Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons

By Gitta H. Lubke, B. Muthén. 2004

524 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c8f5a41772250559f8f7ffb359c669965f22de94

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Citations in the corpus

Confirmatory Factor Analytic Procedures for the Determination of Measurement Invariance (2006, methodology): “However, treatment of ordinal data as continuous in MCFA (a) violates multivariate normality, (b) may distort a factor structure across groups, and (c) could lead to inaccurate measurement invariance tests (Lubke & Muthén, 2004).”
Multigroup Confirmatory Factor Analysis: Locating the Invariant Referent Sets (2008, result): “These sample sizes are consistent with previous MI simulation research (eg, Lubke & Muthen, 2004 ; Meade & Lautenschlager, 2004 ) and reflect real data conditions.”
A cross-country and cross-time comparison of the human values measurements with the second round of the European Social Survey (2008, unknown): “Lubke and Muthén (2004) argued that an analysis of Likert data under the assumption of multivariate normality may distort the factor structure differently across groups.”
The relations between implicit intelligence beliefs, autonomous academic motivation, and school persistence intentions: a mediation model (2015, background): “The choice to rely on WLSMV estimation is linked to the fact that this estimator is more suited to the ordered-categorical nature of Likert scales than traditional maximum likelihood estimation (Beauducel and Yorck Herzberg 2006; Finney and DiStefano 2006; Lubke and Muth’en 2004) as well because dro”
Measurement Invariance in International Surveys: Categorical Indicators and Fit Measure Performance (2017, methodology): “…we then analyzed the data assuming that the observed variables were ordered-categorical, which recognizes the actual distribution of the observed variables and avoids documented problems that stem from fitting a normal factor model to non-normal observed variables (Lubke & Muthén, 2004).”
Spirituality in the Workplace: Theory and Measurement (2011, unknown): “…researchers have discussed the potential problems resulting from treating ordered-categorical data as continuous data (e.g., Flora & Curran, 2004; Lubke & Muthen, 2004; Millsap & Jenn, 2004; Muthen, 1984; Muthen & Kaplan, 1985) Future research can treat survey data collected using Likert-type…”
Testing for comparability of human values across countries and time with the third round of the European Social Survey (2010, methodology): “Although its use with Likert data (i.e. data that are obviously ordinal and often not normally distributed) has been criticized in the literature (Lubke and Muthén, 2004), researchers have shown that it still works well even when data are not continuous or normally distributed (De Beuckelaer, 2005;…”
Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study. (2012, unknown): “…of the indicator variables and for clustering of the data (see below) and that has been shown to be robust when ordered categorical variables are treated as continuous (e.g., Beauducel & Herzberg, 2006; Dolan, 1994; Lei, 2009; Lubke & Muthén, 2004; Rhemtulla, BrosseauLiard, & Savalei, 2010).”
Testing Measurement Invariance and Comparing Latent Factor Means Within a Confirmatory Factor Analysis Framework (2011, methodology): “Fortunately, previous methodological research has shed light on this issue by revealing that WLSMV may be more theoretically appropriate (Lubke & Muthén, 2004; Millsap & Yun-Tein, 2004) and may provide superior model estimation under certain conditions (Beauducel & Herzberg, 2006; Flora & Curran,…”
An Investigation of the Alignment Method With Polytomous Indicators Under Conditions of Partial Measurement Invariance (2018, unknown): “Modeling Likert scaled items as continuous in the context of multiple-group factor analysis can make evaluating model fit indexes difficult, as it is unclear if model misfit stems from measurement differences across groups or ignoring the categorical nature of the data (Lubke & Muthén, 2009).”