The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Estimating Statistical Power and Required Sample Sizes for Organizational Research Using Multilevel Modeling
Abstract
The use of multilevel modeling to investigate organizational phenomena is rapidly increasing. Unfortunately, little advice is readily available for organizational researchers attempting to determine statistical power when using multilevel models or when determining sample sizes for each level that will maximize statistical power. This article presents an introduction to statistical power in multilevel models. The unique factors influencing power in multilevel models and calculations for estimating power for simple fixed effects, variance components, and cross-level interactions are presented. The results of simulation studies and the existing general rules of thumb are discussed, and the available power analysis software is reviewed.
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Cites
Sufficient Sample Sizes for Multilevel Modeling
Maas, C. J. M., & Hox, J. J. (2005) · Methodology · doi:10.1027/1614-2241.1.3.86 · Cited by 3,582
Hox, J., Moerbeek, M., & van de Schoot, R. (2002) · doi:10.4324/9781410604118 · Cited by 2,419
Robustness issues in multilevel regression analysis
Maas, C. J. M., & Hox, J. J. (2004) · Statistica Neerlandica · doi:10.1046/j.0039-0402.2003.00252.x · Cited by 837
Standard Errors and Sample Sizes for Two-Level Research
Snijders, T. A. B., & Bosker, R. J. (1993) · Journal of Educational Statistics · doi:10.3102/10769986018003237 · Cited by 487 · cited 8×
“Snijders and Bosker (1993, 1999) presented the following formula based on the t test to determine the maximum value of the standard error for a coefficient that will achieve a given level of power for a particular two-tailed level of a and effect size: Standard Error≤ Effect Size Z1−a=2 + Z1−b…”
BackgroundMethodResultInfluentialBrown, C. H., & Liao, J. (1999) · American Journal of Community Psychology · doi:10.1023/a:1022142021441 · Cited by 142
Cited by
Gabriel, A. S., Podsakoff, N. P., Beal, D. J., Scott, B. A., Sonnentag, S., Trougakos, J. P., & Butts, M. M. (2019) · Organizational Research Methods · doi:10.1177/1094428118802626 · Cited by 755
Schmiedel, T., Müller, O., & vom Brocke, J. (2019) · Organizational Research Methods · doi:10.1177/1094428118773858 · Cited by 319
The benefits of teleworking in the public sector: reality or rhetoric?
de Vries, H., Tummers, L., & Bekkers, V. (2019) · Review of Public Personnel Administration · doi:10.1177/0734371x18760124 · Cited by 294
Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012) · Journal of Abnormal Psychology · doi:10.1037/a0027978 · Cited by 292
On Measuring and Modeling Physiological Synchrony in Dyads
Helm, J. L., Miller, J. G., Kahle, S., Troxel, N. R., & Hastings, P. D. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1459292 · Cited by 112
Metadata
- Authors
- Scherbaum, C. A., & Ferreter, J. M.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2008-04-08
- Volume
- 12
- Issue
- 2
- Pages
- 347-367
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2099665323
- Cited by
- 665
- References
- 5
- FWCI
- 5.41
- Type
- journal article
- Language
- en
- Open access
- closed
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