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Mixture Rasch models (MRMs) reveal distinct subpopulations within survey data. This analysis of higher education survey data identified three unique respondent classes, enhancing understanding of survey scale functionality.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Traditional survey analysis may overlook subgroup differences.
  • Mixture Rasch models (MRMs) offer advanced methods for latent class detection.
  • Understanding respondent heterogeneity is crucial for accurate scale interpretation.

Purpose of the Study:

  • To demonstrate the utility of mixture Rasch models (MRMs), specifically a mixture partial credit model (MPCM), for analyzing survey data.
  • To identify latent classes (unobserved subpopulations) within a higher education survey dataset.
  • To examine how item parameters differ across identified latent classes.

Main Methods:

  • Application of a mixture partial credit model (MPCM) framework.
  • Estimation and selection of the MRM using real survey data from college seniors.
  • Analysis of item parameter estimates to characterize latent classes.

Main Results:

  • Identification of three distinct latent classes among college seniors.
  • Characterization of each class based on unique patterns of item parameter estimates.
  • Investigation of the relationship between class assignment and institutional affiliation.

Conclusions:

  • MRMs are effective for uncovering hidden structures and respondent heterogeneity in survey data.
  • The identified latent classes provide nuanced insights into how the survey scale functions across different subpopulations.
  • Findings highlight the importance of considering subgroup differences for a comprehensive understanding of survey results.