Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares

By Cheng-Hsien Li. Behavior Research Methods, 2016

2,483 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/132190a0d8a541e282e7464109853c80eb2a4bf6

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Citations in the corpus

Teachers’ emotional labour, discrete emotions, and classroom management self-efficacy (2018, background): “We completely acknowledge that a desirable sample size is one of the important factors to conduct CFA (e.g. Li, 2016).”
The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust (2022, methodology): “The estimator was DWLS (diagonally weighted least squares) because the item-level data were ordinal (Li, 2016).”
Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data (2020, methodology): “WLSMV tended to overestimate inter-factor correlations and yielded more variable standard error estimates when sample was small and/or when latent distribution was moderately nonnormal (Li, 2016).”
Is coworker feedback more important than supervisor feedback for increasing innovative behavior? (2019, neutral): “We performed a confirmatory factor analysis to establish the discriminant validity of the study measures using a weighted least squares means and variance adjusted (WLSMV) estimator in Mplus, specifically designed for ordinal data such as those used in this study (Li, 2016).”
Factor Structure of the Difficulties in Emotion Regulation Scale (DERS) in Adult Outpatients Receiving Dialectical Behavior Therapy (DBT) (2017, methodology): “WLSMV has been found to perform well for ordinal items (Beauducel and Herzberg 2006; Flora and Curran 2004; Li 2016) and to outperform robust ML estimation in providing accurate estimates of factor loadings (Li 2016).”
The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence (2022, methodology): “Fit measures were then also assessed using the DWLS (diagonally weighted least squares) estimator on JASP software, in line with the ordinal nature of the data (Li, 2016; Schepman & Rodway, 2022).”