Validity Threats

Understanding validity threats is key in AP Research. These threats can skew results and impact the reliability of findings. Recognizing issues like selection bias, maturation, and confounding variables helps ensure your research is accurate and meaningful.

1. Selection bias

   • Occurs when the participants included in a study are not representative of the larger population.
   • Can lead to skewed results and limit the generalizability of findings.
   • Often arises from non-random sampling methods or self-selection of participants.

2. Maturation

   • Refers to changes in participants that occur naturally over time, independent of the study.
   • Can affect longitudinal studies where participants are observed over extended periods.
   • May lead to misinterpretation of results if changes are attributed to the intervention rather than natural development.

3. History

   • Involves external events occurring during the study that can influence participants' responses.
   • These events can be significant (e.g., political events) or minor (e.g., local weather).
   • Can confound results if not accounted for, making it difficult to isolate the effect of the treatment.

4. Testing effects

   • Occurs when participants' performance is influenced by prior exposure to the test or measurement.
   • Familiarity with the test can lead to improved scores (practice effect) or fatigue (test-wiseness).
   • Important to consider when using pre-tests and post-tests in research designs.

5. Instrumentation

   • Refers to changes in the measurement tools or procedures over the course of the study.
   • Can lead to inconsistencies in data collection, affecting reliability and validity.
   • Researchers must ensure that instruments remain stable and calibrated throughout the study.

6. Regression to the mean

   • Describes the phenomenon where extreme scores tend to move closer to the average upon retesting.
   • Can mislead researchers into thinking that an intervention caused a change when it may simply be statistical fluctuation.
   • Important to recognize in studies with extreme initial measurements.

7. Attrition

   • Refers to the loss of participants during a study, which can bias results if the dropouts differ from those who remain.
   • High attrition rates can threaten the validity of findings and reduce statistical power.
   • Researchers should track and report attrition rates and reasons for dropout.

8. Experimenter bias

   • Occurs when a researcher's expectations or beliefs influence the study's outcome.
   • Can manifest in data collection, analysis, or interpretation of results.
   • Blinding and randomization are strategies to minimize this threat.

9. Demand characteristics

   • Refers to cues in a study that may influence participants to respond in a way they believe is expected.
   • Can lead to altered behavior, affecting the authenticity of the data collected.
   • Researchers should strive to minimize these cues to ensure genuine responses.

10. Confounding variables

    • Variables other than the independent variable that may affect the dependent variable.
    • Can obscure the true relationship between the variables being studied.
    • Identifying and controlling for confounding variables is crucial for establishing causal relationships.