leadership: Example data set on leadership style and job satisfaction
In simongrund1/mitml: Tools for Multiple Imputation in Multilevel Modeling
leadership R Documentation
Example data set on leadership style and job satisfaction
Description
Data set based on the data simulated by Paul D. Bliese as described in Klein et al. (2000) with slight modifications.
The data set consists of 750 employees, nested within 50 work groups, and includes employees' ratings on negative leadership style, job satisfaction, and workload as well as a measure for each work group's cohesion.
The original data set is available in the multilevel package and was modified by (a) transforming workload into a categorical variable, (b) transforming cohesion into a group-level variable, and (c) by inducing missing values.
Usage
data(leadership)
Format
A data frame containing 750 observations on 5 variables.
References
Bliese, P. D. (2013). multilevel: Multilevel functions (Version 2.5) [Computer software]. Retrieved from http://CRAN.R-project.org/package=multilevel
Klein, K. J., Bliese, P. D., Kozlowski, S. W. J., Dansereau, F., Gavin, M. B., Griffin, M. A., ... Bligh, M. C. (2000). Multilevel analytical techniques: Commonalities, differences, and continuing questions. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 512-553). San Francisco, CA: Jossey-Bass.
simongrund1/mitml documentation built on Jan. 26, 2024, 11:08 a.m.
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| leadership | R Documentation |
Example data set on leadership style and job satisfaction
Description
Data set based on the data simulated by Paul D. Bliese as described in Klein et al. (2000) with slight modifications. The data set consists of 750 employees, nested within 50 work groups, and includes employees' ratings on negative leadership style, job satisfaction, and workload as well as a measure for each work group's cohesion.
The original data set is available in the multilevel package and was modified by (a) transforming workload into a categorical variable, (b) transforming cohesion into a group-level variable, and (c) by inducing missing values.
Usage
data(leadership)Format
A data frame containing 750 observations on 5 variables.
References
Bliese, P. D. (2013). multilevel: Multilevel functions (Version 2.5) [Computer software]. Retrieved from http://CRAN.R-project.org/package=multilevel
Klein, K. J., Bliese, P. D., Kozlowski, S. W. J., Dansereau, F., Gavin, M. B., Griffin, M. A., ... Bligh, M. C. (2000). Multilevel analytical techniques: Commonalities, differences, and continuing questions. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 512-553). San Francisco, CA: Jossey-Bass.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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