Structural Factor Analysis Experiments with Incomplete Data
- 1 October 1994
- journal article
- conference paper
- Published by Taylor & Francis in Multivariate Behavioral Research
- Vol. 29 (4) , 409-454
- https://doi.org/10.1207/s15327906mbr2904_5
Abstract
This article presents some benefits and limitations of structural equation models for multivariate experiments with incomplete data. Examples from studies of latent variable path models of cognitive performances illustrate analyses with four different kinds of incomplete data: (a) latent variables, (b) omitted variables, (c) randomly missing data, and (d) non- randomly missing data. Power based cost-benefit analyses for experimental design and planning are also presented. These incomplete data approaches are closely related to models used in classical experimental design, interbattery measurement analysis, longitudinal analyses, and behavioral genetic analyses. These structural equation methods for old experimental design problems indicate some new opportunities for future multivariate research.Keywords
This publication has 70 references indexed in Scilit:
- Experimental Designs for Model DiscriminationJournal of the American Statistical Association, 1993
- Alternative common factor models for multivariate biometric analysesBehavior Genetics, 1990
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Pseudo-Maximum Likelihood Estimation of Mean and Covariance Structures with Missing DataJournal of the American Statistical Association, 1990
- An Introduction to Sample Selection Bias in Sociological DataAmerican Sociological Review, 1983
- Formalizing Subjective Notions about the Effect of Nonrespondents in Sample SurveysJournal of the American Statistical Association, 1977
- Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent VariableJournal of the American Statistical Association, 1975
- Path analysis: Psychological examples.Psychological Bulletin, 1970
- Path Analysis: Sociological ExamplesAmerican Journal of Sociology, 1966
- Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are MissingJournal of the American Statistical Association, 1957