Who Are Our Students? Cluster Analysis as a Tool for Understanding Community College Student Populations by ProQuest

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This study showcases cluster analysis as a useful tool for those who seek to understand the types of students their community colleges serve. Although educational goal, academic program, and demographics are often used as descriptive variables, it is unclear which, if any, of these are the best way to classify community college students. Cluster analyses at two points in time each identified nine distinct clusters in our data. These clusters had a 67% overlap, indicating method validity and consistency over time. The differences between the two years could be due to differences in enrollment over time, but are likely a result of changes in questions asked of the students from year one to year two. The results of this study suggest the utility of cluster analysis as a way for stakeholders to describe and classify their students. Furthermore, once established, cluster membership can be used to predict later success, usage of student services, and other important outcomes. When administrators understand these differences among students, they can better serve all groups of students and identify ways to market to them and address their unique needs. [PUBLICATION ABSTRACT]

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Who Are Our Students? Cluster Analysis as a Tool for
Understanding Community College Student Populations
Bridget V. Ammon                                 Jamillah Bowman                                     Roger Mourad
University of Michigan                           Stanford University                                 Washtenaw Community College


This study showcases cluster analysis as a useful tool for those who seek to understand the types of students their community colleges
serve. Although educational goal, academic program, and demographics are often used as descriptive variables, it is unclear which, if any,
of these are the best way to classify community college students. Cluster analyses at two
								
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