A primer on distributional assumptions and model linearity in repeated measures data analysis
Repeated measures data are widely used in social and behavioral sciences, e.g., to investigate the trajectory of an underlying phenomenon over time. A variety of different mixed-effects models, a type of statistical modeling approach for repeated measures data, have been proposed and they differ mai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Université d'Ottawa
2018-10-01
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| Series: | Tutorials in Quantitative Methods for Psychology |
| Subjects: | |
| Online Access: | https://www.tqmp.org/RegularArticles/vol14-3/p199/p199.pdf |
| _version_ | 1818412120985829376 |
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| author | Peralta, Yadira Kohli, Nidhi Wang, Chun |
| author_facet | Peralta, Yadira Kohli, Nidhi Wang, Chun |
| author_sort | Peralta, Yadira |
| collection | DOAJ |
| description | Repeated measures data are widely used in social and behavioral sciences, e.g., to investigate the trajectory of an underlying phenomenon over time. A variety of different mixed-effects models, a type of statistical modeling approach for repeated measures data, have been proposed and they differ mainly in two aspects: (1) the distributional assumption of the dependent variable and (2) the linearity of the model. Distinct combinations of these characteristics encompass a variety of modeling techniques. Although these models have been independently discussed in the literature, the most flexible framework -- the generalized nonlinear mixed-effects model (GNLMEM) -- can be used as a modeling umbrella to encompass these modeling options for repeated measures data. Therefore, the aim of this paper is to explicate on the different mixed-effects modeling techniques guided by the distributional assumption and model linearity choices using the GNLMEM as a general framework. Additionally, empirical examples are used to illustrate the versatility of this framework. |
| first_indexed | 2024-12-14T10:42:16Z |
| format | Article |
| id | doaj.art-e3a49d27ed2649c7a5d8473c753987f8 |
| institution | Directory Open Access Journal |
| issn | 1913-4126 |
| language | English |
| last_indexed | 2024-12-14T10:42:16Z |
| publishDate | 2018-10-01 |
| publisher | Université d'Ottawa |
| record_format | Article |
| series | Tutorials in Quantitative Methods for Psychology |
| spelling | doaj.art-e3a49d27ed2649c7a5d8473c753987f82022-12-21T23:05:39ZengUniversité d'OttawaTutorials in Quantitative Methods for Psychology1913-41262018-10-0114319921710.20982/tqmp.14.3.p199A primer on distributional assumptions and model linearity in repeated measures data analysisPeralta, YadiraKohli, NidhiWang, ChunRepeated measures data are widely used in social and behavioral sciences, e.g., to investigate the trajectory of an underlying phenomenon over time. A variety of different mixed-effects models, a type of statistical modeling approach for repeated measures data, have been proposed and they differ mainly in two aspects: (1) the distributional assumption of the dependent variable and (2) the linearity of the model. Distinct combinations of these characteristics encompass a variety of modeling techniques. Although these models have been independently discussed in the literature, the most flexible framework -- the generalized nonlinear mixed-effects model (GNLMEM) -- can be used as a modeling umbrella to encompass these modeling options for repeated measures data. Therefore, the aim of this paper is to explicate on the different mixed-effects modeling techniques guided by the distributional assumption and model linearity choices using the GNLMEM as a general framework. Additionally, empirical examples are used to illustrate the versatility of this framework.https://www.tqmp.org/RegularArticles/vol14-3/p199/p199.pdfrepeated measures datadistributional assumptionsmodel linearity.SAS, R |
| spellingShingle | Peralta, Yadira Kohli, Nidhi Wang, Chun A primer on distributional assumptions and model linearity in repeated measures data analysis Tutorials in Quantitative Methods for Psychology repeated measures data distributional assumptions model linearity. SAS, R |
| title | A primer on distributional assumptions and model linearity in repeated measures data analysis |
| title_full | A primer on distributional assumptions and model linearity in repeated measures data analysis |
| title_fullStr | A primer on distributional assumptions and model linearity in repeated measures data analysis |
| title_full_unstemmed | A primer on distributional assumptions and model linearity in repeated measures data analysis |
| title_short | A primer on distributional assumptions and model linearity in repeated measures data analysis |
| title_sort | primer on distributional assumptions and model linearity in repeated measures data analysis |
| topic | repeated measures data distributional assumptions model linearity. SAS, R |
| url | https://www.tqmp.org/RegularArticles/vol14-3/p199/p199.pdf |
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