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Modelling inter-individual differences in latent within-person variation: The confirmatory factor level variability

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  • 1University of Münster, Germany.

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This study introduces a new statistical model for analyzing individual differences in psychological variability using multiple measures. The model extends multilevel approaches to handle complex longitudinal data, enhancing psychological research capabilities.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Psychometrics

Background:

  • Psychological theories frequently address individual differences in within-person variability.
  • Multilevel models are common for analyzing longitudinal data, assessing between-person and within-person differences.
  • Existing multilevel approaches are limited to single-indicator variables.

Purpose of the Study:

  • To develop a novel statistical model for analyzing individual differences in within-person variability across multiple measures.
  • To extend multilevel modeling capabilities to accommodate multi-indicator constructs in psychological research.
  • To enable the estimation of individual differences in latent mean-level and latent within-person variability factors.

Main Methods:

  • Integration of the single-indicator multilevel model with confirmatory factor analysis.
  • Development of a new model to estimate individual differences in latent factors for both mean levels and within-person variability.
  • Application of a maximum likelihood estimator for model parameter estimation.

Main Results:

  • The proposed model successfully estimates individual differences in latent mean-level factors.
  • The model also estimates individual differences in latent within-person variability factors.
  • The approach is demonstrated with intensive longitudinal data, showing its practical applicability.

Conclusions:

  • The new combined model expands the analytical possibilities for psychological research involving multiple measures and longitudinal data.
  • Researchers can now investigate between- and within-person differences in latent constructs more effectively.
  • This methodology provides a robust framework for understanding psychological variability in complex datasets.