date: 2020-11-16T13:16:55Z
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pdf:docinfo:title: Model Fit after Pairwise Maximum Likelihood
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subject: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed.
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dc:title: Model Fit after Pairwise Maximum Likelihood
modified: 2020-11-16T13:16:55Z
cp:subject: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed.
pdf:docinfo:subject: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed.
pdf:docinfo:creator: F. J. Oort
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meta:creation-date: 2016-04-20T10:13:44Z
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Creation-Date: 2016-04-20T10:13:44Z
Author: F. J. Oort
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dc:description: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed.
Keywords: discrete data, pairwise maximum likelihood analysis, weighted least squares analysis, fit statistics
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dc:creator: F. J. Oort
description: Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed.
dcterms:created: 2016-04-20T10:13:44Z
Last-Modified: 2020-11-16T13:16:55Z
dcterms:modified: 2020-11-16T13:16:55Z
title: Model Fit after Pairwise Maximum Likelihood
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creator: F. J. Oort
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dc:subject: discrete data, pairwise maximum likelihood analysis, weighted least squares analysis, fit statistics
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