Structural Equation Modeling: Foundations and Extensions

By D. Kaplan. 2000

1,747 citations recorded by the source corpus.

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Citations in the corpus

The performance of MLR, USLMV, and WLSMV estimation in structural regression models with ordinal variables (2014, result): “As with previous studies, ML produced unacceptable 61 rejection rates and TML exhibited extremely deviant distributional behavior in all asymmetric data conditions (e.g., Kaplan, 2009; Muthén & Kaplan, 1992).”
Conceptualizing and Measuring Collaboration (2007, methodology): “The Akaike Information Coefficient (AIC), widely regarded as a useful measure for comparing alternate formulations of a model (Kaplan 2000, 114), is used as a base measure of fit, and the value for this model is 1209.53 (Akaike 1987).”
Applications of Structural Equation Modeling in Social Sciences Research (2014, background): “The second step centers around fitting the structural model by measuring the significance of the relationship between latent variables, which is often accomplished through path analysis ( Hoyle, 1995; Kaplan, 2000).”
Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis (2011, methodology): “…factor analysis (FA) procedures such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to investigate latent variables has become common for such areas as instrument development, longitudinal data analysis, comparing group means, and so on (see Cudeck & MacCallum, 2007).”
Social Discrimination in the Corporate Elite (2013, methodology): “The results were also robust to using factor scores derived from the principal factor method and the iterated principal factor method (Kaplan, 2009).”
Reporting practices in confirmatory factor analysis: an overview and some recommendations. (2009, background): “, Curran, West, & Finch, 1996; Powell & Schafer, 2001) and downward biased standard errors (Bandalos, 2002; Kaplan, 2000; Nevitt & Hancock, 2001), and may undermine the assumptions inherent in several ancillary fit measures (Yuan, 2005).”
Occupational stress, supervisor support, job satisfaction, and work-related burnout: perceptions of Turkish National Police (TNP) members (2017, background): “The structural equation model includes all latent and control variables and the theoretically driven relationships among them, to evaluate the significance of the hypothesis paths and the explanatory power of the model by computing the R2 values for each endogenous variable (Kaplan, 2000).”
A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data (2014, background): “In these situations, item-level data are crude measures of a latent construct due to the imperfection of the scaling technique (Bollen, 1989; Kaplan, 2000).”
Abiotic and biotic influences on Bromus tectorum invasion and Artemisia tridentata recovery after fire (2011, methodology): “Chi-square statistics, their associated P values and the root mean square error of approximation (RMSEA) provide complementary measures of model fit (Kaplan 2000).”
The Longitudinal Relationship Between School Belonging and Subjective Well-Being in School Among Elementary School Students (2015, methodology): “With regard to RMSEA and SRMR, values less than 0.06 are typically considered indicative of good fit, while RMSEA values between 0.06 and 0.10 are considered adequate fit and SRMR below 0.09 meant the model was acceptable Z (Hu and Bentler 1999; Kaplan 2000).”
A Brief Guide to Structural Equation Modeling (2006, methodology): “In addition to data collection, the steps are model specification, identification, estimation, evaluation, and modification (Hoyle, 1995; Kaplan, 2000; Kline, 2005; Schumacker & Lomax, 2004).”
Multidimensional Self-Concept Structure for Preadolescents With Mild Intellectual Disabilities (2006, methodology): “CFAs and structural equation models (SEMs) were conducted with LISREL (Version 8.54) using maximum likelihood estimation (for further discussion, see Bollen, 1989; Byrne, 1998; Jöreskog & Sörbom, 1996; Kaplan, 2000).”