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Causal research: definition, examples and how to use it.

16 min read Causal research enables market researchers to predict hypothetical occurrences & outcomes while improving existing strategies. Discover how this research can decrease employee retention & increase customer success for your business.

What is causal research?

Causal research, also known as explanatory research or causal-comparative research, identifies the extent and nature of cause-and-effect relationships between two or more variables.

It’s often used by companies to determine the impact of changes in products, features, or services process on critical company metrics. Some examples:

• How does rebranding of a product influence intent to purchase?
• How would expansion to a new market segment affect projected sales?
• What would be the impact of a price increase or decrease on customer loyalty?

To maintain the accuracy of causal research, ‘confounding variables’ or influences — e.g. those that could distort the results — are controlled. This is done either by keeping them constant in the creation of data, or by using statistical methods. These variables are identified before the start of the research experiment.

As well as the above, research teams will outline several other variables and principles in causal research:

• Independent variables

The variables that may cause direct changes in another variable. For example, the effect of truancy on a student’s grade point average. The independent variable is therefore class attendance.

• Control variables

These are the components that remain unchanged during the experiment so researchers can better understand what conditions create a cause-and-effect relationship.  

This describes the cause-and-effect relationship. When researchers find causation (or the cause), they’ve conducted all the processes necessary to prove it exists.

• Correlation

Any relationship between two variables in the experiment. It’s important to note that correlation doesn’t automatically mean causation. Researchers will typically establish correlation before proving cause-and-effect.

• Experimental design

Researchers use experimental design to define the parameters of the experiment — e.g. categorizing participants into different groups.

• Dependent variables

These are measurable variables that may change or are influenced by the independent variable. For example, in an experiment about whether or not terrain influences running speed, your dependent variable is the terrain.  

Why is causal research useful?

It’s useful because it enables market researchers to predict hypothetical occurrences and outcomes while improving existing strategies. This allows businesses to create plans that benefit the company. It’s also a great research method because researchers can immediately see how variables affect each other and under what circumstances.

Also, once the first experiment has been completed, researchers can use the learnings from the analysis to repeat the experiment or apply the findings to other scenarios. Because of this, it’s widely used to help understand the impact of changes in internal or commercial strategy to the business bottom line.

Some examples include:

• Understanding how overall training levels are improved by introducing new courses
• Examining which variations in wording make potential customers more interested in buying a product
• Testing a market’s response to a brand-new line of products and/or services

So, how does causal research compare and differ from other research types?

Well, there are a few research types that are used to find answers to some of the examples above:

1. Exploratory research

As its name suggests, exploratory research involves assessing a situation (or situations) where the problem isn’t clear. Through this approach, researchers can test different avenues and ideas to establish facts and gain a better understanding.

Researchers can also use it to first navigate a topic and identify which variables are important. Because no area is off-limits, the research is flexible and adapts to the investigations as it progresses.

Finally, this approach is unstructured and often involves gathering qualitative data, giving the researcher freedom to progress the research according to their thoughts and assessment. However, this may make results susceptible to researcher bias and may limit the extent to which a topic is explored.

2. Descriptive research

Descriptive research is all about describing the characteristics of the population, phenomenon or scenario studied. It focuses more on the “what” of the research subject than the “why”.

For example, a clothing brand wants to understand the fashion purchasing trends amongst buyers in California — so they conduct a demographic survey of the region, gather population data and then run descriptive research. The study will help them to uncover purchasing patterns amongst fashion buyers in California, but not necessarily why those patterns exist.

As the research happens in a natural setting, variables can cross-contaminate other variables, making it harder to isolate cause and effect relationships. Therefore, further research will be required if more causal information is needed.

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How is causal research different from the other two methods above?

Well, causal research looks at what variables are involved in a problem and ‘why’ they act a certain way. As the experiment takes place in a controlled setting (thanks to controlled variables) it’s easier to identify cause-and-effect amongst variables.

Furthermore, researchers can carry out causal research at any stage in the process, though it’s usually carried out in the later stages once more is known about a particular topic or situation.

Finally, compared to the other two methods, causal research is more structured, and researchers can combine it with exploratory and descriptive research to assist with research goals.

Summary of three research types

What are the advantages of causal research?

• Improve experiences

By understanding which variables have positive impacts on target variables (like sales revenue or customer loyalty), businesses can improve their processes, return on investment, and the experiences they offer customers and employees.

• Help companies improve internally

By conducting causal research, management can make informed decisions about improving their employee experience and internal operations. For example, understanding which variables led to an increase in staff turnover.

• Repeat experiments to enhance reliability and accuracy of results

When variables are identified, researchers can replicate cause-and-effect with ease, providing them with reliable data and results to draw insights from.

• Test out new theories or ideas

If causal research is able to pinpoint the exact outcome of mixing together different variables, research teams have the ability to test out ideas in the same way to create viable proof of concepts.

• Fix issues quickly

Once an undesirable effect’s cause is identified, researchers and management can take action to reduce the impact of it or remove it entirely, resulting in better outcomes.

What are the disadvantages of causal research?

• Provides information to competitors

If you plan to publish your research, it provides information about your plans to your competitors. For example, they might use your research outcomes to identify what you are up to and enter the market before you.

• Difficult to administer

Causal research is often difficult to administer because it’s not possible to control the effects of extraneous variables.

• Time and money constraints

Budgetary and time constraints can make this type of research expensive to conduct and repeat. Also, if an initial attempt doesn’t provide a cause and effect relationship, the ROI is wasted and could impact the appetite for future repeat experiments.

• Requires additional research to ensure validity

You can’t rely on just the outcomes of causal research as it’s inaccurate. It’s best to conduct other types of research alongside it to confirm its output.

• Trouble establishing cause and effect

Researchers might identify that two variables are connected, but struggle to determine which is the cause and which variable is the effect.

• Risk of contamination

There’s always the risk that people outside your market or area of study could affect the results of your research. For example, if you’re conducting a retail store study, shoppers outside your ‘test parameters’ shop at your store and skew the results.

How can you use causal research effectively?

To better highlight how you can use causal research across functions or markets, here are a few examples:

Market and advertising research

A company might want to know if their new advertising campaign or marketing campaign is having a positive impact. So, their research team can carry out a causal research project to see which variables cause a positive or negative effect on the campaign.

For example, a cold-weather apparel company in a winter ski-resort town may see an increase in sales generated after a targeted campaign to skiers. To see if one caused the other, the research team could set up a duplicate experiment to see if the same campaign would generate sales from non-skiers. If the results reduce or change, then it’s likely that the campaign had a direct effect on skiers to encourage them to purchase products.

Improving customer experiences and loyalty levels

Customers enjoy shopping with brands that align with their own values, and they’re more likely to buy and present the brand positively to other potential shoppers as a result. So, it’s in your best interest to deliver great experiences and retain your customers.

For example, the Harvard Business Review found that an increase in customer retention rates by 5% increased profits by 25% to 95%. But let’s say you want to increase your own, how can you identify which variables contribute to it?Using causal research, you can test hypotheses about which processes, strategies or changes influence customer retention. For example, is it the streamlined checkout? What about the personalized product suggestions? Or maybe it was a new solution that solved their problem? Causal research will help you find out.

Discover how to use analytics to improve customer retention.

Improving problematic employee turnover rates

If your company has a high attrition rate, causal research can help you narrow down the variables or reasons which have the greatest impact on people leaving. This allows you to prioritize your efforts on tackling the issues in the right order, for the best positive outcomes.

For example, through causal research, you might find that employee dissatisfaction due to a lack of communication and transparency from upper management leads to poor morale, which in turn influences employee retention.

To rectify the problem, you could implement a routine feedback loop or session that enables your people to talk to your company’s C-level executives so that they feel heard and understood.

How to conduct causal research first steps to getting started are:

1. Define the purpose of your research

What questions do you have? What do you expect to come out of your research? Think about which variables you need to test out the theory.

2. Pick a random sampling if participants are needed

Using a technology solution to support your sampling, like a database, can help you define who you want your target audience to be, and how random or representative they should be.

3. Set up the controlled experiment

Once you’ve defined which variables you’d like to measure to see if they interact, think about how best to set up the experiment. This could be in-person or in-house via interviews, or it could be done remotely using online surveys.

4. Carry out the experiment

Make sure to keep all irrelevant variables the same, and only change the causal variable (the one that causes the effect) to gather the correct data. Depending on your method, you could be collecting qualitative or quantitative data, so make sure you note your findings across each regularly.

5. Analyze your findings

Either manually or using technology, analyze your data to see if any trends, patterns or correlations emerge. By looking at the data, you’ll be able to see what changes you might need to do next time, or if there are questions that require further research.

6. Verify your findings

Your first attempt gives you the baseline figures to compare the new results to. You can then run another experiment to verify your findings.

7. Do follow-up or supplemental research

You can supplement your original findings by carrying out research that goes deeper into causes or explores the topic in more detail. One of the best ways to do this is to use a survey. See ‘Use surveys to help your experiment’.

Identifying causal relationships between variables

To verify if a causal relationship exists, you have to satisfy the following criteria:

• Nonspurious association

A clear correlation exists between one cause and the effect. In other words, no ‘third’ that relates to both (cause and effect) should exist.

• Temporal sequence

The cause occurs before the effect. For example, increased ad spend on product marketing would contribute to higher product sales.

• Concomitant variation

The variation between the two variables is systematic. For example, if a company doesn’t change its IT policies and technology stack, then changes in employee productivity were not caused by IT policies or technology.

How surveys help your causal research experiments?

There are some surveys that are perfect for assisting researchers with understanding cause and effect. These include:

• Employee Satisfaction Survey – An introductory employee satisfaction survey that provides you with an overview of your current employee experience.
• Manager Feedback Survey – An introductory manager feedback survey geared toward improving your skills as a leader with valuable feedback from your team.
• Net Promoter Score (NPS) Survey – Measure customer loyalty and understand how your customers feel about your product or service using one of the world’s best-recognized metrics.
• Employee Engagement Survey – An entry-level employee engagement survey that provides you with an overview of your current employee experience.
• Customer Satisfaction Survey – Evaluate how satisfied your customers are with your company, including the products and services you provide and how they are treated when they buy from you.
• Employee Exit Interview Survey – Understand why your employees are leaving and how they’ll speak about your company once they’re gone.
• Product Research Survey – Evaluate your consumers’ reaction to a new product or product feature across every stage of the product development journey.
• Brand Awareness Survey – Track the level of brand awareness in your target market, including current and potential future customers.
• Online Purchase Feedback Survey – Find out how well your online shopping experience performs against customer needs and expectations.

That covers the fundamentals of causal research and should give you a foundation for ongoing studies to assess opportunities, problems, and risks across your market, product, customer, and employee segments.

If you want to transform your research, empower your teams and get insights on tap to get ahead of the competition, maybe it’s time to leverage Qualtrics CoreXM.

Qualtrics CoreXM provides a single platform for data collection and analysis across every part of your business — from customer feedback to product concept testing. What’s more, you can integrate it with your existing tools and services thanks to a flexible API.

Qualtrics CoreXM offers you as much or as little power and complexity as you need, so whether you’re running simple surveys or more advanced forms of research, it can deliver every time.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Causal Research: Definition, Design, Tips, Examples

Appinio Research · 21.02.2024 · 34min read

Ever wondered why certain events lead to specific outcomes? Understanding causality—the relationship between cause and effect—is crucial for unraveling the mysteries of the world around us. In this guide on causal research, we delve into the methods, techniques, and principles behind identifying and establishing cause-and-effect relationships between variables. Whether you're a seasoned researcher or new to the field, this guide will equip you with the knowledge and tools to conduct rigorous causal research and draw meaningful conclusions that can inform decision-making and drive positive change.

What is Causal Research?

Causal research is a methodological approach used in scientific inquiry to investigate cause-and-effect relationships between variables. Unlike correlational or descriptive research, which merely examine associations or describe phenomena, causal research aims to determine whether changes in one variable cause changes in another variable.

Importance of Causal Research

Understanding the importance of causal research is crucial for appreciating its role in advancing knowledge and informing decision-making across various fields. Here are key reasons why causal research is significant:

• Establishing Causality:  Causal research enables researchers to determine whether changes in one variable directly cause changes in another variable. This helps identify effective interventions, predict outcomes, and inform evidence-based practices.
• Guiding Policy and Practice:  By identifying causal relationships, causal research provides empirical evidence to support policy decisions, program interventions, and business strategies. Decision-makers can use causal findings to allocate resources effectively and address societal challenges.
• Informing Predictive Modeling :  Causal research contributes to the development of predictive models by elucidating causal mechanisms underlying observed phenomena. Predictive models based on causal relationships can accurately forecast future outcomes and trends.
• Advancing Scientific Knowledge:  Causal research contributes to the cumulative body of scientific knowledge by testing hypotheses, refining theories, and uncovering underlying mechanisms of phenomena. It fosters a deeper understanding of complex systems and phenomena.
• Mitigating Confounding Factors:  Understanding causal relationships allows researchers to control for confounding variables and reduce bias in their studies. By isolating the effects of specific variables, researchers can draw more valid and reliable conclusions.

Causal Research Distinction from Other Research

Understanding the distinctions between causal research and other types of research methodologies is essential for researchers to choose the most appropriate approach for their study objectives. Let's explore the differences and similarities between causal research and descriptive, exploratory, and correlational research methodologies .

Descriptive vs. Causal Research

Descriptive research  focuses on describing characteristics, behaviors, or phenomena without manipulating variables or establishing causal relationships. It provides a snapshot of the current state of affairs but does not attempt to explain why certain phenomena occur.

Causal research , on the other hand, seeks to identify cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. Unlike descriptive research, causal research aims to determine whether changes in one variable directly cause changes in another variable.

Similarities:

• Both descriptive and causal research involve empirical observation and data collection.
• Both types of research contribute to the scientific understanding of phenomena, albeit through different approaches.

Differences:

• Descriptive research focuses on describing phenomena, while causal research aims to explain why phenomena occur by identifying causal relationships.
• Descriptive research typically uses observational methods, while causal research often involves experimental designs or causal inference techniques to establish causality.

Exploratory vs. Causal Research

Exploratory research  aims to explore new topics, generate hypotheses, or gain initial insights into phenomena. It is often conducted when little is known about a subject and seeks to generate ideas for further investigation.

Causal research , on the other hand, is concerned with testing hypotheses and establishing cause-and-effect relationships between variables. It builds on existing knowledge and seeks to confirm or refute causal hypotheses through systematic investigation.

• Both exploratory and causal research contribute to the generation of knowledge and theory development.
• Both types of research involve systematic inquiry and data analysis to answer research questions.
• Exploratory research focuses on generating hypotheses and exploring new areas of inquiry, while causal research aims to test hypotheses and establish causal relationships.
• Exploratory research is more flexible and open-ended, while causal research follows a more structured and hypothesis-driven approach.

Correlational vs. Causal Research

Correlational research  examines the relationship between variables without implying causation. It identifies patterns of association or co-occurrence between variables but does not establish the direction or causality of the relationship.

Causal research , on the other hand, seeks to establish cause-and-effect relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. It goes beyond mere association to determine whether changes in one variable directly cause changes in another variable.

• Both correlational and causal research involve analyzing relationships between variables.
• Both types of research contribute to understanding the nature of associations between variables.
• Correlational research focuses on identifying patterns of association, while causal research aims to establish causal relationships.
• Correlational research does not manipulate variables, while causal research involves systematically manipulating independent variables to observe their effects on dependent variables.

How to Formulate Causal Research Hypotheses?

Crafting research questions and hypotheses is the foundational step in any research endeavor. Defining your variables clearly and articulating the causal relationship you aim to investigate is essential. Let's explore this process further.

1. Identify Variables

Identifying variables involves recognizing the key factors you will manipulate or measure in your study. These variables can be classified into independent, dependent, and confounding variables.

• Independent Variable (IV):  This is the variable you manipulate or control in your study. It is the presumed cause that you want to test.
• Dependent Variable (DV):  The dependent variable is the outcome or response you measure. It is affected by changes in the independent variable.
• Confounding Variables:  These are extraneous factors that may influence the relationship between the independent and dependent variables, leading to spurious correlations or erroneous causal inferences. Identifying and controlling for confounding variables is crucial for establishing valid causal relationships.

2. Establish Causality

Establishing causality requires meeting specific criteria outlined by scientific methodology. While correlation between variables may suggest a relationship, it does not imply causation. To establish causality, researchers must demonstrate the following:

• Temporal Precedence:  The cause must precede the effect in time. In other words, changes in the independent variable must occur before changes in the dependent variable.
• Covariation of Cause and Effect:  Changes in the independent variable should be accompanied by corresponding changes in the dependent variable. This demonstrates a consistent pattern of association between the two variables.
• Elimination of Alternative Explanations:  Researchers must rule out other possible explanations for the observed relationship between variables. This involves controlling for confounding variables and conducting rigorous experimental designs to isolate the effects of the independent variable.

3. Write Clear and Testable Hypotheses

Hypotheses serve as tentative explanations for the relationship between variables and provide a framework for empirical testing. A well-formulated hypothesis should be:

• Specific:  Clearly state the expected relationship between the independent and dependent variables.
• Testable:  The hypothesis should be capable of being empirically tested through observation or experimentation.
• Falsifiable:  There should be a possibility of proving the hypothesis false through empirical evidence.

For example, a hypothesis in a study examining the effect of exercise on weight loss could be: "Increasing levels of physical activity (IV) will lead to greater weight loss (DV) among participants (compared to those with lower levels of physical activity)."

By formulating clear hypotheses and operationalizing variables, researchers can systematically investigate causal relationships and contribute to the advancement of scientific knowledge.

Causal Research Design

Designing your research study involves making critical decisions about how you will collect and analyze data to investigate causal relationships.

Experimental vs. Observational Designs

One of the first decisions you'll make when designing a study is whether to employ an experimental or observational design. Each approach has its strengths and limitations, and the choice depends on factors such as the research question, feasibility , and ethical considerations.

• Experimental Design: In experimental designs, researchers manipulate the independent variable and observe its effects on the dependent variable while controlling for confounding variables. Random assignment to experimental conditions allows for causal inferences to be drawn. Example: A study testing the effectiveness of a new teaching method on student performance by randomly assigning students to either the experimental group (receiving the new teaching method) or the control group (receiving the traditional method).
• Observational Design: Observational designs involve observing and measuring variables without intervention. Researchers may still examine relationships between variables but cannot establish causality as definitively as in experimental designs. Example: A study observing the association between socioeconomic status and health outcomes by collecting data on income, education level, and health indicators from a sample of participants.

Control and Randomization

Control and randomization are crucial aspects of experimental design that help ensure the validity of causal inferences.

• Control: Controlling for extraneous variables involves holding constant factors that could influence the dependent variable, except for the independent variable under investigation. This helps isolate the effects of the independent variable. Example: In a medication trial, controlling for factors such as age, gender, and pre-existing health conditions ensures that any observed differences in outcomes can be attributed to the medication rather than other variables.
• Randomization: Random assignment of participants to experimental conditions helps distribute potential confounders evenly across groups, reducing the likelihood of systematic biases and allowing for causal conclusions. Example: Randomly assigning patients to treatment and control groups in a clinical trial ensures that both groups are comparable in terms of baseline characteristics, minimizing the influence of extraneous variables on treatment outcomes.

Internal and External Validity

Two key concepts in research design are internal validity and external validity, which relate to the credibility and generalizability of study findings, respectively.

• Internal Validity: Internal validity refers to the extent to which the observed effects can be attributed to the manipulation of the independent variable rather than confounding factors. Experimental designs typically have higher internal validity due to their control over extraneous variables. Example: A study examining the impact of a training program on employee productivity would have high internal validity if it could confidently attribute changes in productivity to the training intervention.
• External Validity: External validity concerns the extent to which study findings can be generalized to other populations, settings, or contexts. While experimental designs prioritize internal validity, they may sacrifice external validity by using highly controlled conditions that do not reflect real-world scenarios. Example: Findings from a laboratory study on memory retention may have limited external validity if the experimental tasks and conditions differ significantly from real-life learning environments.

Types of Experimental Designs

Several types of experimental designs are commonly used in causal research, each with its own strengths and applications.

• Randomized Control Trials (RCTs): RCTs are considered the gold standard for assessing causality in research. Participants are randomly assigned to experimental and control groups, allowing researchers to make causal inferences. Example: A pharmaceutical company testing a new drug's efficacy would use an RCT to compare outcomes between participants receiving the drug and those receiving a placebo.
• Quasi-Experimental Designs: Quasi-experimental designs lack random assignment but still attempt to establish causality by controlling for confounding variables through design or statistical analysis . Example: A study evaluating the effectiveness of a smoking cessation program might compare outcomes between participants who voluntarily enroll in the program and a matched control group of non-enrollees.

By carefully selecting an appropriate research design and addressing considerations such as control, randomization, and validity, researchers can conduct studies that yield credible evidence of causal relationships and contribute valuable insights to their field of inquiry.

Causal Research Data Collection

Collecting data is a critical step in any research study, and the quality of the data directly impacts the validity and reliability of your findings.

Choosing Measurement Instruments

Selecting appropriate measurement instruments is essential for accurately capturing the variables of interest in your study. The choice of measurement instrument depends on factors such as the nature of the variables, the target population , and the research objectives.

• Surveys :  Surveys are commonly used to collect self-reported data on attitudes, opinions, behaviors, and demographics . They can be administered through various methods, including paper-and-pencil surveys, online surveys, and telephone interviews.
• Observations:  Observational methods involve systematically recording behaviors, events, or phenomena as they occur in natural settings. Observations can be structured (following a predetermined checklist) or unstructured (allowing for flexible data collection).
• Psychological Tests:  Psychological tests are standardized instruments designed to measure specific psychological constructs, such as intelligence, personality traits, or emotional functioning. These tests often have established reliability and validity.
• Physiological Measures:  Physiological measures, such as heart rate, blood pressure, or brain activity, provide objective data on bodily processes. They are commonly used in health-related research but require specialized equipment and expertise.
• Existing Databases:  Researchers may also utilize existing datasets, such as government surveys, public health records, or organizational databases, to answer research questions. Secondary data analysis can be cost-effective and time-saving but may be limited by the availability and quality of data.

Ensuring accurate data collection is the cornerstone of any successful research endeavor. With the right tools in place, you can unlock invaluable insights to drive your causal research forward. From surveys to tests, each instrument offers a unique lens through which to explore your variables of interest.

At Appinio , we understand the importance of robust data collection methods in informing impactful decisions. Let us empower your research journey with our intuitive platform, where you can effortlessly gather real-time consumer insights to fuel your next breakthrough.   Ready to take your research to the next level? Book a demo today and see how Appinio can revolutionize your approach to data collection!

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Sampling Techniques

Sampling involves selecting a subset of individuals or units from a larger population to participate in the study. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population of interest.

• Probability Sampling:  Probability sampling methods involve randomly selecting participants from the population, ensuring that each member of the population has an equal chance of being included in the sample. Common probability sampling techniques include simple random sampling , stratified sampling, and cluster sampling .
• Non-Probability Sampling:  Non-probability sampling methods do not involve random selection and may introduce biases into the sample. Examples of non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling.

The choice of sampling technique depends on factors such as the research objectives, population characteristics, resources available, and practical constraints. Researchers should strive to minimize sampling bias and maximize the representativeness of the sample to enhance the generalizability of their findings.

Ethical Considerations

Ethical considerations are paramount in research and involve ensuring the rights, dignity, and well-being of research participants. Researchers must adhere to ethical principles and guidelines established by professional associations and institutional review boards (IRBs).

