Analyzing Faculty Workload, Performance, and Compensation

Catalog description

Develop research skills to analyze faculty workload and performance in teaching, research, outreach, and compensation. HIED 850 Analyzing Faculty Workload, Performance, and Compensation (3) This course provides researchers with an overview of faculty issues with the analytical skills and tools associated with analyzing faculty workload and performance in teaching, scholarship, and outreach. The course is designed for those entering careers in institutional research and planning, particularly those whose work supports the Provost, as well as for those whose work is related to faculty analysis and reporting in other higher education settings. Topics include an overview of needed local and existing national databases, measuring faculty workload, evaluating faculty research productivity, using student ratings of instruction, providing support for academic program reviews, conducting salary studies, addressing issues of equity/diversity, and assessing faculty satisfaction, turnover, and flow. Curricular goals: Upon completion of this course, students will be able to: - Understand concepts, methodologies, research practices, and information systems that support academic decision making in the Provost's Office. - Use NSOPF, NSF, IPEDS, HERI, and other national databases that collect faculty information. - Develop appropriate metrics to gauge faculty work in instruction, research, and service. - Understand the diversity of academic work-life and labor market issues at national and institutional levels. - Carryout at a basic level the major Institutional Research faculty-related analyses, including instructional analysis, research productivity, benchmarking, salary equity, and turnover projections. - Utilize SPSS software, make power-point presentations, and produce effective reports related to faculty issues. This course has established start and end dates and includes interaction with others throughout the course. The course is structured around learning units, each roughly corresponding to one week of a Penn State semester. Learning units are self-contained and built around a single theme or topic. Each contains an introduction, objectives, reading assignments, professor's content, and learning activities. While it is possible to accelerate or vary the reading and research schedule, the discussion components among peers should adhere roughly to the time frame (the week) within which each Unit is presented. Pre-requisite: Working knowledge of intermediate statistics such as OLS regression.