Historically, AACSB International has classified business school size categories by the number of full-time (FT) faculty reported on the annual Business School Questionnaire (BSQ), according to the following parameters: small (35 or fewer), medium (36-74), and large (75 or more). Since the recent release of the BSQ data from the 2016–17 iteration of the survey, however, we decided to explore some other potential parameters for size categorization, to see whether any significant differences would result. For the purpose of this experiment, we chose total overall enrollment, the numbers of full-time equivalent (FTE) faculty (the sum of the headcount of FT faculty and the reported FTE of part-time faculty), and student/faculty ratios for both FT and FTE faculty totals as new, alternate parameters.
Figure 1. Comparison of Size Categories, Standard vs. Alternate Parameters
As the data show, of these different ways that business schools can be categorized by size, some cause the categorization to differ more significantly than others from our standard classification scheme. For example, the alternate parameters for FTE faculty and for total enrollment each retain the majority of schools in each size category from the standard FT faculty parameters. By contrast, the differences from the standard parameters in the number of schools that fall into each size category for the alternate parameters for student/faculty ratios are far greater.
Notably, institutional control type also appears to have a significant effect on the proportion of schools that change size categories, regardless of which alternative parameters are used:
Table 1. Percent of Schools Remaining in Original Size Categories, by Control Type and Alternate Parameters
The figures in Table 1 indicate the proportion of schools in each set that did not change size category when alternate parameters were applied. Further, any change in size category can be in either direction. Depending on the alternate parameters used, there were some schools that shifted from large to small, and vice versa, and others that moved only one step up or down.
The numbers clearly indicate that the effect of alternate parameters is greater when applied to private institutions than when applied to public institutions, in that public institutions are less likely than private ones to change size category when alternate parameters are applied, regardless of which alternate parameter was used. Being cognizant of such variabilities can be useful for school administrators, particularly when selecting peer schools for benchmarking in DataDirect.
Look out for Part II of this series, which will delve into the effects of the alternate parameters in conjunction with the types of degrees offered by institutions.