• Informed Consent:  Participants should be fully informed about the nature and purpose of the study, potential risks and benefits, their rights as participants, and any confidentiality measures in place. Informed consent should be obtained voluntarily and without coercion.
• Privacy and Confidentiality:  Researchers should take steps to protect the privacy and confidentiality of participants' personal information. This may involve anonymizing data, securing data storage, and limiting access to identifiable information.
• Minimizing Harm:  Researchers should mitigate any potential physical, psychological, or social harm to participants. This may involve conducting risk assessments, providing appropriate support services, and debriefing participants after the study.
• Respect for Participants:  Researchers should respect participants' autonomy, diversity, and cultural values. They should seek to foster a trusting and respectful relationship with participants throughout the research process.
• Publication and Dissemination:  Researchers have a responsibility to accurately report their findings and acknowledge contributions from participants and collaborators. They should adhere to principles of academic integrity and transparency in disseminating research results.

By addressing ethical considerations in research design and conduct, researchers can uphold the integrity of their work, maintain trust with participants and the broader community, and contribute to the responsible advancement of knowledge in their field.

Causal Research Data Analysis

Once data is collected, it must be analyzed to draw meaningful conclusions and assess causal relationships.

Causal Inference Methods

Causal inference methods are statistical techniques used to identify and quantify causal relationships between variables in observational data. While experimental designs provide the most robust evidence for causality, observational studies often require more sophisticated methods to account for confounding factors.

• Difference-in-Differences (DiD):  DiD compares changes in outcomes before and after an intervention between a treatment group and a control group, controlling for pre-existing trends. It estimates the average treatment effect by differencing the changes in outcomes between the two groups over time.
• Instrumental Variables (IV):  IV analysis relies on instrumental variables—variables that affect the treatment variable but not the outcome—to estimate causal effects in the presence of endogeneity. IVs should be correlated with the treatment but uncorrelated with the error term in the outcome equation.
• Regression Discontinuity (RD):  RD designs exploit naturally occurring thresholds or cutoff points to estimate causal effects near the threshold. Participants just above and below the threshold are compared, assuming that they are similar except for their proximity to the threshold.
• Propensity Score Matching (PSM):  PSM matches individuals or units based on their propensity scores—the likelihood of receiving the treatment—creating comparable groups with similar observed characteristics. Matching reduces selection bias and allows for causal inference in observational studies.

Assessing Causality Strength

Assessing the strength of causality involves determining the magnitude and direction of causal effects between variables. While statistical significance indicates whether an observed relationship is unlikely to occur by chance, it does not necessarily imply a strong or meaningful effect.

• Effect Size:  Effect size measures the magnitude of the relationship between variables, providing information about the practical significance of the results. Standard effect size measures include Cohen's d for mean differences and odds ratios for categorical outcomes.
• Confidence Intervals:  Confidence intervals provide a range of values within which the actual effect size is likely to lie with a certain degree of certainty. Narrow confidence intervals indicate greater precision in estimating the true effect size.
• Practical Significance:  Practical significance considers whether the observed effect is meaningful or relevant in real-world terms. Researchers should interpret results in the context of their field and the implications for stakeholders.

Handling Confounding Variables

Confounding variables are extraneous factors that may distort the observed relationship between the independent and dependent variables, leading to spurious or biased conclusions. Addressing confounding variables is essential for establishing valid causal inferences.

• Statistical Control:  Statistical control involves including confounding variables as covariates in regression models to partially out their effects on the outcome variable. Controlling for confounders reduces bias and strengthens the validity of causal inferences.
• Matching:  Matching participants or units based on observed characteristics helps create comparable groups with similar distributions of confounding variables. Matching reduces selection bias and mimics the randomization process in experimental designs.
• Sensitivity Analysis:  Sensitivity analysis assesses the robustness of study findings to changes in model specifications or assumptions. By varying analytical choices and examining their impact on results, researchers can identify potential sources of bias and evaluate the stability of causal estimates.
• Subgroup Analysis:  Subgroup analysis explores whether the relationship between variables differs across subgroups defined by specific characteristics. Identifying effect modifiers helps understand the conditions under which causal effects may vary.

By employing rigorous causal inference methods, assessing the strength of causality, and addressing confounding variables, researchers can confidently draw valid conclusions about causal relationships in their studies, advancing scientific knowledge and informing evidence-based decision-making.

Causal Research Examples

Examples play a crucial role in understanding the application of causal research methods and their impact across various domains. Let's explore some detailed examples to illustrate how causal research is conducted and its real-world implications:

Example 1: Software as a Service (SaaS) User Retention Analysis

Suppose a SaaS company wants to understand the factors influencing user retention and engagement with their platform. The company conducts a longitudinal observational study, collecting data on user interactions, feature usage, and demographic information over several months.

• Design:  The company employs an observational cohort study design, tracking cohorts of users over time to observe changes in retention and engagement metrics. They use analytics tools to collect data on user behavior , such as logins, feature usage, session duration, and customer support interactions.
• Data Collection:  Data is collected from the company's platform logs, customer relationship management (CRM) system, and user surveys. Key metrics include user churn rates, active user counts, feature adoption rates, and Net Promoter Scores ( NPS ).
• Analysis:  Using statistical techniques like survival analysis and regression modeling, the company identifies factors associated with user retention, such as feature usage patterns, onboarding experiences, customer support interactions, and subscription plan types.
• Findings: The analysis reveals that users who engage with specific features early in their lifecycle have higher retention rates, while those who encounter usability issues or lack personalized onboarding experiences are more likely to churn. The company uses these insights to optimize product features, improve onboarding processes, and enhance customer support strategies to increase user retention and satisfaction.

Example 2: Business Impact of Digital Marketing Campaign

Consider a technology startup launching a digital marketing campaign to promote its new product offering. The company conducts an experimental study to evaluate the effectiveness of different marketing channels in driving website traffic, lead generation, and sales conversions.

• Design:  The company implements an A/B testing design, randomly assigning website visitors to different marketing treatment conditions, such as Google Ads, social media ads, email campaigns, or content marketing efforts. They track user interactions and conversion events using web analytics tools and marketing automation platforms.
• Data Collection:  Data is collected on website traffic, click-through rates, conversion rates, lead generation, and sales revenue. The company also gathers demographic information and user feedback through surveys and customer interviews to understand the impact of marketing messages and campaign creatives .
• Analysis:  Utilizing statistical methods like hypothesis testing and multivariate analysis, the company compares key performance metrics across different marketing channels to assess their effectiveness in driving user engagement and conversion outcomes. They calculate return on investment (ROI) metrics to evaluate the cost-effectiveness of each marketing channel.
• Findings:  The analysis reveals that social media ads outperform other marketing channels in generating website traffic and lead conversions, while email campaigns are more effective in nurturing leads and driving sales conversions. Armed with these insights, the company allocates marketing budgets strategically, focusing on channels that yield the highest ROI and adjusting messaging and targeting strategies to optimize campaign performance.

These examples demonstrate the diverse applications of causal research methods in addressing important questions, informing policy decisions, and improving outcomes in various fields. By carefully designing studies, collecting relevant data, employing appropriate analysis techniques, and interpreting findings rigorously, researchers can generate valuable insights into causal relationships and contribute to positive social change.

How to Interpret Causal Research Results?

Interpreting and reporting research findings is a crucial step in the scientific process, ensuring that results are accurately communicated and understood by stakeholders.

Interpreting Statistical Significance

Statistical significance indicates whether the observed results are unlikely to occur by chance alone, but it does not necessarily imply practical or substantive importance. Interpreting statistical significance involves understanding the meaning of p-values and confidence intervals and considering their implications for the research findings.

• P-values:  A p-value represents the probability of obtaining the observed results (or more extreme results) if the null hypothesis is true. A p-value below a predetermined threshold (typically 0.05) suggests that the observed results are statistically significant, indicating that the null hypothesis can be rejected in favor of the alternative hypothesis.
• Confidence Intervals:  Confidence intervals provide a range of values within which the true population parameter is likely to lie with a certain degree of confidence (e.g., 95%). If the confidence interval does not include the null value, it suggests that the observed effect is statistically significant at the specified confidence level.

Interpreting statistical significance requires considering factors such as sample size, effect size, and the practical relevance of the results rather than relying solely on p-values to draw conclusions.

Discussing Practical Significance

While statistical significance indicates whether an effect exists, practical significance evaluates the magnitude and meaningfulness of the effect in real-world terms. Discussing practical significance involves considering the relevance of the results to stakeholders and assessing their impact on decision-making and practice.

• Effect Size:  Effect size measures the magnitude of the observed effect, providing information about its practical importance. Researchers should interpret effect sizes in the context of their field and the scale of measurement (e.g., small, medium, or large effect sizes).
• Contextual Relevance:  Consider the implications of the results for stakeholders, policymakers, and practitioners. Are the observed effects meaningful in the context of existing knowledge, theory, or practical applications? How do the findings contribute to addressing real-world problems or informing decision-making?

Discussing practical significance helps contextualize research findings and guide their interpretation and application in practice, beyond statistical significance alone.

Addressing Limitations and Assumptions

No study is without limitations, and researchers should transparently acknowledge and address potential biases, constraints, and uncertainties in their research design and findings.

• Methodological Limitations:  Identify any limitations in study design, data collection, or analysis that may affect the validity or generalizability of the results. For example, sampling biases , measurement errors, or confounding variables.
• Assumptions:  Discuss any assumptions made in the research process and their implications for the interpretation of results. Assumptions may relate to statistical models, causal inference methods, or theoretical frameworks underlying the study.
• Alternative Explanations:  Consider alternative explanations for the observed results and discuss their potential impact on the validity of causal inferences. How robust are the findings to different interpretations or competing hypotheses?

Addressing limitations and assumptions demonstrates transparency and rigor in the research process, allowing readers to critically evaluate the validity and reliability of the findings.

Communicating Findings Clearly

Effectively communicating research findings is essential for disseminating knowledge, informing decision-making, and fostering collaboration and dialogue within the scientific community.

• Clarity and Accessibility:  Present findings in a clear, concise, and accessible manner, using plain language and avoiding jargon or technical terminology. Organize information logically and use visual aids (e.g., tables, charts, graphs) to enhance understanding.
• Contextualization:  Provide context for the results by summarizing key findings, highlighting their significance, and relating them to existing literature or theoretical frameworks. Discuss the implications of the findings for theory, practice, and future research directions.
• Transparency:  Be transparent about the research process, including data collection procedures, analytical methods, and any limitations or uncertainties associated with the findings. Clearly state any conflicts of interest or funding sources that may influence interpretation.

By communicating findings clearly and transparently, researchers can facilitate knowledge exchange, foster trust and credibility, and contribute to evidence-based decision-making.

Causal Research Tips

When conducting causal research, it's essential to approach your study with careful planning, attention to detail, and methodological rigor. Here are some tips to help you navigate the complexities of causal research effectively:

• Define Clear Research Questions:  Start by clearly defining your research questions and hypotheses. Articulate the causal relationship you aim to investigate and identify the variables involved.
• Consider Alternative Explanations:  Be mindful of potential confounding variables and alternative explanations for the observed relationships. Take steps to control for confounders and address alternative hypotheses in your analysis.
• Prioritize Internal Validity:  While external validity is important for generalizability, prioritize internal validity in your study design to ensure that observed effects can be attributed to the manipulation of the independent variable.
• Use Randomization When Possible:  If feasible, employ randomization in experimental designs to distribute potential confounders evenly across experimental conditions and enhance the validity of causal inferences.
• Be Transparent About Methods:  Provide detailed descriptions of your research methods, including data collection procedures, analytical techniques, and any assumptions or limitations associated with your study.
• Utilize Multiple Methods:  Consider using a combination of experimental and observational methods to triangulate findings and strengthen the validity of causal inferences.
• Be Mindful of Sample Size:  Ensure that your sample size is adequate to detect meaningful effects and minimize the risk of Type I and Type II errors. Conduct power analyses to determine the sample size needed to achieve sufficient statistical power.
• Validate Measurement Instruments:  Validate your measurement instruments to ensure that they are reliable and valid for assessing the variables of interest in your study. Pilot test your instruments if necessary.
• Seek Feedback from Peers:  Collaborate with colleagues or seek feedback from peer reviewers to solicit constructive criticism and improve the quality of your research design and analysis.

Conclusion for Causal Research

Mastering causal research empowers researchers to unlock the secrets of cause and effect, shedding light on the intricate relationships between variables in diverse fields. By employing rigorous methods such as experimental designs, causal inference techniques, and careful data analysis, you can uncover causal mechanisms, predict outcomes, and inform evidence-based practices. Through the lens of causal research, complex phenomena become more understandable, and interventions become more effective in addressing societal challenges and driving progress. In a world where understanding the reasons behind events is paramount, causal research serves as a beacon of clarity and insight. Armed with the knowledge and techniques outlined in this guide, you can navigate the complexities of causality with confidence, advancing scientific knowledge, guiding policy decisions, and ultimately making meaningful contributions to our understanding of the world.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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22 Causal Explanation

Department of Psychology University of California, Berkeley Berkeley, California, USA

• Published: 10 May 2017
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Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. This chapter considers what we can learn about causal reasoning from research on explanation. In particular, it reviews an emerging body of work suggesting that explanatory considerations—such as the simplicity or scope of a causal hypothesis—can systematically influence causal inference and learning. It also discusses proposed distinctions among types of explanations and reviews the effects of each explanation type on causal reasoning and representation. Finally, it considers the relationship between explanations and causal mechanisms and raises important questions for future research.

A doctor encounters a patient: Why does she have a fever and a rash? An engineer investigates a failure: Why did the bridge collapse? A parent wonders about her child: Why did she throw a tantrum? In each of these cases, we seek an explanation for some event—an explanation that’s likely to appeal to one or more antecedent causes . The doctor might conclude that a virus caused the symptoms, the engineer that defects in cast iron caused the collapse, and the parent that the toy’s disappearance caused the tantrum.

Not all explanations are causal, and not all causes are explanatory. Explanations in mathematics, for example, are typically taken to be non-causal, and many causal factors are either not explanatory at all, or only explanatory under particular circumstances. (Consider, for instance, appealing to the big bang as an explanation for today’s inflation rates, or the presence of oxygen as an explanation for California wildfires.) Nonetheless, causation and explanation are closely related, with many instances of causal reasoning featuring explanations and explanatory considerations, and many instances of abductive inference and explanation appealing to causes and causal considerations. The goal of the present chapter is to identify some of the connections between explanation and causation, with a special focus on how the study of explanation can inform our understanding of causal reasoning.

The chapter is divided into five sections. In the first three, we review an emerging body of work on the role of explanation in three types of causal reasoning: drawing inferences about the causes of events, learning novel causal structures, and assigning causal responsibility. In the fourth section, we consider different kinds of explanations, including a discussion of whether each kind is properly “causal” and how different kinds of explanations can differentially influence causal judgments. In the fifth section, we focus on causal explanations that appeal to mechanisms, and consider the relationship between explanation, causal claims, and mechanisms. Finally, we conclude with some important questions for future research.

Causal Inference and Inference to the Best Explanation

Consider a doctor who infers, on the basis of a patient’s symptoms, that the patient has a particular disease—one known to cause that cluster of symptoms. We will refer to such instances of causal reasoning as “causal inference,” and differentiate them from two other kinds of causal reasoning that we will discuss in subsequent sections: causal learning (which involves learning about novel causes and relationships at the type level) and assigning causal responsibility (which involves attributing an effect to one or more causes, all of which could have occurred and could have contributed to the effect).

How might explanation influence causal inference? One possibility is that people engage in a process called “inference to the best explanation” (IBE). IBE was introduced into the philosophical literature by Gilbert Harman in a 1965 paper, but the idea is likely older, and closely related to what is sometimes called “abductive inference” ( Douven, 2011 ; Lombrozo, 2012 , 2016 ; Peirce, 1955 ). The basic idea is that one infers that a hypothesis is likely to be true based on the fact that it best explains the data. To borrow vocabulary from another influential philosopher of explanation, Peter Lipton, one uses an explanation’s “loveliness” as a guide to its “likeliness” ( Lipton, 2004 ).

A great deal of work has aimed to characterize how people go about inferring causes from patterns of evidence ( Cheng, 1997 ; Cheng & Novick, 1990 , 1992 ; Glymour & Cheng, 1998 ; Griffiths & Tenenbaum, 2005 ; Kelley, 1973 ; Perales & Shanks, 2003 ; Shanks & Dickinson, 1988 ; Waldmann & Hagmayer, 2001 ; see Buehner, 2005 ; Holyoak & Cheng, 2011 ; Waldmann & Hagmayer, 2013 , for reviews), and this work is summarized in other chapters of this volume (see Part I , “Theories of Causal Cognition,” and Meder & Mayrhofer, Chapter 23 , on diagnostic reasoning). Thus a question that immediately presents itself is whether IBE is distinct from the kinds of inference these models typically involve, such as analyses of covariation or Bayesian inference. For most advocates of IBE, the answer is “yes”: IBE is a distinct inferential process, where the key commitment is that explanatory considerations play a role in guiding judgments. These considerations can include the simplicity, scope, or other “virtues” of the explanatory hypotheses under consideration.

To provide evidence for IBE as a distinctly explanatory form of inference, it is thus important to identify explanatory virtues, and to demonstrate their role in inference. The most direct evidence of this form comes from research on simplicity ( Bonawitz & Lombrozo, 2012 ; Lombrozo, 2007 ; Pacer & Lombrozo, in preparation), scope ( Khemlani, Sussman, & Oppenheimer, 2011 ), and explanatory power (Douven & Schupbach, 2015a , 2015b ). We focus on this research for the remainder of the section.

In one study from Lombrozo (2007) , participants learned novel causal structures describing the relationships between diseases and symptoms on an alien planet. For example, the conjunction of two particular symptoms—say “sore minttels” and “purple spots”—could be explained by appeal to a single disease that caused both symptoms (Tritchet’s syndrome), or by appeal to the conjunction of two diseases that each caused one symptom (Morad’s disease and a Humel infection). Lombrozo set out to test whether participants would favor the explanation that was simpler in the sense that it invoked a single common cause over two independent causes, and whether they would do so even when probabilistic evidence, in the form of disease base rates, favored the more complex explanation. Lombrozo found that participants’ explanation choices were a function of both simplicity and probability, with a substantial proportion of participants selecting the simpler explanation even when it was less likely than the complex alternative. This is consistent with the idea that an explanation’s “loveliness”—in this case, its simplicity—is used as a basis for inferring its “likeliness.”

In subsequent work, Bonawitz and Lombrozo (2012) replicated the same basic pattern of results with 5-year-old children in a structurally parallel task: children observed a toy generating two effects (a light and a spinning fan), and had to infer whether one block (which generated both effects) or two blocks (which each generated one effect) fell into the toy’s activator bin. In this case, probabilistic information was manipulated across participants by varying the number of blocks of each type and the process by which they fell into the bin. Interestingly, adults did not show a preference for simplicity above and beyond probability in this task, while the 5-year-olds did. Bonawitz and Lombrozo suggest that in the face of probabilistic uncertainty—of the kind that is generated by a more complex task like the alien diagnosis problems used in Lombrozo (2007) —adults rely on explanatory considerations such as simplicity to guide assessments of probability. But when a task involves a transparent and seemingly deterministic causal system, and when the numbers involved are small (as was the case for the task developed for young children in Bonawitz and Lombrozo, 2012 ), adults may engage in more explicit probabilistic reasoning, and may bypass explanatory considerations altogether. Consistent with this idea, adults in Lombrozo (2007) also ceased to favor simplicity when they were explicitly told that the complex hypothesis was most likely to be true.

In more recent work, Pacer and Lombrozo (in preparation) provide a more precise characterization of how people assess an explanation’s simplicity. They differentiate two intuitive metrics for causal explanations, both of which are consistent with prior results: “node simplicity,” which involves counting the number of causes invoked in an explanation; and “root simplicity,” which involves counting the number of unexplained causes invoked in an explanation. For example, suppose that Dr. Node explains a patient’s symptoms by appeal to pneumonia and sarcoma—two diseases. And that Dr. Root explains the symptoms by appeal to pneumonia, sarcoma, and HIV , where HIV is a cause (or at least a contributing factor) for both pneumonia and sarcoma. Dr. Root has invoked more causes than Dr. Node (three versus two), and so her explanation is less simple according to node simplicity. But Dr. Root has explained the symptoms by appeal to only one unexplained cause (HIV), as opposed to Dr. Node’s two (pneumonia and sarcoma), so her explanation is simpler according to root simplicity. Extending the basic method developed by Lombrozo (2007) , Pacer and Lombrozo found strong evidence that people favor explanations with low root simplicity (above and beyond what is warranted on the basis of the frequency information which they were provided), but no evidence that people are sensitive to node simplicity. By using appropriate causal structures, they were able to rule out alternative explanations for these results (e.g., that people prefer explanations that involve intervening variables).

These findings suggest that in drawing causal inferences, people do not simply engage in probabilistic inference on the basis of frequency information. In addition to frequency information, they use explanatory considerations (in this case, low root simplicity) to guide their judgments, at least in the face of probabilistic uncertainty. The findings therefore suggest that IBE plays a role in inferences concerning causal events. But is this effect restricted to simplicity, or do other explanatory considerations play a role as well? Research to date supports a role for two additional factors: narrow latent scope and explanatory power .

An explanation’s “latent scope” refers to the number of unverified effects that the explanation predicts. For example, an observed symptom could be explained by appeal to a disease that predicts that single symptom, or by appeal to a disease that additionally predicts an effect that has not yet been tested for and is hence unobserved (e.g., whether the person has low blood levels of some mineral). In this case, the former explanation has narrower latent scope. Khemlani, Sussman, and Oppenheimer (2011) found that people favor explanations with narrow latent scope, even if the two diseases are equally prevalent. Importantly, they also find that latent scope affects probability estimates: explanations with narrow latent scope are judged more likely than those with broader latent scope (see also Johnson, Johnston, Toig, & Keil, 2014 , for evidence that explanatory scope informs causal strength inferences, and Johnston, Johnson, Koven, & Keil, 2015 , for evidence of latent scope bias in children). Thus latent scope appears to be among the cues to explanatory “loveliness” that affects the perceived “likeliness” of explanatory hypotheses.

Finally, recent work by Douven and Schupbach ( 2015a , 2015b ) provides further evidence of a role for explanatory considerations in inference, with hints that the relevant consideration is “explanatory power.” Employing a quite different paradigm, Douven and Schupbach demonstrate that people’s explanatory judgments better predict their estimates of posterior probability than do objective probabilities on their own. In a study reported in Douven and Schupbach (2015a) , participants observed 10 balls successively drawn from one of two urns, which was selected by a coin flip. One urn contained 30 black balls and 10 white balls, and the other contained 15 black balls and 25 white ones. After each draw, participants were asked to consider the evidence so far, and to rate the “explanatory goodness” of each of two hypotheses: the hypothesis that the balls were drawn from the 30/10 urn, or the hypothesis that the balls were drawn from the 15/25 urn. Participants were also asked to estimate a posterior probability for each hypothesis after each draw. In a series of models, Douven and Schupbach tested whether people’s judgments of the explanatory “goodness” of each hypothesis improved model predictions of their subjective posterior probabilities, above and beyond the objective posteriors calculated on the basis of the data presented to each participant. They found that models incorporating these explanatory judgments outperformed alternatives, even when appropriately penalized for using additional predictors.

