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Use of Demographic and Quantitative Admissions Data to Predict Academic Difficulty Among Professional Physical Therapist Students

RR Utzman, PT, MPH, PhD, is Associate Professor and Academic Coordinator of Clinical Education, Division of Physical Therapy, West Virginia University School of Medicine, PO Box 9226, Morgantown, WV 26506-9226 (USA).
DL Riddle, PT, PhD, FAPTA, is Otto D Payton Professor, Department of Physical Therapy, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, Va.
DV Jewell, PT, PhD, CCS, FAACVPR, is Assistant Professor, Department of Physical Therapy, Medical College of Virginia Campus, Virginia Commonwealth University.

rutzman{at}hsc.wvu.edu

Background and Purpose: The purpose of this study was to determine whether admissions data could be used to estimate physical therapist students' risk for academic difficulty.

Subjects: A nationally representative sample of 20 physical therapist education programs provided data on 3,582 students.

Methods: Programs provided data regarding student demographic characteristics, undergraduate grade point average (uGPA), quantitative and verbal Graduate Record Examination scores (qGRE, vGRE), and academic difficulty. Data were analyzed using logistic regression. Rules for predicting risk of academic difficulty were developed.

Results: A prediction rule that included uGPA, vGRE, qGRE, age, and race or ethnicity was developed from the entire sample. Prediction rules for individual programs showed large variation.

Discussion and Conclusion: Undergraduate grade point average, GRE scores, age, and race or ethnicity can be useful for estimating student academic risk. Programs should calculate their own estimates of student risk. Academic programs should use risk estimates in combination with other data to recruit, admit, and retain students.

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