Hierarchical Linear Model: Thinking Outside the Traditional Repeated-Measures Analysis-of-Variance Box
| Autor: | Christopher C. Cheatham, Monica R. Lininger, Jessaca Spybrook |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Engineering
Longitudinal study Short Report Physical Therapy Sports Therapy and Rehabilitation Machine learning computer.software_genre Sports Medicine Field (computer science) Athletic training Statistics Humans Orthopedics and Sports Medicine Longitudinal Studies Analysis of Variance business.industry Multilevel model Linear model Repeated measures design General Medicine Variance (accounting) Research Design Data Interpretation Statistical Linear Models Artificial intelligence Cross-sequential study business computer Software |
| Popis: | Longitudinal designs are common in the field of athletic training. For example, in the Journal of Athletic Training from 2005 through 2010, authors of 52 of the 218 original research articles used longitudinal designs. In 50 of the 52 studies, a repeated-measures analysis of variance was used to analyze the data. A possible alternative to this approach is the hierarchical linear model, which has been readily accepted in other medical fields. In this short report, we demonstrate the use of the hierarchical linear model for analyzing data from a longitudinal study in athletic training. We discuss the relevant hypotheses, model assumptions, analysis procedures, and output from the HLM 7.0 software. We also examine the advantages and disadvantages of using the hierarchical linear model with repeated measures and repeated-measures analysis of variance for longitudinal data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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