Piecewise latent growth modeling : introduction, moderation, and demonstration in R.
| dc.contributor.advisor | Tomek, Sara. | |
| dc.creator | Jiang, Shan, 1991- | |
| dc.creator.orcid | 0000-0002-5277-8194 | |
| dc.date.accessioned | 2024-07-30T12:44:35Z | |
| dc.date.available | 2024-07-30T12:44:35Z | |
| dc.date.created | 2023-12 | |
| dc.date.issued | 2023-12 | |
| dc.date.submitted | December 2023 | |
| dc.date.updated | 2024-07-30T12:44:35Z | |
| dc.description.abstract | Piecewise latent growth modeling (PLGM) is a class of longitudinal models using a structural equation modeling framework to describe stage-like, discontinuous change of individuals over time. PLGM breaks the overall time window into non-overlapped segments where separate functions can be fitted to represent differential growth patterns for each segment, rather than a single growth pattern over the entire length of time. Because of this flexibility, PLGM is useful in many areas, including education and development sciences, as growth patterns are often different based on stages of development or differential ages. That is, growth is more likely to be considered stagewise in nature rather than following a linear change pattern. Conceptually, PLGM is an extension of a latent growth modeling with the addition of extra intercepts or slopes. This makes it more straightforward to implement, as it is similar to latent growth modeling. However, given those advantages, PLGM is still largely underutilized in social sciences due to the complexity in interpretation. In the modeling of change over time, it is often useful for researchers to assume fluctuations in growth can be accounted for by individual factors. Similarly, moderation effects in growth models assume that these individual factors can either accelerate or decelerate growth in a multiplicative effect. If utilizing PLGM, the addition of moderation effects also increases the complexity of the effect. In this dissertation, I present a comprehensive introduction to the use of basic PLGM and its step-by-step implementation on a free software platform R. Next, I will present a comprehensive introduction to the use of moderation effects within PLGMs and its step-by-step implementation within R. Within this context, I will introduce and explain parameterization, covariates, interpretation of moderation effects, and time-specific variance. And finally, I will present practical applications of this analysis utilizing a public data set ECLS-K: 2011. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.identifier.uri | https://hdl.handle.net/2104/12885 | |
| dc.language.iso | English | |
| dc.rights.accessrights | Worldwide access | |
| dc.title | Piecewise latent growth modeling : introduction, moderation, and demonstration in R. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Baylor University. Dept. of Educational Psychology. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.name | Ph.D. | |
| thesis.degree.program | Educational Psychology | |
| thesis.degree.school | Baylor University |
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