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Karl Jöreskog

Biographic Data

ID9251615
NAMEKarl Jöreskog
GIVEN NAMESKarl
FAMILY NAMEJöreskog
SIGNATUREJÖRESKOG K
AFFILIATIONSEducational Testing Service
VERIFIEDNo
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR1973
LATEST PUBLICATION YEAR2010
H-INDEX0
  • Confirmatory Factor Analysis of Ordinal Variables With Misspecified Models

    Fan Yang-Wallentin, Fan Yang‐Wallentin et al.•ARTICLE•Structural Equation Modeling: A…•2010

    Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS)…

  • A Congeneric Model for Platonic True Scores

    Open Access•Charles E Werts, Robert L Linn et al.•ARTICLE•Educational and Psychological…•1973

    To resolve recent controversy between Klein and Cleary and Levy, a model for dichotomous congeneric items is presented which has mean errors of zero, dichotomous true scores that are uncorrelated with errors, and errors that are mutually uncorrelated

No prominent works on this page.

  • A Congeneric Model for Platonic True Scores

    Open Access•Charles E Werts, Robert L Linn et al.•ARTICLE•Educational and Psychological…•1973

    To resolve recent controversy between Klein and Cleary and Levy, a model for dichotomous congeneric items is presented which has mean errors of zero, dichotomous true scores that are uncorrelated with errors, and errors that are mutually uncorrelated

  • Confirmatory Factor Analysis of Ordinal Variables With Misspecified Models

    Fan Yang-Wallentin, Fan Yang‐Wallentin et al.•ARTICLE•Structural Equation Modeling: A…•2010

    Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS)…

Econometrics (2 works) · Mathematics (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Bayesian Modeling and Causal Inference (1 work) · Correlation (1 work) · Covariance (1 work) · Developmental psychology (1 work) · Item response theory (1 work) · Ordinal data (1 work)

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