The Model-Size Effect on Traditional and Modified Tests of Covariance Structures

By W. Herzog, Anne Boomsma, Sven Reinecke. 2007

125 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b775c1a249c25dfb8b825af689239b8c78b2c7f4

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In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data (2018, methodology): “RELATED LITERATURE WITH LARGE MODELS When fitting factor analysis models with continuous data under ML, researchers have noted a model size effect with inflated chi-square statistics and increasing Type I error as model size increased (Anderson & Gerbing, 1984; Beauducel & Herzberg, 2006; Herzog et ”
Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research (2016, methodology): “The χ2 statistic is sensitive to model size, and Herzog, Boomsma, and Reinecke (2007) recommended using a Swain correction to the χ2 (which corrects for sample size and model size) when large models are tested.”
The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are Due to the Size of the Covariance Matrix (2012, methodology): “More recently, Herzog et al. (2007) investigated the performance of the TML statistic and various modifications thereof in models involving 12 to 48 manifest variables with the degrees of freedom ranging from 48 to 960.”
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.”
Small-Sample Robust Estimators of Noncentrality-Based and Incremental Model Fit (2009, unknown): “However, T MLb more closely follows a chi–square distribution when N is small (Fouladi, 2000; Herzog et al., 2007; Nevitt & Hancock, 2004).”
Brand-Specific Leadership: Turning Employees into Brand Champions (2009, methodology): “Moreover, because of a small ratio of sample size to estimated parameters (N:t = 1.7:1), we corrected the scaled Satorra–Bentler chi-square variate using Swain’s (1975) multiplier (see Herzog and Boomsma 2009; Herzog, Boomsma, and Reinecke 2007).”
A Note on Sample Size and Solution Propriety for Confirmatory Factor Analytic Models (2013, background): “Herzog et al. (2007) indicated that asymptotically, the Swain corrected ML ¦(2) statistic matches the ML ¦(2) distribution.”
Understanding the Model Size Effect on SEM Fit Indices (2018, methodology): “As discussed in the model size literature (e.g., Herzog et al., 2007; Moshagen, 2012), these fit indices are also likely to be influenced by the model size because they are functions of the LR chi-square statistic that tends to be upwardly biased in large models.”
Daily Dynamics of Personal Identity and Self–Concept Clarity (2011, background): “James (1890), one of the founders ofmodern psychology, devotedmuch of his writing to the ways in which people view themselves, and to the ways in which these self-views affect well-being, distress and personal relationships.”
Revisiting the Model Size Effect in Structural Equation Modeling (2018, methodology): “For example, in the Herzog et al. (2007) simulation, the number of observed variables (p) ranged from 12 to 48, whereas for each p, only one single value of q was considered.”
Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison (2010, methodology): “Herzog, Boomsma, & Reinecke, 2007; Nevitt & Hancock, 2004).”