Best Practice: Interrogating Association Claims

We have based our Best Practice series on Dr. Beth Morling's Research Methods in Psychology (5th ed.). We encourage you to purchase it for your bookshelf. If you teach research methods, consider adopting this best-of-class text for your classes.

Dr. Beth Morling is a distinguished Fulbright scholar and was honored as the 2014 Professor of the Year by the Carnegie Foundation for the Advancement of Teaching.

With more than two decades of experience as a researcher and professor of research methods, she is an internationally recognized expert and a passionate advocate for the Research Methods course. Morling's primary objective is to empower students to become discerning critical thinkers, capable of evaluating research and claims presented in the media.

In this post, we will explore a question addressed in Chapter 3: "How do researchers interrogate association claims?"

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We will explore how psychological researchers interrogate association claims—statements suggesting that two measured variables are related. Unlike frequency claims, which focus on how often something occurs, or causal claims, which aim to show that one variable causes another, association claims occupy the middle ground: they imply a connection but stop short of asserting cause and effect. You’ll learn how researchers use construct validity, external validity, and statistical validity to evaluate the strength and credibility of such claims.

This post will guide you through how variables should be measured, how findings might generalize, and how to interpret statistics like correlation coefficients, p-values, and confidence intervals. These skills are critical for identifying reliable associations in both scholarly research and everyday media. We will focus on association claims—those that suggest a relationship between two measured variables. An association claim might say, “Playing an instrument is linked to better cognition” or “More time on social media is associated with increased anxiety.”

These claims do not assert causality, but they do suggest that changes in one variable relate to changes in another. To evaluate how well a study supports an association claim, we focus on three big validities: construct validity, external validity, and statistical validity. Internal validity is less of a concern here because association claims are not about cause and effect. The goal is to determine whether the study accurately measured both variables, whether the findings can generalize, and whether the observed association is statistically sound and replicable.

Construct validity is crucial because it concerns how well the researchers measured each variable in the study. If a study claims that smartphone use is associated with lower attention spans, we must ask: How was smartphone use measured? Was it self-reported, tracked with an app, or recorded through daily diaries? And how was attention span measured—by questionnaires, performance on tasks, or teacher ratings? If either measurement is vague or poorly defined, the association itself becomes questionable. Strong construct validity requires clear, reliable, and valid operational definitions that accurately represent the conceptual variables being studied.

External validity asks whether the results of the association claim apply to other people, settings, or times. Suppose a study linking music practice to cognitive performance was conducted only with Scottish children in a particular age range. Can the results be generalized to American adolescents, or to adults in non-Western countries? Did the sample represent the broader population the researchers are interested in? Were participants randomly selected or drawn from a convenient location?

These questions help us judge whether the association observed in the study is likely to hold elsewhere or whether it’s context-specific. A strong association in one group doesn’t guarantee the same result in a different population.

Statistical validity focuses on how strong the relationship between the variables is, whether the association is statistically significant, and how precise the estimate is. Strength refers to the size of the effect—how tightly the two variables move together. A correlation coefficient (e.g., r = .50) gives a numerical representation of this strength. Precision refers to the confidence interval around that estimate: the narrower the interval, the more precise the result. Statistical significance tells us whether the association is likely to have occurred by chance. Replication adds another layer of trust—if multiple studies find the same association, we can be more confident that it’s real.

While internal validity is not the focus of interrogating association claims, it’s still important to avoid making the mistake of interpreting a correlation as causation. This is where students, journalists, and even some researchers can go wrong. If a study shows that teens who sleep less report more anxiety, that’s a valid association claim. But claiming that less sleep causes anxiety would require an experimental design with random assignment. Readers should always be cautious when causal language creeps into interpretations of correlational research.

Interrogating association claims means asking three core questions: Were the variables measured well (construct validity)? Can the results be generalized to other populations (external validity)? And is the association statistically strong, significant, and precise (statistical validity)?

By practicing these questions consistently, you’ll become adept at spotting overstatements, identifying well-supported relationships, and distinguishing between correlation and causation. Association claims are everywhere—from academic journals to social media headlines—and knowing how to interrogate them is a core skill for scientific thinking.

Construct Validity of Association Claims

Construct validity is especially important in association claims because researchers must measure not just one, but two variables—and the quality of each measurement affects how trustworthy the association is.

