Accommodating Continuous Time Metrics Within the Discrete-Time Latent Change Score Model Using Definition Variables
Bibliographic Data
| ID | 12424902 |
|---|---|
| Authors | Sarfaraz Serang (0000-0002-7985-4951, University of South Carolina), Shawn D Whiteman (0000-0001-9782-2120, Utah State University), Annabelle H Reese (University of South Carolina) |
| Year | 2025 |
| Volume | 32 |
| Issue | 5 |
| Pages | 814-831 |
| Publication date | 2025-05-30 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2025.2500035 |
| PMID | 41322109 |
| OpenAlex | W4410884753 |
| Language | EN |
| References cited | 48 |
Longitudinal models typically represent change as a function of a single time metric. However, the COVID-19 pandemic prompted researchers to consider whether changes are a function of phases of the pandemic while simultaneously accommodating age. This paper proposes an extension of the discrete-time latent change score modeling framework to model wave-to-wave changes while accounting for time more precisely by including continuous time metrics via regressing out initial age and using definition variables instead of bins. The approach is motivated by and applied to data involving adolescent sibling influence in expectations about marijuana. A simulation study shows how our approach compares to models that use wave without regressing out initial age or using definition variables
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| Citation velocity | historical |
|---|---|
| Highly cited | No |