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CiteWeb id: 19800000009

CiteWeb score: 13470

Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. 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. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.

The publication "Significance Tests and Goodness of Fit in the Analysis of Covariance Structures" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 100. Additionally, the publicaiton "Significance Tests and Goodness of Fit in the Analysis of Covariance Structures" is placed in the Top 100 among other scientific works published in 1980.
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