Efforts to develop common higher education data standards are expanding both within and across countries. The institutional research (IR) community plays a critical role in assuring that these efforts capture the diverse manifestations of the postsecondary and tertiary education systems and promote responsible comparisons. This chapter provides…

The purpose of this chapter is to discuss the conceptual, methodological, and statistical challenges in selecting appropriate peer institutions for comparative purposes. The authors' approach embraces a Western scientific tradition that physical things and phenomena can be "reduced into a set of key variables--identifiable parts--that make key…

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,…

The Andrew W. Mellon Foundation funded the College Sports Project (CSP) in 2003 to focus on academics and athletics within Division III. The CSP includes a longitudinal data collection effort comparing athletes to nonathletes at over 80 Division III institutions in the United States. This chapter draws on the CSP to highlight the challenges small…

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)

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 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)

Stresses the importance of common understandings of cost definitions and data collection in order to create reliable databases with optimal utility for inter-institutional analysis. Examines definitions of common expenditure categories, discusses cost-accumulation rules governing financial reporting, and explains differences between direct costs…

Since cost study data have their greatest impact when viewed within the context of institutional peers, this paper provides sources and strategies for the most effective use of comparative expenditure data. Considers comparisons at both the institutional and program level, as well as political considerations in using comparative data. Most…

Success in exchanging data with other institutions depends on the purpose of the exchange, the complexity of the data involved, the effort required to participate, and the frequency with which responses are requested. Common sense, diplomacy, and selling the responding institution on the benefits of participating assure a successful exchange. (MLW)

Setting up a successful interinstitutional data-sharing project involves commonsense guidelines: be clear about what is wanted, take advantage of what already exists, use appropriate technology, staff adequately, recognize institutional differences, and concentrate on achieving insight rather than precision. (Author/MSE)

The future of faculty development programs will benefit most from a systematic assessment of program goals, a factor often ignored or taken for granted while program process and product are evaluated. (MSE)

Comparative data are relatively easy to obtain, but effective use of the information requires careful planning and may produce unanticipated results. (MSE)

The issues surrounding faculty reduction will require increasing sophistication in planning and budgeting. The institutional research professional must play a central role, including analysis of data on (1) instructional production by department, (2) faculty profiles, (3) cost variations by program and instructional level, and (4) financing of…

The costs to colleges and universities of establishing a data-sharing exchange are examined, and it is argued that in most cases, the financial costs are minimal when compared to the potential opportunity cost of making decisions without access to good comparative information. (Author/MSE)