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Effect size guidelines for cross-lagged effects.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Cross-lagged models are frequently used to assess prospective effects between constructs.
  • Lack of established guidelines hinders the interpretation of cross-lagged effect sizes.
  • This gap impacts the reliability and comparability of findings across studies.

Purpose of the Study:

  • To establish empirical benchmarks for interpreting the magnitude of cross-lagged effects.
  • To provide guidance for the cross-lagged panel model (CLPM) and random intercept cross-lagged panel model (RI-CLPM).
  • To assist researchers in power analyses and sample size estimations.

Main Methods:

  • A meta-analysis of 1,028 CLPM and 302 RI-CLPM effect sizes from 174 samples across four psychology subfields.
  • Analysis focused on establishing percentile distributions for effect sizes.
  • Investigated moderation by concurrent correlations and predictor stability.

Main Results:

  • For CLPM, benchmarks are .03 (small), .07 (medium), and .12 (large).
  • For RI-CLPM, benchmarks are .02 (small), .05 (medium), and .11 (large).
  • Effect sizes were not significantly different between CLPM and RI-CLPM, nor moderated by subfield or design characteristics, but were moderated by concurrent correlation and predictor stability.

Conclusions:

  • Proposed benchmarks of .03, .07, and .12 for small, medium, and large cross-lagged effects, respectively.
  • These benchmarks are applicable to both CLPM and RI-CLPM.
  • Findings will enhance the interpretation of cross-lagged effects and inform study design.