The Benefits of Longitudinal Data and Multilevel Modeling to Measure Change in Adventure Education Research

Abstract

Background: A common critique of adventure education research methodology is the overreliance on pre-/post-study designs to measure change. Purpose: This paper compares and contrasts two methods of data analysis on the same adventure education data set to show how these distinct approaches provide starkly different results and interpretation. Methodology/Approach: Using secondary data analysis, we employ a longitudinal data set of the social skill development of urban middle school students who participated in an adventure education program over the course of three academic years. First, change was assessed using a pre-/post-design by a traditional analysis of variance (ANOVA). Next, change was assessed with a six-wave, longitudinal design using multilevel modeling. Findings/Conclusions: Results show that the multilevel modeling approach revealed nonlinear change in the social skill development of middle school students, resulting in more accurate, nuanced estimation of change in social skill development than the ANOVA approach. Implications: The use of longitudinal data and multilevel modeling can be a useful methodological approach and statistical tool for adventure education researchers to not only address the criticisms of quantitative research in adventure education, but more importantly, provide a more thorough understanding of the impact of adventure education on human development.