Academic journal article Research Quarterly for Exercise and Sport

Multilevel Research Design and Data Analysis: An Overview. (Peer-Reviewed Symposia)

Academic journal article Research Quarterly for Exercise and Sport

Multilevel Research Design and Data Analysis: An Overview. (Peer-Reviewed Symposia)

Article excerpt

Many health problems, such as obesity, are caused by determinants at different levels, e.g., intra- and interpersonal, social and environmental levels. In addition, much research data are also collected from clustered sampling units, such as classes, schools, health clubs, communities, states or metropolitan areas. As a result, the commonly used single-level research design and analytical models (e.g., analysis of variance) are no longer appropriate for this kind of hierarchical, or multilevel, research problems and data analysis. This is because observational units (e.g., students or participants) nested within experimental units (e.g., schools or communities) are not independent from each other, which violates the basic assumption of most statistical tests that replications of the experiment are independent. Fortunately, the problems can be overcome by employing the recently developed hierarchical linear model (HLM). Derived from the idea of the "slopes-as-outcome" analytical model (Burstein, 1980), HLM is a multilevel analytical model, which takes the hierarchical data structure into consideration in the data analysis. …

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