Date of Award

Winter 1991

Document Type


Degree Name

Doctor of Philosophy (PhD)


Urban Services - Urban Education

Committee Director

Jack E. Robinson

Committee Member

Maurice R. Berube

Committee Member

W. Randolph Nichols

Committee Member

Robert Lucking

Committee Member

Donald A. Myers


This research was conducted to determine a methodology for the early identification of potential dropouts in the Chesapeake Public School System. A review of research literature determined that many discriminating characteristics had been identified as influential in a student's decision to drop out or stay in school. This study sought to be useful in a practical school setting. Therefore, the study limited its scope to those discriminating characteristics available in student records and thus readily accessible to school personnel. The predictor variables listed below are well documented in the research as discriminating characteristics relating to a student dropping out of school. Data was collected on the following characteristics that were available in the student records of the Chesapeake Public School System to be utilized as predictor variables: (1) absences; (2) achievement test scores--Reading; (3) achievement test scores--English; (4) achievement test scores--Mathematics; (5) achievement test scores--composite; (6) father's education; (7) father's occupation; (8) grade point average; (9) mother's education; (10) mother's occupation; (11) parent's marital status; (12) race; (13) retentions; (14) school attended; (15) sex; (16) transfers. In addition, the student's status during the period of study (dropout or nondropout) was available.

A random sample was selected from the 1988-1989 Chesapeake Public School rolls and a number of analyses, primarily discriminant analysis, were conducted. The analyses were replicated with a fresh sample from 1989-1990 school rolls.

The findings of the analyses indicated that potential school dropouts could be identified in the Chesapeake Public School System with between 90 percent and 98 percent accuracy, depending on the methodology employed. The major predictor variables that consistently emerged as most predictive were absences, retentions, transfers, and mother's education. In addition, schools serving populations more urban in nature exhibited higher dropout rates, and the number of absences proved more predictive in those schools.

The study recommends that this methodology be employed in the school system and results monitored longitudinally to determine if the accuracy of the classification of potential dropouts remains consistent over time. Furthermore, the study recommends that appropriate interventions be developed and monitored to maximize the benefits derived from the early identification of potential dropouts.