As we become increasingly acquainted with the rich opportunities that analytics systems can provide, there is a commensurate need to consider the extent to which analytics tools are effectively integrated, with proper training, into the day-to-day functioning of higher education professionals. This chapter explores the extent to which predictive…

This chapter provides examples of innovative methods and tools to collect, analyze, and report both quantitative and qualitative data in student affairs assessment.

This chapter provides institutional researchers a foundation to understand workforce data and how they can be accessed and used within an institutional research operating culture. Specific wage methodologies, earnings reporting, and future directions for using wage data are provided.

The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…

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…

There is a steady line of research to understand faculty diversity and its impact on the work life quality of faculty and learning outcomes of students in the U.S. higher education systems. What makes this volume unique is that, rather than treating diversity as a static and simplistic concept, the chapter authors presented information to show…

Most assessment arguments are about measurement. When is it better to use direct versus indirect measures of student learning? Is one standardized test of critical thinking better than another? Is applying rubrics to student work better than using standardized tests? How valid are self-reported measures of learning? Although these arguments are…

The sophisticated analytical techniques available to institutional researchers give them an array of procedures to estimate a causal effect using observational data. But as many quantitative researchers have discovered, access to a wider selection of statistical tools does not necessarily ensure construction of a better analytical model. Moreover,…

Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is…

Assessment research is at its best when it packages research results and data so that they can be digested by multiple audiences. Too many assessment researchers spend all their efforts planning and executing the research project with little attention to closing the loop at the end. If assessment findings are not communicated effectively, the…

In this article, the author argues that current strategies to study and understand students' identities fall short of fully capturing their complexity. A multi-dimensional perspective and a mixed-methods approach can reveal nuance that is missed with current approaches. The author offers an illustration of how mixed-methods research can promote a…

This chapter summarizes ten selected issues and common problems that arise in most assessment research projects. These include: (1) the uses of grades in assessment; (2) institutional review boards; (3) research design as a compromise; (4) standardized testing; (5) self-reported measures; (6) missing data; (7) weighting data; (8) conditional…

The author introduces her critical and feminist perspective, elaborates on her critical approach, and discusses how this influenced the development of her research questions, methodology, data analysis, interpretation, and presentation of findings.

This case study documents a successful application of data-mining techniques in enrollment management through a partnership between the admissions office, a business administration master's-degree program, and the institutional research office at Willamette University (Salem, Oregon). (Contains 1 table and 3 figures.)

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)