Identifying Factors that Influence the Successful Transition of Criminal Justice Transfer Students

Abstract

Higher educational institutions place a priority on the retention and timely graduation of students. Previous literature has identified transfer students to have unique concerns and that these concerns vary by major. While previous retention research has reported factors that influence students’ decision to remain in college, many of these studies treated transfer status as a homogenous group. The university in this study enrolls a high percentage of transfer students, and a large percentage of these transfers students enroll in Criminal Justice classes and become Criminal Justice majors. To determine if there are unique risk factors among Criminal Justice transfer students, this study uses multiple measures of transfer status to identify factors that might impact a students’ (1) university involvement, (2) GPA, (3) satisfaction with and sense of belonging to their university and (4) thereby influence their decision to remain in school. The proposition that transfer students as compared to native (nontransfer) students differ on how they face university challenges was also examined. While several variables were found to be important to students’ adjustment to the University, transfer status does not appear to be significant risk factor. Implications of the results are discussed.

Notes

1. While Leiber et al. (1993) examined transfer students in criminal justice, the purpose of their research was to determine the impact of the type of program on transfer student success.

2. The gender, race, age, and transfer status of our sample was compared with the demographics of both the College of Liberal Arts where the department is housed and the University as a whole. The only significant difference that existed was that the criminal justice department had a slightly greater proportion of females than either the college or the University (55.3 percent vs. 44.3 and 47.4 percent respectively).

3. All of the equations were checked for multicollinearity which was not evident. All the variance inflation factors were under three.

4. Probabilities calculated using the following formula (see Peterson 1985): (A) L ₀ = ln [P / 1 − P]; (B) L ₁ = L ₀ + B; (C) [exp(L ₁)/ 1 + exp(L ₁)] – [exp(L ₀) / 1 + exp (L ₀)].

5. Nagelskere r ² is reported for the logistic regression. Although not perfectly analogous with the r ² in linear regression, this statistic, ranging from 0 to 1, still allows us to estimate how well the model predicts the dependent variable (Nagelkerke 1991).