Journal ArticleDOI
Power analysis and determination of sample size for covariance structure modeling.
TLDR
In this article, a framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented, where the value of confidence intervals for fit indices is emphasized.Abstract:
A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes.read more
Citations
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Journal ArticleDOI
Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
Li-tze Hu,Peter M. Bentler +1 more
TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Journal ArticleDOI
Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification
Li-tze Hu,Peter M. Bentler +1 more
TL;DR: In this article, the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under conditions that varied sample size and distribution.
Journal ArticleDOI
Structural Equation Modelling: Guidelines for Determining Model Fit
TL;DR: In this article, a selection of fit indices that are widely regarded as the most informative indices available to researchers is presented, along with guidelines on their use and strategies for their use.
Book
Confirmatory Factor Analysis for Applied Research
TL;DR: In this article, the authors present a detailed, worked-through example drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology.
Journal ArticleDOI
Evaluating the use of exploratory factor analysis in psychological research.
TL;DR: This paper reviewed the major design and analytical decisions that must be made when conducting exploratory factor analysis and notes that each of these decisions has important consequences for the obtained results, and the implications of these practices for psychological research are discussed.
References
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Book
Statistical Power Analysis for the Behavioral Sciences
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Journal ArticleDOI
Alternative Ways of Assessing Model Fit
Michael W. Browne,Robert Cudeck +1 more
TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
Book
Structural Equations with Latent Variables
TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Journal ArticleDOI
Significance tests and goodness of fit in the analysis of covariance structures
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.