The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Investigating population heterogeneity with factor mixture models.
Abstract
Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A step-by-step analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point.
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
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Sample size in factor analysis.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.1.84 · Cited by 5,497
Testing the number of components in a normal mixture
Lo, Y. (2001) · Biometrika · doi:10.1093/biomet/88.3.767 · Cited by 4,640
Measurement Invariance, Factor Analysis and Factorial Invariance
Meredith, W. (1993) · Psychometrika · doi:10.1007/bf02294825 · Cited by 4,536
Byrne, B. M., Shavelson, R. J., & Muthén, B. (1989) · Psychological Bulletin · doi:10.1037//0033-2909.105.3.456 · Cited by 4,115
Self-citationAnalyzing developmental trajectories: A semiparametric, group-based approach.
Nagin, D. S. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.2.139 · Cited by 2,346 · cited 2×
“There are different approaches to compare models and to decide on the number of classes (see, for instance, Nagin, 1999 ).”
MethodSimultaneous Factor Analysis in Several Populations
Jöreskog, K. G. (1971) · Psychometrika · doi:10.1007/bf02291366 · Cited by 2,051 · cited 2×
“Regarding the first criterion, well-known methods for observed group membership are discriminant analysis (DA), logistic (or ordered polytomous) regression (LR), multivariate analysis of variance (MANOVA), and multigroup common factor analysis (MG-CFA; Jöreskog, 1971; Stevens, 1992).”
BackgroundMethodThe SAGE Handbook of Quantitative Methodology for the Social Sciences
Kaplan, D. (2004) · doi:10.4135/9781412986311 · Cited by 1,454
Item bias and item response theory
Mellenbergh, G. J. (1989) · International Journal of Educational Research · doi:10.1016/0883-0355(89)90002-5 · Cited by 553 · cited 2×
“Measurement invariance (MI) as defined by Mellenbergh (1989) and Meredith (1993) implies that, given a certain score on the underlying factor, an observed score does not depend on subpopulation membership.”
Background
Cited by
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701575396 · Cited by 11,415
Self-citationIdentifying careless responses in survey data.
Meade, A. W., & Craig, S. B. (2012) · Psychological Methods · doi:10.1037/a0028085 · Cited by 3,865
Vrieze, S. I. (2012) · Psychological Methods · doi:10.1037/a0027127 · Cited by 1,948
Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (2009) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510902751010 · Cited by 1,245
Lubke, G., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510709336735 · Cited by 852
Self-citation
Metadata
- Authors
- Lubke, G. H., & Muthén, B.
- Venue
- Psychological Methods
- Published
- 2005-03
- Volume
- 10
- Issue
- 1
- Pages
- 21-39
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2146177007
- Cited by
- 1,143
- References
- 11
- FWCI
- 27.73
- 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: Investigating population heterogeneity with factor mixture models. https://app.cochat.ai/papers/10.1037/1082-989x.10.1.21
- The paper
- Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods. https://doi.org/10.1037/1082-989x.10.1.21
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.
