Georgia’s Gratuitous General Electoral Laws
H 8 As the turnout quotient decreases, the percent of the vote for Senator Chambliss in the runoff increases. H 9 Counties within metro Atlanta will have a lower average percentage of the vote for Senator Chambliss than counties outside of metro Atlanta. H 10 Counties with greater percentages of religious adherents will have a higher average percentage of the vote for Senator Chambliss. The data analysis software Wadsworth Microcase Student Version and SPSS Version 17.0 were used to conduct the analysis for this study.
Findings For the variable of region, a difference of means test was conducted to discover if metro Atlanta voted differently from the way the rest of Georgia voted. The hypothesis of, “counties within metro Atlanta will have a lower average percentage of the vote for Senator Chambliss, than counties outside of metro Atlanta,” can be rejected upon viewing the data in Table 2. The counties in the Atlanta MSA actually had a higher mean percentage for Chambliss than the rest of Georgia; therefore, the study failed to reject the null hypothesis of, “there is no difference between the average percentage of the vote for Chambliss inside metro Atlanta versus the rest of Georgia.” For this study, the concept that metro Atlanta votes differently from the rest of Georgia can also be rejected, as this was not the case in the December runoff. The difference of means test failed to produce a tscore that would indicate such a conclusion. Table 2 Difference of Means
An analysis of variance (ANOVA) was conducted to explore the relationship between the dependent variable and county religiosity. A box and whisker plot displays the results in Figure 1, with each of the dots representing a Georgia county, and the rectangles highlighting the cases that fall within one standard deviation above and one standard deviation below the mean. Upon viewing Figure 1, it is apparent that the mean vote for Chambliss increases as it crosses all four categories. The ANOVA produced an Eta square of .068, which indicates that the percentage of religious adherence in a county accounts for 6.8% of the variance in the vote for Chambliss in the runoff. Descriptive statistics for each of the four individual categories of the religiosity variable are displayed in Table 3. The same relationship that is
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