Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 3-14 |
| Number of pages | 12 |
| Journal | Leadership Quarterly |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2002 |
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ASJC Scopus subject areas
- Business and International Management
- Organizational Behavior and Human Resource Management
- Applied Psychology
- Sociology and Political Science
Cite this
Benchmarking multilevel methods in leardership. The articles, the model, and the data set. / Bliese, Paul D.; Halverson, Ronald R.; Schriesheim, Chester.
In: Leadership Quarterly, Vol. 13, No. 1, 2002, p. 3-14.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Benchmarking multilevel methods in leardership. The articles, the model, and the data set
AU - Bliese, Paul D.
AU - Halverson, Ronald R.
AU - Schriesheim, Chester
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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U2 - 10.1016/S1048-9843(01)00101-1
DO - 10.1016/S1048-9843(01)00101-1
M3 - Article
AN - SCOPUS:0036200685
VL - 13
SP - 3
EP - 14
JO - Leadership Quarterly
JF - Leadership Quarterly
SN - 1048-9843
IS - 1
ER -