Confirmatory Factor Analytic Procedures for the Determination of Measurement Invariance

By Brian French, W. H. Finch. 2006

321 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9f6769eba09253f6585df0af35a33dfcffd1eee0

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Citations in the corpus

Differential item function across meditators and non-meditators on the Five Facet Mindfulness Questionnaire (2009, background): “Covariance modeling and IRT approaches require large samples to satisfy assumptions and achieve convergence (e.g., Embretson & Reise, 2000; French & Finch, 2006).”
Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages (2020, methodology): “Until recently, most methodological research in this area relied on two-group comparisons (Chen, 2007; Cheung & Rensvold, 2002; French & Finch, 2006; Meade, Johnson, & Braddy, 2008); however, recent research has pointed to the fact that comparisons across more than two groups create added…”
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 hypothesized factor structure by applying it to both waves…”
Linking Academic Entitlement and Student Incivility Using Latent Means Modeling (2013, background): “Because the χ2 values and CFI values can result in different substantive conclusions (French & Finch, 2006), we also examined changes in standardized covariance and mean residuals.”
An Investigation of the Alignment Method With Polytomous Indicators Under Conditions of Partial Measurement Invariance (2018, methodology): “…of choosing an anchor item for model identification (Johnson, Meade, & DuVernet, 2009; Wang & Yeh, 2003), selecting the best way to compare model fit (Cheung & Rensvold, 2002; French & Finch, 2006), and ensuring adequate power (Cohen, Kim, & Wollack, 1996; Kaplan, 1989; Meade & Bauer, 2007).”
Using PLS path modeling in new technology research: updated guidelines (2016, background): “There is a plethora of papers discussing how to assess the measurement invariance of factor models (see e.g. French and Finch, 2006), there is only one approach for assessing the measurement invariance of composite models (Henseler et al., forthcoming).”
Assessing the Hypothesis of Measurement Invariance in the Context of Large-Scale International Surveys (2014, methodology): “TALIS researchers supported their findings regarding measurement invariance with research that was limited in scope to few groups and relatively small sample sizes (Chen, 2007; French & Finch, 2006).”
Measurement Equivalence of Ordinal Items: A Comparison of Factor Analytic, Item Response Theory, and Latent Class Approaches (2011, methodology): “…of the CFA and IRT methods is the sample size, i.e., because of lack of power a smaller sample size reduces the ability of the two approaches to detect inequivalence in data (Meade and Lautenschlager, 2004a; Meade and Bauer, 2007; French and Finch, 2006; Meade and Lautenschlager, 2004b).”
Multigroup Confirmatory Factor Analysis: Locating the Invariant Referent Sets (2008, background): “Researchers have addressed this issue to some degree (e.g., French & Finch, 2006; Meade & Lautenschlager, 2004) and identified conditions in which these methods appear most effective.”
Self-Efficacy for Self-Regulated Learning (2008, methodology): “Two specific indicators were used as criteria for measurement invariance: a nonsignificant change in chi-square (see French & Finch, 2006) and a change in CFI of less than .01 (Cheung & Rensvold, 2002).”
Sources of self-efficacy in mathematics: A validation study (2009, unknown): “A nonsignificant change in chi-square (see French & Finch, 2006) and a change in CFI of less than .01 (Cheung & Rensvold, 2002) were indicative of model invariance.”
Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research (2016, background): “…have shifted from a focus on absolute fit in terms of χ2 to a focus on alternative fit indices because χ2 is overly sensitive to small, unimportant deviations from a “perfect” model in large samples (Chen, 2007; Cheung & Rensvold, 2002; French & Finch, 2006; Meade, Johnson, & Braddy, 2008).”
Measurement Invariance in International Surveys: Categorical Indicators and Fit Measure Performance (2017, methodology): “This finding was supported by French and Finch (2006) in the multivariate normal context.”
Conducting Measurement Invariance Tests with Ordinal Data: A Guide for Social Work Researchers (2015, background): “Often, the change in χ2(Δχ2) per degree of freedom (df ) is used to evaluate whether fit has deteriorated significantly, but change in other fit indices have also been recommended (Cheung & Rensvold, 2002; French & Finch, 2006).”