Validity Types

Why This Matters

In political research, validity is the foundation that separates credible scholarship from flawed conclusions. You're being tested on your ability to evaluate research designs, identify weaknesses in studies, and understand why certain findings can (or can't) be trusted. Every time you read a study claiming that campaign ads influence voter turnout or that economic conditions predict election outcomes, validity questions should immediately come to mind: Did they actually measure what they claimed? Can we trust the causal story? Does this apply beyond this one case?

The different validity types work together as a system of checks on research quality. Internal and external validity address whether findings are true and generalizable. Construct and content validity ask whether we're measuring the right thing. Statistical conclusion validity examines whether we're analyzing correctly. Criterion validity (and its subtypes) tests whether measures perform as expected. Don't just memorize definitions—know which validity type is threatened in specific research scenarios and how researchers can strengthen each one.

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Causal Inference Validity

These validity types address the core question of whether a study can establish trustworthy cause-and-effect relationships. They focus on the research design itself rather than the measurement tools.

Internal Validity

• Establishes causal relationships—the degree to which a study can confidently claim that the independent variable (not something else) caused the observed effect
• Threats include confounding variables, selection bias, and history effects—any factor that provides an alternative explanation for your results undermines internal validity
• Experimental designs maximize internal validity—randomization and control groups help isolate the causal mechanism, which is why true experiments are the gold standard for causal claims

External Validity

• Concerns generalizability—whether findings from your specific study apply to other populations, settings, or time periods
• Trade-off with internal validity—tightly controlled lab experiments often sacrifice external validity, while field studies in natural settings may struggle with internal validity
• Sample characteristics matter most—a study of college students may not generalize to the broader electorate; always ask "to whom do these findings apply?"

Statistical Conclusion Validity

• Accuracy of statistical inferences—whether the statistical tests used are appropriate, assumptions are met, and conclusions are correctly drawn from the data
• Common threats include low statistical power and data dredging—underpowered studies miss real effects, while running multiple tests until something is "significant" inflates false positives
• Replication addresses this validity type—if findings can't be reproduced with new data, statistical conclusion validity is suspect

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Measurement Validity

These validity types focus on whether your operationalization actually captures the theoretical concept you're trying to study. They ask: "Are we measuring what we think we're measuring?"

Construct Validity

• Alignment between theory and measurement—the degree to which your operational definition truly represents the abstract concept (construct) you're studying
• Assessed through factor analysis and correlations—statistical techniques can reveal whether your measure behaves as the underlying theory predicts
• Central to political science concepts—constructs like "political efficacy," "authoritarianism," or "polarization" require careful operationalization to achieve construct validity

Content Validity

• Coverage of the full construct domain—whether your measure captures all relevant dimensions of the concept, not just one narrow slice
• Assessed through expert judgment and literature review—researchers consult existing scholarship to ensure nothing important is omitted
• Example: measuring "civic engagement"—a valid measure should include voting, volunteering, contacting officials, and other forms—not just one behavior

Face Validity

• Surface-level plausibility—whether a measure appears to assess what it claims, based on common-sense judgment
• Necessary but not sufficient—a measure can look valid but actually miss the construct entirely; face validity is a starting point, not a guarantee
• Useful for participant buy-in—respondents are more likely to take seriously a survey that seems relevant to the stated research purpose

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Criterion-Related Validity

These validity types assess whether a measure performs as expected when compared against external standards or outcomes. They test measures against real-world benchmarks.

Criterion Validity

• Correlation with relevant outcomes—the extent to which a measure relates to an external criterion it should theoretically predict or align with
• Divided into predictive and concurrent subtypes—depending on whether the criterion is measured later (predictive) or simultaneously (concurrent)
• Essential for applied measures—if a "likely voter" scale doesn't correlate with actual voting behavior, it lacks criterion validity

Predictive Validity

• Forecasting future outcomes—how well a measure predicts behavior or performance that hasn't yet occurred
• Evaluated through longitudinal designs—you need follow-up data to assess whether early measurements predicted later outcomes
• Example: pre-election polls—their predictive validity is tested against actual election results; accurate forecasts indicate high predictive validity

Concurrent Validity

• Correlation with established measures at the same time—whether a new measure aligns with existing validated instruments when administered simultaneously
• Useful for validating new assessments—if you develop a shorter political interest scale, it should correlate highly with the established longer version
• Faster to establish than predictive validity—no waiting period required, making it practical for initial validation studies

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Convergent and Related Validity

This validity type examines relationships between measures to confirm that similar constructs produce correlated results. It provides evidence that your measure belongs to the right "family" of concepts.

Convergent Validity

• Correlation between related measures—the degree to which different measures of the same or similar constructs actually correlate with each other
• Assessed through correlation coefficients and factor analysis—high correlations between theoretically related measures support convergent validity
• Works alongside discriminant validity—measures should correlate with related constructs (convergent) but not with unrelated constructs (discriminant); both are needed for full validation

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Quick Reference Table

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Self-Check Questions

1. A researcher conducts a tightly controlled experiment in a university lab using only political science majors as participants. Which two validity types are most in tension here, and why?

2. You're evaluating a new "democratic attitudes" scale. What's the difference between establishing its construct validity versus its content validity?

3. A study finds a statistically significant relationship, but the sample size was small and the researchers tested 20 different hypotheses before finding this one result. Which validity type is most threatened?

4. Compare and contrast predictive validity and concurrent validity. In what research situation would you prioritize one over the other?

5. A measure of "political sophistication" correlates highly with measures of political knowledge and news consumption but shows no correlation with measures of extraversion. What does this pattern suggest about the measure's validity, and which specific validity types does it support?