Latent Class Modeling with Covariates: Two Improved Three-Step Approaches

By Jeroen K. Vermunt. Political Analysis, 2010

2,224 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eeb48c0db84240ed1dde18393f3c9fc62c640029

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Citations in the corpus

A Comparison of Classification Approaches for Cyberbullying and Traditional Bullying Using Data From Six European Countries (2015, methodology): “This stepwise approach starts with the model building process using a set of items, then it assigns individuals to the latent classes and finally estimates a multinomial logistic regression model (for more information see Vermunt, 2010).”
Fuzzy Profiles (2018, methodology): “In conducting our analyses, we followed the previously described automatic three-step approach for LPA (e.g., Asparouhov & Muthén, 2014; Lanza et al., 2013; Vermunt, 2010).”
Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches (2013, methodology): “Their correction method was later extended by Vermunt (2010), who showed how to adjust for the downward bias in the SE s obtained by the initial method and how to include continuous covariates in the step-three model.”
Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, methodology): “The recommendations in this article are consistent with the more recently advocated three-step approaches to examining predictors and outcomes of latent class membership in mixture models (Asparouhov & Muthén, 2013; NylundGibson et al., 2014; Vermunt, 2010).”
A Latent Transition Mixture Model Using the Three-Step Specification (2014, methodology): “A few other applications of the three-step method have been used (Asparouhov & Muthén, 2013; Vermunt, 2010), but this article is the first to use the three-step method within the LTA framework using”
Multiple-Group Analysis of Similarity in Latent Profile Solutions (2016, methodology): “…tests of predictive and explanatory similarity require the direct incorporation of covariates into the model and have not yet been implemented using covariates specified as inactive (i.e., using auxiliary functions, e.g., Asparouhov & Muthén, 2014; Lanza, Tan, & Bray, 2013; Vermunt, 2010).”
Flourishing and Floundering in Emerging Adult College Students (2013, background): “More specifically, given the number of covariates in the current study, it was not practical to perform a one-step model, given our desire for the classification not to be impacted by the covariates (Vermunt, 2010).”
A person‐centered approach to commitment research: Theory, research, and methodology (2016, background): “Whenever this is the case, associations between inactive outcomes and the profiles can also be easily tested using a variety of auxiliary approaches (e.g., Asparouhov & Muthén, 2014; Lanza, Tan, & Bray, 2013; Vermunt, 2010).”
Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models (2016, result): “Similar values are frequently seen in simulation studies (e.g., Asparouhov & Muthen, 2014; Bakk, Oberski, & Vermunt, 2014; Vermunt, 2010).”
Latent Class Analysis: A Guide to Best Practice (2020, methodology): “Previously, researchers would include covariates in the same model as the model used to identify the class solution (Vermunt, 2010).”
An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. (2014, background): “Predictors and distal outcomes of latent classes are active areas of inquiry, with some emerging techniques such as the three-step (Asparouhov & Muthén, 2013; Vermunt, 2010) and stepwise (Lanza, Flaherty, & Collins, 2003) approaches showing promise in certain situations in terms of potentially being”
Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models (2019, methodology): “to as the LTB approach in Bakk and Vermunt (2016), is also conducted in a stepwise fashion.”
A comparison of approaches for assessing covariate effects in latent class analysis (2015, positive): “Vermunt (2010) devised a pair of bias-‐adjusted estimation methods, referring to these as “Modal ML” and “Proportional ML”.”
Bias-Adjusted Three-Step Latent Markov Modeling With Covariates (2016, positive): “Vermunt (2010) proposed an alternative maximum likelihood (ML) based bias correction (referred to as the ML method) which involves estimating a speci ﬁ c type of LC model in the third step, which incorporates the classi ﬁ cation error in the class assignments explicitly.”