Updated June 2025 by R. Eckardt
Overview of Multilevel Research
Eckardt, R., Yammarino, F. J., Dionne, S. D., & Spain, S. M. (2021). Multilevel methods and statistics: The next frontier. Organizational Research Methods, 24(2), 187-218.
Gully, S. M., & Phillips, J. M. (2019). On finding your level. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 11-38). Washington, DC: American Psychological Association.
Hackman, J. R. (2003). Learning more by crossing levels: Evidence from airplanes, hospitals, and orchestras. Journal of Organizational Behavior, 24(8), 905-922.
Humphrey, S. E., & LeBreton, J. M. (2019). The handbook of multilevel theory, measurement, and analysis. Washington, DC: American Psychological Association.
Klein, K. J., Dansereau, F. & Hall, R., J. 1994. Levels issues in theory development, data collection, and analysis. Academy of Management Review, 19, 195-229.
Klein, K. J. & Kozlowski, S. W. J. (eds.) 2000. Multilevel Theory, Research, and Methods in Organizations, San Francisco, CA: Jossey-Bass Publishers.
Klein, K. J. & Kozlowski, S. W. J. 2000. From micro to meso: Critical steps in conceptualizing and conducting multilevel research. Organizational Research Methods, 3, 211-236
Klein, K.J., Tosi, H., & Cannella, A.A. 1999. Multilevel theory building: Benefits, barriers, and new developments. Academy of Management Review, 24, 243-248.
Mathieu, J.E. & Chen, G. 2011. The etiology of the multilevel paradigm in management research. Journal of Management. 37, 610-641
Mathieu, J. E. & Luciano, M. M. in press. Multilevel emergence in work collectives. In S. E. Humphrey & J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement and Analysis.
Mossholder, K. W., & Bedeian, A. G. (1983). Cross-level inference and organizational research: Perspectives on interpretation and application. Academy of Management Review, 8(4), 547-558.
Rousseau, D. M. 1985. Issues of level in organizational research: Multi-level and cross-level perspectives. In Cummings, L. L. & Staw, B. M. (eds.) Research in Organizational Behavior. Greenwich, CT: JAI Press, Inc., 1-37.
Bridging Micro-Macro Research Areas
Aguinis, H., Boyd, B. K., Pierce, C. A., & Short, J. C. 2011. Walking new avenues in management research methods and theories: Bridging micro and macro domains. Journal of Management, 37(2), 395-403.
Bliese, P. D., Schepker, D. J., Essman, S. M., & Ployhart, R. E. (2020). Bridging methodological divides between macro-and micro-research: Endogeneity and methods for panel data. Journal of Management, 46(1), 70-99.
Buckley, M. R., Hamdani, M. R., Klotz, A. C., & Valcea, S. 2011. Into the great wide open: Bridging the micro-macro divide in the organizational sciences. In D. Bergh & D. J. Ketchen (eds.) Research Methodology in Strategy and Management, 6, 31-68.
Eckardt, R., Crocker, A., Ahn, Y., Floyd, S. W., Boyd, B. K., Hodgkinson, G. P., ... & Starbuck, W. H. (2019). Reflections on the micro–macro divide: Ideas from the trenches and moving forward. Strategic Organization, 17(3), 385-402.
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.
House, Rousseau, & Thomas-Hunt, 1995. The meso paradigm: A framework for the integration of micro and macro organizational behavior. Research in Organizational Behavior, 17, 71-114
Molloy, J. C., Ployhart, R. E., & Wright, P. M. 2011. The myth of the micro–macro divide: Bridging system-level and disciplinary divides. Journal of Management. 37, 1496-1518
Rousseau, D. M. (2011). Reinforcing the micro/macro bridge: Organizational thinking and pluralistic vehicles. Journal of Management, 37(2), 429-442.
Microfoundations Overview
Barney, J. A. Y., & Felin, T. (2013). What are microfoundations?. Academy of Management Perspectives, 27(2), 138-155.
Coleman, J. S. (1987). Microfoundations and macrosocial behavior. The micro-macro link, 153- 173.
