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Journal ArticleDOI

Comparative fit indexes in structural models

Peter M. Bentler
- 01 Mar 1990 - 
- Vol. 107, Iss: 2, pp 238-246
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 sizes

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Citations
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A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research

TL;DR: In this paper, a review of the literature on structural model evaluation is presented, focusing on the use of fit indices, the influential work of James, Mulaik, and Brett, and recent developments in model evaluation presented since James et al.
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Proactivity during organizational entry: The role of desire for control.

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Development and validation of the sociocultural attitudes towards appearance questionnaire

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Teacher's Corner: Testing Measurement Invariance of Second-Order Factor Models

TL;DR: In this paper, measurement invariance in a second-order factor model using a quality-of-life dataset (n = 924) was tested across two groups at a set of hierarchically structured levels.
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

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.
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Is 0.76 is acceptable value of Normal fit index NFI?

A Normal Fit Index (NFI) value of 0.76 may not be acceptable due to potential underestimation, especially in smaller samples, as NFI can be influenced by sample size.