In my last post, I described the benefits of secondary data in formalized research and in practical application. I primarily focused on how the use of government data available through IPEDS facilitates peer comparisons by enabling faculty and academic leaders to compare institutions on key characteristics. A number of sources for identifying peer institutions exist beyond IPEDS including a network mapping tool from the Chronicle of Higher Education, Carnegie Classifications Look Up, and U.S. News & World Reports. Additionally, many tools, such as Gray Associates and Emsi, have been created from a marketing and program development perspective to provide valuable data to guide institutional program investment decisions. Where does one begin? First, it is important to recognize that each source has a unique method for gathering data and using that data to profile institutions. Acknowledge that the source is presenting one way of conceptualizing the criteria at hand, and these systems do not proclaim what is empirically best (McCormick & Mei, 2005).
Next, consider the purpose of why you are comparing institutions. In my example from last week, faculty were comparing institutions to identify peer programs to facilitate and peer comparison as part of an evaluation of program effectiveness. The sources you use may differ based on your purpose. Are you comparing and contrasting institutions for research purposes? If so, you may want to consider attributes such as level/degree levels offered, size of student population, and enrollment profile to ensure you are comparing data across similar institutions. From there, your sources and attributes will vary based on your research question. If researching culture, for example, the size of the institution, number of employees, proportion of tenure track faculty, mission, and type of control (e.g., for-profit, private, public) would provide valuable context for understanding the cultural differences between institutions. Finding sources with the desired data is key, and Carnegie Classifications would be a valuable source for many of these attributes. Are you comparing and contrasting for purposes of benchmarking and strategic planning? If so, you may want to consider attributes such as level, enrollment profile, and type of control if benchmarking against similar institutions (e.g., Carnegie Classifications would again be useful); however, when strategic planning, you may want to examine aspirational peers, which would enable you to conduct a gap analysis to see how your institution compares to one which you strive to be or surpass. Attributes such as graduation rate, satisfaction scores, licensure pass rates, and gainful employment may be more important to drive strategic planning.
Finally, when using the data from your selected sources, you will want to include relevant definitions and factors that are necessary for your audience to understand the data as you are using them. I’m a big fan of using footnotes to include these contextual definitions because they detract less from the narrative while providing the important information all readers need.
Through my current research, I am exploring organizational learning at colleges and universities. After compiling a list of all colleges and universities cited in the literature for having organizational learning models, I could use peer comparison tools and secondary data sources to explore institutions of higher education with organizational learning models and compare them to my own institution based on type of control, enrollment size, and employee counts. These factors may significantly impact an organization’s ability to implement organizational learning initiatives. A comparison of this type would be a solid base to understanding what models for organizational learning exist in what types of institutions. This would inform considerations as to how generalizable my model, if effective, might be to other institutions of differing types. An entire study could focus on analyzing the types of institutions with organizational learning models and the differing characteristics between the models and institutions. This could reveal areas in which certain characteristics are more well-suited for specific types of universities and colleges compared to others.
Reference
McCormick, A. & Mei, C. (2005). Rethinking and reframing the Carnegie Classification. Change, 51-57.