Felin, T., & Foss, N. J. (2005). Strategic organization: A field in search of micro-foundations. Strategic Organization, 3(4), 441-455.
Felin, T., Foss, N. J., & Ployhart, R. E. 2015. The microfoundations movement in strategy and organization theory. Academy of Management Annals, 9, 575-632
Ployhart, R. E., & Hale, D. (2014). The Fascinating Psychological Microfoundations of Strategy and Competitive Advantage. Annual Review of Organizational Psychology & Organizational Behavior, 1(1), 145-172.
Ployhart, R. E., & Hendricks, J. L. (2019). The missing levels of microfoundations: A call for bottom-up theory and methods. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 141-162). Washington, DC: American Psychological Association.
Multilevel Measurement / Data Aggregation
Beal, D.J. 2015. State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2: 383-407.
Biemann, T. & Kearney, E. Size does matter: How varying group sizes in a sample affect the most common measures of group diversity. Organizational Research Methods, 13, 582-599
Bliese, P. D. 2000. Within-group agreement, non-independence, and reliability: Implications for data aggregation and analyses. In K. J. Klein and S. W. J. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations: 349-381.
Bliese, P. D., Maltarich, M. A., Hendricks, J. L., Hofmann, D. A., & Adler, A. B. (2019). Improving the measurement of group-level constructs by optimizing between-group differentiation. Journal of Applied Psychology, 104(2), 293.
Chaffin, D., Heidl, R., Hollenbeck, J. R., Howe, M., Yu, A., Voorhees, C., & Calantone, R. 2017. The promise and perils of wearable sensors in organizational research. Organizational Research Methods, 20: 3-31.
Chan, D. 1998. Functional relations among constructs in the same content domain at different levels of analysis: A typology of composition models. Journal of Applied Psychology, 83, 234-246
Chan, D. (2019). Team-level constructs. Annual Review of Organizational Psychology and Organizational Behavior, 6, 325-348.
Chen, G., Mathieu, J., & Bliese, P. (2004). A framework for conducting multi-level construct validation. In F. J. Yammarino & F. Dansereau (Eds.), Multi-level issues in organizational behavior and processes (Vol. 3 of Research in Multi-Level Issues; pp. 273-303). Oxford, UK: JAI.
Croon, M. A. & van Veldhoven, M. J. P. M. 2007. Predicting group-level outcome variables from variables measured at the individual level: A latent variable multilevel model. Psychological Methods, 12, 45-57.
Fioretti, G. 2012 Agent-based simulation models in organization science. Organizational Research Methods, 16: 227-242.
Harrison, D. A. & Klein, K. J. 2007. What’s the difference? Diversity constructs as separation, variety, or disparity in organizations. Academy of Management Review, 32, 1199-1228.
Jebb, A. T., Tay, L., Ng, V., & Woo, S. (2019). Construct validation in multilevel studies. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 253-278). Washington, DC: American Psychological Association.
Emergence / Dynamics / Bottom-Up Effects
Ablowitz, R. (1939). The theory of emergence. Philosophy of Science, 6(1), 1-16.
Acton, B. P., Braun, M. T., & Foti, R. J. (2020). Built for unity: Assessing the impact of team composition on team cohesion trajectories. Journal of Business and Psychology, 35(6), 751-766.
Aiken, J. R., Hanges, P. J., & Chen, T. (2019). The means are the end: Complexity science in organizational research. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 115-140). Washington, DC: American Psychological Association.
Almaatouq, A., Alsobay, M., Yin, M., & Watts, D. J. (2021). Task complexity moderates group synergy. Proceedings of the National Academy of Sciences, 118(36), e2101062118.
Ballard, T., Palada, H., Griffin, M., & Neal, A. (2021). An integrated approach to testing dynamic, multilevel theory: Using computational models to connect theory, model, and data. Organizational Research Methods, 24(2), 251-284.
Bell, S. T., Brown, S. G., Colaneri, A., & Outland, N. (2018). Team composition and the ABCs of teamwork. American Psychologist, 73(4), 349.
