A Case for Using Hierarchical Linear Modeling in Exercise Science Research

被引：1
|
作者：
Mitchell, Sid [1 ]
Loovis, E. Michael [2 ]
Butterfield, Stephen A. [1 ]
机构：
[1] Univ Maine, Orono, ME 04469 USA
[2] Cleveland State Univ, Cleveland, OH 44115 USA
来源：
JOURNAL OF MOTOR LEARNING AND DEVELOPMENT | 2020年 / 8卷 / 01期
关键词：
children; motor performance; physical activity;
D O I：
10.1123/jmld.2019-0003
中图分类号：
B844 [发展心理学（人类心理学）];
学科分类号：
040202 ;
摘要：
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.
引用
收藏
页码：166 / 173
页数：8
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