A qualitative description investigation of U.S. higher education quantitative datasets

Abstract

Currently, the U.S. system of higher education is almost exclusively evaluated by quantitative data based on traditional student trajectories and university structured programs. This could be problematic for community colleges and post-traditional students, who are a growing population at all institutions. Therefore, we conducted a pilot, qualitative description analysis of three U.S. quantitative national datasets to assess their accuracy and identify factors that influence classifications. We interviewed individuals (n=13) who would qualitatively be considered success stories, specifically individuals who attended community colleges during their undergraduate studies and ultimately high ranking graduate programs, to gather information about their educational timelines. In some cases, the datasets would classify these individuals as completers but not always. Participants would be classified as non-completers for two major reasons: transfer prior to Associate degree completion and limitations with prescribed timelines. The latter is complicated by the perceived freedom of the open door policy at community colleges. The results from this study indicate a need to modify existing quantitative metrics to purposefully incorporate community colleges and their students, and the findings reinforce the importance of qualitative research in higher education.