Bliese, P. D., Kautz, J., & Lang, J. W. (2020). Discontinuous growth models: Illustrations, recommendations, and an R function for generating the design matrix. In Handbook on the temporal dynamics of organizational behavior (pp. 319-350). Edward Elgar Publishing.
Bliese, P. D., & Lang, J. W. (2016). Understanding relative and absolute change in discontinuous growth models: Coding alternatives and implications for hypothesis testing. Organizational Research Methods, 19(4), 562-592.
Bliese, P. D. & Ployhart, R. E. Growth modeling using random coefficient models: Model building, testing, and illustrations. Organizational Research Methods, 5: 362-387.
Corning, P. A. (2012). The re-emergence of emergence, and the causal role of synergy in emergent evolution. Synthese, 185(2), 295-317.
Cronin, M. A., & Vancouver, J. B. (2019). The only constant is change: Expanding theory by incorporating dynamic properties into one’s models. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 89-114). Washington, DC: American Psychological Association.
Eckardt, R., Crocker, A., & Tsai, C. Y. (2021). Clarifying and empirically assessing the concept of human capital resource emergence. International Journal of Human Resource Management, 32(2), 279-306.
Eckardt, R., & Jiang, K. (2019). Human capital resource emergence: Theoretical and methodological clarifications and a path forward. In A. Nyberg & T. P. Moliterno (Eds.), Handbook of research on strategic human capital resources (pp. 77-112). Cheltenham, UK: Edward Elgar.
Emich, K. J., McCourt, M., Lu, L., Ferguson, A., & Peterson, R. (2024). Team composition revisited: Expanding the team member attribute alignment approach to consider patterns of more than two attributes. Organizational Research Methods, 27(2), 329-348.
Fulmer, C. A., & Ostroff, C. (2016). Convergence and emergence in organizations: An integrative framework and review. Journal of Organizational Behavior, 37, S122-S145.
Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49-72.
Grand, J. A., et al. 2016. The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101, 1353-1385
Griffin, M.A. 1997. Interactions between individuals and situations: Using HLM procedures to estimate reciprocal relationships. Journal of Management, 23, 759-773.
Kozlowski, S. W. J. & Chao, G. T. 2018. Unpacking team process dynamics and emergent phenomena: Challenges, conceptual advances, and innovative methods. American Psychologist, 73: 576-592.
Kozlowski, S. W. J., & Klein, K. J. 2000. A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Ed.), Multilevel Theory, Research, and Methods in Organizations: 3-90.
Kozlowski, S. W. J., et al. 2013. Advancing multilevel research design: Capturing the dynamics of emergence. Organizational Research Methods, 16, 581 – 615.
Kozlowski, S. W. J., et al. 2014. Capturing the multilevel dynamics of emergence: Computational modeling, simulation, and virtual experimentation. Organizational Psychology Review, 6, 3 – 33.
Kozlowski, S. W. J., et al. 2015. Team dynamics: using "big data" to advance the science of team effectiveness. In S. Tonidandel, E. King, & J. Cortina (Eds.), Big Data at Work: The Data Science Revolution and Organizational Psychology: 272-309.
Lang, J. W., & Bliese, P. D. (2019). A temporal perspective on emergence: Using three-level mixed-effects models to track consensus emergence in groups. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 519-540). Washington, DC: American Psychological Association.
Lang, J. W., Bliese, P. D., & Adler, A. B. (2019). Opening the black box: A multilevel framework for studying group processes. Advances in Methods and Practices in Psychological Science, 2(3), 271-287.
Lang, J. W., Bliese, P. D., & Runge, J. M. (2021). Detecting consensus emergence in organizational multilevel data: Power simulations. Organizational Research Methods, 24(2), 319-341.
Lang, J. W., Bliese, P. D., & de Voogt, A. (2018). Modeling consensus emergence in groups using longitudinal multilevel methods. Personnel Psychology, 71(2), 255-281.
Lavoie, R., Baer, M., & Rouse, E. D. (2024). Group flow: A theory of group member interactions in the moment and over time. Academy of Management Review
Ludtke et al., 2008. The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 3, 203-229.
