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King, Joe P.; Hernandez, Jose M.; Lott, Joe L., II – New Directions for Institutional Research, 2012
Multilevel modeling (MLM) gives researchers the ability to make inferences about organizations where nesting factors will bias results and the assumption of independence is not tenable. This article provides an overview of the variety of data sources that lend themselves to conducting institutional research (IR). It not only serves as a repository…
Descriptors: Institutional Research, Computer Software, Data Analysis, Inferences
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Chen, Pu-Shih Daniel; Gonyea, Robert M.; Sarraf, Shimon A.; BrckaLorenz, Allison; Korkmaz, Ali; Lambert, Amber D.; Shoup, Rick; Williams, Julie M. – New Directions for Institutional Research, 2009
Colleges and universities in the United States are being challenged to assess student outcomes and the quality of programs and services. One of the more widely used sources of evidence is student engagement as measured by a cluster of student engagement surveys administered by the Center for Postsecondary Research at Indiana University. They…
Descriptors: Data Analysis, Data Interpretation, National Surveys, College Students
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Xu, Yonghong Jade; Ishitani, Terry T. – New Directions for Institutional Research, 2008
In recent years, rapid advancement has taken place in computing technology that allows institutional researchers to efficiently and effectively address data of increasing volume and structural complexity (Luan, 2002). In this chapter, the authors propose a new data analytical technique, Bayesian belief networks (BBN), to add to the toolbox for…
Descriptors: Institutional Research, Classification, Researchers, College Faculty
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Brinkman, Paul T.; Teeter, Deborah J. – New Directions for Institutional Research, 1987
Institutional comparison groups can be selected in several ways, depending on the comparison issue. The method chosen involves both technical and political considerations. (Author/MSE)
Descriptors: Comparative Analysis, Data Analysis, Data Interpretation, Higher Education