Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit

By Albert Maydeu-Olivares. 2017

345 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e67845bb52982871c8deb02c8a36323c270c33c8

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Understanding the Model Size Effect on SEM Fit Indices (2018, methodology): “When maximum likelihood is used to estimate a model, the likelihood ratio (LR) test statistic is regarded as the most commonly used test for assessing the overall goodness of fit (Jöreskog, 1969; Maydeu-Olivares, 2017).”
Goodness of Fit in Item Factor Analysis: Effect of the Number of Response Alternatives (2017, methodology): “…surmise that the SRMR was less influenced than either the χ2 or the RMSEA as it is a standardized statistic that does not invoke the use of a weighted mean of residual covariances (correlations) in its computation (the χ2 and RMSEA are both weighted and unstandardized: Maydeu-Olivares, 2017b).”
Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study (2020, methodology): “See Maydeu-Olivares (2017b) for a detailed account of the differences between the various implementations of ML estimation for continuous outcomes.”
Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, background): “Such adjustments often allow SEs to better estimate the amount of sampling variability in the parameter estimates (for a tutorial, see Savalei, 2014), and this result is supported by simulation research (Chou, Bentler & Satorra, 1991; Curran, 1994; Maydeu-Olivares, 2017).”
Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit (2018, methodology): “The item-level nonnormal data with desired skewness and kurtosis were generated by discretizing continuous data through selected thresholds (DiStefano & Morgan, 2014; Maydeu-Olivares, 2017b; Muthén & Kaplan, 1985).”