Analysis of structural equation models with interval and polytomous data

Wai-Yin Poon and Yin-Ping Leung

Statistics & Probability Letters, 1993, vol. 17, issue 2, 127-137

Abstract: It is suggested that in some situations, observations for random variables should be collected in the form of intervals. This kind of data is called interval data. By assuming the underlying continuous variables have a multivariate normal distribution, a two-stage procedure which utilizes the methods of partition maximum likelihood and generalized least squares is established to study the correlation structure of the continuous variables. Basic statistical properties useful for further inference are discussed and a Monte Carlo study is conducted to investigate the performance of these estimates.

Keywords: Structural; equation; models; polytomous; data; interval; data; partition; maximum; likelihood; generalized; least; squares; simulation; study (search for similar items in EconPapers)
Date: 1993
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