Benchmarking multilevel methods in leadership: The articles,the model,and the data set

Multilevel data-analytic techniques are rarely simultaneously employed and directly contrasted with each other. In this special issue of The Leadership Quarterly, hierarchical linear models (HLM), within-and-between analysis (WABA), and random group resampling (RGR) are compared and contrasted by testing the hypothesis that leadership moderates the relationship between stressors and well-being—a hypothesis that has important practical implications for the U.S. Army. This first article plays the groundwork for subsequent comparisons by testing for moderating effects using data collected from 2042 U.S. Army soldiers deployed to Haiti in November and December of 1994. Raw-score or individual-level analyses failed to find evidence of moderating effects. However, a preliminary set of group-level analyses indicated that the data had significant group-level properties that had not been modeled in the individual-level analyses. The group-level properties of the data set the stage for the three multilevel data-analytic approaches (HLM, WABA, and RGR) that are employed in three articles that follow and that are then compared and contrasted in the final article of this special issue.