The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators
Abstract
We study the properties of a three-step approach to estimating the parameters of a latent structure model for categorical data and propose a simple correction for a common source of bias. Such models have a measurement part (essentially the latent class model) and a structural (causal) part (essentially a system of logit equations). In the three-step approach, a stand-alone measurement model is first defined and its parameters are estimated. Individual predicted scores on the latent variables are then computed from the parameter estimates of the measurement model and the individual observed scoring patterns on the indicators. Finally, these predicted scores are used in the causal part and treated as observed variables. We show that such a naive use of predicted latent scores cannot be recommended since it leads to a systematic underestimation of the strength of the association among the variables in the structural part of the models. However, a simple correction procedure can eliminate this systematic bias. This approach is illustrated on simulated and real data. A method that uses multiple imputation to account for the fact that the predicted latent variables are random variables can produce standard errors for the parameters in the structural part of the model.
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“However, our approach of assigning class membership had misclassification bias that has been shown to produce underestimated associations (Bolck, Croon, and Hagenaars, 2004).”
From Health lifestyles across the transition to adulthood: implications for health (2017) · cites this paper as Bolck, Croon, and Hagenaars, 2004
Citations
Cites
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Hagenaars, J. A. P., & McCutcheon, A. L. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511499531 · Cited by 1,983
Self-citationGraphical Models in Applied Multivariate Statistics
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Research works printed but not linked: 5
- Vermunt, 1997a, Loglinear Models for Event History Analysis
- Bolck, 1997, On the Use of Latent Scores in Causal Models for Categorical Variables
- Lazarsfeld, 1950b, Measurement and Prediction, 362
- Vermunt, 2000, Latent Gold; User's Guide
- Van de Pol, 1996, PANMARK 3 User's Manual
Cited by
Sorted by citations
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using M plus
Asparouhov, T., & Muthén, B. (2014) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2014.915181 · Cited by 3,842
Latent Class Modeling with Covariates: Two Improved Three-Step Approaches
Vermunt, J. K. (2010) · Political Analysis · doi:10.1093/pan/mpq025 · Cited by 2,370
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
Heckman, J., Pinto, R., & Savelyev, P. (2013) · American Economic Review · doi:10.1257/aer.103.6.2052 · Cited by 1,628
poLCA : An R Package for Polytomous Variable Latent Class Analysis
Linzer, D. A., & Lewis, J. B. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i10 · Cited by 1,649
Metadata
- Authors
- Bolck, A., Croon, M., & Hagenaars, J.
- Affiliations
- Netherlands Forensic Institute (NL)
- Tilburg University (NL)
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2004
- Volume
- 12
- Issue
- 1
- Pages
- 3-27
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W2061736358
- Cited by
- 1,406
- References
- 39
- Citations by year
- 2026: 284 · 2025: 186 · 2024: 148 · 2023: 140 · 2022: 131 · 2021: 130 · 2020: 95 · 2019: 74 · 2018: 62 · 2017: 43 · 2016: 38 · 2015: 24 · 2014: 14 · 2013: 14 · 2012: 4
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Political Analysis (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Keywords
- latent structure models, categorical data, latent class models, one-step estimation, bias correction, measurement model, structural equation modeling, multiple imputation, standard errors, associative strength
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