Benchmarking multilevel methods in leardership. The articles, the model, and the data set

Paul D. Bliese, Ronald R. Halverson, Chester A. Schriesheim

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


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. Rawscore 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. Published by Elsevier Science Inc.

Original languageEnglish (US)
Pages (from-to)3-14
Number of pages12
JournalLeadership Quarterly
Issue number1
StatePublished - Mar 19 2002

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Sociology and Political Science
  • Organizational Behavior and Human Resource Management


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