Power and sensitivity of alternative fit indices in tests of measurement invariance.

By Adam W. Meade, Emily C. Johnson, Phillip W. Braddy. Journal of Applied Psychology, 2008

1,616 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/91ff529e2a705b4033ec4fef858babc7b4c33a68

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Citations in the corpus

Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys (2014, background): “Given the wealth of studies that examine the performance of MG-CFA in the two-group case (e.g., Chen, 2007; Cheung & Rensvold, 2002; French & Finch, 2006; Meade et al., 2008; Meade & Lautenschlager, 2004), we do not delve deeply into the details surrounding MG-CFA.”
Assessing statistical aspects of test fairness with structural equation modelling (2013, background): “In computer simulation studies, Meade, Johnson, and Braddy (2008) concluded that group sizes of at least 400 may be needed for adequate statistical power in multiplegroup CFA when testing for measurement invariance.”
An Updated Guideline for Assessing Discriminant Validity (2020, methodology): “The idea behind using DCFI in measurement invariance assessment is that the degrees of freedom of the invariance hypothesis depend on the model complexity, and the CFI index and consequently DCFI are less affected by this than the w(2) (Meade et al., 2008).”
An Examination of the Latent Structure of the Difficulties in Emotion Regulation Scale (2012, neutral): “Meade et al. (2008) identified a ΔCFI value of less than or equal to .002 as representing functionally trivial differences in parameter estimates amongmodels.”
Rumination for innovation? Analysing the longitudinal effects of work-related rumination on creativity at work and off-job recovery (2017, result): “The ΔCFI between the weak and strong measurement invariance model was .003, and therefore below the threshold of .01 recommended by Cheung and Rensvold (2002), and close to the threshold of .002 suggested by Meade, Johnson, and Braddy (2008).”
Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research (2016, methodology): “Meade et al. (2008) recommended a ΔCFI < .002 based on the results of their Monte Carlo simulation, but Little (2013) suggested that the simulation parameters used by Meade and colleagues were too Dev Rev. Author manuscript; available in PMC 2017 September 01.”
A Direct Comparison Approach for Testing Measurement Invariance (2012, methodology): “The DCFI > 0.002 threshold recommended by Meade et al. (2008) was derived with CFI based on the weighted least squares w2 using LISREL 8.0.”
Use of multi-group confirmatory factor analysis in examining measurement invariance in counseling psychology research (2017, methodology): “As can be seen by the values of RMSEA (.06 and .04), SRMR (.06 and .05), and CFI (.92 and .97), the two-factor structure fit the data well in the Chinese sample as well as fitting in the U.S. sample.”
Measurement Invariance in International Surveys: Categorical Indicators and Fit Measure Performance (2017, background): “This approach reflects a lack of scholarly research on measurement invariance in a large-scale international survey context; however, calls for more research into the performance of fit indices when the number of groups is greater than two (Meade et al., 2008) and when the sample sizes are large (Fr”
A Cross-Cultural Examination of Subordinates' Perceptions of and Reactions to Abusive Supervision (2015, methodology): “Tsui et al. (2007) pointed out that although some leadership behaviors have shown varying cross-cultural effects, many behaviors, such as transformational and charismatic leadership, do not.”