Douven and Schupbach’s (2015a) results suggest that explanatory considerations do inform assessments of probability, and that these considerations diverge from posterior probability. However, the findings do not pinpoint the nature of the explanatory considerations themselves. On what basis were participants judging one hypothesis more or less explanatory than the other? Additional analyses of these data, reported in Douven and Schupbach (2015b) , provide some hints: models that took into account some measure of “explanatory power”—computed on the basis of the objective probabilities—outperformed the basic model that only considered posteriors. The best-performing model employed a measure based on Good (1960) that roughly tracks confirmation : it takes the log of the ratio of the probability of the data given the hypothesis to the probability of the data. In other work, Schupbach (2011) finds evidence that people’s judgments of an explanation’s “goodness” are related to another measure of explanatory power, proposed by Schupbach and Sprenger (2011) , which is also related to Bayesian measures of confirmation.

These findings suggest that explanatory considerations—in the form of root simplicity, latent scope, and explanatory power—inform causal inference, and in so doing reveal something potentially surprising: that while people’s responses to evidence are systematic, they do not (always) lead to causal inferences that track the posterior probabilities of each causal hypothesis. This not only supports a role for explanatory considerations in causal inference, but also challenges the idea that identifying causes to explain effects is essentially a matter of conditionalizing on the effects to infer the most likely cause. Further challenging this idea, Pacer, Williams, Chen, Lombrozo, and Griffiths (2013) compare judgments of explanatory goodness from human participants to those generated by four distinct computational models of explanation in causal Bayesian networks, and find that models that compute measures of evidence or information considerably outperform those that compute more direct measures of (posterior) probability.

In sum, there is good evidence that people engage in a process like IBE when drawing inferences about causal events: they use explanatory considerations to guide their assessments of which causes account for observed effects, and of how likely candidate hypotheses are to be true. The most direct evidence to date concerns root simplicity, latent scope, and explanatory power, but there is indirect evidence that other explanatory considerations, such as coherence, completeness, and manifest scope, may play a similar role ( Pennington & Hastie, 1992 ; Read & Marcus-Newhall, 1993 ; Preston & Epley, 2005 ; Thagard, 1989 ; Williams & Lombrozo, 2010 ).

Before concluding this section on IBE in causal inference, it is worth considering the normative implications of this work. It is typically assumed that Bayesian updating provides the normatively correct procedure for revising belief in causal hypotheses in light of the evidence. Do the findings reported in this section describe a true departure from Bayesian inference, and therefore a systematic source of error in human judgment? This is certainly one possibility. For example, it could be that IBE describes an imperfect algorithm by which people approximate Bayesian inference. If this is the case, it becomes an interesting project to spell out when and why explanatory considerations ever succeed in approximating more direct probabilistic inference.

There are other possibilities, however. In particular, an appropriately specified Bayesian model could potentially account for these results. In fact, some have argued that IBE-like inference could simply fall out of hierarchical Bayesian inference with suitably assigned priors and likelihoods ( Henderson, 2014 ), in which case there could be a justified, Bayesian account of this behavior. It could also be that the Bayesian models implicit in the comparisons between people’s judgments and posterior probabilities fail to describe the inference that people are actually making. In their chapter in this volume on diagnostic reasoning, for example, Meder and Mayrhofer (Chapter 23 ) make the important point that there can be more than one “Bayesian” model for a given inference, and in fact find different patterns of inference for models that make different assumptions when it comes to elemental diagnostic reasoning: inferring the value of a single binary cause from a single binary effect, which has clear parallels to the cases considered here. In particular, they argue for a model that takes into account uncertainty in causal structures over one that simply computes the empirical conditional probability of a cause given an effect. Similarly, it could be that the “departures” from Bayesian updating observed here reflect the consequences of a Bayesian inference that involves more than a straight calculation of posteriors.

Finally, some argue that IBE corresponds to a distinct but normatively justifiable alternative to Bayesianism (e.g., Douven & Schupbach, 2015a ). In particular, while Bayesian inference may be the best approach for minimizing expected inaccuracy in the long run, it could be that a process like IBE dominates Bayesian inference when the goal is, say, to get things mostly right in the short term, or to achieve some other aim ( Douven, 2013 ). It could also be that explanation judgments take considerations other than accuracy into account, such as the ease with which the explanation can be communicated, remembered, or used in subsequent processing. These are all important possibilities to explore in future research.

Causal Learning and the Process of Explaining

Consider a doctor who, when confronted with a recurring pattern of symptoms, posits a previously undocumented disease, or a previously unknown link between some pathogen and those symptoms. In each case, the inference involves a change in the doctor’s beliefs about the causal structure of the world, not only about the particular patient’s illness. This kind of inference, which we will refer to as causal model learning , differs from the kinds of causal inferences considered in the preceding section in that the learner posits a novel cause or causal relation, not (only) a new token of a known type.

Just as explanatory considerations can influence causal inference, it is likely that a process like IBE can guide causal model learning. In fact, “Occam’s Razor,” the classic admonition against positing unnecessary types of entities ( Baker, 2013 ), is typically formulated and invoked in the context of positing novel types, not tokens of known types. However, research to date has not (to our knowledge) directly explored IBE in the context of causal model learning. Doing so would require assessing whether novel causes or causal relations are more likely to be inferred when they provide better explanations.

What we do know is that engaging in explanation— the process —can affect the course of causal learning. In particular, a handful of studies with preschool-aged children suggest that being prompted to explain, even without feedback on the content or quality of explanations, can promote understanding of number conservation ( Siegler, 1995 ) and of physical phenomena (e.g., a balance beam; Pine & Siegler, 2003 ), and recruit causal beliefs that are not invoked spontaneously to guide predictions ( Amsterlaw & Wellman, 2006 ; Bartsch & Wellman, 1995 ; Legare, Wellman, & Gelman, 2009 ). Prompts to explain can also accelerate children’s understanding of false belief ( Amsterlaw & Wellman, 2006 ; Wellman & Lagattuta, 2004 ; see Wellman & Liu, 2007 , and Wellman, 2011 , for reviews), which requires a revision from one causal model of behavior to a more complex model involving an unobserved variable (belief) and a causal link between beliefs and behavior (e.g., Goodman et al., 2006 ). Finally, there is evidence that prompting children to explain can lead them to preferentially learn about and remember causal mechanisms over causally irrelevant perceptual details ( Legare & Lombrozo, 2014 ), and that prompting children to explain makes them more likely to generalize internal parts and category membership from some objects to others on the basis of shared causal affordances as opposed to perceptual similarity ( Walker, Lombrozo, Legare, & Gopnik, 2014 ; see also Muentener & Bonawitz, Chapter 33 in this volume, for more on children’s causal learning).

To better understand the effects of explanation on children’s causal learning, Walker, Lombrozo, Williams, Rafferty, and Gopnik (2016) set out to isolate effects of explanation on two key factors in causal learning: evidence and prior beliefs. Walker et al. used the classic “blicket detector” paradigm ( Gopnik & Sobel, 2000 ), in which children observe blocks placed on a machine, where some of the blocks make the machine play music. Children have to learn which blocks activate the machine, which can involve positing a novel kind corresponding to a subset of blocks, and/or positing a novel causal relationship between those blocks (or some of their features) and the machine’s activation.

In Walker et al.’s studies, 5-year-old children observed eight blocks successively placed on the machine, where four activated the machine and four did not. Crucially, half the children were prompted to explain after each observation (“Why did [didn’t] this block make my machine play music?”), and the remaining children, in the control condition, were asked to report the outcome (“What happened to my machine when I put this block on it? Did it play music?”). This control task was intended to match the explanation condition in eliciting a verbal response and drawing attention to the relaionship between each block and the machine, but without requiring that the child explain.

Across studies, Walker et al. (2016) varied the properties of the blocks to investigate whether prompting children to explain made them more likely to favor causal hypotheses that were more consistent with the data (i.e., one hypothesis accounted for 100% of observations and the other for 75%) and/or more consistent with prior beliefs (i.e., one hypothesis involved heavier blocks activating the machine, which matched children’s initial asumptions; the other involved blocks of a given color activating the machine). When competing causal hypotheses were matched in terms of prior beliefs but varied in the evidence they accounted for, children who were prompted to explain were significantly more likely than controls to favor the hypothesis with stronger evidence. And when competing causal hypotheses were matched in terms of evidence but varied in their consistency with prior beliefs, children who were prompted to explain were significantly more likely than controls to favor the hypothesis with a higher prior. In other words, explaining made children more responsive to both crucial ingredients of causal learning: evidence and prior beliefs.

In their final study, Walker et al. (2016) considered a case in which evidence and prior beliefs came into conflict: a hypothesis that accounted for 100% of the evidence (“blue blocks activate the machine”) was pitted against a hypothesis favored by prior beliefs (“big blocks activate the machine”), but that only accounted for 75% of the evidence. In this case, children who were prompted to explain were significantly more likely than controls to go with prior beliefs, guessing that a novel big block rather than a novel blue block would activate the machine. This pattern of responses was compared against the predictions of a Bayesian model that incorporated children’s own priors and likelihoods as estimated from an independent task. The results suggested that children who were prompted to explain were less likely than children in the control condition to conform to Bayesian inference. This result may seem surprising in light of explainers’ greater sensitivity to both evidence and prior beliefs, which suggests that explaining results in “better” performance. However, it is less surprising in light of the findings reported in the previous section, which consistently point to a divergence between explanation-based judgments and assessments of posterior probability.

While the evidence summarized thus far is restricted to preschool-aged children, it is likely that similar processes operate in older children and adults. For instance, Kuhn and Katz (2009) had fourth-grade children engage in a causal learning task that involved identifying the causes of earthquakes by observing evidence. The children subsequently participated in a structurally similar causal learning task involving an ocean voyage, where half were instructed to explain the basis for each prediction that they made, and those in a control group were not. When the same students completed the earthquake task in a post-test, those who had explained generated a smaller number of evidence-based inferences; instead, they seemed to rely more heavily on their (mistaken) prior beliefs, in line with the findings from Walker et al. (2016) . In a classic study with eighth-grade students, Chi, De Leeuw, Chiu, and LaVancher (1994) prompted students to “self-explain” as they read a passage about the circulatory system, with students in the control condition instead prompted to read the text twice. Students who explained were significantly more likely to acquire an accurate causal model of the circulatory system, in part, they suggest, because explaining “involved the integration of new information into existing knowledge”—that is, the coordination of evidence with prior beliefs. Finally, evidence with adults investigating the effects of explanation in categorization tasks mirrors the findings from Walker et al. (2016) , with participants who explain both more responsive to evidence ( Williams & Lombrozo, 2010 ) and more likely to recruit prior beliefs ( Williams & Lombrozo, 2013 ).

Why does the process of explaining affect causal learning? One possibility is that explaining simply leads to greater attention or engagement. This is unlikely for a variety of reasons. Prior work has found that while explaining leads to some improvements in performance, it also generates systematic impairments. In one study, children prompted to explain were significantly less likely than controls to remember the color of a gear in a gear toy ( Legare & Lombrozo, 2014 ); in another, they were significantly less likely to remember which sticker was placed on a block ( Walker et al., 2014 ). Research with adults has also found that a prompt to explain can slow learning and increase error rates in a category learning task ( Williams, Lombrozo, & Rehder, 2013 ). Moreover, the findings from the final study of Walker et al. (2016) suggest that prompting children to explain makes them look less, not more, like ideal Bayesian learners. Far from generating a global boost in performance, explanation seems to generate highly selective benefits.

A second possibility is that explaining plays a motivational role that is specifically tied to causal learning. In a provocatively titled paper (“Explanation as Orgasm and the Drive for Causal Understanding”), Gopnik (2000) argues that the phenomenological satisfaction that accompanies a good explanation is part of what motivates us to learn about the causal structure of the world. Prompting learners to explain could potentially ramp up this motivational process, directing children and adults to causal relationships over causally irrelevant details (consistent with Legare & Lombrozo, 2014 ; Walker et al., 2014 ). Explaining could also affect the course of causal inquiry itself, with effects on which data are acquired and how they inform beliefs (see Legare, 2012 , for preliminary evidence that explanation guides exploration).

Finally (and not mutually exclusively), it could be that effects of explanation on learning are effectively a consequence of IBE—that is, that in the course of explaining, children generate explanatory hypotheses, and these explanatory hypotheses are evaluated with “loveliness” as a proxy for “likeliness.” For instance, in Walker et al. (2016) , children may have favored the hypothesis that accounted for more evidence because it had greater scope or coverage, and the hypothesis consistent with prior knowledge because it provided a specification of mechanism or greater coherence. We suspect that this is mostly, but only mostly, correct. Some studies have found that children who are prompted to explain outperform those in control conditions even when they fail to generate the right explanation , or any explanation at all ( Walker et al., 2014 ). This suggests the existence of some effects of engaging in explanation that are not entirely reducible to the effects of having generated any particular explanation.

While such findings are puzzling on a classic interpretation of IBE, they can potentially be accommodated with a modified and augmented version (Lombrozo, 2012 , 2016 ; Wilkenfeld & Lombrozo, 2015 ). Wilkenfeld and Lombrozo (2015) argue for what they call “explaining for the best inference” (EBI), an inferential practice that differs from IBE in focusing on the process of explaining as opposed to candidate explanations themselves. While IBE and EBI are likely to go hand in hand, there could be cases in which the explanatory processes that generate the best inferences are not identical with those promoted by possessing the best explanations, and EBI allows for this possibility.

In sum, there is good evidence that the process of engaging in explanation influences causal learning. This is potentially driven by effects of explanation on the evaluation of both evidence and prior beliefs ( Walker et al., 2016 ). One possibility is that by engaging in explanation, learners are more likely to favor hypotheses that offer “lovely” explanations (Lombrozo, 2012 , 2016 ), and to engage in cognitive processes that affect learning even when a lovely or accurate explanation is not acquired ( Wilkenfeld & Lombrozo, 2015 ). It is not entirely clear, however, whether and when these effects of explanation lead to “better” causal learning. The findings from Amsterlaw and Wellman (2006) and Chi et al. (1994) suggest that effects can be positive, accelerating conceptual development and learning. Other findings are more mixed (e.g., Kuhn & Katz, 2009 ), with the modeling result from Walker et al. (2016) suggesting that prompting children to explain makes them integrate evidence and prior beliefs in a manner that corresponds less closely to Bayesian inference. Better delineating the contours of explanation’s beneficial and detrimental effects will be an important step for future research. It will also be important to investigate how people’s tendency to engage in explanation spontaneously corresponds to these effects. That is, are the conditions under which explaining is beneficial also the conditions under which people tend to spontaneously explain?

Assigning Causal Responsibility

The previous sections considered two kinds of causal reasoning, one involving novel causal structures and the other causal events generated by known structures. Another important class of causal judgments involves the assignment of causal responsibility : to which cause(s) do we attribute a given effect? For instance, a doctor might attribute her patient’s disease to his weak immune system or to a cold virus, when both are in fact present and play a causal role.

Causal attribution has received a great deal of attention within social psychology, with the classic conundrum concerning the attribution of some behavior to a person (“she’s so clumsy!”) versus a situation (“the staircase is so slippery!”) (for reviews, see Fiske & Taylor, 2013 ; Kelley & Michela, 1980 ; Malle, 2004 ). While this research is often framed in terms of causation, it is natural to regard attribution in terms of explanation, with attributions corresponding to an answer to the question of why some event occurred (“Why did Ava slip?”). In his classic “ANOVA model,” Kelley ( 1967 , 1973 ) proposed that people effectively carry out an analysis of covariation between the behavior and a number of internal and external factors, such as the person, stimulus, and situation. For example, to explain why Ava slipped on the staircase yesterday, one would consider how this behavior fares along the dimensions of consensus (did other people slip?), the distinctiveness of the stimulus (did she slip only on that staircase?), and consistency across situations (does she usually slip, or was it the only time she did so?). Subsequent work, however, has identified a variety of additional factors that influence people’s attributions (e.g., Ahn, Kalish, Medin, & Gelman, 1995 ; Försterling, 1992 ; Hewstone & Jaspars, 1987 ; McArthur, 1972 ), and some have challenged the basic dichotomy on which the person-versus-situation analysis is based (Malle, 1999 , 2004 ; Malle, Knobe, O’Laughlin, Pearce, & Nelson, 2000 ). (We direct readers interested in social attribution to Hilton, Chapter 32 in this volume.)

Assignments of causal responsibility also arise in the context of what is sometimes called “causal selection”: the problem of deciding which cause or causes in a chain or other causal structure best explain or account for some effect. Such judgments are especially relevant in moral and legal contexts, where they are closely tied to attributions of blame. For example, suppose that someone steps on a log, which pushes a boulder onto a picnic blanket, crushing a chocolate pie. The person, the log, and the boulder all played a causal role in the pie’s destruction, but various factors might influence our assignment of causal responsibility, including the location of each factor in the chain, whether and by how much it increased the probability of the outcome, and whether the person intended and foresaw the culinary catastrophe (see, e.g., Hart & Honoré, 1985 ; Hilton, McClure, & Sutton, 2009 ; Lagnado & Channon, 2008 ; McClure, Hilton, & Sutton, 2007 ; Spellman, 1997 ). (Chapter 29 in this volume, in which Lagnado and Gerstenberg discuss moral and legal reasoning, explores these issues in detail; also relevant is Chapter 12 by Danks on singular causation.)

While research has not (to our knowledge) investigated whether explanatory considerations such as simplicity and explanatory power influence judgments of causal responsibility, ideas from the philosophy and psychology of explanation can usefully inform research on this topic. For example, scholars of explanation often emphasize the ways in which an explanation request is underspecified by a why-question itself. When we ask, “Why did Ava slip on the stairs?” the appropriate response is quite different if we’re trying to get at why Ava slipped (as opposed to Boris) than if we’re trying to get at why Ava slipped on the stairs (as opposed to the landing). These questions involve a shift in what van Fraassen (1980) calls a “contrast class,” that is, the set of alternatives to the target event that the explanation should differentiate from the target via some appropriate relation (see also Cheng & Novick, 1991 ).

McGill (1989) showed in a series of studies that a number of previously established effects in causal attribution—effects of perspective (actor vs. observer; Jones & Nisbett, 1971 ), covariation information (consensus and distinctiveness; Kelley, 1967 ), and the valence of the behavior being explained (positive vs. negative; Weiner, 1985 )—are related to shifts in the contrast class. Specifically, by manipulating the contrast class adopted by participants, McGill was able to eliminate the actor–observer asymmetry, interfere with the roles of consensus and distinctiveness information, and counteract self-serving attributions of positive versus negative performance. These findings underscore the close relationship between attribution and explanation.

Focusing on explanation is also helpful in bringing to the foreground questions of causal relevance as distinct from probability . In a 1996 paper, Hilton and Erb presented a set of studies designed to clearly differentiate these notions. In one study, Hilton and Erb showed that contextual information can influence the perceived “goodness” and relevance of an explanation without necessarily affecting its probability. For example, participants were asked to rate the following explanation of why a watch broke (an example adapted from Einhorn & Hogarth, 1986 ): “the watch broke because the hammer hit it.” This explanation was rated as fairly good, relevant, and likely to be true; however, after learning that the hammer hit the watch during a routine testing procedure at a watch factory, participants’ ratings of explanation quality and relevance dropped. In contrast, ratings of probability remained high, suggesting that causal relevance and the probability of an explanation can diverge, and that these two factors differ in their susceptibility to this contextual manipulation. It is possible that these effects were generated by a shift in contrast, from “Why did this watch break now (as opposed to not breaking now)?” to “Why did this watch break (as opposed to some other watch breaking)?”

More recently, Chin-Parker and Bradner (2010) showed that effects of background knowledge and implicit contrasts extend to the generation of explanations. They manipulated participants’ background assumptions by presenting a sequence of causal events that either did or did not seem to unfold toward a particular functional outcome (when it did, the sequence appeared to represent a closed-loop system functioning in a self-sustaining manner). Participants’ explanations of an ambiguous observation at the end of the sequence tended to invoke a failure of a system to perform its function in the former case, but featured proximal causes in the latter case. (In contrast to prior research, context did not affect explanation evaluation in this design.)

Taken together, these studies offer another set of examples of how explanatory considerations (in this case, the contextually determined contrast class) can influence causal judgments, and suggest that ascriptions of causal responsibility may vary depending on how they are framed: in terms of causal relevance and explanation, or in terms of probability and truth. It is also possible that considerations such as simplicity and scope play a role in assigning causal responsibility, above and beyond their roles in causal inference and learning. These are interesting questions for future research.

The Varieties of Causal Explanation

There is no agreed-upon taxonomy for explanations; in fact, even the distinction between causal and non-causal explanation generates contested cases. For instance, consider an example from Putnam (1975) . A rigid board has a round hole and a square hole. A peg with a square cross-section passes through the square hole, but not the round hole. Why? Putnam suggests that this can be explained by appeal to the geometry of the rigid objects (which is not causal), without appeal to lower-level physical phenomena (which are presumably causal). Is this a case of non-causal explanation? Different scholars provide different answers.

One taxonomy that has proven especially fruitful in the psychological study of explanation has roots in Aristotle’s four causes (efficient, material, final, and formal), which are sometimes characterized not as causes per se, but in terms of explanation—as distinct answers to a “why?” question ( Falcon, 2015 ). Efficient causes, which identify “the primary source of the change or rest” (e.g., a carpenter who makes a table), seem like the most canonically causal. Material causes, which specify “that out of which” something is made (e.g., wood for a table), are not causal in a narrow sense (for instance, we wouldn’t say that the wood causes or is a cause of the table), but they nonetheless play a clear causal role in the production of an object. Final and formal causes are less clearly causal; but, as we consider in the following discussion, there are ways in which each could be understood causally, as well.

First, consider final causes, which offer “that for the sake of which a thing is done.” Final cause explanations (or perhaps more accurately, their contemporary counterparts) are also known as teleological or functional explanations, as they offer a goal or a function. For instance, we might explain the detour to the café by appeal to a goal (getting coffee), or the blade’s sharpness by appeal to its function (slicing vegetables). On the face of it, these explanations defy the direction of causal influence: they explain a current event (the detour) or property (the sharpness) by appeal to something that occurs only later (the coffee acquisition or the vegetable slicing). Nonetheless, some philosophers have argued that teleological explanations can be understood causally (e.g., Wright, 1976 ), and there is evidence that adults ( Lombrozo & Carey, 2006 ) and children ( Kelemen & DiYanni, 2005 ) treat them causally, as well (see also Chaigneau, Barsalou, & Sloman, 2004 , and Lombrozo & Rehder, 2012 , for more general investigations of the causal structure of functions).

How can teleological explanations be causal? On Wright’s view, teleological explanations do not explain the present by appeal to the future—rather, the appeal to an unrealized goal or function is a kind of shorthand for a complex causal process that brought about (and hence preceded ) what is being explained. In cases of intentional action, the function or goal could be a shorthand for the corresponding intention that came first: the detour to the café was caused by a preceding intention to get coffee, and the blade’s sharpness was caused by the designer’s antecedent intention to create a tool for slicing vegetables. Other cases, however, can be more complex. For instance, we might explain this zebra’s stripes by appeal to their biological function (camouflage) because its ancestors had stripes that produced effective camouflage, and in part for that reason, stripes were increased or maintained in the population. If past zebra stripes didn’t produce camouflage, then this zebra wouldn’t have stripes (indeed, this zebra might not exist at all). In this case, the function can be explanatory because it was produced by “a causal process sensitive to the consequences of changes it produces” ( Lombrozo & Carey, 2006 ; Wright, 1976 ), even in the absence of a preceding intention to realize the function.

