A Case for Using Hierarchical Linear Modeling in Exercise Science Research

in Journal of Motor Learning and Development
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  • 1 University of Maine
  • 2 Cleveland State University
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Analyzing data in the exercise sciences can be challenging when trying to account for physical changes brought about by maturation (e.g., growth in height, weight, heart/lung capacity, muscle-to-fat ratio). In this paper, we present an argument for using hierarchical linear modeling (HLM) as an approach to analyzing physical performance data. Using an applied example from Butterfield, Lehnhard, Lee, and Coladarci, we will show why HLM is an appropriate analysis technique and provide other examples of where HLM will be beneficial.

Mitchell and Butterfield are with the University of Maine, Orono, ME. Loovis is with Cleveland State University, Cleveland, OH.

Butterfield (stephen.butterfield@maine.edu) is corresponding author.
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