The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M plus
Abstract
This article discusses alternatives to single-step mixture modeling. A 3-step method for latent class predictor variables is studied in several different settings, including latent class analysis, latent transition analysis, and growth mixture modeling. It is explored under violations of its assumptions such as with direct effects from predictors to latent class indicators. The 3-step method is also considered for distal variables. The Lanza, Tan, and Bray (2013) method for distal variables is studied under several conditions including violations of its assumptions. Standard errors are also developed for the Lanza method because these were not given in Lanza et al. (2013).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
The SAGE Handbook of Quantitative Methodology for the Social Sciences
Kaplan, D. (2004) · doi:10.4135/9781412986311 · Cited by 1,454
Estimating latent structure models with categorical variables: one-step versus three-step estimators
Bolck, A., Croon, M., & Hagenaars, J. (2004) · Political Analysis · doi:10.1093/pan/mph001 · Cited by 1,406
Research works printed but not linked: 7
- 10.1177/0081175012470644 · DOI · Paper page
- 10.1080/10705511.2013.742377 · DOI · Paper page
- 10.4135/9781412986311.n19 · DOI · Paper page
- Muthén B., 2009, Longitudinal data analysis, 143
- 10.1111/j.0006-341X.1999.00463.x · DOI · Paper page
- 10.1093/pan/mpq025 · DOI · Paper page
- 10.1198/016214505000000501 · DOI
Cited by
Latent class analysis: a guide to best practice
Weller, B. E., Bowen, N. K., & Faubert, S. J. (2020) · Journal of Black Psychology · doi:10.1177/0095798420930932 · Cited by 2,223
The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies
van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2017) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2016.1247646 · Cited by 721
Multiple-Group Analysis of Similarity in Latent Profile Solutions
Morin, A. J. S., Meyer, J. P., Creusier, J., & Biétry, F. (2016) · Organizational Research Methods · doi:10.1177/1094428115621148 · Cited by 664
The development of self-regulation across early childhood.
Montroy, J. J., Bowles, R. P., Skibbe, L. E., McClelland, M. M., & Morrison, F. J. (2016) · Developmental Psychology · doi:10.1037/dev0000159 · Cited by 629
A person‐centered approach to commitment research: Theory, research, and methodology
Meyer, J. P., & Morin, A. J. S. (2016) · Journal of Organizational Behavior · doi:10.1002/job.2085 · Cited by 487
Metadata
- Authors
- Asparouhov, T., & Muthén, B.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2014-06-09
- Volume
- 21
- Issue
- 3
- Pages
- 329-341
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2147200107
- Cited by
- 3,535
- References
- 3
- FWCI
- 164.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M plus. https://app.cochat.ai/papers/10.1080/10705511.2014.915181
- The paper
- Asparouhov, T., & Muthén, B. (2014). Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M plus. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2014.915181
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.