Morgeson, F. P., & Hofmann, D. A. (1999). The structure and function of collective constructs: Implications for multilevel research and theory development. Academy of Management Review, 24(2), 249-265.
Zhou, L., Wang, M., & Zhang, Z. (2021). Intensive longitudinal data analyses with dynamic structural equation modeling. Organizational Research Methods, 24(2), 219-250.
Sayama, H. 2015. Introduction to the Modeling and Analysis of Complex Systems. Geneseo, NY: Open SUNY
Waller, M. J., Okhuysen, G.O., & Saghafian, M. 2016. Conceptualizing emergent states: A strategy to advance the study of group dynamics. Academy of Management Annals, 10, 561 – 598
Wang, M., Zhou, L., Zhang, Z. 2016. Dynamic modeling. Annual Review of Organizational Psychology and Organizational Behavior, 3: 241-266.
Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686-688.
Multilevel Statistical Techniques
Aguinis, H., Gottfredson, R. K. & Culpepper, S. A. 2013. Best practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39, 1490-1528.
Aguinis, H., & Culpepper, S. A. 2015. An expanded decision-making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods, 18, 155-176
Antonakis, J., Bastardoz, N., & Rönkkö, M. (2021). On ignoring the random effects assumption in multilevel models: Review, critique, and recommendations. Organizational Research Methods, 24(2), 443-483.
Bliese, P. D., Maltarich, M. A., Hendricks, J. L. 2018. Back to basics with mixed-effects models: Nine take-away points. Journal of Business and Psychology, 33, 1-23.
Dansereau, F. & Yammarino, F. J. 2000. Within and between analysis: The variant paradigm as an underlying approach to theory building and testing. In K. J. Klein & S. W. J. Kozlowski (Ed.), Multilevel Theory, Research, and Methods in Organizations: 3-90.
Enders, C. K. & Tofighi, D. 2007. Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 2, 121-138
Gelman, A. & Hill, J. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models, New York, NY, Cambridge University Press.
Gonzalez-Roma, V. & Hernandez, A. 2017. Multilevel modeling: Research-based lessons for substantive researchers. Annual Review of Organizational Psychology and Organizational Behavior, 4, 183-210.
Han, 2005. Crossover linear modeling: Combining multilevel heterogeneities in crossover relationships. Organizational Research Methods, 8, 290 - 316.
Hofmann, D. A. 1997. An overview of the logic and rationale of hierarchical linear models. Journal of Management, 23, 723-744.
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.
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: 292-310.
Lester, H. F., Cullen-Lester, K. L., & Walters, R. W. (2021). From nuisance to novel research questions: Using multilevel models to predict heterogeneous variances. Organizational Research Methods, 24(2), 342-388.
McNeish, D. (2021). Specifying location-scale models for heterogeneous variances as multilevel SEMs. Organizational Research Methods, 24(3), 630-653.
McNeish, D., & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods.
McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22(1), 114.
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31(4), 437-448.
Raudenbush, S. W. & Bryk, A. S. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods, Thousand Oaks, California, SAGE Publications, Inc.
Short, J. C., Ketchen, D. J., Bennett, N. & du Toit, M. 2006. An examination of firm, industry, and time effects on performance using random coefficients modeling. Organizational Research Methods, 9, 259-284.
Vandenberg, R. J., & Richardson, H. A. (2019). A primer on multilevel structural modeling: User-friendly guidelines. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 449-472). Washington, DC: American Psychological Association.
Williams, L. J., Vandenberg, R. J., & Edwards, J. R. (2009). Structural equation modeling in management research: A guide for improved analysis. Academy of Management Annals, 3(1), 543-604.
Multilevel Mediation
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209-233.
Zhang, Z., Zyphur, M. J. & Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695-719.
Zyphur, M. J., Zhang, Z., Preacher, K. J., & Bird, L. J. (2019). Moderated mediation in multilevel structural equation models: Decomposing effects of race on math achievement within versus between high schools in the United States. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 473-494). Washington, DC: American Psychological Association.
