A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918380149145600 |
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| author | Wayman, Eric Alan Culpepper, Steven Andrew Douglas, Jeff Bowers, Jesse |
| author_facet | Wayman, Eric Alan Culpepper, Steven Andrew Douglas, Jeff Bowers, Jesse |
| contents | We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit specification and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13143 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates Wayman, Eric Alan Culpepper, Steven Andrew Douglas, Jeff Bowers, Jesse Methodology We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit specification and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past. |
| title | A Restricted Latent Class Model with Polytomous Attributes and Respondent-Level Covariates |
| topic | Methodology |
| url | https://arxiv.org/abs/2408.13143 |