On Examining the Underlying Normal Variable Assumption in Latent Variable Models With Categorical Indicators
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Summary
A multiple testing approach is outlined that can be used to examine the assumption of underlying normal variables in latent variable models with categorical indicators in research based on structural equation modeling using models containing discrete outcomes.
- Type
- article
- Published
- 2015-02-27
- Cited by
- 12
- References
- 21
- OpenAlex
- https://openalex.org/W2042394538
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121220216
Keywords
Categorical variable, Latent variable, Structural equation modeling, Variable (mathematics), Latent variable model
References
- Introduction to Psychometric Theory
- An Introduction to Applied Multivariate Analysis
- Applied Multivariate Statistical Analysis
- Lisrel 8: User's Reference Guide
- THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY
- A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators
- Alternative Ways of Assessing Model Fit
- Factor Models for Ordinal Variables With Covariate Effects on the Manifest and Latent Variables: A Comparison of LISREL and IRT Approaches
- EQS : structural equations program manual
- Structural Equations with Latent Variables
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
- Testing the assumptions underlying tetrachoric correlations
- Introduction to R
- Testing EBUmgf Class of Life Distributions based on Goodness of Fit approach
- Applied Multivariate Statistical Analysis
- Using Multivariate Statistics
- Structural Equation Modeling with Ordinal Variables using LISREL
Cited by
- Interrater Agreement Evaluation
- The utility of item response modeling in marketing research
- Intraclass Correlation Coefficients in Hierarchical Design Studies With Discrete Response Variables
- Longitudinal Measurement Non-Invariance with Ordered-Categorical Indicators: How are the Parameters in Second-Order Latent Linear Growth Models Affected?
- Pernicious Polychorics: The Impact and Detection of Underlying Non-normality
- Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods
- How to Estimate Absolute-Error Components in Structural Equation Models of Generalizability Theory
- Transformativist Measurement Development Methodology: A Mixed Methods Approach to Scale Construction
- Bivariate Distributions Underlying Responses to Ordinal Variables
- How to Derive Expected Values of Structural Equation Model Parameters when Treating Discrete Data as Continuous
- Assessing the Properties and Functioning of Model-Based Sum Scores in Multidimensional Measures With Local Item Dependencies: A Comprehensive Proposal
- Why Ordinal Variables Can (Almost) Always be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods
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