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)

Provides guidelines for building a consistent and reliable database of expenditures within an institution of higher education. Stresses the importance of involving key constituencies, including both data providers and data users, in decisions about building the data elements for such a database and the resulting campus cost studies. (DB)

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)

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)

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)

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)

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)

Technology can be an obstacle or an advantage to cooperative data sharing within and between colleges and universities. Taking a technology-oriented approach before the technology is ripe for general use can be counterproductive. Many technical difficulties can be overcome by encouraging consortium staff to promote new methods of information…

Five categories of student outcomes in graduate and professional education are identified: timely degree completion; knowledge of the discipline; preparedness for professional practice; preparation for research and inquiry; preparation for teaching. A framework for assessing each is offered and various methods for collecting data on student…

A discussion of methods for measuring college faculty workload first reviews course load analysis, the traditional method used to define, measure, and report faculty workload, then suggests some data alternatives and related issues faced by institutional research offices. Suggestions are offered for locating existing workload data, compiling a…

Models for college and university Interinstitutional data sharing are redefined by examining the organization and activities of existing data-sharing groups across 11 dimensions: data-sharing purpose; nature of data-sharing structures; definer of process; calendar; scope of surveys; participant characteristics; source of data-sharing initiative;…

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)