Conducting Three-Level Longitudinal Analyses
Bibliographic Data
| ID | 11672010 |
|---|---|
| Authors | James L Peugh (0000-0002-1134-5667, Cincinnati Children’s Hospital Medical Center, OH, USA, corresponding author), Ronald H Heck (0000-0001-6314-5352, University of Hawaii at Manoa, Honolulu, USA) |
| Year | 2016 |
| Volume | 37 |
| Issue | 1 |
| Pages | 7-58 |
| Publication date | 2016-05-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Early Adolescence (JOURNAL) |
| Journal identifiers | ISSN: 0272-4316 • E-ISSN: 1552-5449 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/0272431616642329 |
| OpenAlex | W2400085626 |
| Language | EN |
| Citations received | 18 |
| References cited | 33 |
Researchers in the field of early adolescence interested in quantifying the environmental influences on a response variable of interest over time would use cluster sampling (i.e., obtaining repeated measures from students nested within classrooms and/or schools) to obtain the needed sample size. The resulting longitudinal data would be nested at three levels (e.g., repeated measures [Level 1], collected across participants [Level 2], and nested within different schools [Level 3]). A previous publication addressed statistical analysis issues specific to cross-sectional three-level data analytic designs. This article expands upon the previous cross-sectional three-level publication to address topics specific to longitudinal three-level data analyses efforts. Although all analysis examples are demonstrated using SAS, the equivalent SPSS and Mplus syntax scripts, as well as the generated example data and additional supplemental materials, are available online
Cluster sampling · Data mining · Environmental health · Longitudinal data · Nested set model · Programming language · Sample (material · Sample size determination · Scripting language · Statistics · Variable (mathematics · Computer Science · Mathematics · Medicine · Psychology · Psychometric Methodologies and Testing · Statistical Methods and Applications · Statistical Methods and Bayesian Inference
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| Citations per year | 2.25 |
|---|---|
| Citation span | 2018 - 2026 (9) |
| Highly cited | No |