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Hom, Willard C. – Journal of Applied Research in the Community College, 2010
Analysts of institutional performance have occasionally used a peer grouping approach in which they compared institutions only to other institutions with similar characteristics. Because analysts historically have used cluster analysis to define peer groups (i.e., the group of comparable institutions), the author proposes and demonstrates with…
Descriptors: Multivariate Analysis, Robustness (Statistics), Classification, Comparative Analysis
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Bahr, Peter Riley; Bielby, Rob; House, Emily – New Directions for Institutional Research, 2011
One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…
Descriptors: College Students, Classification, Multivariate Analysis, Community Colleges
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Saenz, Victor B.; Hatch, Deryl; Bukoski, Beth E.; Kim, Suyun; Lee, Kye-hyoung; Valdez, Patrick – Community College Review, 2011
This study employs survey data from the Center for Community College Student Engagement to examine the similarities and differences that exist across student-level domains in terms of student engagement in community colleges. In total, the sample used in the analysis pools data from 663 community colleges and includes more than 320,000 students.…
Descriptors: Learner Engagement, Community Colleges, Classification, Multivariate Analysis
Zeidenberg, Matthew; Scott, Marc – Community College Research Center, Columbia University, 2011
Community college students typically have access to a large selection of courses and programs, and therefore the student transcripts at any one college or college system tend to be very diverse. As a result, it is difficult for faculty, administrators, and researchers to understand the course-taking patterns of students in order to determine what…
Descriptors: College Students, Technical Institutes, Community Colleges, Course Selection (Students)
Luan, Jing – Online Submission, 2004
This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…
Descriptors: Educational Strategies, Evaluation Methods, Student Behavior, College Students
Crosta, Peter M.; Leinbach, Timothy; Jenkins, Davis – Community College Research Center, Columbia University, 2006
Colleges and state higher education agencies too often lack accurate information about the socioeconomic status (SES) of their students. This paper describes the methodology that Community College Research Center (CCRC) researchers used to estimate the SES of individual students in the Washington State community and technical college system using…
Descriptors: Socioeconomic Status, Community Characteristics, Census Figures, Two Year College Students