关键词:
cross-level analysis
interactions
measurement models
quantitative multilevel research
CENTERING PREDICTOR VARIABLES
MODERATED MULTIPLE-REGRESSION
LIKELIHOOD RATIO TESTS
EMOTIONAL EXHAUSTION
PERFORMANCE
IMPACT
WORK
EXCHANGE
CLIMATE
LEADER
摘要:
Cross-level interaction effects lay at the heart of multilevel contingency and interactionism theories. Also, practitioners are particularly interested in such effects because they provide information on the contextual conditions and processes under which interventions focused on individuals (e.g., selection, leadership training, performance appraisal, and management) result in more or less positive outcomes. We derive a new intraclass correlation, , to assess the degree of lower-level outcome variance that is attributed to higher-level differences in slope coefficients. We provide analytical and empirical evidence that is an index of variance that differs from the traditional intraclass correlation and use data from recently published articles to illustrate that assesses differences across collectives and higher-level processes (e.g., teams, leadership styles, reward systems) but ignores the variance attributed to differences in lower-level relationships (e.g., individual level job satisfaction and individual level performance). Because and provide information on two different sources of variability in the data structure (i.e., differences in means and differences in relationships, respectively), our results suggest that researchers contemplating the use of multilevel modeling, as well those who suspect nonindependence in their data structure, should expand the decision criteria for using multilevel approaches to include both types of intraclass correlations. To facilitate this process, we offer an illustrative data set and the icc beta R package for computing in single- and multiple-predictor situations and make them available through the Comprehensive R Archive Network (i.e., CRAN).