Multivariate Correlational Designs Study Notes

Multivariate Correlational Designs

• Definition: These designs examine the relationships among multiple variables without manipulating them. They help in understanding how variables correlate with each other over time and under various conditions.

Longitudinal Design

• Overview: Longitudinal studies track the same participants over a period of time, providing insights into how variables may influence each other across different ages or stages.
• Example: A child development researcher investigates if authoritarian parenting leads to conduct disorder in children.
• Study Design:
  • Participants: 200 parents with children aged 4-5 (Time 1).
  • Processes: Parents interviewed about parenting style; children assessed for conduct disorder symptoms.
  • Follow-up: Same parents and children assessed again at ages 7-8 (Time 2).

Conclusions from the Study

• Authoritarian Parenting and Conduct Disorder:

• Finding: A statistically significant cross-lagged correlation (r = .31, p < .05) suggests that authoritarian parenting style predicts conduct disorder, supporting the researcher's hypothesis.

• Implication: This indicates that the parenting style influences the children's behavior rather than the reverse.

• Stability of Conduct Disorder:

• Evidence: Positive autocorrelation at Time 1 and Time 2 (r = .29, p < .05) shows that conduct disorder symptoms persist over time, indicating stability.

• Causality:

• Claim: The researcher cannot claim causation; while cross-lagged correlations support temporal precedence, other variables might influence the relationship (third variable problem).

• Reasoning: Correlational designs lack internal validity, which is crucial for causal claims.

Internal Validity and Correlational Studies

• Internal Validity:

• Definition: The extent to which a study can show a cause-effect relationship without confounding factors.

• Note: Correlational studies cannot establish causation due to potential unmeasured variables.

• Causality Criteria:

1. Covariance: Show correlation.
2. Temporal Precedence: The cause must occur before the effect.
3. Internal Validity: Must rule out alternative explanations; achieved only through well-designed experimental studies.

Third Variable Problem

• Definition: When two variables correlate but only due to their relationship with a third variable.
• Ruling Out Third Variables:

1. Statistical Control: Holding variables constant during analysis.
2. Experimental Control: Controlling variables across experimental conditions.

Multiple Regression Analysis

• Purpose: To control for third variables and examine direct relationships between primary variables.

• Example: Examining the relationship between physical health, relationship satisfaction, and financial security.

• Interpretation: Findings indicate whether a significant relationship remains after accounting for other factors.

Moderation and Mediation

• Moderation: Tests if the strength/direction of a relationship changes based on another variable.

• Example: The effect of studying on exam performance may depend on study types (e.g., practicing retrieval vs. reviewing notes).

• Mediation: Explores the mechanism through which one variable influences another.

• Steps:

  1. Establish correlation between predictor and outcome.
  2. Show predictor influences mediator.
  3. Demonstrate the mediator affects the outcome when accounting for the predictor.
  4. Assess if the direct relationship disappears when controlling for the mediator.

Practice Question Examples

1. Mediation Model: Lack of sleep predicting relationship conflict through perspective-taking ability.

• Paths:
  • a: Link between sleep and perspective-taking.
  • b: Link between perspective-taking and relationship conflict.
  • c: Overall effect of sleep on conflict.
  • ab: Indirect effect via perspective-taking.
  • c': Direct effect of sleep on conflict.

1. Results Interpretation: If 85% of the relationship between sleep and conflict is explained by perspective-taking, this indicates complete mediation.

Evaluating Validity in Multivariate Designs

• Construct Validity: Measurement accuracy of constructs involved.
• External Validity: Generalizability of findings to broader populations.
• Statistical Validity: Consideration of effect sizes and sample sizes; acknowledging that internal validity is absent in correlational studies.

Assignment 3

• Task: Design an experimental study to test a causal claim. Must manipulate the independent variable and measure the dependent variable.
• Due date: March 19, 2025, at 11:59 pm.