Using Small-Variance Priors to Detect Covariate Misspecifications in Latent Class Analysis Models
Bibliographic Data
| ID | 12424907 |
|---|---|
| Authors | Sarah Depaoli (0000-0002-1277-0462, University of California), Fan Jia (0000-0003-3855-532X, University of California), Marieke Visser (0000-0003-3240-7801, University of California) |
| Year | 2025 |
| Volume | 32 |
| Issue | 5 |
| Pages | 780-800 |
| Publication date | 2025-06-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2025.2505635 |
| OpenAlex | W4411141483 |
| Language | EN |
| References cited | 59 |
Bayesian probability · Class (philosophy · Covariate · Econometrics · Economics · Latent class model · Prior probability · Statistics · Variance (accounting · Advanced Statistical Methods and Models · Computer Science · Imbalanced Data Classification Techniques · Mathematics · Artificial Intelligence
Latent Class Analysis and Finite Mixture Modeling
Latent Class Analysis
Loglinear models with Latent Variables
Covariates and Mixture Modeling
A systematic review of Bayesian articles in psychology
Local solutions in the estimation of growth mixture models.
Beyond SEM
Bayesian structural equation modeling
Prediction from Latent Classes
General Growth Mixture Analysis with Antecedents and Consequences of Change
Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling
Auxiliary Variables in Mixture Modeling
Stepwise Latent Class Analysis in the Presence of Missing Values on the Class Indicators
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
A Guide to Detecting and Modeling Local Dependence in Latent Class Analysis Models
The MIMIC Method With Scale Purification for Detecting Differential Item Functioning
Comparison of Multiple-Indicators, Multiple-Causes– and Item Response Theory–Based Analyses of Subgroup Differences
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
On Inclusion of Covariates for Class Enumeration of Growth Mixture Models
Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions
Observations on the Use of Growth Mixture Models in Psychological Research
Exploring patterns of Latino/a children's school readiness at kindergarten entry and their relations with Grade 2 achievement
Simultaneous Latent Structure Analysis in Several Groups
Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches
Latent Class Modeling with Covariates
Estimating Latent Structure Models with Categorical Variables
Multinomial Logit Latent-Class Regression Models
| Citation velocity | historical |
|---|---|
| Highly cited | No |