Journal ArticleDOI
Comparative fit indexes in structural models
TLDR
A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models and two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes.Abstract:
Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model A drawback of existing indexes is that they estimate no known population parameters A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI) FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics An example illustrates the behavior of these indexes under conditions of correct specification and misspecification The new fit indexes perform very well at all sample sizesread more
Citations
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Storytelling Neighborhood Paths to Belonging in Diverse Urban Environments
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Confirmatory factor analyses of the full and short versions of the Marlowe–Crowne Social Desirability Scale.
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Evaluating existing and new validity evidence for the Academic Motivation Scale
TL;DR: In this article, the authors provide a narrative review synthesizing past research, and then conduct a validity investigation of the scores from the AMS, concluding that support for the scale's simplex structure, which would represent the self-determination theory continuum, was not fully substantiated.
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Statistical issues in the study of temporal data: daily experiences.
TL;DR: The conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
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Leadership emergence in autonomous work teams: Antecedents and outcomes.
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References
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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.
Journal ArticleDOI
A reliability coefficient for maximum likelihood factor analysis
Ledyard R Tucker,Charles Lewis +1 more
TL;DR: In this paper, a reliability coefficient is proposed to indicate quality of representation of interrelations among attributes in a battery by a maximum likelihood factor analysis, which can indicate that an otherwise acceptable factor model does not exactly represent the interrelations between the attributes for a population.
Journal ArticleDOI
Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions.
TL;DR: In this article, the entropy-based information criterion (AIC) has been extended in two ways without violating Akaike's main principles: CAIC and CAICF, which make AIC asymptotically consistent and penalize overparameterization more stringently.
Journal ArticleDOI
Goodness-of-fit indexes in confirmatory factor analysis : The effect of sample size
TL;DR: In this paper, the influence of sample size on different goodness-of-fit indices used in confirmatory factor analysis (CFA) was examined and the results are consistent with the observation that the amount of random, unexplained variance varies inversely with sample size.