Modeling Predictors of Latent Classes in Regression Mixture Models
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Minjung [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © Taylor & Francis Group, LLC |
|---|
| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Structural equation modeling - Philadelphia, Pa. : Psychology Press, Taylor & Francis Group, 1994, 23(2016), 4, Seite 601 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:4 ; pages:601 |
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| DOI / URN: |
10.1080/10705511.2016.1158655 |
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Kim, Minjung |
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10.1080/10705511.2016.1158655 |
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modeling predictors of latent classes in regression mixture models |
| title_auth |
Modeling Predictors of Latent Classes in Regression Mixture Models |
| abstract |
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students' academic achievement outcome. Implications of the study are discussed. |
| abstractGer |
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students' academic achievement outcome. Implications of the study are discussed. |
| abstract_unstemmed |
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students' academic achievement outcome. Implications of the study are discussed. |
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| title_short |
Modeling Predictors of Latent Classes in Regression Mixture Models |
| url |
http://dx.doi.org/10.1080/10705511.2016.1158655 http://www.tandfonline.com/doi/abs/10.1080/10705511.2016.1158655 http://search.proquest.com/docview/1791391476 |
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| author2 |
Vermunt, Jeroen Bakk, Zsuzsa Jaki, Thomas Van Horn, M. Lee |
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Vermunt, Jeroen Bakk, Zsuzsa Jaki, Thomas Van Horn, M. Lee |
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| up_date |
2024-07-03T14:17:37.750Z |
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