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Sideridis, Georgios D.; Jaffari, Fathima – Measurement and Evaluation in Counseling and Development, 2022
The utility of the maximum likelihood F-test was demonstrated as an alternative to the omnibus Chi-square test when evaluating model fit in confirmatory factor analysis with small samples, as it has been well documented that the likelihood ratio test (T[subscript ML]) with small samples is not Chi-square distributed.
Descriptors: Maximum Likelihood Statistics, Factor Analysis, Alternative Assessment, Sample Size
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van Smeden, Maarten; Hessen, David J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In this article, a 2-way multigroup common factor model (MG-CFM) is presented. The MG-CFM can be used to estimate interaction effects between 2 grouping variables on 1 or more hypothesized latent variables. For testing the significance of such interactions, a likelihood ratio test is presented. In a simulation study, the robustness of the…
Descriptors: Multivariate Analysis, Robustness (Statistics), Sample Size, Statistical Analysis
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Lui, Kung-Jong; Cumberland, William G. – Psychometrika, 2004
When the underlying responses are on an ordinal scale, gamma is one of the most frequently used indices to measure the strength of association between two ordered variables. However, except for a brief mention on the use of the traditional interval estimator based on Wald's statistic, discussion of interval estimation of the gamma is limited.…
Descriptors: Intervals, Sample Size, Maximum Likelihood Statistics, Monte Carlo Methods
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Olsson, Ulf Henning; Foss, Tron; Troye, Sigurd V.; Howell, Roy D. – Structural Equation Modeling, 2000
Used simulation to demonstrate how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Discusses results for maximum likelihood (ML), generalized least squares (GLS), and weighted least…
Descriptors: Estimation (Mathematics), Goodness of Fit, Least Squares Statistics, Maximum Likelihood Statistics