Addressing Missing Data in Latent Class Analysis When Using a Three-Step Estimation Approach
Bibliographic Data
| ID | 12424871 |
|---|---|
| Authors | Sarah Depaoli (0000-0002-1277-0462, University of California, corresponding author), Fan Jia (0000-0003-3855-532X, University of California), Marieke Visser (0000-0003-3240-7801, University of California) |
| Year | 2024 |
| Volume | 32 |
| Issue | 2 |
| Pages | 287-303 |
| Publication date | 2024-10-29 |
| 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 | 10.1080/10705511.2024.2410240 |
| OpenAlex | W4403862531 |
| Language | EN |
| Citations received | 3 |
| References cited | 60 |
Class (philosophy · Econometrics · Economics · Estimation · Latent class model · Missing data · Statistics · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
Analysis of Incomplete Multivariate Data
Latent Class Analysis and Finite Mixture Modeling
Loglinear models with Latent Variables
Covariates and Mixture Modeling
Adding Missing-Data-Relevant Variables to Fiml-Based Structural Equation Models
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Beyond SEM
Exploratory latent structure analysis using both identifiable and unidentifiable models
Subtypes, Severity, and Structural Stability of Peer Victimization
Prediction from Latent Classes
General Growth Mixture Analysis with Antecedents and Consequences of Change
MplusAutomation
Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes
Multilevel Latent Class Analysis
Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters
The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling
Missing data
Multiple imputation using chained equations
Auxiliary Variables in Mixture Modeling
Is Item Imputation Always Better? An Investigation of Wave-Missing Data in Growth Models
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
Correcting for Nonresponse in Latent Class Analysis
On Inclusion of Covariates for Class Enumeration of Growth Mixture Models
Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions
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
How to Impute Interactions, Squares, and other Transformed Variables
Relating Latent Class Assignments to External Variables
Latent Class Modeling with Covariates
Estimating Latent Structure Models with Categorical Variables
Multinomial Logit Latent-Class Regression Models
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |