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Considering between- and within-person relations in auto-regressive cross-lagged panel models for developmental data.

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Auto-regressive cross-lagged panel models analyze longitudinal data but often confound between-person and within-person associations. This study clarifies model selection for accurate longitudinal data analysis.

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Longitudinal Data Analysis

Background:

  • Longitudinal data analysis allows inferences at both between-person and within-person levels.
  • Auto-regressive cross-lagged panel models are commonly used for examining time-lagged relations in longitudinal data.
  • Existing implementations often fail to adequately distinguish between-person and within-person associations, leading to inaccurate results.

Purpose of the Study:

  • To familiarize analysts with common auto-regressive cross-lagged panel model variants.
  • To guide researchers in selecting appropriate models based on data characteristics and research questions.
  • To improve the accuracy of longitudinal data analysis by addressing model specification issues.

Main Methods:

  • Focus on auto-regressive cross-lagged panel models.
  • Analysis of common model variants and their interpretations.
  • Guidance on selecting models for distinct sources of association in longitudinal data.

Main Results:

  • Many common implementations of these models confound between-person and within-person relations.
  • Substantial differences in interpretation arise from seemingly minor model specification differences.
  • Clearer understanding of how model choices impact results in longitudinal studies.

Conclusions:

  • Accurate longitudinal data analysis requires careful selection of statistical models.
  • Distinguishing between-person and within-person associations is crucial for valid inferences.
  • This work provides guidance for selecting appropriate models to avoid common pitfalls in longitudinal research.