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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
Peer reviewedSwain, A. J. – Psychometrika, 1975
Considers a class of estimation procedures for the factor model. The procedures are shown to yield estimates possessing the same asymptotic sampling properties as those from estimation by maximum likelihood or generalized last squares, both special members of the class. General expressions for the derivatives needed for Newton-Raphson…
Descriptors: Factor Analysis, Least Squares Statistics, Matrices, Maximum Likelihood Statistics
Peer reviewedAnderson, James C.; Gerbing, David W. – Psychometrika, 1984
This study of maximum likelihood confirmatory factor analysis found effects of practical significance due to sample size, the number of indicators per factor, and the number of factors for Joreskog and Sorbom's (1981) goodness-of-fit index (GFI), GFI adjusted for degrees of freedom, and the root mean square residual. (Author/BW)
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Mathematical Models
Peer reviewedCudeck, Robert; Browne, Michael W. – Psychometrika, 1992
A method is proposed for constructing a population covariance matrix as the sum of a particular model plus a nonstochastic residual matrix, with the stipulation that the model holds with a prespecified lack of fit. The procedure is considered promising for Monte Carlo studies. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Estimation (Mathematics), Factor Analysis

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