Abstract
Multiple-level (or mixed linear) modeling (MLM) can simultaneously test hypotheses at several levels of analysis (usually two or three), or control for confounding effects at one level while testing hypotheses at others. Advances in multi-level modeling allow increased precision in quantitative international business (IB) research, and open up new methodological and conceptual possibilities. However, they create new challenges, and they are still not frequently used in IB research. In this editorial we outline some key methodological issues for the uses of MLM in IB, including criteria, sample size, and measure equivalence issues. We then examine promising directions for future multilevel IB research considering comparative opportunities at nation, multiple-nation cluster, and within-nation region levels, including large multilevel databases. We also consider its promise for MNE research about semi-globalization, interorganizational effects across nations, clusters within nations, and teams and subsidiaries within MNEs.
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Notes
- 1.
Beside this Optimal Design software, other packages to determine the appropriate sample size based on power estimates in specific cases of MLM include PinT (http://www.stats.ox.ac.uk/~snijders/multilevel.htm) and RMASS (http://www.rmass.org/)
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Acknowledgements
The authors thank Shige Makino and Anand Swaminathan for contributions to an earlier version of this editorial; and Paul Bliese, Fons van de Vijver, the JIBS Editors and Area Editors, and three anonymous reviewers for their useful comments.
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Peterson, M.F., Arregle, JL., Martin, X. (2020). Multilevel Models in International Business Research. In: Eden, L., Nielsen, B.B., Verbeke, A. (eds) Research Methods in International Business. JIBS Special Collections. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-22113-3_23
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