Prediction of a school superintendent's tenure using regression and Bayesian analyses

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Virginia Polytechnic Institute and State University

A model was developed to incorporate the major forces impacting upon a school superintendent and the descriptors, stability measures, intentions and processes of those forces. Tenure was determined to be the best outcome measure, thus the model became a quantitative method for predicting tenure. A survey measuring characteristics of the community, School Board, and the superintendent was sent to superintendents nationwide who had left a superintendency between 1983 and 1985. Usable forms were returned by 835 persons.

The regression analysis was significant (p ≤ .0000) and accounted for 40% of the variance in superintendent tenure. In developing the equation, statistical applications included Mallows CP for subset selection, Rousseeuw’s Least Median of Squares for outlier diagnostics, and the PRESS statistic for validation.

The survey also included 24 hypothetical situations randomly selected out of a set of 290 items with four optional courses of action. The answers were weighted by the tenure groups of the superintendents. and the responses analyzed using a Bayesian joint probability formula. Predictions of the most probable tenure based on these items were accurate for only 18% of the superintendents.

Variables found to contribute significantly in every candidate equation included per pupil expenditure, recent board member defeat, years in the contract, use of a formal interview format, age, being in the same etlmic group as the community, intention to move to another superintendency, orienting new Board members, salary, enrollment, and Board stability. Variables which were significant in some equations were region of the country, state turnover rate, proportion of Board support, whether changes were expected, use of a regular written evaluation, community power structure, number of Board members, grade levels in the district, gender, and having worked in the same school district. Variables which did not contribute were per capita income, whether the board was elected or appointed, educational degree and type of community.