Small-Sample Robust Estimators of Noncentrality-Based and Incremental Model Fit

By W. Herzog, Anne Boomsma. 2009

162 citations recorded by the source corpus.

Subfields: Food Science, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/964378851dea7ca23651d0fc308c8e55672a96a9

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Citations in the corpus

On the adoption of partial least squares in psychological research: Caveat emptor (2015, methodology): “For example, corrections to the chi-square statistic will help adjust for the “model size effect” (i.e., low N:p ratio; Herzog & Boomsma, 2009; Yuan, Tian, & Yanagihara, 2013) and non-normal distributions can be addressed with numerous techniques including multivariate transformations (e.g., Yuan,…”
The performance of MLR, USLMV, and WLSMV estimation in structural regression models with ordinal variables (2014, result): “This is in line with Curran et al. (2002) and Herzog & Boomsma (2009), who found that RMSEA is upward biased in smaller sample size conditions.”
Small Sample Size Solutions (2020, background): “Herzog, Boomsma, and Reinecke (2007) and Herzog and Boomsma (2009) compared these corrections and concluded that the Swain correction worked best; however, they only looked at complete and normal data.”
The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are Due to the Size of the Covariance Matrix (2012, result): “…of approximation (RMSEA) are based on the TML statistic and, consequently, might also produce misleading results under these conditions (Chen, Curran, Bollen, Kirby, & Paxton, 2008; Curran et al., 2002; Ding, Velicer, & Harlow, 1995; Herzog & Boomsma, 2009; Jackson, 2007; Kenny & McCoach, 2003).”
Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modeling (2016, background): “Herzog and Boomsma (2009) studied several small-sample corrected estimators of the noncentrality parameter, including Yuan’s (2005) approach and Swain’s (1975) approach, and found that Swain’s approach provided a good approximation in small samples (i.e., N 200).”
A Note on Sample Size and Solution Propriety for Confirmatory Factor Analytic Models (2013, methodology): “Because Herzog and Boomsma (2009) found it valuable to correct fit measures using the Swain correction, we also examine corrected and uncorrected versions of fit measures.”
Redesigning work through idiosyncratic deals (2014, background): “As moderate sample size and low degrees of freedom are known to inflate the RMSEA and create a wider CI (Chen et al., 2008; Herzog & Boomsma, 2009; Kelley & Lai, 2011), somewhat more lenient standards were applied to models with fewer degrees of freedom.”
A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling (2013, background): “SEM techniques for small samples (e.g., Herzog & Boomsma, 2009) and for estimating sample size requirements (e.g., Lai & Kelley, 2011) are actively studied, but fundamental laws of probability limit what can be accomplished.”