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Random intercept item factor analysis.

Albert Maydeu-Olivares1, Donna L Coffman

  • 1Department of Psychology, University of Barcelona, Barcelona, Spain. amaydeu@ub.edu

Psychological Methods
|December 13, 2006
PubMed
Summary
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This study introduces a flexible factor model where intercepts vary across participants, improving analysis of questionnaire data. This approach enhances modeling accuracy when common coefficients are too restrictive.

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • The common factor model assumes fixed coefficients, which may be too restrictive for individual response patterns in questionnaires.
  • Idiosyncratic use of response scales can violate the common coefficient assumption.

Purpose of the Study:

  • To propose a partially relaxed common factor model allowing participant-specific intercepts.
  • To offer a more flexible alternative to traditional factor models for analyzing individual differences in response data.

Main Methods:

  • Developed a factor model that relaxes the fixed intercept assumption, allowing intercepts to vary across participants.
  • Utilized structural equation modeling software for fitting the proposed single-level data model.
  • Compared the proposed model with bifactor and correlated trait-correlated method minus 1 models using empirical data.

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Main Results:

  • The proposed model adequately reproduces data where m+1 factors are practically needed despite m factors being theoretically expected.
  • Demonstrated the model's utility with an empirical dataset on optimism.

Conclusions:

  • The proposed model offers a valuable extension to the common factor model for handling participant heterogeneity in response styles.
  • This flexible approach improves the accuracy of latent variable modeling when traditional assumptions are violated.