To interrogate construct validity, we ask how well the researchers defined and measured their variables.

For example, suppose a study claims that coffee consumption is associated with lower levels of depression. First, we need to ask how coffee consumption was measured. Did researchers ask participants to recall how many cups they drank last week? Did they use daily tracking logs, or perhaps obtain purchase receipts? Next, we ask how depression was assessed. Did participants complete a standardized clinical scale like the Beck Depression Inventory, or was the measurement based on a single yes/no question about mood? The stronger and more detailed the operational definitions, the more confident we can be in the reported association.

One major component of construct validity is the match between the operational definition and the conceptual variable. A study that claims to measure “anxiety” but uses only one vague item like “I feel nervous” may not capture the full scope of anxiety symptoms. A better approach would involve multiple items assessing physiological arousal, worry, avoidance behavior, and emotional distress. Similarly, if a study claims to measure “academic performance,” using only GPA might miss other important aspects like critical thinking, class participation, or study habits. The closer the operational definition mirrors the theoretical construct, the higher the construct validity.

Researchers can also strengthen construct validity by using previously validated scales or combining multiple methods. For example, if a study on aggression uses both teacher ratings and self-reports, and the two correlate well, we have more confidence in the accuracy of the aggression measure. Using behavioral indicators, like the number of aggressive acts in a simulation, can further complement subjective ratings. Triangulating measures in this way helps compensate for the limitations of any single method and provides a fuller picture of the variable being studied.

Reliability is another pillar of construct validity. A reliable measure is one that gives consistent results across time or across different raters. For instance, if someone completes a depression inventory twice in the same week and scores vary widely, that raises concerns about the measure’s reliability. Similarly, if two researchers observe the same behaviors but record very different data, we have low interrater reliability. A measure must be both reliable and valid: reliability ensures consistency, while validity ensures accuracy. Without reliability, even a well-defined measure can produce misleading results.

It’s also worth evaluating the scope of the construct being measured. Does the measure capture subtle variations, or does it lump all responses into broad categories? A study that groups people into “low” and “high” coffee consumption might miss meaningful differences among moderate consumers. Additionally, researchers need to be transparent about how they coded or scored responses. Did they reverse-score any items? Did they average multiple items into a scale? Were cutoff points clearly defined and justified? Transparency helps others replicate the study and evaluate the construct validity more fully.

Construct validity in association claims depends on clear operational definitions, matched closely to the conceptual variable, and measured reliably. As a student reading psychological research, your job is to examine how each variable was defined and assessed. Was a validated tool used? Were multiple methods employed? Did the measure capture the full scope of the concept?

Asking these questions helps you determine whether the reported association reflects a real relationship between well-measured variables or is merely the result of poor operationalization. Solid construct validity is essential for making trustworthy inferences about associations in psychological research.

External Validity of Association Claims

External validity is all about the generalizability of a study’s findings. In the case of association claims—statements that suggest a relationship between two variables—external validity asks whether the observed association would hold in other populations, settings, or times.

Suppose a study finds that students who sleep more tend to score higher on standardized tests. To evaluate the external validity, we must ask: Who were these students? Were they from different types of schools, geographic regions, or socioeconomic backgrounds? Were they all from the same age group or cultural context? If the sample was diverse and selected using random sampling methods, the study is more likely to have good external validity. But if it was based on a small, homogeneous group—say, college students from a single university—the ability to generalize the findings to other groups may be limited.

Generalizability also depends on the context in which the study was conducted. For example, an association between mindfulness and stress reduction might hold in a quiet laboratory setting, but would it apply in high-pressure workplaces or during times of crisis? If the study was conducted during the COVID-19 pandemic, the results might not generalize to post-pandemic conditions. Similarly, cultural factors can influence whether certain associations apply universally. For instance, the association between family closeness and mental well-being might be stronger in collectivist cultures than in individualist ones. A study with strong external validity will carefully report on the sample and setting, so readers can judge how well the results apply to other contexts.

It’s also important to distinguish between the generalizability of individual variables and the generalizability of the association between them. A study might measure screen time in a representative sample of teenagers, and stress levels in a representative sample of adults—but that doesn’t mean the observed relationship between screen time and stress applies across both age groups. Just because each variable has good external validity doesn’t guarantee that their association does. Researchers must consider whether the relationship observed in one group holds true in others. Ideally, this is tested through replication in different populations.

