Is Employee Turnover Related to Higher Education Institutional Performance? An Empirical Analysis
By: Randy Glazer
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: Employee turnover continues to be discussed as an outcome in Human Resources (HR), but comparatively few studies have examined the relationship between turnover as the independent variable and institutional outcomes. Although the call to HR practitioners has often been made over the past 20 years regarding the importance of tying HR programs and measures to institutional goals, there has been limited reporting of such initiatives among higher education institutions, which typically focus on student outcomes equally or more prominently than financial outcomes. While the HR Analytics field has been growing and there is a robust community of academics involved in data analysis of organizations, the field in Higher Education is still in its development stages. The purpose of this cross-sectional study was to test whether employee turnover in various iterations can be a statistically significant predictor of (a) student completion rate, and (b) aggregate organizational external research funding.

The study also tested whether such measures can be established by strictly using current institutional “legacy” data, as opposed to gathering any data that are not currently collected or available from normal business operations. Reviewing these questions through a theoretical framework of general systems theory and using student data, employee data, and financial data of a single higher education institution, this study was designed for the HR practitioner to review the use of models to predict whether employee turnover statistics are meaningful in explaining operational goals of an organization that are not financial. Six years of data (2006-2011) from a single higher education institution were used in the analysis. The sample subject group comprised students enrolled in various Master’s degree programs across 10 academic departments at the University. The analysis was conducted using ordinary least squares regression and via binomial logistic regression. Other forms of analysis were considered as part of the review.

Overall, findings suggested that employee turnover (operationalized as employee instability rate) is statistically significant in models that predict student completion rate. Furthermore, employee turnover is statistically significant in models that predict the University’s external research funding levels (operationalized as indirect cost recovery statistics reported annually).
Notes: Department: Interdisciplinary Studies in Education.

Thesis advisor: John P. Allegrante.

Thesis advisor: Young-Sun Lee.

Thesis (Ed.D.)--Teachers College, Columbia University, 2019.