Lombrozo and Carey (2006) tested these ideas as a descriptive account of the conditions under which adults accept teleological explanations. In one study, they presented participants with causal stories in which a functional property did or did not satisfy Wright’s conditions. For example, participants learned about genetically engineered gophers that eat weeds, and whose pointy claws damage the roots of weeds as they dig, making them popular among farmers. The causal role of “damaging roots” in bringing about the pointy claws varied across conditions, from no role (the genetic engineer accidentally introduced a gene sequence that resulted in gophers with pointy claws), to a causal role stemming from an intention to damage roots (the genetic engineer intended to help eliminate weeds, and to that end engineered pointy claws), to a causal role without an intention to damage roots (the genetic engineer didn’t realize that pointy claws damaged weed roots, but did notice that the pointy claws were popular and decided to create all of his gophers with pointy claws). Participants then rated the acceptability and quality of teleological (and other) explanations. For the vignette involving genetically engineered gophers, they were asked why the gophers had pointy claws, and rated “because the pointy claws damage weed roots” as a response.

In this and subsequent studies, Lombrozo and Carey (2006) found that teleological explanations are understood causally in the sense that participants only accepted teleological explanations when the function or goal invoked in the explanation played an appropriate causal role in bringing about what was being explained. More precisely, this causal requirement was necessary for teleological explanations to be accepted, but not sufficient . In the preceding examples, teleological explanations were accepted at high levels when the function was intended, at moderate levels when the function played a non-intentional causal role, and at low levels when the function played no causal role at all. Lombrozo and Carey suggest (and provide evidence) that in addition to satisfying certain causal requirements, teleological explanations might call for the existence of a general pattern that makes the function predictively useful.

Kelemen and DiYanni (2005) conducted a study with elementary school children (6–7 and 9–10-year-olds) investigating the relationship between their acceptance and generation of teleological explanations for natural phenomena, on the one hand, and their causal commitments concerning their origins, on the other hand—specifically, whether they believed that an intentional designer of some kind (“someone or something”) made them or they “just happened.” The tendency to endorse and generate teleological explanations of natural events, non-living natural objects, and animals was significantly correlated with belief in the existence of an intentional creator of some kind, be it God, a human, or an unspecified force or agent. While these findings do not provide direct support for the idea that teleological explanations are grounded in a preceding intention to produce the specific function in question, the link between teleological explanations and intentional design more generally is consistent with the idea that teleological explanations involve some basic causal commitments. Along the same lines, Kelemen, Rottman, and Seston (2013) found that adults (including professional scientists) who believe in God or “Gaia” are more likely to accept scientifically unwarranted teleological explanations (see also ojalehto, Waxman, & Medin, 2013 , for a relevant discussion). Thus, the findings to date suggest that teleological explanations are understood causally by both adults and children.

What about formal explanations? Within Aristotle’s framework, a formal explanation offers “the form” of something or “the account of what-it-is-to-be.” Within psychology, what little work there is on formal explanation has focused on explanations that appeal to category membership. For example, Prasada and Dillingham (2006) define formal explanations as stating that tokens of a type have certain properties because they are the kinds of things they are (i.e., tokens of the respective type): we can say that Zach diagnoses ailments because he is a doctor , or that a particular object is sharp because it is a knife .

In their original paper and in subsequent work, Prasada and Dillingham ( 2006 , 2009 ) argue that formal explanations are not causal, but instead are explanatory by virtue of a part–whole relationship. They show that only properties that are considered to be aspects of the kind support formal explanations, in contrast to “statistical” properties that are merely reliably associated with the kind. For example, people accepted a formal explanation of why something has four legs by reference to its category (“because it’s a dog”), and also accepted the claim that “having four legs” is one aspect of being a dog. In contrast, participants rejected formal explanations such as “that (pointing to a barn) is red because it’s a barn,” and also denied that being red is one aspect of being a barn (even though most barns are red). Prasada and Dillingham (2009) argue that the relationship underlying such formal explanation is constitutive (not causal): aspects are connected to kinds via a part–whole relationship, and such relationships are explanatory because the “existence of a whole presupposes the existence of its parts, and thus the existence of a part is rendered intelligible by identifying the whole of which it is a part” (p. 421).

Prasada and Dillingham offer two additional pieces of evidence for the proposal that formal explanations are constitutive, and not causal. First, they demonstrate the explanatory potential of the part–whole relationship by showing that when this relationship is made explicit, even statistical features can support formal explanations. For example, we can explain, “Why is that (pointing to a barn) red? Because it is a red barn,” where being red is understood as part of being a red barn ( Prasada & Dillingham, 2009 ). This explanation isn’t great, but neither is it tautological: it identifies the source of the redness in something about the red barn, as opposed, for instance, to the light that happens to be shining on it (see also Cimpian & Salomon, 2014 , on “inherent” explanations). Less convincingly, they attempt to differentiate formal explanations from causal-essentialist explanations. On causal-essentialist accounts, a category’s essence is viewed as the cause of the category members’ properties ( Gelman, 2003 ; Gelman & Hirschfeld, 1999 ; Medin & Ortony, 1989 ), which could ground formal explanations in a causal relationship. To test this, Prasada and Dillingham had participants evaluate explanations such as “Why does that (pointing to a dog) have four legs? Because it has the essence of a dog which causes it to have four legs” ( Prasada & Dillingham, 2006 ). While there was a trend for formal explanations to be rated more highly than causal-essentialist explanations for properties that were taken to be aspects of a given kind, the results were inconclusive. As Prasada and Dillingham acknowledge, the wording of the causal-essentialist explanations was awkward, which could partially account for their middling acceptance. It thus remains a possibility that at least some formal explanations are understood causally, as pointers to some category-associated essence or causal factor responsible for the properties being explained.

One reason it is valuable to recognize the diversity of explanations is that different kinds of explanations lead to systematically different patterns of causal judgment. For example, Lombrozo (2009) investigated the relationship between different kinds of causal explanations and the relative importance of features in classification (see also Ahn, 1998 ). Participants learned about novel artifacts and organisms with three causally related features. To illustrate, one item involved “holings,” a type of flower with “brom” compounds in its stem, which makes it bend over as it grows, which means its pollen can be spread to other flowers by wandering field mice. Participants were asked a why-question about the middle feature (e.g., “Why do holings typically bend over?”), which was ambiguous as a request for a mechanistic explanation (e.g., “Because of the brom compounds”) or a teleological explanation (e.g., “In order to spread their pollen”). Participants provided an explanation and were subsequently asked to decide whether novel flowers were holings, where some shared the mechanistic feature (brom compounds) and some shared the functional feature (bending over). Lombrozo found that participants who provided functional explanations in response to the ambiguous why-question were significantly more likely than participants who did not to then privilege the functional feature relative to the mechanistic feature when it came to classification. Similarly, a follow-up study found that experimentally prompting participants to generate a particular explanation type by disambiguating the why-question (“In other words, what purpose might bending over serve?”) had the same effect (see also Lombrozo & Rehder, 2012 , for additional evidence about the relationship between functions and kind classification).

Additional studies suggest that the effects of mechanistic versus functional explanations extend beyond judgments of category membership. Lombrozo and Gwynne (2014) employed a method similar to Lombrozo (2009) , presenting participants with causal chains consisting of three elements, such as a certain gene that causes a speckled pattern in a plant, which attracts butterflies that play a role in pollination. Participants explained the middle feature (the speckled pattern) and generalized a number of aspects of that feature (e.g., its density, contrast, and color) to novel entities that shared either a causal or a functional feature with the original. Lombrozo and Gwynne found that explaining a property functionally (versus mechanistically) promoted the corresponding type of generalization.

Vasilyeva and Coley (2013) demonstrated a similar link between explanation and generalization in an open-ended task. Participants learned about plants and animals possessing novel but informative properties (e.g., ducks have parasite X [or X-cells ]) and generated hypotheses about which other organisms might share the property. In the course of generating these hypotheses, participants spontaneously produced formal, causal, and teleological explanations in a manner consistent with the property they reasoned about. Of most importance the type of explanation predicted the type of generalization: for example, people were most likely to generalize properties to entities related via causal interactions (e.g., plants and insects that ducks eat, or things that eat ducks) after generating causal explanations (e.g., they got it from their food). In a separate set of studies, Vasilyeva and Coley (in preparation) ruled out an alternative account based exclusively on the direct effects of generalized properties on generalizations.

Beyond highlighting some causal relationships over others, different kinds of explanations could change the way participants represent and reason about causal structure. Indeed, findings from Lombrozo (2010) suggest that this is the case. In a series of studies, Lombrozo presented participants with causal structures drawn from the philosophical literature and intended to disambiguate two accounts of causation: those based on some kind of dependence relationship (see Le Pelley, Griffiths, and Beesley, Chapter 2 in this volume) and those based on some kind of transference (see Wolff and Thorstad, Chapter 9 in this volume). According to one version of the former view, C is a cause of E if it is the case that had C not occurred, E would not have occurred. In other words, E depends upon C in the appropriate way, in this case counterfactually. According to one version of transference views, C is a cause of E if there was a physical connection between C and E—some continuous mechanism or conserved physical quantity, such as momentum.

While dependence and transference often go hand in hand, they can come apart in cases of “double prevention” and “overdetermination.” Lombrozo presented participants with such cases and found that judgments were more closely aligned with dependence views than transference views when the causal structures were directed toward a function or goal, and therefore supported a teleological explanation. Lombrozo (2010) explains this result, in part, by appeal to the idea of equifinality : when a process is goal-directed, the end may be achieved despite variations in the means. To borrow William James’s famous example, Romeo will find his way to Juliet whatever obstacle is placed in his path ( James, 1890 ). He might scale a fence or wade through a river, but the end—reaching Juliet—will remain the same. When participants reason about a structure in teleological or goal-directed terms, they may similarly represent it as means- or mechanism-invariant, and therefore focus on dependence relationships irrespective of the specific transference that happened to obtain.

In sum, pluralism has long been recognized as a feature of explanation, with Aristotle’s taxonomy providing a useful starting point for charting variation in explanations (although it is by no means the only taxonomy of explanation; see, for example, Cimpian & Salomon, 2014 , on inherent versus extrinsic explanations). We have reviewed evidence that teleological explanations are causal explanations, but that they are nonetheless treated differently from mechanistic explanations, which do not appeal to functions or goals. The evidence concerning formal explanations is less conclusive, but points to a viable alternative to a causal interpretation, with formal explanation instead depending on constitutive part–whole relations.

Recognizing explanatory pluralism can provide a useful road map for thinking about pluralism when it comes to causation and causal relations. In fact, as we have seen, different kinds of explanations do lead to systematic differences in classification and inference, with evidence that causal relationships themselves may be represented differently under different “explanatory modes.” In the following section, we take a closer look at mechanistic explanations and their relationship to causation and mechanisms.

Explanation and Causal Mechanisms

The “mechanistic explanations” considered in the previous section concerned the identification of one or more causes that preceded some effect. Often, however, causal explanations do not simply identify causes, but instead aim to articulate how the cause brought about the effect. That is, they involve a mechanism . But what, precisely, is a mechanism? Are all mechanisms causal? And do mechanisms have a privileged relationship to explanation? In this section, we begin to address these questions about the relationship between mechanisms and explanations. For a more general discussion of mechanisms, we direct readers to the chapter on mechanisms by Johnson and Ahn (Chapter 8 in this volume).

Within psychology, there is growing interest in the role of mechanisms in causal reasoning. For example, Ahn, Kalish, Medin, and Gelman (1995) found that people seek “mechanistic” information in causal attribution. Park and Sloman (2013) found that people’s violations of the Markov assumption depended on their “mechanistic” beliefs about the underlying causal structure. Buehner and McGregor (2006) showed that beliefs about mechanism type moderate effects of temporal contiguity in causal judgments (see also Ahn & Bailenson, 1996 ; Buehner & May, 2004 ; Fugelsang & Thompson, 2000 ; Koslowski & Okagaki, 1986 ; Koslowski, Okagaki, Lorenz, & Umbach, 1989 ; for reviews, see Ahn & Kalish, 2000 ; Johnson & Ahn, Chapter 8 in this volume; Koslowski, 1996 , 2012 ; Koslowski & Masnik, 2010 ; Sloman & Lagnado, 2014 ; Waldmann & Hagmayer, 2013 ). Despite these frequent appeals to mechanisms and mechanistic information, however, there is no explicitly articulated and widely endorsed conception of “mechanism.”

Most often, a mechanism is taken to spell out the intermediate steps between some cause and some effect. For example, Park and Sloman (2014) define a mechanism as “the set of causes, enablers, disablers, and preventers that are directly involved in producing an effect, along with information about how the effect comes about, including how it unfolds over time” (p. 807). Research that adopts a perspective along these lines often goes further in explicitly identifying such mechanisms as explanations (and these terms are often used interchangeably, as in Koslowski & Masnik, 2010 ). Other work operationalizes mechanisms using measures of explanation, implicitly suggesting a correspondence. For example, to validate a manipulation of mechanism, Park and Sloman asked participants whether the same explanation applies to both effects in a common-cause structure (see also Park & Sloman, 2013 ). Similarly, in a study examining mental representations of mechanisms, Johnson and Ahn (2015) considered (but did not ultimately endorse) an “explanatory” sense of mechanism, which they operationalized by asking participants to rate the extent to which some event B explains why event A led to event C.

Shifting from psychology to philosophy, we find a class of accounts of explanation that likewise associate explanations with a specification of mechanisms (e.g., Bechtel & Abrahamsen, 2005 ; Glennan, 1996 , 2002 ; Machamer, Darden, & Craver, 2000 ; Railton, 1978 ; Salmon, 1984 ). Consistent with the empirical work reviewed earlier, some of these accounts (e.g., Railton, 1978 ; Salmon, 1984 ) consider mechanisms to be “sequences of interconnected events” ( Glennan, 2002 , p. S345). Canonical examples include causal chains or networks of events leading to a specific outcome, such as a person who kicks a ball, which bounces off a pole, which breaks a window. On these views, explanation, causation, and mechanisms are not only intimately related, but potentially interdefined.

A second view of mechanisms within philosophy, however, departs more dramatically from work in psychology, and also suggests a more circumscribed role for causation. These views analyze mechanisms as complex systems that involve a (typically hierarchical) structure and arrangement of parts and processes, such as that exhibited by a watch, a cell, or a socioeconomic system (e.g., Bechtel & Abrahamsen, 2005 ; Glennan, 1996 , 2002 ; Machamer, Darden, & Craver, 2000 ). Within this framework, Craver and Bechtel (2007) offer an insightful analysis of causal and non-causal relationships within a multilevel mechanistic system. Specifically, they suggest that interlevel (i.e., “vertical”) relationships within a mechanism are not causal, but constitutive . For instance, a change in rhodopsin in retinal cells can partially explain how signal transduction occurs, but we wouldn’t say that this change causes signal transduction; it arguably is signal transduction (or one aspect of it). Craver and Bechtel point out that constitutive relations conflict with many common assumptions about event causation: that causes and effects must be distinct events, that causes precede their effects, that the causal relation is asymmetrical, and so on. Unlike causation, explanation can accommodate both causal (intralevel) relationships and constitutive (interlevel) relationships, of the kind documented by Prasada and Dillingham’s (2009) work on formal explanation.

Although Craver and Bechtel convincingly argue that the causal reading of interlevel relationships is erroneous (see also Glennan, 2010 , for related claims), as a descriptive matter, it could be that laypeople nonetheless interpret them in causal terms. An example from the Betty Crocker Cookbook , discussed by Patricia Churchland (1994) , illustrates the temptation. In the book, Crocker is correct to explain that microwave ovens work by accelerating the molecules comprising the food, but she wrongly states that the excited molecules rub against one another and that their friction generates heat. Crocker assumes that the increase in mean kinetic energy of the molecules causes heat, when in fact heat is constituted by the mean kinetic energy of the molecules ( Craver & Bechtel, 2007 ). A study by Chi, Roscoe, Slotta, Roy, and Chase (2012) showed that eighth and ninth graders, like Crocker, tended to misconstrue non-sequential, emergent processes as direct sequential causal relationships. It’s possible that adults might make similar errors as well, assimilating non-causal explanations to a causal mold.

There are thus many open questions about how best to define mechanisms for the purposes of psychological theory, and about the extent to which mechanisms are represented in terms of strictly causal relationships. What we do know, however, is that explanations and mechanisms seem to share a privileged relationship. More precisely, there is evidence that the association between mechanisms and explanation claims is closer than that between mechanisms and corresponding causal claims ( Vasilyeva & Lombrozo, 2015 ).

The studies by Vasilyeva and Lombrozo (2015) used “minimal pairs”: causal and explanatory claims that were matched as closely as possible. For example, participants read about a person, PK, who spent some time in the portrait section of a museum and made an optional donation to the museum. They were then asked to evaluate how good they found an explanation for the donation (“Why did PK make an optional donation to the museum? Because PK spent some time in the portrait section”), or how strongly they endorsed a causal relationship (“Do you think there exists a causal relationship between PK spending some time in a portrait section and PK making an optional donation to the museum?”).

Vasilyeva and Lombrozo varied two factors across items and participants: the strength of covariation evidence between the candidate cause and effect, and knowledge of a mediating mechanism. In the museum example, some participants learned the speculative hypothesis that “being surrounded by many portraits (as opposed to other kinds of paintings) creates a sense that one is surrounded by watchful others. This reminds the person of their social obligations, which in turn encourages them to donate money to the public museum.” Both explanation and causal judgments were affected by these manipulations of covariation and mechanism information. However, they were not affected equally: specifying a mechanism had a stronger effect on explanation ratings than on causal ratings, while the strength of covariation evidence had a stronger effect on causal ratings than on explanation ratings.

The findings from Vasilyeva and Lombrozo (2015) support a special relationship between explanations and mechanisms. They also challenge views that treat explanations as equivalent to identifying causal relationships, since matched explanation and causal claims were differentially sensitive to mechanisms and covariation. The findings thus raise the possibility that explanatory and causal judgments are tuned to support different cognitive functions. For example, explanation could be especially geared toward reliable and broad generalizations ( Lombrozo & Carey, 2006 ), which can benefit from mechanistic information: when we understand the mechanism by which some cause generates some effect, we can more readily infer whether the same relationship will obtain across variations in circumstances. By learning the mechanism that mediates the relationship between visiting a portrait gallery and making an optional museum donation, for example, we are in a better position to predict whether visiting a figurative versus an abstract sculpture garden will have the same effect. This benefit can potentially be realized with quite skeletal mechanistic ( Rozenblit & Keil, 2002 ) or functional understanding ( Alter, Oppenheimer, & Zemla, 2010 ); people need not understand a mechanism in full detail to gain some inferential advantage. Causal claims, by contrast, could more closely track the evidence concerning a particular event or relationship, rather than the potential for broad generalization.

In sum, the picture that emerges is one of partial overlap between causality, explanation, and mechanisms. Work in philosophy offers a variety of proposals emphasizing different aspects of mechanisms: structure, functions, temporally unfolding processes connecting starting conditions to the end state, and so on. Explanatory and causal judgments could track different aspects of mechanisms, resulting in the patterns of association and divergence observed. We suspect that adopting more explicit and sophisticated notions of mechanism will help research in this area move forward. On a methodological note, we think the strategy adopted in Vasilyeva and Lombrozo (2015) —of contrasting the characteristics of causal explanation claims with “matched” causal claims—could be useful in driving a wedge between different kinds of judgments, thus shedding light on their unique characteristics and potentially unique roles in human cognition. This strategy can also generalize to other kinds of judgments. For example, Dehghani, Iliev, and Kaufmann (2012) and Rips and Edwards (2013) both report systematic patterns of divergence between explanations and counterfactual claims, another judgment with a potentially foundational relationship to both explanation and causation.

Conclusions

Throughout the chapter, we have presented good evidence that explanatory considerations affect causal reasoning, with implications for causal inference, causal learning, and attribution. We have also considered different kinds of explanations, including their differential effects on causal generalizations and causal representation, and the role of mechanisms in causal explanation. However, many questions remain open. We highlight four especially pressing questions here.

First, we have observed many instances in which explanation leads to departures from “normative” reasoning, at least on the assumption that one ought to infer causes and causal relationships by favoring causal hypotheses with the highest posterior probabilities. Are these departures truly errors? Or have we mischaracterized the relevant competence? In particular, could it be that explanatory judgments are well-tuned to some cognitive end, but that end is not the approximation of posterior probabilities?

Second, we have focused on a characterization of explanations and the effects of engaging in explanation, with little attention to underlying cognitive mechanisms. How do people actually go about generating and evaluating causal explanations? How do the mental representations that support explanation relate to those that represent causal structure? And how do explanatory capacities arise over the course of development?

Third, what is the relationship between causal and non-causal explanations? Are they both explanatory by virtue of some shared explanatory relationship, or are causal explanations explanatory by virtue of being causal, with non-causal explanations explanatory for some other reason (for instance, because they embody a part–whole relationship)? On each view, what are the implications for causation?

Finally, we have seen how debates in explanation (from both philosophy and psychology) can inform the study of causation, with examples including inference to the best explanation, the idea of a “contrast class,” and pluralism about explanatory kinds. Can the literature on levels of explanation (e.g., Potochnik, 2010 ) perhaps inspire some new debates about levels of causation (as in, e.g., Woodward, 2010 )? Recent work on hierarchical Bayesian models and hierarchical causal structures is beginning to move in this direction, with the promise of a richer and more powerful way to understand humans’ remarkable ability to reason about and explain the causal structure of the world.

Acknowledgments

The preparation of this chapter was partially supported by the Varieties of Understanding Project funded by the Templeton Foundation, as well as an NSF CAREER award to the first author (DRL-1056712). We are also grateful to David Danks, Samuel Johnson, and Michael Waldmann for helpful comments on a previous draft if this chapter.

Ahn, W. ( 1998 ). Why are different features central for natural kinds and artifacts? The role of causal status in determining feature centrality.   Cognition , 69 (2), 135–178.

Google Scholar

Ahn, W. K. , & Bailenson, J. ( 1996 ). Causal attribution as a search for underlying mechanisms: An explanation of the conjunction fallacy and the discounting principle.   Cognitive Psychology , 31 (1), 82–123.

Ahn, W. K. , & Kalish, C. ( 2000 ). The role of mechanism beliefs in causal reasoning. In F. C. Keil (Ed.), Explanation and cognition . Cambridge, MA: MIT Press.

Google Preview

Ahn, W. K. , Kalish, C. W. , Medin, D. L. , & Gelman, S. A. ( 1995 ). The role of covariation versus mechanism information in causal attribution.   Cognition , 54 , 299–352.

Alter, A. L. , Oppenheimer, D. M. , & Zemla, J. C. ( 2010 ). Missing the trees for the forest: A construal level account of the illusion of explanatory depth.   Journal of Personality and Social Psychology , 99 , 436–451.

Amsterlaw, J. , & Wellman, H. M. ( 2006 ). Theories of mind in transition: A microgenetic study of the development of false belief understanding.   Journal of Cognition and Development , 7 (2), 139–172.

Baker, A. ( 2013 ). Simplicity. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2013 ed.). http://plato.stanford.edu/archives/fall2013/entries/simplicity/ .

Bartsch, K. , & Wellman, H. M. ( 1995 ). Children talk about the mind . Oxford: Oxford University Press.

Bechtel, W. , & Abrahamsen, A. ( 2005 ). Explanation: A mechanist alternative.   Studies in History and Philosophy of Science Part C :Studies in History and Philosophy of Biological and Biomedical Sciences , 36 (1995), 421–441.

Bonawitz, E. B. , & Lombrozo, T. ( 2012 ). Occam’s rattle: Children’s use of simplicity and probability to constrain inference.   Developmental Psychology , 48 (4), 1156–1164.

Buehner, M. J. ( 2005 ). Contiguity and covariation in human causal inference.   Learning & Behavior: A Psychonomic Society Publication , 33 (2), 230–238.

Buehner, M. J. , & May, J. ( 2004 ). Abolishing the effect of reinforcement delay on human causal learning.   The Quarterly Journal of Experimental Psychology , 57 B , 179–191.