External validity can be strengthened by using probability sampling methods. These include random digit dialing, systematic sampling, or stratified sampling, which increase the likelihood that the sample reflects the broader population. However, these methods can be expensive and time-consuming, so researchers sometimes rely on convenience samples. When they do, it’s important that they acknowledge the limitations of their sample and avoid overgeneralizing the findings. Transparent reporting about sampling procedures helps other scientists—and critical readers like you—assess how far the findings can reasonably be extended.

Replication is another tool for evaluating external validity. If a study shows that coffee consumption is associated with reduced depression in middle-aged adults in France, and another study finds a similar result in young adults in Brazil, confidence in the generalizability of that association increases. On the other hand, if similar studies in different populations yield conflicting results, that’s a sign that the association may be context-dependent or influenced by cultural, environmental, or methodological differences. The more consistently an association appears across varied studies, the more we can trust its generalizability.

External validity helps us decide whether the associations we see in research studies apply beyond the original participants. When reading an association claim, always ask: Who was studied? How were they selected? What setting did the study take place in? Could the results change over time or in different cultures?

Thinking through these questions allows you to judge whether the findings are robust or limited in scope. Strong external validity doesn't guarantee that an association is real, but it does increase our confidence that the findings are meaningful across a wider range of situations and people.

Statistical Validity of Association Claims

Statistical validity focuses on how well the numbers support an association claim. It asks whether the association between two variables is statistically significant, how strong the relationship is, and how precise the estimate appears. For example, if researchers report that increased social media use is associated with higher levels of anxiety, we need to examine whether this result is likely to have occurred by chance, or whether it reflects a real trend in the population.

To do this, researchers rely on statistical tools such as correlation coefficients, p-values, and confidence intervals. A statistically significant association typically has a p-value less than 0.05, indicating there’s less than a 5% probability that the observed effect occurred by random chance. However, statistical significance alone isn’t enough. The size and practical relevance of the association—called the effect size—also matter.

Effect size is a measure of how strong the relationship between two variables is. A correlation of r = .10 is small, r = .30 is moderate, and r = .50 or higher is considered large in many contexts. For example, if time spent exercising correlates with well-being at r = .50, that’s a relatively strong association. If the correlation is only r = .10, even if it’s statistically significant, it may not be practically meaningful. That’s why effect size provides critical context when interpreting results.

Additionally, confidence intervals show the precision of the estimate—narrow intervals suggest more certainty, while wide intervals indicate more variability and less confidence in the exact effect size.

Another important aspect of statistical validity is whether the researchers used the correct statistical test for their data. Different types of data require different analyses. For instance, correlations are appropriate when both variables are continuous, but if one variable is categorical—like gender—other tests such as t-tests or ANOVAs might be needed. Misapplying statistical tests can lead to incorrect conclusions. Furthermore, multiple testing without correction increases the likelihood of Type I errors—finding a significant result by chance when none exists. Good researchers report how they handled multiple comparisons and whether they corrected for the increased risk of error.

Power is another statistical consideration. Statistical power refers to a study’s ability to detect an effect if one truly exists. Low-powered studies, often due to small sample sizes, are more likely to miss real associations (Type II errors) and to produce unstable results. A study that finds a weak association in a sample of 20 participants is far less convincing than one with the same effect size in a sample of 2,000. Power analysis, conducted before data collection, helps determine the appropriate sample size needed to detect the expected effect with sufficient confidence.

Transparency in reporting is also critical for evaluating statistical validity. Reliable studies clearly state their sample size, the exact p-values, the effect sizes, the type of statistical tests used, and the confidence intervals for their findings. They also describe any data exclusions or outliers that were removed and whether any tests were pre-registered. These practices reduce the risk of p-hacking (manipulating analyses until significant results emerge) and help ensure that reported associations are trustworthy.

Statistical validity is about much more than getting a p-value under .05. It’s about using appropriate methods, reporting results transparently, interpreting effect sizes accurately, and considering the likelihood that findings replicate in future studies.

