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Activity Number:
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307
- Challenges and Advances in Psychological and Behavioral Data Analysis
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #317270
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Title:
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Latent Structure Models for Multiclass Data: Identifiability and Estimation
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Author(s):
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Steven Culpepper and Ying Liu*
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Companies:
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University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
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Keywords:
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Multiclass response;
Restricted latent class model;
Identifiability;
Bayesian;
Psychometrics
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Abstract:
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Restricted latent class models (RLCMs) provide an important framework for diagnosing and classifying respondents on a collection of multivariate binary responses. Recent research made significant advances in theory for establishing identifiability conditions for RLCMs with binary and polytomous response data. Multiclass data, which are unordered nominal response data, are also widely collected in the social sciences. We establish new identifiability conditions for parameters of RLCMs for multiclass data and discuss the implications for substantive applications. We propose a Bayesian framework for inferring model parameters and assess parameter recovery in a Monte Carlo simulation study.
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Authors who are presenting talks have a * after their name.
