The NSSE National Data Project is an element of ongoing engagement research and implementation practice in Canada. It has two primary objectives. The first is the construction of detailed NSSE reports (items means and frequencies, benchmarks and learning scales) at the academic program- and student subgroup-level for individual institutions rather than for peer groups. The second is the development of statistical (regression) models to measure the relative contribution to engagement variation of student characteristics, program mix and institutional character at both the student record- and institution-level. Both objectives address the broader goals of providing greater focus to engagement improvement efforts, identifying clusters of promising practices and best engagement results, supporting improved interpretation and use of institutional engagement scores, and informing the development of institutional accountability procedures and metrics.
The core of the project is a record-level data file containing the approximately 69,000 2008 or 2009 NSSE responses and additional student records system data representing 44 Canadian universities. Student responses were classified into 10 general academic programs (e.g., Social Sciences) and over 75 specific academic programs (e.g., History, Biology) and over 30 student subgroups (including first generation, First Nations and international).
The detailed NSSE reports indicate a considerable level of variation in student characteristics and program mix across Canadian universities; large differences in engagement item scores and benchmarks across academic program clusters and specific programs within clusters, and across student subgroups; and wide engagement variability across institutions of differing size. A summary of the results from these detailed reports is presented below. The program- and student subgroup-level NSSE reports provide a more focused basis for comparing engagement university by university, and strongly suggest that institution-level engagement comparisons should take account of student, program and size variation and should not be presented without context in ranked format.
The regression models provide a more formal basis for identifying and quantifying the role of student, program and size variation in engagement, and permit a number of conclusions. First, student characteristics, program mix and institutional character all contribute to a comprehensive statistical explanation of engagement variation. Second, the wide variation in institutional engagement scores is reduced considerably when student characteristics, program mix and institutional size are controlled. Third, each engagement benchmark requires a distinct statistical explanation: factors important to one benchmark are often quite different from those important to another. Fourth, Francophone and Anglophone institutions differ with respect to certain key engagement dynamics. And finally, the models suggest several approaches to defining the institutional contribution to engagement and the scope of institutional potential to
modify engagement level.