CoChat
Sign Up
Cited by 1,406Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceEconomic and Environmental Valuation

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

Bolck, Annabel, Croon, Marcel, Hagenaars, Jacques·2004·Political Analysis
DOI 10.1093/pan/mph001
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1093/pan/mph001

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 39 references

Research works printed but not linked: 5

  1. Vermunt, 1997a, Loglinear Models for Event History Analysis
  2. Bolck, 1997, On the Use of Latent Scores in Causal Models for Categorical Variables
  3. Lazarsfeld, 1950b, Measurement and Prediction, 362
  4. Vermunt, 2000, Latent Gold; User's Guide
  5. Van de Pol, 1996, PANMARK 3 User's Manual

Cited by

Sorted by citations

Items 201–300 of 1,001

Related

See all

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
DOI
10.1093/pan/mph001
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
Licence
www.cambridge.org/core/terms (unspecified, 4752 d)
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

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