A study of self -selection: Factors affecting the decision to be part of a living -learning community.
Danielson, Cherry Linnea
2005
Abstract
Living-learning communities on college campuses offer an academically linked residential experience for students. Administrators place in these communities their hopes for improved retention, institutional identification and socialization, and ultimately, academic success for participants. However, discovering the effectiveness of these communities can prove troublesome. Research on living-learning communities has been limited by the possibility that students participating in these programs share some common characteristics, thus rendering the results tenuous. More evidence is necessary to either refute or substantiate self-selection as a factor in the effectiveness of living-learning communities. Thus, the purpose of this study was to discover whether there is any empirical basis for the charge that self-selection influences the membership of living-learning communities. University of Michigan students who responded to the Freshmen Survey from the Cooperative Institutional Research Project (CIRP) provided pre-college data for this study. The sample included both living-learning participants and non-participants. The analyses tested for differences between these two populations of students, as well as differences among the eight living-learning communities themselves. The research model employed three categories of independent variables: student background characteristics, student types, and intended academic fields. Astin's (1993) student typology provides a longitudinally reliable template for measuring types of students using CIRP data. Findings show that variables from all three categories distinguished living-learning participants from non-participants. First, the most prominent difference in the two populations emerged from the student-type analyses. The proportion of students who have higher social activism and artist-type scores is significantly greater in the living-learning population. Second, except for the female students in the Women in Science and Engineering program, engineering students are much more likely to be found in the non-living-learning population. Lastly, the influence of the level of education of both parents is significantly higher among living-learning participants. Although the variables show significant differences in the two populations, the model does not effectively predict membership in living-learning communities. Similarly, the variables effectively differentiate among the living-learning communities. However, the accuracy of predicting participation in the communities varies widely. The three models with the best predictability contained very different variables, pointing to the distinguishing characteristics of the communities.Subjects
Affecting Alexander W. Astin Astin, Alexander W. Be Decision Factors Living-learning Community Part Self-selection Student Typology Study
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