Buehner, M. J. , & McGregor, S. ( 2006 ). Temporal delays can facilitate causal attribution: Towards a general timeframe bias in causal induction.   Thinking & Reasoning , 12 , 353–378.

Chaigneau, S. E. , Barsalou, L. W. , & Sloman, S. A. ( 2004 ). Assessing the causal structure of function.   Journal of Experimental Psychology: General , 133 (4), 601–25.

Cheng, P. W. ( 1997 ). From covariation to causation: A causal power theory.   Psychological Review , 104 , 367–405.

Cheng, P. W. , & Novick, L. R. ( 1990 ). A probabilistic contrast model of causal induction.   Journal of Personality and Social Psychology , 58 (4), 545.

Cheng, P. W. , & Novick, L. R. ( 1991 ). Causes versus enabling conditions.   Cognition , 40 (1–2), 83–120.

Cheng, P. W. , & Novick, L. R. ( 1992 ). Covariation in natural causal induction.   Psychological Review , 99 (2), 365–382.

Chi, M. T. H. , De Leeuw, N. , Chiu, M.-H. , & Lavancher, C. ( 1994 ). Eliciting self-explanations improves understanding.   Cognitive Science , 18 (3), 439–477.

Chi, M. T. H. , Roscoe, R. D. , Slotta, J. D. , Roy, M. , & Chase, C. C. ( 2012 ). Misconceived causal explanations for emergent processes.   Cognitive Science , 36 (1), 1–61.

Chin-Parker, S. , & Bradner, A. ( 2010 ). Background shifts affect explanatory style: How a pragmatic theory of explanation accounts for background effects in the generation of explanations.   Cognitive Processing , 11 (3), 227–249.

Churchland, P. S. ( 1994 ). Can neurobiology teach us anything about consciousness?   Proceedings and Addresses of the American Philosophical Association , 67 (4), 23–40.

Cimpian, A. , & Salomon, E. ( 2014 ). The inherence heuristic: An intuitive means of making sense of the world, and a potential precursor to psychological essentialism.   Behavioral and Brain Sciences , 37 (5), 461–480.

Craver, C. F. , & Bechtel, W. ( 2007 ). Top-down causation without top-down causes.   Biology and Philosophy , 22 , 547–563.

Dehghani, M. , Iliev, R. , & Kaufmann, S. ( 2012 ). Causal explanation and fact mutability in counterfactual reasoning.   Mind & Language , 27 (1), 55–85.

Douven, I. ( 2011 ). Abduction. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy . (Spring 2011 ed.). http://plato.stanford.edu/archives/spr2011/entries/abduction/ .

Douven, I. ( 2013 ). Inference to the best explanation, Dutch books, and inaccuracy minimisation.   Philosophical Quarterly , 63 (252), 428–444.

Douven, I. , & Schupbach, J. N. ( 2015 a). The role of explanatory considerations in updating.   Cognition , 142 , 299–311.

Douven, I. , & Schupbach, J. N. ( 2015 b). Probabilistic alternatives to Bayesianism: The case of explanationism.   Frontiers in Psychology , 6 , 1–9.

Einhorn, H. J. , & Hogarth, R. M. ( 1986 ). Judging probable cause.   Psychological Bulletin , 99 (1), 3–19.

Falcon, A. ( 2015 ). Aristotle on causality. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2015 Edition). http://plato.stanford.edu/archives/spr2015/entries/aristotle-causality/ .

Fiske, S. T. , & Taylor, S. E. ( 2013 ). Social cognition: From brains to culture . Thousand Oaks, CA: Sage.

Försterling, F. ( 1992 ). The Kelley model as an analysis of variance analogy: How far can it be taken?   Journal of Experimental Social Psychology , 28 (5), 475–490.

Fugelsang, J. A. , & Thompson, V. A. ( 2000 ). Strategy selection in causal reasoning: When beliefs and covariation collide.   Canadian Journal of Experimental Psychology , 54 , 15–32.

Gelman, S. A. ( 2003 ). The essential child: Origins of essentialism in everyday thought . Oxford: Oxford University Press.

Gelman, S. A. , & Hirschfeld, L. A. ( 1999 ). How biological is essentialism.   Folkbiology , 9 , 403–446.

Glennan, S. ( 1996 ). Mechanisms and the nature of causation.   Erkenntnis , 44 (1), 49–71.

Glennan, S. ( 2002 ). Rethinking mechanistic explanation.   Philosophy of Science , 69 (3), S342–S353.

Glennan, S. ( 2010 ). Mechanisms, causes, and the layered model of the world.   Philosophy and Phenomenological Research , 81 (2), 362–381.

Glymour, C. , & Cheng, P. W. ( 1998 ). Causal mechanism and probablity: A normative approach. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 295–313). Oxford: Oxford University Press.

Good, I. J. ( 1960 ). Weight of evidence, corroboration, explanatory power, information and the utility of experiments.   Journal of the Royal Statistical Society: Series B (Methodological) , 22 (2), 319–331.

Goodman, N. D. , Baker, C. L. , Bonawitz, E. B. , Mansinghka, V. K. , Gopnik, A. , Wellman, H. , et al. ( 2006 ). Intuitive theories of mind: A rational approach to false belief. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual conference of the Cognitive Science Society (pp. 1382–1387). Mahwah, NJ: Lawrence Erlbaum Associates.

Gopnik, A. ( 2000 ). Explanation as orgasm and the drive for causal knowledge: The function, evolution, and phenomenology of the theory formation system. In F. Keil & R. A. Wilson (Eds.), Explanation and cognition (pp. 299–323). Cambridge, MA: MIT Press.

Gopnik, A. , & Sobel, D. M. ( 2000 ). Detecting blickets: How young children use information about novel causal powers in categorization and induction.   Child Development , 71 (5), 1205–1222.

Griffiths, T. L. , & Tenenbaum, J. B. ( 2005 ). Structure and strength in causal induction.   Cognitive Psychology , 51 (4), 334–384.

Harman, G. H. ( 1965 ). The inference to the best explanation.   Philosophical Review , 74 (1), 88–95.

Hart, H. L. A. , & Honoré, T. ( 1985 ). Causation in the Law . Oxford: Oxford University Press.

Henderson, L. ( 2014 ). Bayesianism and inference to the best explanation.   The British Journal for the Philosophy of Science , 65 , 687–715.

Hewstone, M. , & Jaspars, J. ( 1987 ). Covariation and causal attribution: A logical model of the intuitive analysis of variance.   Journal of Personality and Social Psychology , 53 (4), 663–672.

Hilton, D. J. , & Erb, H.-P. ( 1996 ). Mental models and causal explanation: Judgments of probable cause and explanatory relevance.   Thinking and Reasoning , 2 (4), 273–308.

Hilton, D. J. , McClure, J. , & Sutton, R. M. ( 2009 ). Selecting explanations from causal chains: Do statistical principles explain preferences for voluntary causes?   European Journal of Social Psychology , 39 , 1–18.

Holyoak, K. J. , & Cheng, P. W. ( 2011 ). Causal learning and inference as a rational process: The new synthesis.   Annual Review of Psychology , 62 , 135–163.

James, W. ( 1890 ). The principles of psychology . New York: H. Holt.

Johnson, S. G. B. , & Ahn, W. ( 2015 ). Causal networks or causal islands? The Rrepresentation of mechanisms and the transitivity of causal judgment.   Cognitive Science , 1–36.

Johnson, S. G. B. , Johnston, A. M. , Toig, A. E. , & Keil, F. C. ( 2014 ). Explanatory scope informs causal strength inferences. In P. Bello , M. Guarini , M. McShane , & B. Scassellati (Eds.), Proceedings of the 36th annual conference of the Cognitive Science Society (pp. 2453–2458). Austin, TX: Cognitive Science Society.

Johnston, A. M. , Johnson, S. G. B. , Koven M. L. , & Keil, F. C. ( 2015 ). Probabilistic versus heuristic accounts of explanation in children: Evidence from a latent scope bias. In D. C. Noelle , R. Dale , A. S. Warlaumont , J. Yoshimi , T. Matlock , C. D. Jennings , & P. P. Maglio (Eds.), Proceedings of the 37th annual conference of the Cognitive Science Society (pp. 1021–1026). Austin, TX: Cognitive Science Society.

Jones, E. E. , & Nisbett, R. E. ( 1971 ). The actor and the observer: Divergent perceptions of the causes of behavior. In E. E. Jones et al. (Eds.), Attribution: Perceiving the causes of behavior . Morristown, N.J.: General Learning Press.

Kelemen, D. , & DiYanni, C. ( 2005 ). Intuitions about origins: Purpose and intelligent design in children’s reasoning about nature.   Journal of Cognition and Development , 6 , 3–31.

Kelemen, D. , Rottman, J. , & Seston, R. ( 2013 ). Professional physical scientists display tenacious teleological tendencies: Purpose-based reasoning as a cognitive default.   Journal of Experimental Psychology: General , 142 (4), 1074–1083.

Kelley, H. H. ( 1967 ). Attribution theory in social psychology.   Nebraska Symposium on Motivation , 15 , 192–238.

Kelley, H. H. ( 1973 ). The process of causal attributions.   American Psychologist , 28 , 107–128.

Kelley, H. H. , & Michela, J. L. ( 1980 ). Attribution theory and research.   Annual Review of Psychology , 31 , 457–501.

Kemp, C. , Goodman, N. , & Tenenbaum, J. ( 2010 ). “ http://www.psy.cmu.edu/%7Eckemp/papers/kempgt10_learningtolearncausalmodels.pdf ” Learning to learn causal models.   Cognitive Science , 34 (7), 1185–1243.

Khemlani, S. S. , Sussman, A. B. , & Oppenheimer, D. M. ( 2011 ). Harry Potter and the sorcerer’s scope: Latent scope biases in explanatory reasoning.   Memory & Cognition , 39 (3), 527–535.

Koslowski, B. ( 1996 ). Theory and evidence: The development of scientific reasoning . Cambridge, MA: MIT Press.

Koslowski, B. ( 2012 ). Scientific reasoning: Explanation, confirmation bias, and scientific practice. In G. Feist & M. Gorman (Eds.), Handbook of the psychology of science . New York: Springer.

Koslowski, B. , & Masnick, A. ( 2010 ). Causal reasoning and explanation. In U. C. Goswami (Ed.), The Wiley-Blackwell handbook of childhood cognitive development (2nd ed., pp. 377–398). Malden, MA: Wiley-Blackwell.

Koslowski, B. , & Okagaki, L. ( 1986 ). Non-Humean indices of causation in problem-solving situations: Causal mechanism, analogous effects, and the status of rival alternative accounts.   Child Development , 57 (5), 1100–1108.

Koslowski, B. , Okagaki, L. , Lorenz, C. , & Umbach, D. ( 1989 ). When covariation is not enough: The role of causal mechanism, sampling method, and sample size in causal reasoning.   Child Development , 60 (6), 1316–1327.

Kuhn, D. , & Katz, J. ( 2009 ). Are self-explanations always beneficial?   Journal of Experimental Child Psychology , 103 (3), 386–394.

Lagnado, D. A. , & Channon, S. ( 2008 ). Judgments of cause and blame: the effects of intentionality and foreseeability.   Cognition , 108 (3), 754–70.

Legare, C. H. ( 2012 ). Exploring explanation: Explaining inconsistent evidence informs exploratory, hypothesis-testing behavior in young children.   Child Development , 83 (1), 173–85.

Legare, C. H. , & Lombrozo, T. ( 2014 ). Selective effects of explanation on learning during early childhood.   Journal of Experimental Child Psychology , 126 , 198–212.

Legare, C. H. , Wellman, H. M. , & Gelman, S. A. ( 2009 ). Evidence for an explanation advantage in naïve biological reasoning.   Cognitive Psychology , 58 (2), 177–94.

Lipton, P. ( 2004 ). Inference to the best explanation . London: Routledge.

Lombrozo, T. ( 2007 ). Simplicity and probability in causal explanation.   Cognitive Psychology , 55 (3), 232–257.

Lombrozo, T. ( 2009 ). Explanation and categorization: How “why?” informs “what?.”   Cognition , 110 (2), 248–53.

Lombrozo, T. ( 2010 ). Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions.   Cognitive Psychology , 61 (4), 303–32.

Lombrozo, T. ( 2012 ). Explanation and abductive inference. In Oxford handbook of thinking and reasoning (pp. 260–276). Oxford: Oxford University Press.

Lombrozo, T. ( 2016 ). Explanatory preferences shape learning and inference.   Trends in Cognitive Sciences , 20 , 748–759.

Lombrozo, T. , & Carey, S. ( 2006 ). Functional explanation and the function of explanation.   Cognition , 99 (2), 167–204.

Lombrozo, T. , & Gwynne, N. Z. (2014). Explanation and inference: Mechanistic and functional explanations guide property generalization.   Frontiers in Human Neuroscience , 8 (September), 700.

Lombrozo, T. , & Rehder, B. ( 2012 ). Functions in biological kind classification.   Cognitive Psychology , 65 (4), 457–485.

Machamer, P. , Darden, L. , & Craver, C. F. ( 2000 ). Thinking about mechanisms.   Philosophy of Science , 67 (1), 1–25.

Malle, B. F. ( 1999 ). How people explain behavior: A new theoretical framework.   Personality and Social Psychology Review , 3 (1), 23–48.

Malle, B. F. ( 2004 ). How the mind explains behavior: Folk explanations, meaning, and social interaction . Cambridge, MA: MIT Press.

Malle, B. F. , Knobe, J. , O’Laughlin, M. J. , Pearce, G. E. , & Nelson, S. E. ( 2000 ). Conceptual structure and social functions of behavior explanations: Beyond person-situation attributions.   Journal of Personality and Social Psychology , 79 (3), 309–326.

Mansinghka, V. K. , Kemp, C. , Tenenbaum, J. B. , & Griffiths, T. L. (2006). Structured priors for structure learning. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006).

McArthur, L. A. ( 1972 ). The how and what of why: Some determinants and consequences of causal attribution.   Journal of Personality and Social Psychology , 22 (2), 171–193.

McClure, J. , Hilton, D. J. , & Sutton, R. M. ( 2007 ). Judgments of voluntary and physical causes in causal chains: Probabilistic and social functionalist criteria for attributions.   European Journal of Social Psychology , 37 , 879–901.

McGill, A. L. ( 1989 ). Context effects in judgments of causation.   Journal of Personality and Social Psychology , 57 (2), 189–200.

Medin, D. L. , & Ortony, A. ( 1989 ). Psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and Analogical Reasoning (pp. 179–195). Cambridge: Cambridge University Press.

ojalehto, b. , Waxman, S. R. , & Medin, D. L. ( 2013 ). Teleological reasoning about nature: Intentional design or relational perspectives?   Trends in Cognitive Sciences , 17 (4), 166–171.

Pacer, M. , & Lombrozo, T. ( 2015 ). Ockham’s Razor cuts to the root: simplicity in causal explanation. Manuscript in revision.

Pacer, M. , Williams, J. J. , Chen, X. , Lombrozo, T. , & Griffiths, T. L. ( 2013 ). Evaluating computational models of explanation using human judgments. In A. Nicholson & P. Smyth (Eds.), Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (pp. 498–507). Corvallis, Oregon: AUAI Press.

Park, J. , & Sloman, S. A. ( 2014 ). Causal explanation in the face of contradiction.   Memory & Cognition , 42 (5), 806–820.

Park, J. , & Sloman, S. A. ( 2013 ). Mechanistic beliefs determine adherence to the Markov property in causal reasoning.   Cognitive Psychology , 67 (4), 186–216.

Peirce, C. S. ( 1955 ). Abduction and induction. In Philosophical writings of Peirce (Vol. 11). New York: Dover.

Pennington, N. , & Hastie, R. ( 1992 ). Explaining the evidence: Tests of the Story Model for juror decision making.   Journal of Personality and Social Psychology , 62 (2), 189–206.

Perales, J. C. , & Shanks, D. R. ( 2003 ). Normative and descriptive accounts of the influence of power and contingency on causal judgement.   The Quarterly Journal of Experimental Psychology. A: Human Experimental Psychology , 56 (6), 977–1007.

Pine, K. J. , & Siegler, R. S. (2003). The role of explanatory activity in increasing the generality of thinking. Paper presented at the biennial meeting of the Society for Research in Child Development, Tampa, FL.

Potochnik, A. ( 2010 ). Levels of explanation reconceived.   Philosophy of Science , 77 (1), 59–72.

Prasada, S. , & Dillingham, E. M. ( 2006 ). Principled and statistical connections in common sense conception.   Cognition , 99 (1), 73–112.

Prasada, S. , & Dillingham, E. M. ( 2009 ). Representation of principled connections: A window onto the formal aspect of common sense conception.   Cognitive Science , 33 (3), 401–48.

Preston, J. , & Epley, N. ( 2005 ). Explanations versus applications: The explanatory power of valuable beliefs.   Psychological Science , 16 (10), 826–832.

Putnam, H. ( 1975 ). Philosophy and our mental life. In H. Putnam , Mind, Language and Reality: Philosophical Papers (Vol. 2). New York: Cambridge University Press.

Railton, P. ( 1978 ). A deductive-nomological model of probabilistic explanation.   Philosophy of Science , 44 (2), 206–226.

Read, S. J. , & Marcus-Newhall, A. ( 1993 ). Explanatory coherence in social explanations: A parallel distributed processing account.   Journal of Personality and Social Psychology , 65 (3), 429.

Rips, L. J. , & Edwards, B. J. ( 2013 ). Inference and explanation in counterfactual reasoning.   Cognitive Science , 37 (6), 1107–35.

Rozenblit, L. , & Keil, F. ( 2002 ). The misunderstood limits of folk science: An illusion of explanatory depth.   Cognitive Science , 26 , 521–562.

Salmon, W. ( 1984 ). Scientific explanation and the causal structure of the world . Princeton, NJ: Princeton University Press.

Schupbach, J. N. ( 2011 ). Comparing probabilistic measures of explanatory power.   Philosophy of Science , 78 (5), 813–829.

Schupbach, J. N. , & Sprenger, J. ( 2011 ). The logic of explanatory power.   Philosophy of Science , 78 (1), 105–127.

Shanks, D. R. , & Dickinson, A. ( 1988 ). Associative accounts of causality judgment.   Psychology of Learning and Motivation: Advances in Research and Theory , 21 (C), 229–261.

Siegler, R. S. ( 1995 ). How does change occur: A microgenetic study of number conservation.   Cognitive Psychology , 28 , 225–273.

Sloman, S. A , & Lagnado, D. ( 2014 ). Causality in thought.   Annual Review of Psychology , 66 , 223–247.

Spellman, B. A. ( 1997 ). Crediting causality.   Journal of Experimental Psychology: General , 126 (4), 323–348.

Thagard, P. ( 1989 ). Explanatory coherence.   Behavioral and Brain Sciences , 12 , 435–502.

van Fraassen, B. C. ( 1980 ). The scientific image . Oxford University Press.

Vasilyeva, N. , & Coley, J.C. ( 2013 ). Evaluating two mechanisms of flexible induction: Selective memory retrieval and evidence explanation. In M. Knauff , M. Pauen , N. Sebanz , & I. Wachsmuth (Eds.), Proceedings of the 35th annual conference of the Cognitive Science Society (pp. 3645–3650). Austin, TX: Cognitive Science Society.

Vasilyeva, N. , & Lombrozo, T. ( 2015 ). Explanation and causal judgments are differentially sensitive to covariation and mechanism information. In Proceedings of the 37th annual conference of the Cognitive Science Society (pp. 2663–2668). Austin, TX: Cognitive Science Society.

Waldmann, M. R. , & Hagmayer, Y. ( 2001 ). Estimating causal strength: The role of structural knowledge and processing effort.   Cognition , 82 (1), 27–58.

Waldmann, M. R. , & Hagmayer, Y. ( 2013 ). Causal reasoning. In D. Reisberg (Ed.), Oxford handbook of cognitive psychology (pp. 733–752). New York: Oxford University Press.

Walker, C. M. , Lombrozo, T. , Legare, C. H. , & Gopnik, A. ( 2014 ). Explaining prompts children to privilege inductively rich properties.   Cognition , 133 (2), 343–57.

Walker, C.M. , Lombrozo, T. , Williams, J. J. , Rafferty, A. , & Gopnik, A. ( 2016 ). Explaining constrains causal learning in childhood.   Child Development .

Weiner, B. ( 1985 ). An attributional theory of achievement motivation and emotion.   Psychological Review , 92 (4), 548–573.

Wellman, H. M. ( 2011 ). Reinvigorating explanations for the study of early cognitive development.   Child Development Perspectives , 5 (1), 33–38.

Wellman, H. M. , & Lagattuta, K. H. ( 2004 ). Theory of mind for learning and teaching: The nature and role of explanation.   Cognitive Development , 19 , 479–497.

Wellman, H. M. , & Liu, D. ( 2007 ). Causal reasoning as informed by the early development of explanations. In A. Gopnik & L. Schulz (Eds.), Causal Learning: Psychology, Philosophy, and Computation (pp. 261–279). Oxford: Oxford University Press.

Wilkenfeld, D. A. , & Lombrozo, T. ( 2015 ). Infernece to the Best Explanation (IBE) vs. Explaining for the Best Inference (EBI).   Science and Education , 24 (9–10), 1059–1077.

Williams, J. J. , & Lombrozo, T. ( 2010 ). The role of explanation in discovery and generalization: Evidence from category learning.   Cognitive Science , 34 (5), 776–806.

Williams, J. J. , & Lombrozo, T. ( 2013 ). Explanation and prior knowledge interact to guide learning.   Cognitive Psychology , 66 (1), 55–84.

Williams, J. J. , Lombrozo, T. , & Rehder, B. ( 2013 ). The hazards of explanation: Overgeneralization in the face of exceptions.   Journal of Experimental Psychology. General , 142 (4), 1006–14.

Woodward, J. ( 2010 ). Causation in biology: Stability, specificity, and the choice of levels of explanation.   Biology & Philosophy , 25 (3), 287–318.

Wright, L. ( 1976 ). Teleological explanations: An etiological analysis of goals and functions . Berkeley: University of California Press.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

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BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

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What Is Face Validity In Research? Importance & How To Measure

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This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

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Causal Inference

List of mathematical algorithms machine learning (ml).

Open-Source Internship opportunity by OpenGenus for programmers. Apply now.

The process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system is called Causal inference. Usual statistical methods like correlation does not ensures causality. That's why we need a more scientific method to ensure causation.

Table of Contents:

• Causal reasoning

Methodology

Experimental method, quasi-experimental method, implementation in python, real life applications, causal reasoning:.

Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality was originated in ancient philosophy. Then It also discussed in neuropsychology by correlating the changes in the brain with behavioral outcomes. Assumptions about the nature of causality, may be shown to be functions of a previous event preceding a later one. The first study of cause and effect occurred in Aristotle's Physics. Causal inference is an example of causal reasoning.

In Causal Inference, the measure of one variable is suspected to affect the measure of another variable in a system. The first step is to formulate a falsifiable null hypothesis, which will be tested with statistical methods. Probability of that null hypothesis is true is to be calculated. Bayesian inference is used here to determine the effect of an independent variable.

Suppose, a chemist invented a new drug to cure a disease. Now there are four catagories

• The patient consumed the drug and cured.
• The patient consumed the drug but not cured.
• The patient didn't consume the drug but cured.
• The patient didn't consume the drug and not cured. There are two Uplift Modelling Meta-Learning Techniques

1. Two-Model Approach Now, Individual Treatment Effect

2. Class Transformation Approach Now, Individual Treatment Effect

Randomized control test can be used to find the answers of some questions.

