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Studying item-effect variables and their correlation patterns with multi-construct multi-state models.

Tina H Erhardt1, Timo Gnambs1, Marie-Ann Sengewald1

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This study introduces a new model to analyze item-level method effects in longitudinal data, revealing important interindividual differences and improving measurement accuracy for constructs like life satisfaction.

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Area of Science:

  • Psychometrics
  • Latent Variable Modeling
  • Longitudinal Data Analysis

Background:

  • Method effects at the item level can be modeled as latent difference variables in longitudinal data.
  • These variables capture interindividual differences in responses to specific items within multi-item scales.
  • Including item-effect variables significantly enhances model fit compared to traditional unidimensional models.

Purpose of the Study:

  • To introduce and demonstrate a multi-construct multi-state model incorporating item-effect variables.
  • To systematically investigate correlation patterns of item effects within and between constructs.
  • To provide insights into instrument structure and response processes.

Main Methods:

  • Latent variable analysis using a multi-construct multi-state model.
  • Modeling item-effect variables to account for method effects at the item level.
  • Analysis of longitudinal data from 2,529 Dutch respondents measuring life satisfaction and positive affect over five occasions.

Main Results:

  • Confirmed the presence of significant item effects in ostensibly unidimensional scales.
  • Demonstrated that item effects represent interindividual differences on the item level.
  • Observed specific item effects between constructs and content-aligned correlations within constructs.

Conclusions:

  • Modeling item effects is crucial for accurate psychometric and substantive research.
  • The proposed multi-construct multi-state model effectively examines item effects.
  • Findings highlight the importance of accounting for item-level response variability in longitudinal studies.