TPC Journal V8, Issue 3- FULL ISSUE

The Professional Counselor | Volume 8, Issue 3 269 predictors fell within the p ≤ .20 range. This process took five steps, resulting in the removal of four variables with p values greater than .20: (a) CTI Commitment Anxiety Change, (b) CTI External Conflict Change, (c) Gender, and (d) CTI Decision Making Confusion Change, respectively. The model yielded a Chi-square value of 91.011 ( df = 10, p < .001), a -2 Log likelihood of 453.488, a Cox and Snell R-square value of .198, and a Nagelkerke R-square value of .270. These R-square values indicate that the model can explain between approximately 20% and 27% of the variance in the outcome. The model had a good fit with the data, as evidenced by the Hosmer and Lemeshow Goodness of Fit Test (Chi- square = 6.273, df = 8, p = .617). The final model accurately predicted 73.4% of cases across groups; however, the model predicted the retained students more accurately (89.6% of cases) than the non- retained cases (45.8% of cases). Table 2 explains how each of the six variables retained in the model contributed to the final model. The odds ratio represents an association between a particular independent variable and a particular outcome, or for this study, the extent that the independent variables predict membership in the retained outcome group. With categorical variables, this odds ratio represents the likelihood that being in a category increases the odds of being in the retained group over the reference category (i.e., African American/ Black participants were 1.779 times more likely to be in the retained group than White/Caucasian students, who served as the reference category). With continuous variables, odds ratios represent the likelihood that quantifiable changes in the independent variables predict membership in the retained group (i.e., for every unit increase in SAT math score, the odds of being in the retained group increase 1.004 times). The interpretation of odds ratios allows them to be viewed as a measure of effect size, with odds ratios closer to 1.0 having a smaller effect (Tabachnick & Fidell, 2013). Table 2 Variables in the Equation for Hypothesis 1 95% C.I. for O.R. Variable B S.E. Wald O.R. Lower Upper Ethnicity 10.319 * Ethnicity (African American/Black) .576 .393 2.148 1.779 .823 3.842 Ethnicity (Hispanic) .068 .290 .054 1.070 .606 1.889 Ethnicity (Asian/Pacific Islander) 1.889 .637 8.803 ** 6.615 1.899 23.041 Ethnicity (Other) .258 .714 .131 1.295 .320 5.246 Initial Major 35.824 *** Initial Major (Declared STEM) .412 .265 2.422 1.511 .899 2.539 Initial Major (Declared Non-STEM) -1.944 .375 26.905 *** .143 .069 .298 STEM Seminar (Non-CP) .850 .258 10.885 ** 2.340 1.412 3.879 SAT Math .004 .002 2.411 1.004 .999 1.008 Math Placement–Algebra .002 .002 2.080 1.002 .999 1.005 CTI Total Change .017 .007 5.546 * 1.017 1.003 1.032 Constant -2.994 1.378 4.717 .050 Note : B = Coefficient for the Constant; S.E. = Standard Error; O.R. = Odds Ratio; * p < .05; ** p < .01; *** p < .001.