Categories of inference and validity type

Categories of Inference and Validity Type

Inferences About Constructs

• Construct Validity (pages 291-299)

  • Concerns about whether the constructs researchers claim to measure or manipulate are actually being studied.

  • Emphasis on the need for strong definitions and operational definitions of independent and dependent variables to ensure they accurately represent the constructs.

  • Good operational definitions ensure that researchers are measuring what they intend to measure.

Statistical Inferences

• Statistical Conclusion Validity

  • Refers to the correct statistical treatment of data and the soundness of the conclusions drawn from those statistics.

  • Must reflect more than mere random variations; it should allow claims of genuine differences between groups (e.g., strains of rats in a maze).

  • Examines whether researchers' conclusions about the relationships between independent variables (IV) and dependent variables (DV) are based on appropriate statistical analyses.

Causal Inferences

• Internal Validity

  • Assesses confidence that a study demonstrates a causal effect between two variables.

  • Requires three conditions:

    1. Presence of at least two variables (IV and DV).

    2. Establishment of causation.

    3. Clear temporal order of variable changes.

Inferences About Generalizability

• External Validity

  • Concerned with the extent to which findings can be generalized beyond the study context, across populations and settings.

  • Replication is critical for validating original findings and enhancing both internal and external validity.

  • It allows researchers to test findings in different contexts to increase confidence in generalizability.

Ecology and Research Context

• Ecological Validity

  • Focuses on how well responses from a research context translate to natural settings.

  • Involves scientific study of the relationship between living organisms and their environment (Miller, 1988).

  • Factors include the relationship among the researcher, the participant, and the research situation.

Importance of Internal Validity

• Internal Validity

  • Represents the ability to make valid inferences about relationships between DV and IV.

  • The observed effect (measured by the DV) must be caused solely by variations in the IV, avoiding confounding variables.

Threats to Internal Validity (pages 300-304)

1. History

   • Events occurring during the study that can affect the DV but are not part of the experimental manipulation.

   • Example: Studying cognitive development and political attitudes during significant historical events, like 9/11, influences perceptions.

2. Maturation

   • Natural changes in participants over time can influence the DV.

   • Example: Increases in intimacy during adolescence regardless of the number of friends.

3. Testing

   • Changes in participants' responses due to the measurement process itself.

   • Example: Monitoring children can lead to altered behaviors during observations.

4. Instrumentation

   • Changes in measurement instruments can skew results.

   • Example: Observers may become more adept at recognizing behaviors over time.

5. Regression Toward the Mean

   • Extreme scores on a variable will tend to regress towards the mean over time.

6. Attrition (Subject Loss)

   • When participants withdraw, potentially biasing results.

   • Example: Victimized adolescents drop out of a study over time.

7. Selection

   • Pre-existing differences in participant characteristics can mediate results.

   • Example: Parenting programs where parents self-select into conditions.

8. Diffusion of Treatment

   • Participants communicate and reduce differences between experimental groups.

Control Achieved Through Participant Assignment

• Random Selection

  • Ensures everyone in the participant pool has an equal chance of being selected, enhancing generalizability.

Avoiding Potential Confounds

• Elimination Procedure: Remove specific groups that may introduce bias.

• Equating Procedure: Assign similar numbers of individuals to different conditions.

• Counterbalancing Procedure: Change the order of conditions to control for order effects.

Randomization in Experimental Design

• Most powerful for eliminating confounding variables, ensuring controls are in place through random assignment.

• Random sampling vs. random assignment: Sampling refers to selecting a representative sample, while assignment places those samples into specific experimental conditions.