There are experimental methods to verify of causal mechanisms. Suppose, By keeping the other experimental variables constant if we manipulate the variable of interest, A and find that another variable, B is also changing then A is called Independent variable and B is called Dependent variable. If A have statistically significant effect on B then it is considered as a causal effect.

When traditional experimental methods are unavailable, infeasible or illegal Quasi-experimental verification is used. In this method researchers collect the data before the change of A (Independent variable) and after the change of A and work with the collected data for verification of causality.

In Python, the DoWhy python library is used to do causal inference. DoWhy performed this into 4 steps - Modeling, Identification, Estimation, Refutation. Here is an example of evaluation the impact of a signup program on customer spending behavior over time. Creating a dataset

This code simulates a dataset to analyze the causal impact of a signup program on customer spending over time. By defining a causal graph, it models the relationships between treatment (signup), pre-signup spends, and post-signup spends. Using DoWhy, the code identifies and estimates the causal effect, showing that signing up increases spending. A placebo test confirms the robustness of this effect, indicating the observed increase in spending is likely due to the program rather than random chance. This approach effectively demonstrates how to use causal inference techniques to evaluate program impacts.

I. Model a causal problem

Post-process the data based on the graph and the month of the treatment (signup) For each customer, determine their average monthly spend before and after month i

II. Identify a target estimand under the model

III. Estimate causal effect based on the identified estimand

IV. Refute the obtained estimate

In Etiology, it is used to find the correct reason of a disease among multiple factors like pathogen, a perticular gene trait or chemical substrates. Award-winning computer scientist and philosopher Judea Pearl first discussed The concept of causal AI and the limits of machine learning in 2018's "The Book of Why: The New Science of Cause and Effect. In 2020, Columbia University established a Causal AI Lab for research on causal and counterfactual inference under Director Elias Bareinboim to apply in data-driven fields like the health sector, social sciences or consulting firms. On a conceptual level, the idea is to factorize the joint distribution P(Cause, Effect) into P(Cause) * P(Effect | Cause) rather than the factorization into P(Effect) * P(Cause | Effect) to reduce it's complexities. A different family of methods are used to discover causal "footprints" from large amounts of labeled data, and allow the prediction of more flexible causal relations. One practical use of causal AI in organisations is to explain decision-making and the causes for a decision.

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• Open access
• Published: 21 May 2024

Dissecting causal relationships between primary biliary cholangitis and extrahepatic autoimmune diseases based on Mendelian randomization

• Gang Ma 1   na1 ,
• Jiaqi Yang 1   na1 ,
• Xingguo Wang 2   na1 ,
• Erzhuo Xia 1 ,
• Jiahao Yu 1 ,
• Miao Zhang 1 ,
• Yinan Hu 1 ,
• Shuoyi Ma 1 ,
• Xia Zhou 1 ,
• Qingling Fan 1 ,
• Ying Han 1 &
• Jingbo Wang 1  

Scientific Reports volume  14 , Article number:  11528 ( 2024 ) Cite this article

408 Accesses

Metrics details

• Quality of life
• Risk factors
• Signs and symptoms

As an autoimmune disease, up to 73% of patients with primary biliary cholangitis (PBC) have a combination of extrahepatic autoimmune diseases (EHAIDs); however, the causal relationship between PBC and EHAIDs is unclear. The genome-wide association analyses provided 14 GWAS data for PBC and EHAIDs, and bidirectional, two-sample MR analyses were performed to examine the relationship between PBC and EHAIDs. The analysis using MR provides a strong and meaningful estimation of the bidirectional correlation between PBC and 7 EHAIDs: rheumatoid arthritis, systemic lupus erythematosus, Sjögren's syndrome, systemic sclerosis, autoimmune hypothyroidism, inflammatory bowel disease and ulcerative colitis of its types. In addition, PBC increases the risk of autoimmune thyroid diseases such as autoimmune hyperthyroidism and Graves' disease, as well as multiple sclerosis and psoriasis. Additionally, PBC is identified as a risk factor for Crohn's disease and Celiac disease. Based on genetic evidence, there may be connections between PBC and specific EHAIDs: not all coexisting EHAIDs induce PBC, and vice versa. This underscores the significance of prioritizing PBC in clinical practice. Additionally, if any liver function abnormalities are observed during treatment or with EHAIDs, it is crucial to consider the possibility of comorbid PBC.

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Genome-wide association studies

Refining the impact of genetic evidence on clinical success

Introduction.

Epidemiological studies may be biased due to factors that cannot be measured, such as unmeasurable confounding and reverse causality. These factors make it challenging to make specific causal inferences. Although epidemiological studies indicate a link between PBC and EHAIDs, it is still uncertain whether this association is causal. Mendelian randomization (MR) is a statistical analysis that helps infer the cause of diseases based on genetic variants. MR analysis uses single nucleotide polymorphisms (SNPs) as instrumental variables (IV) to explore potential causal relationships between exposure (phenotype) and outcome (disease). These genetic variants are randomly assigned at conception, which minimizes potential bias due to environmental confounders and reverse causality. Therefore, MR analysis is a reliable method to investigate the causal relationship between exposure and outcome 6 , 7 .

A recent study by Fan et al. 8 indicated that PBC may have a causal role in the pathogenesis of RA, but not vice versa. However, the study did not account for the possible influence of MHC regions on the outcome, which could have led to a bias. In the MR study of IBD on PBC 9 , the authors did not utilize the latest GWAS data for PBC, and the impact of UC on PBC was inconsistent. As a result, the findings may not be entirely accurate and require validation with a larger sample size. In addition, there has been little research on the relationship between PBC and other EHAIDs. To address this gap, we conducted a two-sample bidirectional MR study on major EHAIDs with PBC to better understand the causal effects.

Study cohorts and GWAS

We searched PubMed to identify EHAIDs with a relatively high incidence of comorbidity with PBC: rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Sjögren's syndrome (SS), systemic sclerosis (SSc), autoimmune thyroid disease (AITD), autoimmune hyperthyroidism (AHYPER), Graves' disease (GD), autoimmune hypothyroidism (AHYPO), inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), celiac disease (CeD), multiple sclerosis (MS), and psoriasis (PoS). We obtained GWAS data for PBC and EHAIDs from PubMed and online platforms such as GWAS catalog, IEU Open GWAS project, UKBB, and Finngen R9 (Table 1 ). When there were multiple GWAS options for a disease, we chose the best one based on these criteria: all cases and controls were of European origin, no significant overlap between GWAS populations, complete GWAS pooled data, high number of cases, and relatively high number of SNPs detected.

• Mendelian randomization

IV selection

When conducting an MR study, it is crucial to use validated genetic variants (SNPs) that meet specific criteria. First, they should be strongly associated with the exposure being studied. Second, there should be no common cause for the outcome. Last, the SNPs should only affect the outcome through the exposure pathway and not through any confounding factors 7 . In summary, the SNPs must be related to the exposure, unrelated to any confounders, and not affect the outcome or confounding factors.

To choose genetic instruments from each of the 14 exposed GWASs, we utilized the default settings of TwoSampleMR from the R package 10 , 11 . Specifically, our approach involved extracting SNPs that are genome-wide significant (P value < 5.0E−08) while excluding those containing MHC regions 12 . We used standard aggregation parameters that excluded variants with a physical distance of less than 10,000 kb and r 2  < 0.001. When IVs were present in the exposure but not in the outcome, we relied on the LDproxyR tool to replace them with proxy SNPs in high linkage disequilibrium (LD r 2  > 0.8). To estimate the intensity of IV, we calculated the proportion of variance in the exposure explained by the SNP (r 2 ) and the F-statistic 13 . An F-statistic > 10 indicated the appropriate instrumental variable to satisfy the first MR hypothesis. We excluded ambiguous SNPs with inconsistent alleles, echo SNPs with ambiguous chains and SNPs with minor allele frequencies less than 1%. Finally, we utilized IV clumping and then applied Steiger filtering to eliminate SNPs that accounted for more variability in the outcome than in the exposure 14 .

This approach was also applied to the reverse MR process, resulting in a total of 28 two-way MR studies that evaluated EHAIDs and PBC as both exposures and outcomes (Fig.  1 ).

Flowchart for Mendelian randomization analysis.

Mendelian randomization analyses

We conducted a thorough analysis of MR using four different methods, each with varying statistical assumptions: inverse variance weighted method with fixed effects and multiplicative random effects (IVW-FE and IVW-MRE), MR‒Egger regression, weighted median method (WME), and weighted mode method (WM). Our aim was to obtain conclusive β estimates that could be converted to ORs 15 , 16 , 17 . To test heterogeneity, we utilized the Cochran Q test in the IVW and MR‒Egger methods 18 . If the IVW method indicated heterogeneity, we used IVW-MRE. We also employed the MR‒Egger regression intercept method and the Global Test of MR-PRESSO to evaluate the horizontal multiplicity of IVs. To ensure reliable results and eliminate outliers, we conducted MR-PRESSO 19 , 20 and MR analyses multiple times. We removed the outlier corresponding to the smallest P value of MR-PRESSO each time unless all P values were 1. The final MR results were generated when the P values of the pleiotropy test (MR-Egger intercept and MR-PRESSO global test) and the heterogeneity test (IVW Cochran Q test) were greater than 0.05. This approach helped us minimize outliers and retain valid instrumental variables for reliable results. To visually present the results of various MR methods, we created scatter plots using the TwoSampleMR package. Additionally, we conducted a leave-one-out analysis (LOO) to detect the potential impact of any single SNP on the outcome of each MR study. In the absence of horizontal pleiotropy in instrumental variables, we relied on the IVW method as our primary detection tool. To minimize type I errors, we applied FDR correction to the p-values of MR. If the adjusted P-value was less than 0.05, we considered it as a significant genetic causal relationship between the exposure and outcome. If the P-value was less than 0.05 corrected but adjusted P-value not, we considered it as a potential genetic causal relationship between exposure and outcome (Fig.  1 ).

All statistical analyses were carried out using the R program (version 4.3.1) and the TwoSampleMR 11 , MRPRESSO 19 packages.

Results of the MR analyses of PBC on EHAIDs

The main results of MR analysis investigating the relationship between PBC and EHAIDs are shown in Table 2 . PBC was considered significant with a value of P < 5.0E−08 as an exposure factor, and 8–30 eligible SNPs were obtained (Supplementary 2 , S2T1). These SNPs accounted for 3.05% to 11.38% of the total variance in PBC. The F values for individual SNPs and mean-F statistics were > 10, indicating that the instrument strength was adequate. Our IVW results showed that PBC has a positive genetic contribution to EHAIDs, including RA, SLE, SS, SSc, AITD, AHYPO, AHYPER, GD, IBD, UC, MS and PoS, which may increase the risk of developing these diseases, with ORs ranging from 1.06 to 1.44 (Fig.  2 ). Furthermore, the Steiger test confirmed the directionality of causality, as shown in the Supplementary 1 , S1T3.

IVW results of Mendelian randomization analysis for PBC-on-EHAIDs.

Results of the MR analyses of EHAIDs on PBC

Table 3 shows the results of EHAIDs on PBC. The eligible SNPs for EHAIDs ranged from 1 to 56, explaining 0.01% to 9.77% of the total variance in the above EHAIDs, with F values for individual SNPs and all mean-F statistics being > 10 (Supplementary 2 , S2T2). Our research results showed that RA, SLE, SS, SSc, ATHYO, IBD, CD, UC and CeD contributed positively to PBC pathogenesis. The estimated values (OR) ranged from 1.08 to 1.82. AITD, AHYPER, GD, MS, and PoS did not have a genetically positive contribution to PBC development (Fig.  3 ). The Steiger test confirmed the directionality of causality (Supplementary 1 , S1T3).

IVW results of Mendelian randomization analysis for EHAIDs on PBC.

Sensitivity analyses

Estimates for the other three MR methods were consistent with IVW except for SSc as exposure and shown in the supplementary table except that four exposures were left with less than three SNP (SS, SSc, AHYPER, GD) after conducting MR-PRESSO inspection (Tables 2 and 3 , Supplementary 3 ). MR Egger's intercept method and MR-PRESSO's Global Test were conducted to detect the presence of horizontal pleiotropy. Our findings indicated that the P values were greater than 0.05 for all tests of horizontal pleiotropy with EHAIDs as exposure or outcome for both MR‒Egger intercept and Global Test of MR-PESSO (Supplementary 1 , S1T1). And all P values of heterogeneity were greater than 0.05, which increased the reliability of the results (Supplementary 1 , S1T2). We also included LOO plots, forest plots, and funnel plots for IVs to visually demonstrate the outlier distribution and contribution effect of each (Supplementary 4 – 6 ).

Mendelian randomization analysis, with its unique advantages and insights into etiology, has been applied to the study of factors influencing autoimmune diseases 21 . In this study, we performed a comprehensive two-way two-sample MR study to reveal the causal relationship between the risk of several EHAIDs and PBC. Using this approach, we found a genetic causal relationship between RA, SLE, SS, SSc, AHYPO, IBD, CD, UC and CeD, which are responsible for PBC. Moreover, we found that PBC had a causal association with EHAIDs, including RA, SLE, SS, SSc, AITD, AHYPO, AHYPER, GD, IBD, UC, MS, and PoS. These findings align with the results reported by the epidemiological study, further substantiating the potential explanation for the cooccurrence of PBC and EHAIDs. To ensure the robustness and validity of our MR analyses, we conducted multiple sensitivity analyses. These analyses confirmed that our MR hypotheses were satisfied and allowed us to rule out any moderate to severe causal effect of exposure on the outcome. Given the consistency of MR results across these different methods, we have confidence in the reliability of our findings in elucidating the intricate relationship between PBC and EHAIDs.

The development of rheumatic diseases is often accompanied by abnormal liver function, which may be associated with the presence of coexisting autoimmune liver disease, direct involvement of liver parenchyma or drug therapy 22 , 23 . As many as 50% of patients with RA and 60% of patients with SLE may have elevated liver enzymes, 49% of patients with SS have abnormal liver serologic parameters, only 1.1% of patients with SSc have liver involvement, and PBC accounts for approximately 22% of patients with liver involvement 23 .

Fan et al. 8 conducted a study exploring the causal relationship between RA and autoimmune liver disease. Utilizing self-reported GWAS data with a sample size of 5201 RA cases and 457,732 controls, they identified a unidirectional reverse causality between RA and PBC. Similarly, Liu et al. 24 reported identical findings using aggregated data from populations in the UK and Finland. However, their research did not account for the potential interference of MHC region SNPs on the outcomes. Moreover, Liu et al. 24 utilized the IVW random effects model, disregarding the potential impact of heterogeneity on the results. In contrast, our MR analysis used a larger sample size of the RA GWAS dataset and found a bidirectional causal relationship between PBC and RA. Our findings suggest that RA increases the prevalence of PBC by 16%, and PBC, in turn, increases the likelihood of RA by 20%. These results indicate a reciprocal causal relationship between PBC and RA. In clinical settings, PBC is the most common EHAID coexisting with RA, with a prevalence of PBC in RA ranging from 3.8 to 6.3% and RA in PBC ranging from 1.8 to 13% 8 . Our two-sample MR analysis suggests that this correlation may not be coincidental.

SLE is a complex autoimmune disease that affects multiple organs, including the liver. According to a study conducted by Gershwin et al. 25 , out of 1032 patients with PBC, 27 (2.61%) also suffered from SLE. The prevalence of SLE was significantly higher in patients with PBC (27/1032, 2.61%) than in the control group (5/1041, 0.48%). Although most articles on SLE and PBC comorbidity are mainly case reports, Huang et al. 26 summarized 34 cases of SLE combined with PBC. They found that PBC was more common in middle-aged women, while SLE usually affected women of childbearing age. The interval from PBC diagnosis to SLE diagnosis ranged from 7 months to 10 years, with PBC diagnosed 1–19 years after SLE diagnosis. In previously reported MR studies, Liu et al. 24 reported a unidirectional promoting effect of PBC on SLE without considering heterogeneity. However, Huang et al. 27 found that after excluding outliers, their MR results indicated that SLE significantly increased the risk of PBC (OR 1.31, P < 0.01). Similarly, by excluding confounding factors and reverse causality, our results show that SLE and PBC are risk factors for each other. Consistent with our findings, Wu et al. 28 conducted a bidirectional MR analysis using different GWAS data for PBC and found a causal relationship between SLE and PBC, suggesting they mutually promote disease occurrence. Their use of transcriptome overlap analysis further demonstrated the close relationship between PBC and SLE. Therefore, patients with SLE or PBC should be regularly monitored for the possibility of SLE-PBC overlap, even if the onset may occur several years later.

Primary SS (pSS) and secondary SS (sSS) are two subtypes of SS. The clinical and immunological features of PBC and SS are similar, and it has been suggested that PBC is the biliary phenotype of SS, while SS is the exocrine phenotype of PBC 29 . Studies have revealed that SS is the most common autoimmune disease associated with PBC, with a prevalence of 35% according to a meta-analysis 30 . Our MR analysis indicates that PBC genetically promotes SS, and vice versa, even though only one valid SNP was identified. However, in the MR study reported by Liu et al. 24 , the authors did not find a positive causal relationship between SS and PBC. The relatively small number of cases and a large number of controls could have made it difficult to detect disease-related genetic variations, potentially introducing bias into the study's results and preventing an accurate assessment of the relationship between genetic variations and the disease. Further studies on the causal relationship between SS and PBC are needed, especially with regard to subgroup data on pSS and sSS. Since PBC patients are more likely to develop Sicca symptoms, they should be screened early for symptomatic treatment 5 . Those with SS presenting with cholestasis should be promptly screened for PBC comorbidity and UDCA to improve cholestasis, with special attention to AMA-negative PBC patients 31 .

Studies estimate that approximately 2–18% of patients with PBC also have SSc, while approximately 2–10% of SSc patients have PBC 32 . Hepatobiliary issues were found in 7.5% of patients in a large Spanish registry, with PBC being the main cause (4.3%). In PBC patients, characteristic SSc anti-mitochondrial antibodies (ACAs) were present in 9–30%, widespread PBC antibodies were also detectable in SSc patients, and PBC disease has the potential to occur ten years after onset 33 . Our findings suggest that PBC and SSc may increase the risk of each other, which aligns with clinical observations. However, in the MR study reported by Liu et al. 24 , the authors did not find a positive causal relationship between SSc and PBC, which could be related to the relatively small number of cases and the large number of controls. Screening for PBC overlap is recommended when encountering SSc patients in the clinic, as the rate of ACA positivity in SSc-PBC patients is higher than that in PBC cases alone, and early control of gastroesophageal varices may improve prognosis in these patients 32 . Further confirmation through observation of a larger clinical cohort and GWAS dataset is necessary.

Patients diagnosed with PBC often experience abnormal thyroid function, with a significantly higher incidence (ranging from 5.6 to 23.6%) than non-PBC patients 1 . There is also a higher risk of developing AITDs for those with PBC 34 . Our study, which utilized a GWAS dataset with a larger sample size of AITD and PBC, showed that PBC greatly contributed to the development of AITD, including its subgroups of AHYPER and AHYPO, while AITD was not identified as a risk factor for the development of PBC, which is consistent with previous research conducted by HUANG 35 . GD and HT are the most prominent disease components of autoimmune hyperthyroidism and autoimmune hypothyroidism, respectively, with Hashimoto thyroiditis being the most common type of PBC. Floreani et al. 34 reported a prevalence rate as high as 20.4% in PBC patients and 3.2% suffering from Graves' disease. Another study 36 conducted by a European team found that 16.3% of 921 PBC patients had concomitant thyroid disease, with 10.2% having HT, 1.6% having GD, and 41 having other types of thyroid disease. Of these, 77.4% had thyroid disease concurrent with or immediately following PBC. Our study confirmed it may be related to the promotion of PBC by AHYPO rather than AHYPER, and our use of GWAS data for GD and validation yielded the same results as for AHYPER. Unfortunately, we failed to perform approximate equivalence validation for AHYPO due to the lack of a GWAS dataset for HT in the European population.

IBD is a systemic condition that can have extraintestinal manifestations in 5% to 50% of patients, but comorbid PBC is less commonly reported. It is believed that IBD may be most closely associated with primary sclerosing cholangitis (PSC), which has a prevalence of 0.12% to 10.97% in patients with UC 37 . Compared to PSC, comorbidity between PBC and IBD is relatively rare 38 . Our findings suggest that IBD, together with its subtype CD and UC, contributes to the development of PBC. Meanwhile, PBC contributes genetically to IBD and UC. These results were similar to the previous MR results 9 as well as conventional observations 38 , 39 , 40 . Compared to Zhang 9 , we conducted a study with larger sample sizes and achieved inconsistent estimates across the four statistical methods used. In contrast to the findings of Zhao et al. 41 , we emphasized the positive promoting effect of PBC on UC onset. This discrepancy might be attributed to the use of larger sample size GWAS data for PBC and the exclusion of loci within the MHC region that could interfere with causal inference.

Observational studies have described a higher prevalence of PBC in UC patients than in non-UC patients and that UC diagnosis often precedes PBC diagnosis. Our results show that the PBC contributes to the UC, rather than a CD, which seems to explain the results of observational studies. Studies have shown that CD and UC have their own specific immune cell populations 42 , and there may be common pathogenic pathways in the development of PBC and CD 36 . Transcriptomic analysis has shown that CD is more closely associated with viral infection and autoimmunity, whereas microbiome-associated immunity may play a more important role in UC 43 . It may indicate that gut microbiota plays a more prominent role in the pathogenesis of PBC and UC, which needs to be verified by more research areas. If impaired liver function in PBC patients can lead to impaired bile secretion and reduced hepatic detoxification capacity, which in turn affects the intestinal flora, dendritic cells may be more abundant in UC than in CD by sensing the intestinal microbiota and initiating an innate immune response that may be more likely to induce UC disease 43 , 44 , which may be one of the reasons for the greater comorbidity of UC in PBC patients. While subtypes of the same broad disease group may share some similarities in genetic background, significant differences in individual genetics, genetics and immunity remain between CD and UC. Further studies are needed to elucidate the differences between the two subtypes.

Research has shown that up to 7% of patients with PBC also have CeD, and liver abnormalities are often found in conjunction with CeD 45 . In a group of 440 PBC cases, CeD was the most prevalent autoimmune disease affecting the gastrointestinal tract (1.7%) 3 . Another study demonstrated that CeD is more common in PBC patients than in those with other liver diseases 46 . In fact, data collected from a UK registry revealed that 6% of CeD patients also had PBC 47 . However, we analyzed a GWAS dataset with a larger sample size of PBC and discovered a unidirectional positive causal relationship between CeD and PBC, which inconsistent with the research conducted by Li et al. 48 .

There is belief that the connection between PBC and MS is linked to environmental factors affecting those who are genetically vulnerable, and it is uncommon for MS and PBC to co-occur 49 . According to meta-analyses, there may be a significant genetic correlation between MS and PBC 50 . Our research indicates that patients with PBC have a higher chance of developing MS, while patients with MS do not have a higher risk of developing PBC. A study conducted across multiple centers and based on population did not reveal an increased risk of autoimmune disease in MS patients 51 , which aligns with the outcomes of our MR study.

Research has shown that psoriasis occurs more frequently in patients with PBC, with a prevalence of up to 6%, compared to the general population, but there is a lack of studies on larger PBC groups 52 , 53 . Our study indicates that PBC has a positive impact on psoriasis in one direction. This is consistent with the findings of Zhao et al. 54 , despite using different datasets for PBC and PoS, which enhances the reliability of our results. As the onset of PBC is gradual and can be masked during psoriasis treatment, it is crucial for psoriasis patients to distinguish between drug-induced liver enzyme abnormalities and overlapping PBC disease 55 .

