Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling
Bibliographic Data
| ID | 19290366 |
|---|---|
| Authors | Seungwon Chung (0000-0002-3009-2722, Graduate School of Education and Information Studies, University of California, Los Angeles, CA, USA;, corresponding author), Li Cai (0000-0002-6098-1168, Graduate School of Education and Information Studies and Department of Psychology, University of California, Los Angeles, CA, USA) |
| Year | 2019 |
| Volume | 54 |
| Issue | 3 |
| Pages | 323-337 |
| Publication date | 2019-05-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2018.1523000 |
| PMID | 30950634 |
| OpenAlex | W2781627616 |
| Language | EN |
| Citations received | 6 |
| References cited | 40 |
The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai's approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set
Categorical variable · Data mining · Data modeling · Imputation (statistics) · Inference · Machine learning · Missing data · Statistical inference · Statistics · Structural equation modeling · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
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Item factor analysis
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On the Estimation of Polychoric Correlations and their Asymptotic Covariance Matrix
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Evaluating Structural Equation Models for Categorical Outcomes
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A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables
Assessing Approximate Fit in Categorical Data Analysis
Mardia's Multivariate Kurtosis with Missing Data
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| Unique citing works | 6 |
|---|---|
| Citations per year | 1,2 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |