Threats to Internal Validity to Know for Experimental Design

Understanding threats to internal validity is crucial in experimental design. These threats, like history, maturation, and selection bias, can distort results and lead to misleading conclusions. Recognizing and addressing these issues helps ensure more reliable and valid findings in research.

1. History

   • Events occurring outside the experiment that can influence the outcome.
   • Changes in the environment or societal context during the study period.
   • Can lead to confounding variables that affect the dependent variable.

2. Maturation

   • Natural changes in participants over time that can affect results.
   • Includes physical, emotional, or cognitive development.
   • Particularly relevant in long-term studies where time is a factor.

3. Testing

   • The effects of taking a test on subsequent test performances.
   • Familiarity with the test can improve scores (practice effects).
   • May lead to biased results if participants are repeatedly tested.

4. Instrumentation

   • Changes in measurement tools or procedures during the study.
   • Variability in data collection methods can skew results.
   • Ensuring consistency in instruments is crucial for valid comparisons.

5. Statistical regression

   • Tendency for extreme scores to move closer to the average on subsequent measurements.
   • Can mislead interpretations of treatment effects if not accounted for.
   • Important to recognize when analyzing pre-test and post-test data.

6. Selection bias

   • Non-random assignment of participants can lead to unequal groups.
   • Differences in characteristics between groups can confound results.
   • Randomization is essential to minimize this threat.

7. Experimental mortality (attrition)

   • Loss of participants during the study can affect the validity of results.
   • If the dropout rate is related to the treatment, it can skew findings.
   • Tracking and analyzing reasons for attrition is important.

8. Diffusion of treatments

   • Participants in different groups may share information about treatments.
   • Can lead to contamination of control and experimental groups.
   • Maintaining separation between groups is vital to preserve integrity.

9. Compensatory rivalry

   • Control group may work harder to outperform the experimental group.
   • Can create an artificial inflation of results in the control group.
   • Awareness of group assignments can influence participant behavior.

10. Demoralization of control group

    • Control group may feel disadvantaged and perform worse as a result.
    • Negative feelings about not receiving treatment can affect outcomes.
    • Ensuring ethical treatment and communication with participants is essential.