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Journal of Business and Psychology

Erschienen in:

05.04.2017 | Original Paper

Back to Basics with Mixed-Effects Models: Nine Take-Away Points

verfasst von: Paul D. Bliese, Mark A. Maltarich, Jonathan L. Hendricks

Erschienen in: Journal of Business and Psychology | Ausgabe 1/2018

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Abstract

Purpose

Multilevel mixed effects models are widely used in organizational behavior and organizational psychology to test and advance theory. At times, however, the complexity of the models leads researchers to draw erroneous inferences or otherwise use the models in less than optimal ways. We present nine take-away points intended to enhance the theoretical precision and utility of the models.

Approach

We demonstrate our points using two types of simulated data: one in which group membership is irrelevant, and the other in which relationships exist only because of group membership. We then demonstrate that the effects we observe in simulated data replicate in organizational data.

Findings

Little that we address will be new to methodology experts; nonetheless, we draw together a variety of points that we believe will help advance both theory and analytic rigor in multilevel analyses.

Implications

We make two points that run somewhat counter to conventional norms. First, we argue that mixed-effects models are appropriate even when ICC(1) values associated with the outcome data are small and non-significant. Second, we show that high ICC(2) values are not a prerequisite for detecting emergent multilevel relationships.

Originality/Value

The article is designed to be a resource for researchers who are learning about and applying mixed-effects (i.e., multilevel) models.

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We are indebted to the late Larry James who (to our knowledge) was the originator of this example and used it when discussing these ideas in conferences.

There are two ways to calculate ICC(1)—using ANOVA or mixed-effects models (Bartko 1976; Bliese 2000). Using the ANOVA method here would produce a slightly negative ICC(1), which is sometimes truncated to zero in reporting. The differences between the two methods are generally not substantial, but the decision point is worth noting.

Econometricians have noted that random effect models can yield level 1 effects that are biased because they “inherit” group-level effects. Including group means as a predictor in random effect models removes this bias and produces coefficients for x that are comparable to fixed-effects models (see Raudenbush 2009).

The estimate of 0.283 is identical to the values reported in Tables 3 and 4. Table 3 therefore shows that one can remove any group effects from X by group-mean centering or by including the group mean of X into the model as noted in footnote 3.

The appendix provides code to create an X2 variable that randomly sorts the X variable on a group-by-group basis and then re-estimates the components of the covariance theorem. As anticipated, aligning Y variables to different X responses within a group has no meaningful impact on the results.

Interested readers can execute the final simulation in the appendix to see how a group-mean interaction can be detected in a cross-level interaction model and then how group-mean centering corrects the problem.

Aguinis, H., & Molina-Azorin, J. F. (2015). Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research. Strategic Organization, 13(4), 353–364.CrossRef

Alwin, D. F. (1976). Assessing school effects: Some identities. Sociology of Education, 49(4), 294–303.CrossRef

Alwin, D. F., & Hauser, R. M. (1975). The decomposition of effects in path analysis. American Sociological Review, 40, 37–47.CrossRef

Barrick, M. R., Stewart, G. L., Neubert, M. J., & Mount, M. K. (1998). Relating member ability and personality to work-team processes and team effectiveness. Journal of Applied Psychology, 83(3), 377–391.CrossRef

Bartko, J. J. (1976). On various intraclass correlation reliability coefficients. Psychological Bulletin, 83(5), 762–765.CrossRef

Bliese, P. D. (1998). Group size, ICC values, and group-level correlations: A simulation. Organizational Research Methods, 1(4), 355–373.CrossRef

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations (pp. 349–381). San Francisco: Jossey-Bass.

Bliese, P. D. (2006). Social climates: Drivers of soldier well-being and resilience. In A. B. Adller, C. A. Castro, & T. W. Britt (Eds.), Military life: The psychology of serving in peace and combat (Vol. 2. Operational stress, pp. 213–234). Westport: Praeger Security International.

Bliese, P. D., & Halverson, R. R. (1996). Individual and nomothetic models of job stress: An examination of work hours, cohesion and well-being. Journal of Applied Social Psychology, 26(13), 1171–1189.CrossRef

Bliese, P. D., & Hanges, P. J. (2004). Being both too liberal and too conservative: The perils of treating grouped data as though they were independent. Organizational Research Methods, 7(4), 400–417.CrossRef

Bliese, P. D., Chan, D., & Ployhart, R. E. (2007). Multilevel methods: Future directions in measurement, longitudinal analyses, and nonnormal outcomes. Organizational Research Methods, 10(4), 551–563.CrossRef

Bloom, H. S., Bos, J. M., & Lee, S. W. (1999). Using cluster random assignment to measure program impacts: Statistical implications for the evaluation of education programs. Evaluation Review, 23(4), 445–469.CrossRefPubMed

Chen, G., Mathieu, M. J., & Bliese, P. D. (2004). A framework for conducting multilevel construct validation. In F. Dansereau & F. J. Yammarino (Eds.), Research in multi-level issues, volume 3: Multi-level issues in organizational behavior and processes (pp. 273–303). Oxford: Elsevier Science.CrossRef

Dansereau, F., Alutto, J. A., & Yammarino, F. J. (1984). Theory testing in organizational behavior: The varient approach. Englewood Cliffs Prentice-Hall.

Firebaugh, G. (1978). A rule for inferring individual-level relationships from aggregate data. American Sociological Review, 43(4), 557–572.CrossRef

Firebaugh, G. (1980). Groups as contexts and frog ponds. In K. H. Roberts & L. Burstein (Eds.), Issues in aggregation (pp. 43–52). San Francisco: Jossey-Bass.

