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Titolo:
STRUCTURAL FACTOR-ANALYSIS EXPERIMENTS WITH INCOMPLETE DATA
Autore:
MCARDLE JJ;
Indirizzi:
UNIV VIRGINIA,DEPT PSYCHOL,JEFFERSON PSYCHOMETR LAB CHARLOTTESVILLE VA 22903
Titolo Testata:
Multivariate behavioral research
fascicolo: 4, volume: 29, anno: 1994,
pagine: 409 - 454
SICI:
0027-3171(1994)29:4<409:SFEWID>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
CROSS-SECTIONAL DATA; MISSING DATA; MEASUREMENT INVARIANCE; MAXIMUM LIKELIHOOD; LATENT-VARIABLES; PATH ANALYSIS; MODEL; COVARIANCE; POPULATIONS; INFERENCE;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Physical, Chemical & Earth Sciences
CompuMath Citation Index
CompuMath Citation Index
Citazioni:
121
Recensione:
Indirizzi per estratti:
Citazione:
J.J. Mcardle, "STRUCTURAL FACTOR-ANALYSIS EXPERIMENTS WITH INCOMPLETE DATA", Multivariate behavioral research, 29(4), 1994, pp. 409-454

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.

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Documento generato il 27/11/20 alle ore 07:23:26