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Conducting multilevel analyses in medical education

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Abstract

A significant body of education literature has begun using multilevel statistical models to examine data that reside at multiple levels of analysis. In order to provide a primer for medical education researchers, the current work gives a brief overview of some issues associated with multilevel statistical modeling. To provide an example of this technique, we then present a multilevel analysis examining the relationship between two individual-level variables and the “cross-level” interaction between this relationship and a school-level variable. In offering this discussion and example of multilevel modeling, we hope to provide medical educators with a basic introduction to multilevel statistics, including the advantages of utilizing these techniques.

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Notes

  1. While this notation may be somewhat different than that employed in OLS regression, the importance of using HLM notation is threefold. First, this notation is ubiquitous in much literature dealing with multilevel modeling. Second, because this notation often is employed in statistical packages’ equation-based language, our use of this notation allows one to interpret relevant output and to recognize the correspondence between results appearing in software packages and those in research articles. Third, given that multilevel models may incorporate more than two levels, our using unique symbols for each level of analysis yields less confusion in interpreting results than would using the same symbol applied at multiple levels.

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Correspondence to Michael J. Zyphur.

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Zyphur, M.J., Kaplan, S.A., Islam, G. et al. Conducting multilevel analyses in medical education. Adv in Health Sci Educ 13, 571–582 (2008). https://doi.org/10.1007/s10459-007-9078-y

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