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…

As a tour de force, data mining is likely to gain wider use in the next few years. To facilitate this transition, we make several recommendations addressed to both institutional research professionals and the Association of Institutional Research.

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)

This chapter explores how multiple approaches including data mining can help examine how the lengths of student enrollment are associated with varying numbers of advanced placement units. (Contains 3 tables and 5 figures.)

The authors examine the college conditions that contribute to character development, using data from the National Survey of Student Engagement (NSSE). (Contains 4 tables and 5 figures.)

Institutional researchers frequently use national datasets such as those provided by the National Center for Education Statistics (NCES). The authors of this chapter explore the adjustments required when analyzing NCES data collected using complex sample designs. (Contains 8 tables.)

Data-mining technology's predictive modeling was applied to enhance the prediction of enrollment behaviors of admitted applicants at a large state university. (Contains 4 tables and 6 figures.)

Data on which to base interinstitutional comparisons can be obtained directly from institutions or from a variety of secondary and tertiary sources such as national surveys, statistical publications, newsletters, directories, and organizations. Lists of these sources are included. (MSE)

Focusing on student retention and time to degree completion, this study illustrates how institutional researchers may benefit from the power of predictive analyses associated with data-mining tools. The following are appended: (1) Predictors; and (2) Variable Definitions. (Contains 5 figures.)