Extension professionals often assess community needs to determine programs and target audiences. Data can be collected through surveys, focus group and individual interviews, meta-analysis, systematic observation, and other methods. Knowledge gaps are identified, and programs are designed to resolve the deficiencies. However, do Extension…

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.…

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third…

The media communicates the existence of two distinct types of college applicants: the frenzied, overachieving, anxious student who applies to many institutions, and the underprepared, less advantaged student whose parents are not at all familiar with the application process. The purpose of this study is to more realistically describe distinct…

This paper demonstrates a new application of cluster analysis to segment business school students according to their degree of satisfaction with various aspects of the academic program. The resulting clusters provide additional insight into drivers of student satisfaction that are not evident from analysis of the responses of the student body as a…

Introduction: The aim of this study is to gather knowledge about how different groups of Icelanders take advantage of information about health and lifestyle in their everyday life. Method: A random sample of 1,000 people was used in the study and data was gathered as a postal survey. Response rate was 50.8%. Analysis: K-means cluster analysis was…

DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…