Evaluation of Faculty Performance of Higher Education Institution Using Principal Component Analysis

Education is one of the drivers of economy and the role of higher education institutions (HEIs) as knowledge contributors to the nation's economy is significant. Educational organisations being service organizations quality of service depends directly on the capability, commitment, and motivation of faculty who provide it and ensuring quality is a challenge for education managers. One method of ensuring quality is by assessing the performance of faculty and ranking them based on their performance against set standards-Academic Performance Indicators. Teachers of modern education system have to carry out multiple tasks- administrative, teaching, research, societal engagement, mentoring, extra-curricular activities and so on. Hence, setting standards for each of these activities and measuring them on the same yardstick may not yield desired results. This is especially true in multidisciplinary institutions wherein faculty have different tasks and roles as per their specialization and discipline. Therefore, conventional assessment criteria may not suffice the decision makers of educational institutions. Principal Component analysis (PCA) is a standard statistical technique that can be used to reduce the dimensionality of a data set by assessing the dimensional structure of a dataset (Dunteman, 1989) and reducing a large number of variables into a smaller set of linear combinations (components). PCA is a variable reduction method that can be used to reduce the multiple variables in the performance appraisal criteria and result in smaller dataset for further analysis. In this study, the faculty performance scores (API) as per UGC format were analysed using PCA and the most important components were identified contributing to the performance of faculty.