Multilevel Networks
Amati, V., Lomi, A., Mascia, D., & Pallotti, F. (2021). The co-evolution of organizational and network structure: The role of multilevel mixing and closure mechanisms. Organizational Research Methods, 24(2), 285-318.
Brass, D. J., & Borgatti, S. P. (2019). Multilevel thoughts on social networks. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 187-200). Washington, DC: American Psychological Association.
Kim, J. Y., Howard, M., Pahnke, E. C., & Boeker, W. 2016. Understanding network formation in strategy research: Exponential random graph models. Strategic Management Journal, 37, 22-44.
Lomi, A., Robins, G., & Tranmer, M. 2016. Introduction to multilevel social networks. Social Networks, 44, 266-268.
Lusher, D., Koskinen, J. & Robins, G. 2013. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Maupin, C. K., Mohan, G., Choudhury, A., Deepak, P., & Jin, F. (2024). Network-based approaches to leadership: An organizing framework, review, and recommendations. Leadership Quarterly, 35(1), 101753.
Moliterno, T. P. & Mahony, D. M. 2011. Network theory of organization: A multilevel approach. Journal of Management, 37, 443-467.
Paruchuri, S., Goossen, M. C., & Phelps, C. C. (2019). Conceptual foundations of multilevel social networks. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 201-222). Washington, DC: American Psychological Association.
Piepenbrink, A. & Gau, A. S. 2013. Methodological advances in the analysis of bipartite networks: An illustration using board interlocks in Indian firms. Organizational Research Methods, 16, 474 - 496.
Quintane, E., Conaldi, G., Tonellato, M., & Lomi, A. 2014. Modeling relational events: A case study on an open source software project. Organizational Research Methods, 17, 23-50.
Snijders T. A. 2016. The multiple flavours of multilevel issues for networks. In Lazega E., Snijders T. (eds) Multilevel Network Analysis for the Social Sciences.
Snijders, T. A. & Steglich, C. A. 2015. Representing micro-macro linkages by actor-based dynamic network models. Sociological Methods & Research, 44, 222-271.
Snijders, T. A., Van de Bunt, G. G., & Steglich, C. E. 2010. Introduction to stochastic actor-based models for network dynamics. Social networks, 32, 44-60.
Wang, P., Robins, G., Pattison, P., & Lazega, E. 2013. Exponential random graph models for multilevel networks. Social Networks, 35, 96-115.
Wang, P., Robins, G., Pattison, P., & Lazega, E. 2016. Social selection models for multilevel networks. Social Networks, 44, 346-362.
Zappa, P. & Lomi, A. 2015. The analysis of multilevel networks in organizations: Models and empirical tests. Organizational Research Methods, 18, 542-569.
Dyads
Gooty, J. & Yammarino, F. J. 2011. Dyads in organizational research: Conceptual issues and multilevel analyses. Organizational Research Methods, 14, 456 - 483.
Kim, J., Yammarino, F. J., Dionne, S. D., Eckardt, R., Cheong, M., Tsai, C. Y., ... & Park, J. W. (2020). State-of-the-science review of leader-follower dyads research. The Leadership Quarterly, 31(1), 101306.
Kenny, D., Kashy, D. A., & Cook, W. L. 2006. Dyadic Data Analysis. New York: Wiley.
Knight, A. P., & Humphrey, S. E. (2019). Dyadic data analysis. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 423-448). Washington, DC: American Psychological Association.
Krasikova, D. V. & LeBreton, J. M. 2012. Just the two of us: Misalignment of theory and methods in examining dyadic phenomena. Journal of Applied Psychology, 97: 739-757.
Liden, R. C., Anand, S., & Vidyarthi, P. (2016). Dyadic relationships. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 139-166.
Rouse, E. D. (2020). Where you end and I begin: Understanding intimate co-creation. Academy of Management Review, 45(1), 181-204.
Statistical Power in Multilevel Studies
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.
Scherbaum, C. A., & Pesner, E. (2019). Power analysis for multilevel research. In S. Humphrey & J. LeBreton (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 329-352). Washington, DC: American Psychological Association.