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Raykov, Tenko; Marcoulides, George A.; Li, Tenglong – Educational and Psychological Measurement, 2017
The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…
Descriptors: Error of Measurement, Factor Analysis, Research Methodology, Psychometrics
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Koran, Jennifer – Measurement and Evaluation in Counseling and Development, 2016
Proactive preliminary minimum sample size determination can be useful for the early planning stages of a latent variable modeling study to set a realistic scope, long before the model and population are finalized. This study examined existing methods and proposed a new method for proactive preliminary minimum sample size determination.
Descriptors: Factor Analysis, Sample Size, Models, Sampling
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Geiser, Christian; Lockhart, Ginger – Psychological Methods, 2012
Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…
Descriptors: Psychological Studies, Simulation, Measurement, Error of Measurement
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Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria – Psychological Methods, 2012
A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…
Descriptors: Factor Analysis, Computation, Simulation, Sample Size
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Schumacker, Randall E. – Mid-Western Educational Researcher, 1993
Structural equation models merge multiple regression, path analysis, and factor analysis techniques into a single data analytic framework. Measurement models are developed to define latent variables, and structural equations are then established among the latent variables. Explains the development of these models. (KS)
Descriptors: Causal Models, Data Analysis, Error of Measurement, Factor Analysis
Olson, Jeffery E. – 1992
Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…
Descriptors: Error of Measurement, Factor Analysis, Goodness of Fit, Mathematical Models
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Lemery, Kathryn S.; Essex, Marilyn J.; Smider, Nancy A. – Child Development, 2002
This study examined whether item overlap, or measurement confounding, accounts for the correlation between temperament and behavior problem symptoms in children. Experts rated items on Children's Behavior Questionnaire and Preschool Behavior Questionnaire for their fit to both constructs, and then these items were factor analyzed with longitudinal…
Descriptors: Behavior Development, Behavior Problems, Children, Error of Measurement
Werts, Charles E.; Linn, Robert L. – 1972
The objective of this study was to review and integrate the various methodologies used in the study of individual growth (especially academic growth). This was accomplished by means of Joreskog's general model for the analysis of covariance structures, i.e., each of the disparate methodologies available from the literature was shown to be a…
Descriptors: Academic Achievement, Analysis of Covariance, Educational Research, Error of Measurement
Linn, Robert L.; Werts, Charles E. – 1971
Failure to consider errors of measurement when using partial correlation or analysis of covariance techniques can result in erroneous conclusions. Certain aspects of this problem are discussed and particular attention is given to issues raised in a recent article by Brewar, Campbell, and Crano. (Author)
Descriptors: Analysis of Covariance, Analysis of Variance, Comparative Analysis, Correlation
Penfield, Douglas A. – 1972
Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)
Descriptors: Analysis of Variance, Bayesian Statistics, Behavioral Science Research, Computers