Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T01:55:40.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Three Issues in Multilevel Research

Published online by Cambridge University Press:  01 March 2019

Vicente González-Romá
Vicente González-Romá*
Affiliation:
Universitat de València (Spain)
*
*Correspondence concerning this article should be addressed to Vicente González-Romá. Universitat de València. Institut d’ Investigació en Psicologia del RRHH, del Desenvolupament Organitzacional i de la Qualitat de Vida Labora (IDOCAL). Av. Blasco Ibáñez, 21. 46010 Valencia (Spain). E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In this article, three important issues in organizational multilevel research are discussed and clarified, namely: (a) The interpretation of “cross-level direct effects” in theoretical and research multilevel models, (b) the specification of the emergence processes involved in higher-level constructs, and (c) the sample size recommendations for using multilevel statistical methods. By doing so, this article hopes to contribute to the improvement of organizational multilevel research.

Keywords

compilationcompositioncross-level direct effectmultilevelsample size
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 22 , 2019 , E4
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

I want to thank Ana Hernández for her constructive comments on an earlier version of this article.

How to cite this article:

González-Romá, V. (2018). Three issues in multilevel research. The Spanish Journal of Psychology, 22. e4. Doi:10.1017/sjp.2019.3

References

Ashforth, B. E. (1985). Climate formation: Issues and extensions. The Academy of Management Review, 10, 837–847. https://doi.org/10.2307/258051.CrossRefGoogle Scholar
Barsade, S. G., & Knight, A. P. (2015). Group affect. Annual Review of Organizational Psychology and Organizational Behavior, 2, 21–46. https://doi.org/10.1146/annurev-orgpsych-032414-11131CrossRefGoogle Scholar
Bell, B. A., Morgan, G. B., Schoeneberger, J. A., Kromrey, J. D., & Ferron, J. M. (2014). How low can you go? An investigation of the influence of sample size and model complexity on point and interval estimates in two-level linear models. Methodology, 1, 86–92.Google Scholar
Bosker, R. J., Snijders, T. A. B., & Guldemond, H. (2003). PINT (Power IN Two-level designs): Estimating standard errors of regression coefficients in hierarchical linear models for power calculations: User’s manual (Version 2.1). Groningen, The Netherlands: University of Groningen.Google Scholar
Browne, W. J., Lahi, M. G., & Parker, R. M. A. (2009). A guide to sample size calculations for random effect models via simulation and the MLPowSim Software Package. Bristol, UK: University of Bristol.Google Scholar
Burstein, L., Linn, R. L., & Capell, F . J. (1978). Analyzing multilevel data in the presence of heterogeneous within-class regressions. Journal of Educational Statistics, 3, 347–383. https://doi.org/10.3102/10769986003004347CrossRefGoogle Scholar
Cortina, J. M., & Landis, R. S. (2009). When small effect sizes tell a big story, and when large effect sizes don’t. In Lance, C. E. & Vandenberg, R. J. (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences (pp. 287–308). New York, NY: Routledge.Google Scholar
de Leeuw, J., & Kreft, I. (1986). Random coefficient models for multilevel analysis. Journal of Educational Statistics, 11, 57–85. https://doi.org/10.3102/10769986011001057CrossRefGoogle Scholar
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138. https://doi.org/10.1037/1082-989X.12.2.121.CrossRefGoogle Scholar
Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika, 73, 43–56. https://doi.org/10.1093/biomet/73.1.43CrossRefGoogle Scholar
González-Romá, V., & Hernández, A. (2014). Climate uniformity: Its influence on team communication quality, task conflict, and team performance. Journal of Applied Psychology, 99, 1042–1058. https://doi.org/10.1037/a0037868.CrossRefGoogle ScholarPubMed
González-Romá, V., & Hernández, A. (2017). Multilevel modeling: research-based lessons for substantive researchers. Annual Review of Organizational Psychology and Organizational Behavior, 4, 183–210. https://doi.org/10.1146/annurev-orgpsych-041015-062407.CrossRefGoogle Scholar
González-Romá, V., & Peiró, J. M. (2014). Climate and culture strength. In Schneider, B. & Barbera, K. (Eds.), The Oxford handbook of organizational climate and culture (pp. 496–531). New York, NY: Oxford University Press.Google Scholar
González-Romá, V., Peiró, J. M., & Tordera, N. (2002). An examination of the antecedents and moderator influences of climate strength. Journal of Applied Psychology, 87, 465–473. https://doi.org/10.1037/0021-9010.87.3.465.CrossRefGoogle ScholarPubMed
Hitt, M. A., Beamish, P. W., Jackson, S. E., & Mathieu, J. E. (2007). Building theoretical and empirical bridges across levels: Multilevel research in management. Academy of Management Journal, 50, 1385–1399. https://doi.org/10.5465/amj.2007.28166219.CrossRefGoogle Scholar
House, R., Rousseau, D. M., & Thomas-Hunt, M. (1995). The Meso paradigm: A framework for the integration of micro and macro organizational behavior. Research in Organizational Behavior, 17, 71–114.Google Scholar
James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85–98. https://doi.org/10.1037/0021-9010.69.1.85.CrossRefGoogle Scholar
Kelly, J. R. & Barsade, S. G. (2001). Mood and emotions in small groups and work teams. Organizational Behavior and Human Decision Processes, 86, 99–130.CrossRefGoogle Scholar
Klein, K. J., Dansereau, F., & Hall, R. J. (1994). Levels issues in theory development, data-collection, and analysis. The Academy of Management Review, 19, 195–229. https://doi.org/10.5465/amr.1994.9410210745CrossRefGoogle Scholar
Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations. Contextual, temporal, and emergent processes. In Klein, K. J. & Kozlowski, S. W. J. (Eds.), Multilevel theory, research, and methods in organizations (pp. 3–90). San Francisco, CA: Jossey-Bass.Google Scholar
LoPilato, A. C., & Vandenberg, R. J. (2015). The not-so-direct cross-level direct effect. In Lance, C. E. & Vandenberg, R. J. (Eds.), More statistical and methodological myths and urban legends (pp. 292–310). New York, NY: Routledge.Google Scholar
Mathieu, J. E., Aguinis, H., Culpepper, S. A., & Chen, G. (2012). Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97, 951–966. https://doi.org/10.1037/a0028380.CrossRefGoogle ScholarPubMed
Muthén, B. O. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54, 557–585.CrossRefGoogle Scholar
Preacher, K. J, Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209–233. https://doi.org/10.1037/a0020141.CrossRefGoogle ScholarPubMed
Rentsch, J. R. (1990). Climate and culture: Interaction and qualitative differences in organizational meanings. Journal of Applied Psychology, 75, 668–681. https://doi.org/10.1037/0021-9010.75.6.668.CrossRefGoogle Scholar
Rousseau, D. M. (1985). Issues of level in organizational research: multi-level and cross-level perspectives. Research in Organizational Behavior, 7, 1–38.Google Scholar
Tonidandel, S., Williams, E. B., & LeBreton, J. M. (2015). Size mattersjust not in the way that you think. In Lance, C. E. & Vandenberg, R. J. (Eds.), More statistical and methodological myths and urban legends (pp. 162–183). New York, NY: Routledge.Google Scholar