Rotation Local Solutions in Multidimensional Item Response Theory Models.

We conducted an extensive Monte Carlo study of factor-rotation local solutions (LS) in multidimensional, two-parameter logistic (M2PL) item response models. In this study, we simulated more than 19,200 data sets that were drawn from 96 model conditions and performed more than 7.6 million rotations t...

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Publicado en:Educational & Psychological Measurement Vol. 84; no. 6; pp. 1045 - 1076
Autores principales: Nguyen, Hoang V., Waller, Niels G.
Formato: Artículo
Publicado: Sage Publications Inc. Dec2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2024
      vid: 84
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      pub: Sage Publications Inc.
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        10.1177/00131644231223722
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        atl: Rotation Local Solutions in Multidimensional Item Response Theory Models.
      aug:
        au:
          Nguyen, Hoang V.
          Waller, Niels G.
        affil: University of Minnesota, Twin Cities, Minneapolis, USA
      su:
        Multidimensional scaling
        Psychological tests
        Statistical models
        Data analysis
        Descriptive statistics
        Statistics
        Algorithms
      sug:
        subj:
          Multidimensional scaling
          Psychological tests
          Statistical models
          Data analysis
          Descriptive statistics
          Statistics
          Algorithms
      keyword:
        factor rotations
        geomin
        local solutions
        multidimensional item response theory
        oblimin
        factor rotations
        geomin
        local solutions
        multidimensional item response theory
        oblimin
      ab: We conducted an extensive Monte Carlo study of factor-rotation local solutions (LS) in multidimensional, two-parameter logistic (M2PL) item response models. In this study, we simulated more than 19,200 data sets that were drawn from 96 model conditions and performed more than 7.6 million rotations to examine the influence of (a) slope parameter sizes, (b) number of indicators per factor (trait), (c) probabilities of cross-loadings, (d) factor correlation sizes, (e) model approximation error, and (f) sample sizes on the local solution rates of the oblimin and (oblique) geomin rotation algorithms. To accommodate these design variables, we extended the standard M2PL model to include correlated major factors and uncorrelated minor factors (to represent model error). Our results showed that both rotation methods converged to LS under some conditions with geomin producing the highest local solution rates across many models. Our results also showed that, for identical item response patterns, rotation LS can produce different latent trait estimates with different levels of measurement precision (as indexed by the conditional standard error of measurement). Follow-up analyses revealed that when rotation algorithms converged to multiple solutions, quantitative indices of structural fit, such as numerical measures of simple structure, will often misidentify the rotation that is closest in mean-squared error to the factor pattern (or item-slope pattern) of the data-generating model.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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