González-Morales, M. G., Peiró, J. M., Rodriguéz, I., & Bliese, P. D. (2012). Perceived collective burnout: A multilevel explanation of burnout. Anxiety, Stress, and Coping, 25(1), 43–61.CrossRefPubMed

Hedges, L., & Hedberg, E. C. (2007). Intraclass correlation values for planning group randomized trials in education. Educational Evaluation and Policy Analysis, 29(1), 60–87.CrossRef

Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623–641.CrossRef

Kreft, I., & De Leeuw, J. (1998). Introducing multilevel modeling. Thousand Oaks: Sage.CrossRef

LaHuis, D. M., & Ferguson, M. W. (2009). The accuracy of significance tests for slope variance components in multilevel random coefficient models. Organizational Research Methods, 12(3), 418–435.CrossRef

Lang, J., Thomas, J. L., Bliese, P. D., & Adler, A. B. (2007). Job demands and job performance: The mediating effect of psychological and physical strain and the moderating effect of role clarity. Journal of Occupational Health Psychology, 12, 116–124.CrossRefPubMed

Li, Y., Wang, Z., Yang, L.-Q., & Liu, S. (2016). The crossover of psychological distress from leaders to subordinates in teams: The role of abusive supervision, psychological capital, and team performance. Journal of Occupational Health Psychology, 21(2), 142–153.CrossRefPubMed

Liao, H., & Chuang, A. (2007). Transforming service employees and climate: A multilevel, multisource examination of transformational leadership in building long-term service relationships. Journal of Applied Psychology, 92(4), 1006–1019.CrossRefPubMed

Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models. Cary: SAS Institute.

Liu, D., Liao, H., & Loi, R. (2012). The dark side of leadership: A three-level investigation of the cascading effect of abusive supervision on employee creativity. Academy of Management Journal, 55(5), 1187–1212.CrossRef

LoPilato, A. C., & Vandenberg, R. J. (2015). The not so direct cross-level direct effect. In C. E. Lance & R. J. Vandenberg (Eds.), More statistical and methodological myths and urban legends (pp. 292–310). New York: Routledge.

Luciano, M. M., Mathieu, J. E., & Ruddy, T. M. (2014). Leading multiple teams: Average and relative external leadership influences on team empowerment and effectiveness. Journal of Applied Psychology, 99(2), 322–331.CrossRefPubMed

Mathieu, J. E., & Kohler, S. S. (1990). A cross-level examination of group absence influences on individual absence. Journal of Applied Psychology, 75(2), 217–220.CrossRef

McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, Advance online publication. doi:10.1037/met0000078.

Miron-Spektor, E., Erez, M., & Naveh, E. (2011). The effect of conformist and attentive-to-detail members on team innovation: Reconciling the innovation paradox. Academy of Management Journal, 54(4), 740–760.CrossRef

Morgeson, F. P., & Hofmann, D. A. (1999). The structure and function of collective constructs: Implications for multilevel resarch and theory development. Academy of Management Review, 24(2), 249–265.

Morrison, E. W., Wheeler-Smith, S. L., & Kamdar, D. (2011). Speaking up in groups: A cross-level study of group voice climate and voice. Journal of Applied Psychology, 96(1), 183–191.CrossRefPubMed

Murray, D. M., & Short, B. (1995). Intra-class correlation among measures related to alcohol use by young adults: Estimates, correlates and applications in intervention studies. Journal of Studies on Alcohol, 56, 681–694.CrossRefPubMed

Ostroff, C. (1993). Comparing correlations based on individual-level and aggregated data. Journal of Applied Psychology, 78, 569–582.CrossRef

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. New York: Springer-Verlag.CrossRef

Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209–233.CrossRefPubMed

Raudenbush, S. W. (2009). Adaptive centering with random effects: An alternative to the fixed effects model for studying time-varying treatments in school settings. Education, 4, 468–491.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage.

Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357.CrossRef

Sampson, R. J. (2003). The neighborhood context of well-being. Perspectives in Biology and Medicine, 46(3), S53–S64.CrossRefPubMed

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924.CrossRefPubMed

Scherbaum, C. A., & Ferreter, J. M. (2009). Estimating statistical power and required sample sizes for organizational research using multilevel modeling. Organizational Research Methods, 12(2), 347–367.CrossRef

Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks: Sage.

Verbeek, M. (2008). A guide to modern econometrics. John Wiley & Sons.

Wang, X.-H., & Howell, J. M. (2010). Exploring the dual-level effects of transformational leadership on followers. Journal of Applied Psychology, 95(6), 1134–1144.CrossRefPubMed

Whitener, E. M. (2001). Do “high commitment” human resource practices affect employee commitment? A cross-level analysis using hierarchical linear modeling. Journal of Management, 27(5), 515–535.

Wolfinger, R. D. (1997). An example of using mixed models and PROC MIXED for longitudinal data. Journal of Biopharmaceutical Statistics, 7(4), 481–500.CrossRefPubMed

Titel: Back to Basics with Mixed-Effects Models: Nine Take-Away Points
verfasst von: Paul D. Bliese
Mark A. Maltarich
Jonathan L. Hendricks
Publikationsdatum: 05.04.2017
Verlag: Springer US
Erschienen in: Journal of Business and Psychology / Ausgabe 1/2018
Print ISSN: 0889-3268
Elektronische ISSN: 1573-353X
DOI: https://doi.org/10.1007/s10869-017-9491-z

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Abstract

Purpose

Approach

Findings

Implications

Originality/Value

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