Predicting academic success for low -income students in an urban Licensed Practical Nursing (LPN) program
A major challenge for today's educator is to make a difference to those persons who are jobless, working poor or in a dead-end job. Building a career is the key to breaking the never-ending cycle of welfare and hopelessness. The purpose of this study was concerned with the problem of increasing the efficiency of predicting performance among low-income students in an urban Licensed Practical Nursing (LPN) program.^ The students in the eight classes over a 6-year period in a Philadelphia LPN program were studied. The study sample consisted of 391 students, 146 of whom were low-income. Low-income and non low-income students were studied separately. The independent variables included seven demographic and seven academic variables. The two dependent variables were: Graduate/No Graduate form the program, and the students' Grade Point Average (GPA) achieved in the program. A study of correlation and regression coefficients was completed. Predictive formulas were developed from a step-wise multiple regression analysis of the data.^ Based on the findings of this study, completing the program could best be predicted for low-income students by using the variable Marital Status and GPA could best be predicted by the variables Full-time/Part-time, Martial Status and PSB Verbal in that order. The following is the key conclusion drawn from the study: Admission Directors to LPN programs especially in areas of poverty, such as our nation's inner cities, should use this prediction tool to screen and qualify motivated low-income students and, based on the screening results, prepare a remedial action program for those low-income students who seem most vulnerable to failure or poor performance. ^
Education, Tests and Measurements|Health Sciences, Education|Education, Administration
Snook, I. Donald, "Predicting academic success for low -income students in an urban Licensed Practical Nursing (LPN) program" (1997). Dissertations available from ProQuest. AAI9810577.