A two-step estimator for multilevel latent class analysis with covariates
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915093057372160 |
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| author | Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni |
| author_facet | Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni |
| contents | We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (i) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (ii) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_06091 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A two-step estimator for multilevel latent class analysis with covariates Di Mari, Roberto Bakk, Zsuzsa Oser, Jennifer Kuha, Jouni Methodology We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (i) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (ii) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms. |
| title | A two-step estimator for multilevel latent class analysis with covariates |
| topic | Methodology |
| url | https://arxiv.org/abs/2303.06091 |