The connection between PBC and EHAID comorbidity has been a topic of discussion, as their pathogenesis is believed to be related to a common genetic and autoimmune basis. However, there are differences in their pathogenetic features, clinical manifestations and disease interactions that are not fully understood. To address this issue, we conducted an MR study based on the principle of random assignment of alleles using GWAS data from a large number of PBC and EHAID cases by eliminating MHC loci confounding variables, which is a limitation of observational studies. We used multiple MR statistical methods to improve the reliability of our findings, providing new theoretical support for the study of PBC and EHAIDs. However, our study has some limitations, including the low number of GWAS cases for certain EHAIDs (e.g., SSc, AHYPER), which may affect the causal relationship with PBC. In addition, all confounding factors associated with outcomes could not be completely excluded, and we could only exclude them at the level of MR Egger's intercept test and MR PRESSO's Global Test using statistical pleiotropy methods to ensure the robustness of the results. Moreover, due to limitations in the available data, the study was confined to European populations, and more data is needed to validate its applicability to other ethnic groups. Lastly, the summary data we used lacked detailed demographic information, which limited our ability to conduct stratified analyses and understand disease dynamics in different populations.

In summary, the current MR analysis provides genetic evidence for a causal relationship between PBC and EHAIDs, i.e., PBC is an active contributor to the above intriguing EHAIDs except for CD and CeD, and in turn, RA, SLE, SS, SSc, IBD, CD, UC, AHYPO and CeD become drivers of PBC pathogenesis. This underscores the significance of prioritizing PBC in clinical practice. Additionally, if any liver function abnormalities are observed during treatment or with EHAIDs, it is crucial to consider the possibility of comorbid PBC.

Data availability

All GWAS data analysed during this study are from IEU open gwas project ( https://gwas.mrcieu.ac.uk/ ), GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ), and FinnGen consortium ( https://www.finngen.fi/fi ) and shown in Table 1 .

Chalifoux, S. L., Konyn, P. G., Choi, G. & Saab, S. Extrahepatic manifestations of primary biliary cholangitis. Gut Liver. 11 , 771–780 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Floreani, A., de Martin, S., Secchi, M. F. & Cazzagon, N. Extrahepatic autoimmunity in autoimmune liver disease. Eur. J. Intern. Med. 59 , 1–7 (2019).

Article   CAS   PubMed   Google Scholar  

Efe, C. et al. Extrahepatic autoimmune diseases in primary biliary cholangitis: Prevalence and significance for clinical presentation and disease outcome. J. Gastroenterol. Hepatol. 36 , 936–942 (2021).

Chen, S. et al. Concomitant extrahepatic autoimmune diseases do not compromise the long-term outcomes of primary biliary cholangitis. Hepatobil. Pancreat. Dis. Int. 21 , 577–582 (2022).

Article   Google Scholar  

Hirschfield, G. M. et al. EASL clinical practice guidelines: The diagnosis and management of patients with primary biliary cholangitis. J. Hepatol. 67 , 145–172 (2017).

Emdin, C. A., Khera, A. V. & Kathiresan, S. Mendelian randomization. JAMA. 318 , 1925–1926 (2017).

Article   PubMed   Google Scholar  

Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomisation (STROBE-MR): Explanation and elaboration. BMJ. 375 , n2233 (2021).

Fan, J., Jiang, T. & He, D. Genetic link between rheumatoid arthritis and autoimmune liver diseases: A two-sample Mendelian randomization study. Semin. Arthritis Rheum. 58 , 152142 (2023).

Zhang, H., Chen, L., Fan, Z. & Lv, G. The causal effects of inflammatory bowel disease on primary biliary cholangitis: A bidirectional two-sample Mendelian randomization study. Liver Int. 43 , 1741–1748 (2023).

Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. ELife. 7 , 34408 (2018).

Walker, V. M. et al. Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes. Wellcome Open Res. 4 , 113 (2019).

Matzaraki, V., Kumar, V., Wijmenga, C. & Zhernakova, A. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18 , 76 (2017).

Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40 , 755–764 (2011).

Hemani, G., Tilling, K. & Davey, S. G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13 , e1007081 (2017).

Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32 , 377–389 (2017).

Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40 , 304–314 (2016).

Slob, E. A. W. & Burgess, S. A comparison of robust Mendelian randomization methods using summary data. Genet. Epidemiol. 44 , 313–329 (2020).

Bowden, J. et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int. J. Epidemiol. 48 , 728–742 (2019).

Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50 , 693–698 (2018).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44 , 512–525 (2015).

Chen, C. et al. Mendelian randomization as a tool to gain insights into the mosaic causes of autoimmune diseases. Autoimmun. Rev. 21 , 103210 (2022).

Selmi, C., de Santis, M. & Gershwin, M. E. Liver involvement in subjects with rheumatic disease. Arthritis Res. Ther. 13 , 226 (2011).

Gebreselassie, A., Aduli, F. & Howell, C. D. Rheumatologic diseases and the liver. Clin. Liver Dis. 23 , 247–261 (2019).

Liu, Z., Shao, Y. & Duan, X. Genetic link between primary biliary cholangitis and connective tissue diseases in European populations: A two-sample Mendelian randomization study. PLoS ONE. 19 , e0298225 (2024).

Gershwin, M. E. et al. Risk factors and comorbidities in primary biliary cirrhosis: A controlled interview-based study of 1032 patients. Hepatology. 42 , 1194–1202 (2005).

Shizuma, T. Clinical characteristics of concomitant systemic lupus erythematosus and primary biliary cirrhosis: A literature review. J. Immunol. Res. 2015 , 713728 (2015).

Huang, W. et al. Identifying the genetic association between systemic lupus erythematosus and the risk of autoimmune liver diseases. J. Autoimmun. 145 , 103188 (2024).

Wu, L. et al. Causal relationship between systemic lupus erythematosus and primary liver cirrhosis based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis. Arthritis Res. Ther. 26 , 10 (2024).

Sun, Y. et al. The coexistence of Sjögren’s syndrome and primary biliary cirrhosis: A comprehensive review. Clin. Rev. Allerg. Immunol. 48 , 301–315 (2015).

Article   CAS   Google Scholar  

Deng, X. et al. Prevalence and impact of Sjögren’s syndrome in primary biliary cholangitis: A systematic review and meta-analysis. Ann. Hepatol. 27 , 100746 (2022).

Santos, G. A., Brandão, M. & Farinha, F. Prevalence of primary biliary cholangitis in a cohort of primary Sjögren’s syndrome patients. Cureus. 14 , e24590 (2022).

PubMed   PubMed Central   Google Scholar  

Florin, L. et al. Evaluation of the primary biliary cholangitis-related serologic profile in a large cohort of Belgian systemic sclerosis patients. Clin. Chem. Lab. Med. 58 , 416–423 (2020).

Marí-Alfonso, B. et al. Hepatobiliary involvement in systemic sclerosis and the cutaneous subsets: Characteristics and survival of patients from the Spanish RESCLE Registry. Semin. Arthritis Rheum. 47 , 849–857 (2018).

Floreani, A. et al. Extrahepatic autoimmune conditions associated with primary biliary cirrhosis. Clin. Rev. Allerg. Immunol. 48 , 192–197 (2015).

Huang, P. et al. The causal effects of primary biliary cholangitis on thyroid dysfunction: A two-sample Mendelian randomization study. Front. Genet. 12 , 791778 (2021).

Floreani, A. et al. Thyroid dysfunction in primary biliary cholangitis: A comparative study at two European centers. Am. J. Gastroenterol. 112 , 114–119 (2017).

Barberio, B. et al. Prevalence of primary sclerosing cholangitis in patients with inflammatory bowel disease: A systematic review and meta-analysis. Gastroenterology. 161 , 1865–1877 (2021).

Liberal, R., Gaspar, R., Lopes, S. & Macedo, G. Primary biliary cholangitis in patients with inflammatory bowel disease. Clin. Res. Hepatol. Gastroenterol. 44 , e5–e9 (2020).

Alsuleiman, B., Sarmini, M. T., Khoudari, G., Shah, R. S. & Romero-Marrero, C. Mo1484: The association between primary biliary cholangitis and ulcerative colitis: Large inpatient database study. Gastroenterology. 156 , 1323 (2019).

Khoudari, G. et al. Sa1538: The Epidemiology of ulcerative colitis-primary biliary cholangitis association in the USA: A population-based study. Gastroenterology. 156 , 345 (2019).

Zhao, J., Li, K., Liao, X. & Zhao, Q. Causal associations between inflammatory bowel disease and primary biliary cholangitis: A two-sample bidirectional Mendelian randomization study. Sci. Rep. 13 , 10950 (2023).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Mitsialis, V. et al. Single-cell analyses of colon and blood reveal distinct immune cell signatures of ulcerative colitis and Crohn’s disease. Gastroenterology. 159 , 591-608.e10 (2020).

Yang, L. et al. Difference in pathomechanism between Crohn’s disease and ulcerative colitis revealed by colon transcriptome. Inflamm. Bowel Dis. 25 , 722–731 (2019).

Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell. 157 , 121–141 (2014).

Volta, U., Caio, G., Tovoli, F. & de Giorgio, R. Gut-liver axis: An immune link between celiac disease and primary biliary cirrhosis. Expert Rev. Gastroenterol. Hepatol. 7 , 253–261 (2013).

Callichurn, K. et al. Prevalence of celiac disease in patients with primary biliary cholangitis. J. Can. Assoc. Gastroenterol. 4 , 44–47 (2021).

Kingham, J. G. & Parker, D. R. The association between primary biliary cirrhosis and coeliac disease: A study of relative prevalences. Gut. 42 , 120–122 (1998).

Li, T. et al. Causal relationships between autoimmune diseases and celiac disease: A Mendelian randomization analysis. Biotechnol. Genet. Eng. Rev. 2023 , 1–16 (2023).

Google Scholar  

Sattar, M. & Poursadeghfard, M. Concurrence of multiple sclerosis and primary biliary cholangitis: Report of 3 cases. Caspian J. Intern. Med. 11 , 223–226 (2020).

Olafsson, S. et al. Fourteen sequence variants that associate with multiple sclerosis discovered by meta-analysis informed by genetic correlations. NPJ Genom. Med. 2 , 24 (2017).

Ramagopalan, S. V. et al. Autoimmune disease in families with multiple sclerosis: A population-based study. Lancet Neurol. 6 , 604–610 (2007).

Terziroli Beretta-Piccoli, B. et al. Primary biliary cholangitis associated with skin disorders: A case report and review of the literature. Arch. Immunol. Ther. Exp. 65 , 299–309 (2017).

Ohira, H., Rai, T., Takiguchi, J., Abe, K. & Sato, Y. Six cases of primary biliary cirrhosis complicated by psoriasis. Hepatol. Res. 30 , 111–115 (2004).

Zhao, D., Zhao, Q., Xu, F., Zhang, F. & Bai, W. Primary biliary cirrhosis and psoriasis: A two-sample Mendelian randomization study. Front. Immunol. 14 , 1264554 (2023).

Tomše, P. & Balkovec, V. Primary biliary cholangitis: Cause or association with psoriasis: A case report. Acta Dermatovenerol. Alpina Pannonica Adriat. 32 , 1–10 (2023).

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Acknowledgements

We are enormously grateful to IEU open gwas project, GWAS Catalog, and FinnGen consortium for providing summary results data for these analyses

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These authors contributed equally: Gang Ma, Jiaqi Yang and Xingguo Wang.

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Xijing Hospital of Digestive Disease, Xijing Hospital of Air Force Military Medical University, Xi’an, China

Gang Ma, Jiaqi Yang, Erzhuo Xia, Jiahao Yu, Miao Zhang, Yinan Hu, Shuoyi Ma, Xia Zhou, Qingling Fan, Ying Han & Jingbo Wang

Xijing Hospital of Obstetrics and Gynecology, Xijing Hospital of Air Force Military Medical University, Xi’an, China

Xingguo Wang

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JBW and YH conceived and designed the study. GM, JQY and XGW analyzed data and wrote the manuscript. EZX, JHY, MZ, YNH, SYM, XZ and QLF provided support in the data analysis, edited and revised the manuscript. All authors took responsibility for the integrity of the data and the accuracy of the data analysis and approved the final manuscript.

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Ma, G., Yang, J., Wang, X. et al. Dissecting causal relationships between primary biliary cholangitis and extrahepatic autoimmune diseases based on Mendelian randomization. Sci Rep 14 , 11528 (2024). https://doi.org/10.1038/s41598-024-62509-x

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Can service scholarships boost academic performance? Causal evidence from China’s Free Teacher Education scholarship

• Published: 24 May 2024

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• Qi Zheng   ORCID: orcid.org/0000-0002-5290-5720 1 &
• Yan Shi 2  

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This study provides causal evidence on the short-term impacts of the Free Teacher Education scholarship (FTE), a service scholarship for teacher candidates in China, on college academic achievement, employing a regression discontinuity design. Distinguishing itself from existing literature, the study compares academic performance within the same classrooms, drawing from a transcript dataset of around 200,000 course-level observations from a top-ranked normal university. The findings indicate that, overall, the scholarship has no significant impact on course grades or college GPA, except in specific scenarios. Heterogeneity analyses reveal that scholarship recipients from wealthier families perform slightly worse than their peers with similar family backgrounds. Additionally, a negative trend is observed in the scholarship’s impacts on course scores over time: initial performance improvement followed by a subsequent decline. These insights imply that while the FTE scholarship attracts more academically competitive students, it may simultaneously lead to unintended trade-offs in performance.

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Prestige or education: college teaching and rigor of courses in prestigious and non-prestigious institutions in the U.S.

Data availability.

The authors accessed a restricted administrative database containing sensitive information and do not have the right to share the dataset with the public.

“Grants” and “scholarships” are often used interchangeably in this context, for consistency, this paper adopts the term “scholarships”.

Given the challenges in tracking scholarship recipients post-graduation, especially those from nationwide programs, current knowledge about the improvement in teaching effectiveness due to service scholarships is rare.

According to a nationwide survey, over one-third of Chinese college students have loans, and more than 20% struggle to cover basic college-related expenses (Li et al., 2013 ).

The NCEE scores hold validity only for the corresponding year, compelling students to weigh the risks of losing their current admission offers if they choose to retake the exam. Consequently, as Kang et al. ( 2024 ) note, the majority of students admitted to Tier 1 universities typically do not retake the test. For further comprehensive analyses and descriptions of China’s college application and admission system, interested readers can refer to works such as Chen and Kesten ( 2016 ) and Ye ( 2023 ).

In China, high school students enroll in either the art or science stream. Most departments at the sample university admit students from a single stream, except for English literacy and elementary education, which accept students from both streams. Admissions are based on the selection pool of students from the same province-major(-stream) clusters.

Applicants could still be admitted if they chose to comply with the major assignment and their scores rank above the quota limits of the university in their province. Otherwise, applicants will not be admitted and may either be matched with other universities or not receive admission at all.

In our sample university, there is no scholarship other than those provided by the government.

Given that 98.9% of students in the 2018 cohort completed the program in 4 years, the average number of courses (73.6) for this cohort should be very close to the total number of courses to obtain a bachelor’s degree in the sample university.

Student survey responses show no correlation with their FTE status, GPA, or completed credits. See Table A1 in the appendix for details.

See Table A2 in the appendix for full results.

The optimal bandwidth using MSE-optimal selector (Calonico et al., 2014 ) is 21 points.

In addition, we conducted a supplementary analysis exploring students’ underlying motivations for choosing teaching majors using additional data from the survey. This investigation covered factors such as passion for teaching, job security, flexibility, influence from close associates or previous teachers, and attraction to the FTE policy, examining their consistency around the cutoff thresholds. The analysis shows that these motivational factors account for minimal variances for scoring above the thresholds, with adjusted R-squared values ranging from 0.001 to 0.003 across various bandwidths. Notably, none of the factors reach statistical significance given other covariates (see Tables A4 and A5 in the appendix for detailed results).

Of the 1.1% of admitted teaching major students who did not enroll in the sample university, 46% had been accepted into FTE majors, indicating no significant preference for FTE scholarship among unenrolled students and implying minimal impact on the estimation.

Stepwise RD analyses, along with detailed discussions, are presented in the appendix. Figures A1 to A3 display the results of all potential combinations of covariates, while Table A7 outlines key models, underscoring the results are not sensitive to model specifications.

To ensure the power of the heterogeneity analysis, students are grouped into two income categories instead of the original five. Employing the five-category variable, despite the abundance of course-level observations, results in relatively few unique students per subgroup during local analysis.

Balance checks for subsample analysis, justifying assumption 2 should be held. Comprehensive results are presented in Tables A8 to A15 .

We also observe no significant unequal distribution among unenrolled admitted students for the by-grade analyses. For the 1st and 2nd year analyses, the sample includes all students. In the 3rd year analysis, which encompasses the 2018, 2019, and 2020 cohorts, 44% of unenrolled students were admitted to FTE majors. For the 4th year, involving the 2018 and 2019 cohorts, this figure is 45%. Due to the absence of detailed individual characteristics of unenrolled students, we lack specific insights into distribution by family income.

Full results can be found in Tables A16 and A17 in the appendix.

Concerns that the scholarships might benefit low-income students more and that unenrolled admitted students may mostly be from this group, potentially skewing estimates, are noted. However, even if all 38 unenrolled FTE students were low-income and near the cutoff points, their absence would lead to just a 2.8% sample loss within the optimal bandwidths, and 4.9% in the ± 5 bandwidths, both under the 5% threshold, suggesting minimal estimation impact. If this phenomenon is present, we would anticipate more stable and positive estimates in the low-income student analysis.

While the estimate for the polynomial specification with quadratic terms is not statistically significant, this model specification may be flawed because the polynomial terms are not statistically significant. Therefore, it may be appropriate to not refer to this model.

Note that the 4th year sample includes a limited number of students of cohort 2018 and one semester of course observations for cohort 2019 in their final year. Also, students complete most of their coursework before the last academic year. These factors may result in larger standard errors and insignificant estimates for the 4th year. The precision of estimates should improve with a larger sample size. Hence, it should be safe to infer the negative longer-term impact of the FTE scholarship on academic performance.

Bardach, L., & Klassen, R. M. (2020). Smart teachers, successful students? A systematic review of the literature on teachers’ cognitive abilities and teacher effectiveness. Educational Research Review, 30 , 100312. https://doi.org/10.1016/j.edurev.2020.100312

Article   Google Scholar  

Barkowski, E., Nielsen, E., Noel, H., Dodson, M., Sonnenfeld, K., Ye, C., DeMonte, E., Monahan, B., & Eccleston, M. (2018). Study of the teacher education assistance for college and higher education (TEACH) grant program. Office of Planning, Evaluation and Policy Development, US Department of Education . Retrieved from https://files.eric.ed.gov/fulltext/ED613443.pdf

Bärnighausen, T., & Bloom, D. E. (2009). “Conditional scholarships” for HIV/AIDS health workers: Educating and retaining the workforce to provide antiretroviral treatment in sub-Saharan Africa. Social Science & Medicine, 68 (3), 544–551. https://doi.org/10.1016/j.socscimed.2008.11.009

Barrow, L., Richburg-Hayes, L., Rouse, C. E., & Brock, T. (2014). Paying for performance: The education impacts of a community college scholarship program for low-income adults. Journal of Labor Economics, 32 (3), 563–599. https://doi.org/10.1086/675229

Bénabou, R., & Tirole, J. (2006). Incentives and prosocial behavior. American Economic Review, 96 (5), 1652–1678. https://doi.org/10.1257/aer.96.5.1652

Bettinger, E., Gurantz, O., Kawano, L., Sacerdote, B., & Stevens, M. (2019). The long-run impacts of financial aid: Evidence from California’s cal grant. American Economic Journal: Economic Policy, 11 (1), 64–94. https://doi.org/10.1257/pol.20170466

Blundell, R., Dearden, L., Meghir, C., & Sianesi, B. (1999). Human capital investment: The returns from education and training to the individual, the firm and the economy. Fiscal Studies, 20 (1), 1–23. https://doi.org/10.1111/j.1475-5890.1999.tb00001.x

Boatman, A., Evans, B. J., & Soliz, A. (2017). Understanding loan aversion in education: Evidence from high school seniors, community college students, and adults. AERA Open, 3 (1), 2332858416683649. https://doi.org/10.1177/2332858416683649

Brand, J. E. (2010). Civic returns to higher education: A note on heterogeneous effects. Social Forces, 89 (2), 417–433. https://doi.org/10.1353/sof.2010.0095

Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 82 (6), 2295–2326. https://doi.org/10.3982/ECTA11757

Carruthers, C. K., & Özek, U. (2016). Losing HOPE: Financial aid and the line between college and work. Economics of Education Review, 53 , 1–15. https://doi.org/10.1016/j.econedurev.2016.03.014

Chen, Y., & Kesten, O. (2016). Chinese college admissions and school choice reforms: A theoretical analysis. Journal of Political Economy, 125 (1), 99–139. https://doi.org/10.1086/689773

Chinese Ministry of Education. (2007). The guidelines for implementing free teacher education programs at the ministry of education affiliated normal universities (Trial). Retrieved from https://www.gov.cn/zwgk/2007-05/14/content_614039.htm . ( in Chinese ).

Chinese Ministry of Education (2010). National plan for medium and long-term education reform and development (2010–2020). Retrieved from https://www.gov.cn/jrzg/2010-07/29/content_1667143.htm . (in Chinese).

Cornwell, C., Mustard, D. B., & Sridhar, D. J. (2006a). The enrollment effects of merit-based financial aid: Evidence from Georgia’s HOPE program. Journal of Labor Economics, 24 (4), 761–786. https://doi.org/10.1086/506485

Cornwell, C., Lee, K. H., & Mustard, D. B. (2006b). The effects of state-sponsored merit scholarships on course selection and major choice in college. IZA Discussion Paper No. 1953, Available at  https://doi.org/10.2139/ssrn.880430

Curs, B. R., & Harper, C. E. (2012). Financial aid and first-year collegiate GPA: A regression discontinuity approach. The Review of Higher Education, 35 (4), 627–649. https://doi.org/10.1353/rhe.2012.0040

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125 (6), 627.

Dee, T. S. (2004). Are there civic returns to education? Journal of Public Economics, 88 (9), 1697–1720. https://doi.org/10.1016/j.jpubeco.2003.11.002

Deming, D., & Dynarski, S. (2009). Into college, out of poverty? Policies to increase the postsecondary attainment of the poor [presented at “targeting investments in children,” September 26, 2008]. National Bureau of Economic Research Working Paper Series , No. 15387 . https://doi.org/10.3386/w15387

Dynarski, S., & Scott-Clayton, J. (2013). Financial aid policy: Lessons from research. National Bureau of Economic Research Working Paper Series , No. 18710 . https://doi.org/10.3386/w18710

Eng, A., & Matsudaira, J. (2021). Pell grants and student success: Evidence from the universe of federal aid recipients. Journal of Labor Economics, 39 (S2), S413–S454. https://doi.org/10.1086/712556

Fan, E., Meng, X., Wei, Z., & Zhao, G. (2010). Rates of return to university education: The regression discontinuity design . IZA Discussion Paper No. 4749, Available at https://doi.org/10.2139/ssrn.1552683

Feng, L., & Sass, T. R. (2018). The impact of incentives to recruit and retain teachers in “hard-to-staff” subjects. Journal of Policy Analysis and Management, 37 (1), 112–135. https://doi.org/10.1002/pam.22037

Field, E. (2009). Educational debt burden and career choice: Evidence from a financial aid experiment at NYU Law School. American Economic Journal: Applied Economics, 1 (1), 1–21. https://doi.org/10.1257/app.1.1.1

Freeman, R. B. (1997). Working for nothing: The supply of volunteer labor. Journal of Labor Economics, 15 (1, Part 2), S140–S166.

