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…

Although institutional researchers devote a great deal of time mining and using student data to fulfill mandatory federal and state reports and analyze institutional effectiveness, financial and personnel information is also necessary for such endeavors. In this article, the authors discuss the challenges that arise from extracting data from…

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.

Provides information regarding the National Educational Longitudinal Study database that was used for many of the studies in this volume. Provides relevant background and instructions so that others may use this resource from the National Center for Education Statistics. (Author/EV)

The widespread use of comparative data can lead to problems on a campus unless the data are systematically incorporated in the institutional planning cycle. A model has been developed to alleviate a number of problems and increase the power of comparison as a management tool. (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 ultimate goal of the institutional researcher is not always to test a research hypothesis, but more often simply to find an appropriate model to gain an understanding of the underlying characteristics and interrelationships of the data. Exploratory data analysis provides a means of accomplishing this. (Author)

Examines the theoretical basis for data mining, one of the essential knowledge management processes, and uses a case study to describe its application and impact. (EV)

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)

The data collected in higher education research are not always quantitative or continuous. Statistical methods using the log-linear model provide the institutional researcher with a powerful set of tools for addressing research questions when data are categorical. (Author/MSE)

Path analysis and linear structural relations (LISREL) provide the institutional researcher with some extremely powerful statistical tools. However, they must be applied and interpreted carefully with a full understanding of their limitations and the statistical assumptions on which they are based. (Author)

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.)

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.)

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.)