Applications of multilevel modeling in sport injury rehabilitation research

Abstract

Sport injury rehabilitation researchers are using increasingly sophisticated and complex prospective longitudinal research designs. Unfortunately, traditional statistical analysis techniques (e.g., analysis of variance [ANOVA], regression) are inadequate to thoroughly analyze the data sets these designs produce. In this article, the use of multilevel modeling (MLM) is described. MLM is shown to be an effective alternative to regression and ANOVA techniques for two main reasons: (a) MLM is particularly well suited to analyzing repeated‐measures designs when the data are clustered; and (b) MLM results are not adversely affected by random missing data and unequal numbers of observations. Results of MLM analyses highlighting the ability to examine relationships among variables pertaining to both individuals (i.e., within‐subjects analyses) and groups (i.e., between‐subjects analyses) are presented in the context of a longitudinal study of persons with knee injuries. Suggestions for applying MLM to other sport injury rehabilitation research questions are provided.

Keywords:

• multilevel modeling
• hierarchical linear modeling
• longitudinal analysis
• injury rehabilitation.