Testing Measurement Invariance and Comparing Latent Factor Means Within a Confirmatory Factor Analysis Framework

By Daniel A. Sass. 2011

533 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Developmental and Educational Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3d4b92ccfab5a96691e39f1ee707fe08000f831b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Daniel A. Sass
2. J. Veale
3. Zhonglin Wen
4. Zhiyong Zhang
5. Yujiao Mai
6. N. Bowen
7. R. Masa
8. Herbert W. Marsh

In Developmental and Educational Psychology

1. J. Veale
2. Daniel A. Sass
3. N. Bowen
4. R. Masa
5. Zhiyong Zhang
6. Yujiao Mai
7. Zhonglin Wen
8. Thomas A. Schmitt

In Management Science and Operations Research

1. Zhonglin Wen
2. Thomas A. Schmitt
3. Herbert W. Marsh
4. Daniel A. Sass
5. Zhiyong Zhang
6. Yujiao Mai
7. N. Bowen
8. R. Masa

Citations in the corpus

• The Latent Structure of Spatial Skills and Mathematics: A Replication of the Two-Factor Model (2017, methodology): “Second, we carried out a multigroup confirmatory factor analysis (MGCFA; French & Finch, 2006; Sass, 2011) to determine whether the covariance matrices were statistically equivalent across waves and to check the validity of our”
• Factor Structure of the Rosenberg Self-Esteem Scale (2013, methodology): “Given that measurement nonequivalence can lead to biased associations when comparing regression coefficients across cultural groups, we first evaluated whether the RSES items demonstrated measurement equivalence (also called invariance) across the three groups in this study (Sass, 2011; Steenkamp & ”
• Conducting Measurement Invariance Tests with Ordinal Data: A Guide for Social Work Researchers (2015, methodology): “Many researchers consider partial measurement invariance acceptable if the proportion of noninvariant parameters to all parameters tested is small (Dimitrov, 2010; Millsap & Olivera-Alguilar, 2012; Muthén & Asparouhov, 2002; Sass, 2011).”
• Evaluating Model Fit With Ordered Categorical Data Within a Measurement Invariance Framework: A Comparison of Estimators (2014, background): “Thus, the estimator selected and model fit criteria could influence the statistical conclusions (Lubke & Muthén, 2004; Sass, 2011).”
• Teacher efficacy in student engagement, instructional management, student stressors, and burnout: A theoretical model using in-class variables to predict teachers' intent-to-leave (2012, background): “Skaalvik and Skaalvik’s (2009) analysis of Norwegian teachers confirmed a similar connection between emotional exhaustion, personal accomplishment, and job satisfaction.”
• Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables (2018, methodology): “…loadings, exploratory structural equation modeling (ESEM) was proposed as an alternative approach for latent variables analysis, which evaluates the measurement model of latent variables using exploratory factor analysis (EFA) instead of CFA (Marsh et al., 2009; Sass, 2011; Schmitt, 2011).”
• Edinburgh Handedness Inventory – Short Form: A revised version based on confirmatory factor analysis (2014, methodology): “Invariance testing was not conducted using WLSMV estimation because there have been relatively few studies that have conducted this type of analysis, no standards exist for comparing models on fit indices other than the chi-squared likelihood ratio (Sass, 2011), and it was not possible to calculate ”