Using STATA for data analysis
Table 1. Descriptive statistics
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Table 2. Logistic models
The inspection of logit models shown in Table 2 reveals how the studied effects look like. For all three dependent variables, GPA has a positive significant effect. For instance, an increase by one unit in GPS leads to changes in the odds to graduate by 5.68 times. The effect is significant at p<.001. Similar impacts are on graduating in a STEM field and on retention.
ACT Comp has no significant impact, while the higher ACT SCI REAS leads to the expected increase in the likelihood to graduate with a bachelor's degree in STEM fields.
Gender and being a PELL grant recipient makes no significant difference on any indicator, all being controlled for.
The treatment proves to be significant with respect to graduation with a bachelor's degree within 6 years of enrollment in a STEM field. More precisely, for those exposed to the treatment the odds to graduate in a STEM field are by 2.25 times higher. It is true that the level of significance indicates the precision of the estimation is very weak at best (p<.10, therefore the confidence interval is very large). Figure 2 illustrates these relations. One may easily observe the overlapping confidence intervals, the illustration of the non-significant effect.
Nevertheless, larger samples may lead to smaller standard errors, i.e. to observing other significant effects. In other words, some effects may become significant when a larger sample is considered. However, one cannot be sure this holds true, so one needs to stick to the results one currently has.
Figure 2. Marginal effects of the treatment (for the models in Table 1)
One may also consider a reduced model, not including ACT scores. Table 3 introduces the corresponding results for the three outcome variables.
Table 3. Reduced logistic models
One may notice the persistent effect of the GPA (significant at p<.01for all three dependent variables). The treatment proves this time to be significant for all three outcomes. Better GPA scores are positively associated with larger probabilities to graduate in general (p<.05), to graduate in STEM (p<.10), and increase the retention odds by 2.62 times (p<.05). Figure 2 illustrates these results as marginal effects. One may notice that in the absence of controlling for ACT scores, the effect of the treatment appears more important.
Figure 2. Marginal effects of the treatment (for the models in Table 2)
Table 4 explores the additional hypothesis. An interaction effect between gender and being a Pell grant recipient was considered. It turns out that a significant effect can be noticed only for the case of graduation in a STEM field. In this case, being a Pell recipient increases the odds of graduation in STEM fields. Being female also has a positive effect. However, being simultaneously female and Pell recipient decreases the odds to graduate in STEM.
Table 4. Logistic models with interactions between gender and Pell grants
Further developments may include interactions effects of Treatment with GPA, Pell, ACT Sci, and Gender.
Tables 5-8 introduce these models, considering one interaction at a time. We notice no significant interaction effect with GPA (Table 5), ACT Sci scores (Table 7), and gender (Table 8). However, being a PELL recipient increases the odds to graduate from STEM (second model from Table 6), which says the PELL grants are more likely to be effective for students in the field of hard-sciences.
Table 5. Logistic models with interactions between GPA and treatment
Table 6. Logistic models with interactions between PELL and treatment
Table 7. Logistic models with interactions between ACT SCI and treatment
Table 8. Logistic models with interactions between gender and treatment
One may also look to a reduced model, including significant effects only. Table 9 reproduces Table 6, but only GPA, ACT Sci Reasoning, and the interaction Pell grant recipients # Group=intervention/control are retained into the equations as predictors. GPA and ACT Sci Reasoning prove to be significant in predicting graduation likelihood, GAP, and the interaction matter for graduating from STEM, while in the retention model there is no significant predictor.
Table 9. Logistic models with interactions between PELL and treatment: reduced models
Further, one may test the stability of the models considering only those students that have Expected Family Contribution (EFC) (106 out of 123). Table 10 reproduces the same models as in Table 9, but the population includes only students for which EFC is not null and the EFC-treatment is the considered interaction. The results are roughly similar with respect to GPA, the Retention model indicates weak significant effects (at p<.10) for GPA. The interaction term is insignificant in all models. Act Sci Reasoning brings a weak effect only in the prediction of likely to graduate.
Table 10. Logistic models with interactions EFC#treatment: reduced models, cases with EFC only
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