Frey, B. S., & Oberholzer-Gee, F. (1997). The cost of price incentives: An empirical analysis of motivation crowding- out. The American economic review , 87 (4), 746–755. http://www.jstor.org/stable/2951373

Frey, B. S., & Jegen, R. (2001). Motivation crowding theory. Journal of Economic Surveys, 15 (5), 589–611. https://doi.org/10.1111/1467-6419.00150

Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation . Norto: W. W.

Google Scholar  

Gist, C. D., Bianco, M., & Lynn, M. (2018). Examining grow your own programs across the teacher development continuum: Mining research on teachers of color and nontraditional educator pipelines. Journal of Teacher Education, 70 (1), 13–25. https://doi.org/10.1177/0022487118787504

Han, L., & Xie, J. (2020). Can conditional grants attract better students? Evidence from Chinese teachers’ colleges. Economics of Education Review, 78 , 102034. https://doi.org/10.1016/j.econedurev.2020.102034

Hanushek, E. A., Piopiunik, M., & Wiederhold, S. (2019). The value of smarter teachers international evidence on teacher cognitive skills and student performance. Journal of Human Resources, 54 (4), 857–899.

Henry, G. T., Bastian, K. C., & Smith, A. A. (2012). Scholarships to recruit the “best and brightest” into teaching: Who is recruited, where do they teach, how effective are they, and how long do they stay? Educational Researcher, 41 (3), 83–92.

Herbaut, E., & Geven, K. (2020). What works to reduce inequalities in higher education? A systematic review of the (quasi-)experimental literature on outreach and financial aid. Research in Social Stratification and Mobility, 65 , 100442. https://doi.org/10.1016/j.rssm.2019.100442

Hillman, N. W., Tandberg, D. A., & Gross, J. P. K. (2014). Performance funding in higher education: Do financial incentives impact college completions? The Journal of Higher Education, 85 (6), 826–857. https://doi.org/10.1080/00221546.2014.11777349

Hout, M. (2012). Social and economic returns to college education in the United States. Annual Review of Sociology, 38 (1), 379–400. https://doi.org/10.1146/annurev.soc.012809.102503

Jackson, C. K. (2012). School competition and teacher labor markets: Evidence from charter school entry in North Carolina. Journal of Public Economics, 96 (5), 431–448. https://doi.org/10.1016/j.jpubeco.2011.12.006

Jacobsen, C. B., Hvitved, J., & Andersen, L. B. (2014). Command and motivation: How the perception of external internventions relates to intrinsic motivation and public service motivation. Public Administration, 92 (4), 790–806. https://doi.org/10.1111/padm.12024

Johnson, I. (2022). Merit aid and retention: Mediation and moderation. Research in Higher Education, 63 (4), 713–739. https://doi.org/10.1007/s11162-021-09661-9

Kang, C. H., & Park, Y. (2021). Private tutoring and distribution of student academic outcomes: An implication of the presence of private tutoring for educational inequality. Korean Economic Review, 37 (2), 287–326. https://doi.org/10.22841/kerdoi.2021.37.2.004

Kang, L., Lei, Z., Song, Y., & Zhang, P. (2024). Gender differences in reactions to failure in high-stakes competition: Evidence from the national college entrance exam retakes. Journal of Political Economy Microeconomics (available online). https://doi.org/10.1086/729535

Kingston, P. W., Hubbard, R., Lapp, B., Schroeder, P., & Wilson, J. (2003). Why education matters. Sociology of Education, 76 (1), 53–70. https://doi.org/10.2307/3090261

Krueger, A. B., & Lindahl, M. (2001). Education for growth: Why and for whom? Journal of Economic Literature, 39 (4), 1101–1136. https://doi.org/10.1257/jel.39.4.1101

Kunreuther, H., & Easterling, D. (1990). Are risk-benefit tradeoffs possible in siting hazardous facilities? The American Economic Review, 80 (2), 252–256.

Lepper, M. R., & Greene, D. (Eds.). (2015). The hidden costs of reward: New perspectives on the psychology of human motivation . Psychology Press.

Li, W. (2007). Family background, financial constraints and higher education attendance in China. Economics of Education Review, 26 (6), 724–734. https://doi.org/10.1016/j.econedurev.2007.09.001

Li, T., & Zhang, J. (2010). What determines employment opportunity for college graduates in China after higher education reform? China Economic Review, 21 (1), 38–50. https://doi.org/10.1016/j.chieco.2009.10.001

Li, H., Meng, L., Shi, X., & Wu, B. (2013). Poverty in China’s colleges and the targeting of financial aid. The China Quarterly, 216 , 970–992. https://doi.org/10.1017/S0305741013001082

Li, J., Shi, Z., & Xue, E. (2020). The problems, needs and strategies of rural teacher development at deep poverty areas in China: Rural schooling stakeholder perspectives. International Journal of Educational Research, 99 , 101496. https://doi.org/10.1016/j.ijer.2019.101496

Liao, W., & Yuan, R. (2017). Understand an emerging “failure” of an equality-oriented teacher policy in China: A job search perspective. International Journal of Educational Research, 81 , 71–82. https://doi.org/10.1016/j.ijer.2016.11.002

Liou, P.-Y., & Lawrenz, F. (2011). Optimizing teacher preparation loan forgiveness programs: Variables related to perceived influence. Science Education, 95 (1), 121–144. https://doi.org/10.1002/sce.20409

Long, B. T., & Riley, E. (2008). Financial aid: A broken bridge to college access? Harvard Educational Review, 77 (1), 39–63. https://doi.org/10.17763/haer.77.1.765h8777686r7357

McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142 (2), 698–714. https://doi.org/10.1016/j.jeconom.2007.05.005

McEwan, P. J. (1999). Recruitment of rural teachers in developing countries: An economic analysis. Teaching and Teacher Education, 15 (8), 849–859. https://doi.org/10.1016/S0742-051X(99)00025-6

Mirowsky, J., & Ross, C. (2003). Education, social status, and health . Aldine de Gruyter.

Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference . Cambridge University Press.

Nguyen, T. D., Kramer, J. W., & Evans, B. J. (2019). The effects of grant aid on student persistence and degree attainment: A systematic review and meta-analysis of the causal evidence. Review of Educational Research, 89 (6), 831–874. https://doi.org/10.3102/0034654319877156

OECD. (2005). Teachers matter: Attracting, developing and retaining effective teachers. OECD Publishing . https://doi.org/10.1787/9789264018044-en

Park, A., Shi, X., Hsieh, C.-T., & An, X. (2015). Magnet high schools and academic performance in China: A regression discontinuity design. Journal of Comparative Economics, 43 (4), 825–843. https://doi.org/10.1016/j.jce.2015.10.013

Peng, W. J., McNess, E., Thomas, S., Wu, X. R., Zhang, C., Li, J. Z., & Tian, H. S. (2014). Emerging perceptions of teacher quality and teacher development in China. International Journal of Educational Development, 34 , 77–89. https://doi.org/10.1016/j.ijedudev.2013.04.005

Peyton, D. J., van Dijk, W., & Mason-Williams, L. (2022). Meeting the moment: Impact of TEACH grant on US undergraduate education degree completion in high-need content areas. Higher Education Policy . https://doi.org/10.1057/s41307-022-00263-3

Pinto, L. H., & He, K. (2019). ‘In the eyes of the beholder’: The influence of academic performance and extracurricular activities on the perceived employability of Chinese business graduates. Asia Pacific Journal of Human Resources, 57 (4), 503–527. https://doi.org/10.1111/1744-7941.12200

Psacharopoulos, G., & Patrinos, H. A. (2018). Returns to investment in education: A decennial review of the global literature. Education Economics, 26 (5), 445–458. https://doi.org/10.1080/09645292.2018.1484426

Rockoff, J. E., Jacob, B. A., Kane, T. J., & Staiger, D. O. (2011). Can you recognize an effective teacher when you recruit one? Education Finance and Policy, 6 (1), 43–74. https://doi.org/10.1162/EDFP_a_00022

Schleicher, A. (2012). Preparing teachers and developing school leaders for the 21st century: Lessons from around the world . OECD Publishing.

Scott-Clayton, J. (2011). On money and motivation a quasi-experimental analysis of financial incentives for college achievement. Journal of Human Resources, 46 (3), 614–646. https://doi.org/10.3368/jhr.46.3.614

Shen, H., & Ziderman, A. (2009). Student loans repayment and recovery: International comparisons. Higher Education, 57 (3), 315–333. https://doi.org/10.1007/s10734-008-9146-0

Sjoquist, D. L., & Winters, J. V. (2015). State merit-based financial aid programs and college attainment. Journal of Regional Science, 55 (3), 364–390. https://doi.org/10.1111/jors.12161

Stater, M. (2009). The impact of financial aid on college GPA at three flagship public institutions. American Educational Research Journal, 46 (3), 782–815. https://doi.org/10.3102/0002831208329903

Steele, J. L., Murnane, R. J., & Willett, J. B. (2010). Do financial incentives help low-performing schools attract and keep academically talented teachers? Evidence from California. Journal of Policy Analysis and Management, 29 (3), 451–478.

Titmuss, R. (2018). The gift relationship: From human blood to social policy . Policy Press.

Wang, D., & Gao, M. (2013). Educational equality or social mobility: The value conflict between preservice teachers and the Free Teacher Education Program in China. Teaching and Teacher Education, 32 , 66–74. https://doi.org/10.1016/j.tate.2013.01.008

Wei, Y., & Zhou, S. (2019). Are better teachers more likely to move? Examining teacher mobility in rural China. The Asia-Pacific Education Researcher, 28 (2), 171–179. https://doi.org/10.1007/s40299-018-0423-0

Whitfield, J. G., Waxman, H., & Scott, T. (2016). Comparing Robert Noyce Scholars and Non- Robert Noyce Scholars Perceptions of Teaching. Journal of Research in STEM Education , 2 (2), 90–105.

Yang, P. (2010). Who gets more financial aid in China? A multilevel analysis. International Journal of Educational Development, 30 (6), 560–569. https://doi.org/10.1016/j.ijedudev.2009.12.006

Yang, P. (2011). The impact of financial aid on learning, career decisions, and employment. Chinese Education & Society, 44 (1), 27–57. https://doi.org/10.2753/CED1061-1932440102

Ye, X. (2023). Improving college choice in centralized admissions: Experimental evidence on the importance of precise predictions. Education Finance and Policy , 1–75. https://doi.org/10.1162/edfp_a_00397

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Acknowledgements

We extend our heartfelt appreciation to Dr. Nicholas Hillman for his invaluable expertise and encouragement, which were instrumental in shaping this work. Our gratitude also goes to the two anonymous reviewers whose constructive and insightful comments significantly enhanced the quality of this study. We are also thankful to Dr. Daniel Collier for his generous allocation of time and valuable advice. Further, we express our thanks to our colleagues and participants at conferences and department reviews, including but not limited to Drs. Xiaoyang Ye, Ang Yu, Rian Djita, Sam Glick, Xinliang Zhang, and others, for their constructive feedback and support that have notably enriched the quality of our research.

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• “What are the most common stressors faced by college students during final exams?”
• “How does academic performance differ between students who live on campus and those who commute?”
• “What is the relationship between part-time employment and GPA among college students?”
• “How does participation in study abroad programs impact cultural awareness and academic performance?”
• “What are the emerging trends in college students’ use of social media for academic purposes?”
• “Why do some college students engage in academic dishonesty despite awareness of the consequences?”
• “How effective are university mental health services in addressing students’ mental health issues?”
• “How do different learning styles affect the academic success of college students in online courses?”
• “What strategies can be employed to improve retention rates among first-year college students?”
• “How does participation in extracurricular activities influence leadership skills development in college students?”

Research Question Examples in Statistics

• “What are the most common statistical methods used in medical research?”
• “How does the accuracy of machine learning models compare to traditional statistical methods in predicting housing prices?”
• “What is the relationship between sample size and the power of a statistical test in clinical trials?”
• “How does the use of random sampling affect the validity of survey results in social science research?”
• “What are the emerging trends in the application of Bayesian statistics in data science?”
• “Why do some datasets require transformation before applying linear regression models?”
• “How effective are bootstrapping techniques in estimating the confidence intervals of small sample data?”
• “How do different imputation methods impact the results of analyses with missing data?”
• “What strategies can improve the interpretation of interaction effects in multiple regression analysis?”
• “How does the choice of statistical software affect the efficiency of data analysis in academic research?”

Research Question Examples in Socialogy

• “What are the primary social factors contributing to urban poverty in major cities?”
• “How does the level of social integration differ between immigrants and native-born citizens in urban areas?”
• “What is the relationship between educational attainment and social mobility in different socioeconomic classes?”
• “How does exposure to social media influence political participation among young adults?”
• “What are the emerging trends in family structures and their impact on child development?”
• “Why do certain communities exhibit higher levels of civic engagement than others?”
• “How effective are community policing strategies in reducing crime rates in diverse neighborhoods?”
• “How do socialization processes differ in single-parent households compared to two-parent households?”
• “What strategies can be implemented to reduce racial disparities in higher education enrollment?”
• “How does the implementation of public housing policies affect the quality of life for low-income families?”

Research Question Examples in Biology

• “What are the primary characteristics of the various stages of mitosis in eukaryotic cells?”
• “How do the reproductive strategies of amphibians compare to those of reptiles?”
• “What is the relationship between genetic diversity and the resilience of plant species to climate change?”
• “How does the presence of pollutants in freshwater ecosystems impact the growth and development of aquatic organisms?”
• “What are the emerging trends in the use of CRISPR technology for gene editing in agricultural crops?”
• “Why do certain bacteria develop antibiotic resistance more rapidly than others?”
• “How effective are different conservation strategies in protecting endangered species?”
• “How do various environmental factors influence the process of photosynthesis in marine algae?”
• “What strategies can enhance the effectiveness of reforestation programs in tropical rainforests?”
• “How does the method of seed dispersal affect the spatial distribution and genetic diversity of plant populations?”

Research Question Examples in History

• “What were the key social and economic factors that led to the Industrial Revolution in Britain?”
• “How did the political systems of ancient Athens and ancient Sparta differ in terms of governance and citizen participation?”
• “What is the relationship between the Renaissance and the subsequent scientific revolution in Europe?”
• “How did the Treaty of Versailles contribute to the rise of Adolf Hitler and the onset of World War II?”
• “What are the emerging perspectives on the causes and impacts of the American Civil Rights Movement?”
• “Why did the Roman Empire decline and eventually fall despite its extensive power and reach?”
• “How effective were the New Deal programs in alleviating the effects of the Great Depression in the United States?”
• “How did the processes of colonization and decolonization affect the political landscape of Africa in the 20th century?”
• “What strategies did the suffragette movement use to secure voting rights for women in the early 20th century?”
• “How did the logistics and strategies of the D-Day invasion contribute to the Allied victory in World War II?”

Importance of Research Questions

Research questions are fundamental to the success and integrity of any study. Their importance can be highlighted through several key aspects:

• Research questions provide a clear focus and direction for the study, ensuring that the researcher remains on track.
• Example: “How does online learning impact student engagement in higher education?”
• They establish the boundaries of the research, determining what will be included or excluded.
• Example: “What are the effects of air pollution on respiratory health in urban areas?”
• Research questions dictate the choice of research design, methodology, and data collection techniques.
• Example: “What is the relationship between physical activity and mental health in adolescents?”
• They make the objectives of the research explicit, providing clarity and precision to the study’s goals.
• Example: “Why do some startups succeed in securing venture capital while others fail?”
• Well-crafted research questions emphasize the significance and relevance of the study, justifying its importance.
• Example: “How effective are public health campaigns in increasing vaccination rates among young adults?”
• They enable a systematic approach to inquiry, ensuring that the study is coherent and logically structured.
• Example: “What are the social and economic impacts of remote work on urban communities?”
• Research questions offer a framework for analyzing and interpreting data, guiding the researcher in making sense of the findings.
• Example: “How does social media usage affect self-esteem among teenagers?”
• By addressing specific gaps or exploring new areas, research questions ensure that the study contributes meaningfully to the existing body of knowledge.
• Example: “What are the emerging trends in the use of artificial intelligence in healthcare?”
• Clear and precise research questions increase the credibility and reliability of the research by providing a focused approach.
• Example: “How do educational interventions impact literacy rates in low-income communities?”
• They help in clearly communicating the purpose and findings of the research to others, including stakeholders, peers, and the broader academic community.
• Example: “What strategies are most effective in reducing youth unemployment in developing countries?”

Research Question vs. Hypothesis

Chracteristics of research questions.

Research questions are fundamental to the research process as they guide the direction and focus of a study. Here are the key characteristics of effective research questions:

1. Clear and Specific

• The question should be clearly articulated and specific enough to be understood without ambiguity.
• Example: “What are the effects of social media on teenagers’ mental health?” rather than “How does social media affect people?”

2. Focused and Researchable

• The question should be narrow enough to be answerable through research and data collection.
• Example: “How does participation in extracurricular activities impact academic performance in high school students?” rather than “How do activities affect school performance?”

3. Complex and Analytical

• The question should require more than a simple yes or no answer and should invite analysis and discussion.
• Example: “What factors contribute to the success of renewable energy initiatives in urban areas?” rather than “Is renewable energy successful?”

4. Relevant and Significant

• The question should address an important issue or problem in the field of study and contribute to knowledge or practice.
• Example: “How does climate change affect agricultural productivity in developing countries?” rather than “What is climate change?”

5. Feasible and Practical

• The question should be feasible to answer within the constraints of time, resources, and access to information.
• Example: “What are the challenges faced by remote workers in the tech industry during the COVID-19 pandemic?” rather than “What are the challenges of remote work?”

6. Original and Novel

• The question should offer a new perspective or explore an area that has not been extensively studied.
• Example: “How do virtual reality technologies influence empathy in healthcare training?” rather than “What is virtual reality?”
• The question should be framed in a way that ensures the research can be conducted ethically.
• Example: “What are the impacts of privacy laws on consumer data protection in the digital age?” rather than “How can we collect personal data more effectively?”

8. Open-Ended

• The question should encourage detailed responses and exploration, rather than limiting answers to a simple yes or no.
• Example: “In what ways do cultural differences affect communication styles in multinational companies?” rather than “Do cultural differences affect communication?”

9. Aligned with Research Goals

• The question should align with the overall objectives of the research project or study.
• Example: “How do early childhood education programs influence long-term academic achievement?” if the goal is to understand educational impacts.

10. Based on Prior Research

• The question should build on existing literature and research, identifying gaps or new angles to explore.
• Example: “What strategies have proven effective in reducing urban air pollution in European cities?” after reviewing current studies on air pollution strategies.

Benefits of Research Question

Research questions are fundamental to the research process and offer numerous benefits, which include the following:

1. Guides the Research Process

A well-defined research question provides a clear focus and direction for your study. It helps in determining what data to collect, how to collect it, and how to analyze it.

Benefit: Ensures that the research stays on track and addresses the specific issue at hand.

2. Clarifies the Purpose of the Study

Research questions help to articulate the purpose and objectives of the study. They make it clear what the researcher intends to explore, describe, compare, or test.

Benefit: Helps in communicating the goals and significance of the research to others, including stakeholders and funding bodies.

3. Determines the Research Design

The type of research question informs the research design, including the choice of methodology, data collection methods, and analysis techniques.

Benefit: Ensures that the chosen research design is appropriate for answering the specific research question, enhancing the validity and reliability of the results.

4. Enhances Literature Review

A well-crafted research question provides a framework for conducting a thorough literature review. It helps in identifying relevant studies, theories, and gaps in existing knowledge.

Benefit: Facilitates a comprehensive understanding of the topic and ensures that the research is grounded in existing literature.

5. Focuses Data Collection

Research questions help in identifying the specific data needed to answer them. This focus prevents the collection of unnecessary data and ensures that all collected data is relevant to the study.

Benefit: Increases the efficiency of data collection and analysis, saving time and resources.

6. Improves Data Analysis

Having a clear research question aids in the selection of appropriate data analysis methods. It helps in determining how the data will be analyzed to draw meaningful conclusions.

Benefit: Enhances the accuracy and relevance of the findings, making them more impactful.

7. Facilitates Hypothesis Formation

In quantitative research, research questions often lead to the development of hypotheses that can be tested statistically.

Benefit: Provides a basis for hypothesis testing, which is essential for establishing cause-and-effect relationships.

8. Supports Result Interpretation

Research questions provide a lens through which the results of the study can be interpreted. They help in understanding what the findings mean in the context of the research objectives.

Benefit: Ensures that the conclusions drawn from the research are aligned with the original aims and objectives.

9. Enhances Reporting and Presentation

A clear research question makes it easier to organize and present the research findings. It helps in structuring the research report or presentation logically.

Benefit: Improves the clarity and coherence of the research report, making it more accessible and understandable to the audience.

10. Encourages Critical Thinking

Formulating research questions requires critical thinking and a deep understanding of the subject matter. It encourages researchers to think deeply about what they want to investigate and why.

Benefit: Promotes a more thoughtful and analytical approach to research, leading to more robust and meaningful findings.

How to Write a Research Question

Crafting a strong research question is crucial for guiding your study effectively. Follow these steps to write a clear and focused research question:

Identify a Broad Topic:

Start with a general area of interest that you are passionate about or that is relevant to your field. Example: “Climate change”

Conduct Preliminary Research:

Explore existing literature and studies to understand the current state of knowledge and identify gaps. Example: “Impact of climate change on agriculture”

Narrow Down the Topic:

Focus on a specific aspect or issue within the broad topic to make the research question more manageable. Example: “Effect of climate change on crop yields”

Consider the Scope:

Ensure the question is neither too broad nor too narrow. It should be specific enough to be answerable but broad enough to allow for thorough exploration. Example: “How does climate change affect corn crop yields in the Midwest United States?”

Determine the Research Type:

Decide whether your research will be descriptive, comparative, relational, or causal, as this will shape your question. Example: “How does climate change affect corn crop yields in the Midwest United States over the past decade?”

Formulate the Question:

Write a clear, concise question that specifies the variables, population, and context. Example: “What is the impact of increasing temperatures and changing precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

Ensure Feasibility:

Make sure the question can be answered within the constraints of your resources, time, and data availability. Example: “How have corn crop yields in the Midwest United States been affected by climate change-related temperature increases and precipitation changes between 2010 and 2020?”

Review and Refine:

Evaluate the question for clarity, focus, and relevance. Revise as necessary to ensure it is well-defined and researchable. Example: “What are the specific impacts of temperature increases and changes in precipitation patterns on corn crop yields in the Midwest United States from 2010 to 2020?”

What is a research question?

A research question is a specific query guiding a study’s focus and objectives, shaping its methodology and analysis.

Why is a research question important?

It provides direction, defines scope, ensures relevance, and guides the methodology of the research.

How do you formulate a research question?

Identify a topic, narrow it down, conduct preliminary research, and ensure it is clear, focused, and researchable.

What makes a good research question?

Clarity, specificity, feasibility, relevance, and the ability to guide the research effectively.

Can a research question change?

Yes, it can evolve based on initial findings, further literature review, and the research process.

What is the difference between a research question and a hypothesis?

A research question guides the study; a hypothesis is a testable prediction about the relationship between variables.

How specific should a research question be?

It should be specific enough to provide clear direction but broad enough to allow for comprehensive investigation.

What are examples of good research questions?

Examples include: “How does social media affect academic performance?” and “What are the impacts of climate change on agriculture?”

Can a research question be too broad?

Yes, a too broad question can make the research unfocused and challenging to address comprehensively.

What role does a research question play in literature reviews?

It helps identify relevant studies, guides the search for literature, and frames the review’s focus.

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