Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance
This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to ac...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyg.2018.01323/full |
| _version_ | 1818501914293174272 |
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| author | Louisa Hohmann Jana Holtmann Michael Eid |
| author_facet | Louisa Hohmann Jana Holtmann Michael Eid |
| author_sort | Louisa Hohmann |
| collection | DOAJ |
| description | This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models. |
| first_indexed | 2024-12-10T21:02:49Z |
| format | Article |
| id | doaj.art-fc85e4ad659448d79088d7202e6c9a99 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-10T21:02:49Z |
| publishDate | 2018-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-fc85e4ad659448d79088d7202e6c9a992022-12-22T01:33:45ZengFrontiers Media S.A.Frontiers in Psychology1664-10782018-08-01910.3389/fpsyg.2018.01323343407Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical PerformanceLouisa HohmannJana HoltmannMichael EidThis simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models.https://www.frontiersin.org/article/10.3389/fpsyg.2018.01323/fullmixture modelingskew t-distributionlatent state-trait analysislongitudinal datanon-normality |
| spellingShingle | Louisa Hohmann Jana Holtmann Michael Eid Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance Frontiers in Psychology mixture modeling skew t-distribution latent state-trait analysis longitudinal data non-normality |
| title | Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance |
| title_full | Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance |
| title_fullStr | Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance |
| title_full_unstemmed | Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance |
| title_short | Skew t Mixture Latent State-Trait Analysis: A Monte Carlo Simulation Study on Statistical Performance |
| title_sort | skew t mixture latent state trait analysis a monte carlo simulation study on statistical performance |
| topic | mixture modeling skew t-distribution latent state-trait analysis longitudinal data non-normality |
| url | https://www.frontiersin.org/article/10.3389/fpsyg.2018.01323/full |
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