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This study is conducted for the purpose of determining which actions state lawmakers will implement in order to close the budget gap. The data used is obtained from a survey of Texas Registered voters of the age of 18 years and above. The results are based on a Random sample size of 800 voters, which is taken from the entire target population. The study uses different variables to suggest the optimal decision, among which budget is the dependent variable. The objective of the study to determine whether the lawmakers will resort to reducing spending or increasing revenue.
In order to find a decision, various voter characteristics are considered, as well as the relationship between them and their option for closing budget gap is measured. These characteristics are the independent variables (factors), and they include political interest, which is ranked on a scale of one to four with the highest rank referring to those who are extremely interested, and the least rank for those who demonstrate no interest; the political ideology of the voters which range from extremely liberal voters to those who are highly conservative. The voters’ race is also considered for disparities where the blacks and Latinos are compared to Anglos. Apart from that, gender, voter income, residence and the level of education are among the other factors under consideration.
The study investigates the relationships between the dependent variables and the independent variables. Each predictor variable is related to the dependent variable individually while holding the other variables as controls. Again, combinations of some predictor variables are applied to the model so as to examine their effect on the dependent variable.
Multiple regression analysis is used for predicting the budget decision. Each variable contributes to the model. Once the voter characteristics are taken into account, the difference between the groups is not a serious matter anymore. Hence, the above mentioned procdure depicts the correlation between two variables, holding all other predictors constant and using multiple-regression in order to observe how that particular predictor is related to the criterion used in the decision-making process after all other predictors in the model are controlled.
Correlation and multiple regression analyses are conducted to determine the relationship between the budget decision and the various variables. Political ideology is negatively correlated indicating that the liberal voters are in support of the decision criteria, which demonstrates the inverse relationship existing between the variable and the dependent variable. Again, the black race is recognized as having a positive correlation that is significant when implying an impact on the decision. The multiple regression analysis with all the nine predictors produced R-squared value of 0.332 indicating that only 33% of the variations are explained by the model. Political interests and income of the voters have little contribution to the model, as long as R- squared decreases and the standard error of estimates increases.
It is expected that there is a relationship between independent variables and the dependent variables. In the current case, only one dependent variable is used. The relationships between these independent variables with one another, and with the dependent variable are under examination. The study expectations include the major independent variables, that are political interest and political ideology, to have a significant impact on the model and to influence the budget decision arrived at. An inverse relationship is expected between the political ideology variable and the dependent variable (budget). This is due to the fact that naturally conservative voters would want the government to reduce the spending rather than increase the revenue.
Political interest and political ideology are the two major independent variables of interest. The current study demonstrates that political interest is not a highly significant contributor of the model. The variable has a p-value of 0.420 which is not statistically substantial at 5% level. However, it has a low positive correlation that implies that voters with lower political interest are in support of the budget decision which involves increasing the revenue. On the other hand, the variable political ideology has a high negative correlation (- 0.893) implying that more than 89% of the registered voters who are liberal-minded are in support reducing the spending. Moreover, the variable has a p-value of 0.000 which is highly significant thus making a major contribution into the model.
When all the predictor variables are used in the model, both the R-squared and the adjusted R-squared values are 0.332, suggesting that the model is not fit since more than 60% of the variations in it are not accounted. Since the standard error of the estimate is too high the issue of the model unfitness emerges again. Such results may be caused by a relatively small impact of some of the variables on the model.
All the above mentioned factors influence the dependent variable. Yet only some of them are of high significance while the others scarcely have any impact on the dependent variable. For political interest, a positive correlation is registered implying a positive relationship with the budget variable. It should be mentioned that female voters have an influence on the budget decision making. The relationship between them and the budget decision is positive with a correlation coefficient of 0.259, meaning female voters are likely to support the decision that suggests the increase of the revenue. At the same time, the age variable has a very low correlation coefficient. This implies that age has a very low impact on the decision made as regards the budget. Education registers a low positive correlation coefficient (0.16) meaning only 16% of the budget decision-making is explained by education. Finally, the race or ethnicity of the voter has a great influence on the budget option they choose.
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