As a researcher, when you see an association claim, don’t just ask whether it’s statistically significant—ask how strong the association is, how precisely it was estimated, whether the sample was large enough, and whether the appropriate tests were used. These habits will help you evaluate claims more critically and build a stronger foundation for understanding the reliability and relevance of psychological research.

Summary

Association claims are central to psychological science and everyday reasoning. They inform our understanding of trends, risks, and relationships—such as whether screen time correlates with anxiety or whether exercise is associated with better mood. But association does not imply causation. To evaluate these claims responsibly, readers must interrogate three key forms of validity.

Construct validity asks whether the study measured each variable accurately and reliably. Without well-defined operationalizations, the link between variables may be misleading. External validity examines whether the observed relationship holds in other populations, settings, or times. A strong association in a narrowly defined group does not guarantee the same result elsewhere. Statistical validity evaluates the strength and significance of the association and whether the correct analyses were used and transparently reported.

Together, these validity checks empower students to detect exaggerated claims, avoid misinterpreting correlations as causal, and appreciate when association claims are genuinely well-supported. Practicing these interrogations fosters scientific literacy and enhances our ability to navigate research-based conclusions in a data-driven world.

Key Takeaways

1. Association claims suggest a relationship between variables but do not assert causality.

2. Construct validity ensures that each variable is clearly and reliably measured.

3. External validity addresses whether the association generalizes across people, settings, and time.

4. Statistical validity evaluates the strength, precision, and significance of the association.

5. Responsible interpretation of association claims requires rejecting causal language unless supported by experimental evidence.

Glossary

association claim: a statement that one variable is related to another, without asserting a causal connection.

causal claim: a statement asserting that one variable causes a change in another, requiring evidence from experimental designs.

confidence interval: a statistical range that likely contains the true value of an effect, with narrower intervals indicating greater precision.

construct validity: the extent to which a variable has been accurately and reliably operationalized in line with the theoretical concept.

correlation coefficient: a statistic (typically represented as r) that quantifies the strength and direction of the relationship between two continuous variables.

effect size: a measure of the magnitude of a relationship between variables, independent of sample size.

external validity: the degree to which the results of a study can be generalized to other populations, settings, or times.

generalizability: the extent to which findings from a specific sample apply to broader populations or contexts.

interrater reliability: the level of agreement between different observers or raters measuring the same behavior or construct.

operational definition: the specific procedure or method used to measure or manipulate a variable in a study.

p-value: the probability of observing an effect as extreme as the one in the data, assuming the null hypothesis is true.

replication: the process of repeating a study to see whether the original findings can be consistently reproduced.

statistical significance: a result is statistically significant if it is unlikely to have occurred by chance, typically indicated by a p-value below .05.

statistical validity: the extent to which a study’s statistical conclusions are accurate and reasonable, based on appropriate analyses and effect size estimation.

Type I error: the incorrect rejection of a true null hypothesis; a false positive.

Type II error: the failure to reject a false null hypothesis; a false negative.

validity: the overall accuracy or truthfulness of a measure or conclusion in research.

About the Authors

Zachary Meehan earned his PhD in Clinical Psychology from the University of Delaware and serves as the Clinic Director for the university's Institute for Community Mental Health (ICMH). His clinical research focuses on improving access to high-quality, evidence-based mental health services, bridging gaps between research and practice to benefit underserved communities. Zachary is actively engaged in professional networks, holding membership affiliations with the Association for Behavioral and Cognitive Therapies (ABCT) Dissemination and Implementation Science Special Interest Group (DIS-SIG), the BRIDGE Psychology Network, and the Delaware Project. Zachary joined the staff at Biosource Software to disseminate cutting-edge clinical research to mental health practitioners, furthering his commitment to the accessibility and application of psychological science.

Fred Shaffer earned his PhD in Psychology from Oklahoma State University. He is a biological psychologist and professor of Psychology, as well as a former Department Chair at Truman State University, where he has taught since 1975 and has served as Director of Truman’s Center for Applied Psychophysiology since 1977. In 2008, he received the Walker and Doris Allen Fellowship for Faculty Excellence. In 2013, he received the Truman State University Outstanding Research Mentor of the Year award. In 2019, he received the Association for Applied Psychophysiology and Biofeedback (AAPB) Distinguished Scientist award. He teaches Experimental Psychology every semester and loves Beth Morling's 5th edition.