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Faculty Scholarly Productivity Index


The Faculty Scholarly Productivity Index (FSPI), a product of Academic Analytics, is a metric designed to create benchmark standards for the measurement of academic and scholarly quality within and among United States research universities.

The index is based on a set of statistical algorithms developed by Lawrence B. Martin and Anthony Olejniczak. It measures the annual amount and impact of faculty scholarly work in several areas, including:

The FSPI analysis creates, by academic field of study, a statistical score and a ranking based on the cumulative scoring of a program's faculty using these quantitative measures compared against national standards within the particular discipline. Individual program scores can then be combined to demonstrate the quality of the scholarly work of the entire university. This information is gathered for over 230,000 faculty members representing 118 academic disciplines in roughly 7,300 Ph.D. programs throughout more than 350 universities in the United States.

Unlike other annual college and university rankings, e.g., the U.S. News & World Report annual survey, the FSPI focuses on research institutions as defined by the Carnegie Classification of Institutions of Higher Education. It draws on the approach used by the United States National Research Council (NRC), which publishes a ranking of U.S.-based graduate programs approximately every ten years, but focuses on providing a more frequently-gathered set of benchmark measurements that do not include the qualitative and subjective reputation assessments favored by the NRC and other ranking systems.

The system for evaluating university programs that forms the basis of the FSPI was developed by Lawrence Martin and Anthony Olejniczak of Stony Brook University. Martin had been studying, speaking, and writing about faculty scholarly productivity since 1995. During that period, a series of discipline-specific, per-capita regression models was created and tested to evaluate their accuracy and the feasibility of predicting the academic reputation of the faculty of doctoral programs.


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