Literature DB >> 30838499

A Problem with Discretizing Vale-Maurelli in Simulation Studies.

Steffen Grønneberg1, Njål Foldnes2.   

Abstract

Previous influential simulation studies investigate the effect of underlying non-normality in ordinal data using the Vale-Maurelli (VM) simulation method. We show that discretized data stemming from the VM method with a prescribed target covariance matrix are usually numerically equal to data stemming from discretizing a multivariate normal vector. This normal vector has, however, a different covariance matrix than the target. It follows that these simulation studies have in fact studied data stemming from normal data with a possibly misspecified covariance structure. This observation affects the interpretation of previous simulation studies.

Keywords:  Vale–Maurelli; non-normal data; ordinal data; polychoric correlation; structural equation modeling

Mesh:

Year:  2019        PMID: 30838499     DOI: 10.1007/s11336-019-09663-8

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  9 in total

1.  A Monte Carlo study comparing PIV, ULS and DWLS in the estimation of dichotomous confirmatory factor analysis.

Authors:  Steffen Nestler
Journal:  Br J Math Stat Psychol       Date:  2012-04-24       Impact factor: 3.380

2.  Factor Analysis of Ordinal Variables: A Comparison of Three Approaches.

Authors:  K G Jöreskog; I Moustaki
Journal:  Multivariate Behav Res       Date:  2001-07-01       Impact factor: 5.923

3.  An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data.

Authors:  David B Flora; Patrick J Curran
Journal:  Psychol Methods       Date:  2004-12

4.  Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares.

Authors:  Cheng-Hsien Li
Journal:  Behav Res Methods       Date:  2016-09

5.  When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.

Authors:  Mijke Rhemtulla; Patricia É Brosseau-Liard; Victoria Savalei
Journal:  Psychol Methods       Date:  2012-07-16

6.  How General is the Vale-Maurelli Simulation Approach?

Authors:  Njål Foldnes; Steffen Grønneberg
Journal:  Psychometrika       Date:  2014-08-06       Impact factor: 2.500

7.  Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model.

Authors:  Chun Wang; Shiyang Su; David J Weiss
Journal:  Multivariate Behav Res       Date:  2018-04-06       Impact factor: 5.923

8.  Contributions to Estimation of Polychoric Correlations.

Authors:  Scott Monroe
Journal:  Multivariate Behav Res       Date:  2018-01-29       Impact factor: 5.923

9.  Comparing interval estimates for small sample ordinal CFA models.

Authors:  Prathiba Natesan
Journal:  Front Psychol       Date:  2015-10-30
  9 in total
  2 in total

1.  On Identification and Non-normal Simulation in Ordinal Covariance and Item Response Models.

Authors:  Njål Foldnes; Steffen Grønneberg
Journal:  Psychometrika       Date:  2019-09-27       Impact factor: 2.500

2.  Factor Retention Using Machine Learning With Ordinal Data.

Authors:  David Goretzko; Markus Bühner
Journal:  Appl Psychol Meas       Date:  2022-05-04
  2 in total

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