NDLI: Limited information estimation and testing of discretized multivariate normal structural models

Content Provider	Springer Nature Link
Author	Maydeu Olivares, Albert
Copyright Year	2006
Abstract	Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification.
Starting Page	57
Ending Page	77
Page Count	21
File Format	PDF
ISSN	00333123
Journal	Psychometrika
Volume Number	71
Issue Number	1
e-ISSN	18600980
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2006-04-10
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	GLS WLS estimation LISREL categorical data analysis data sparseness goodness-of-fit limited information estimation pseudo-maximum likelihood estimation IRT polychoric correlations structural equation models Psychometrics Statistical Theory and Methods Assessment, Testing and Evaluation Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Psychology

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