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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators |
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Identification Number: |
doi:10.7910/DVN/YFREQV |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2010-02-16 |
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Version: |
1 |
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Bibliographic Citation: |
Annabel Bolck; Marcel Croon; Jacques Hagenaars, 2010, "Replication data for: Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators", https://doi.org/10.7910/DVN/YFREQV, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication data for: Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators |
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Identification Number: |
doi:10.7910/DVN/YFREQV |
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Authoring Entity: |
Annabel Bolck (Netherlands Forensic Institute) |
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Marcel Croon (Tilburg University) |
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Jacques Hagenaars (Tilburg University) |
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Producer: |
Political Analysis |
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Date of Production: |
2004 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Murray Research Archive |
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Date of Deposit: |
2010-02-09 |
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Holdings Information: |
https://doi.org/10.7910/DVN/YFREQV |
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Study Scope |
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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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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Annabel Bolck, Marcel Croon and Jacques Hagenaars. 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators." Political Analysis, 12(1), 3-27. <a href= "http://pan.oxfordjournals.org/cgi/reprint/12/1/3" target= "_new">subscribe to Political Analysis to access the full article</a> |
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Bibliographic Citation: |
Annabel Bolck, Marcel Croon and Jacques Hagenaars. 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators." Political Analysis, 12(1), 3-27. <a href= "http://pan.oxfordjournals.org/cgi/reprint/12/1/3" target= "_new">subscribe to Political Analysis to access the full article</a> |
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Label: |
croon.web.doc |
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Text: |
Data from the real data analysis in Annabel Bolck, Marcel Croon & Jacques Hagenaars 2003. “Predicted latent scores in causal models for categorical data.” Political Analysis. |
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Notes: |
application/msword |