Volu m e II - Purdue University Calumet

As we can see, this relationship is statistically significant at the .000 level. As we know if a value is
less than 0.05 we can be confident that the results reflect a similar relationship in the population than those
arising from sampling error. Since our value .000 we can be extremely confident that such results did not
occur only by chance. Since our relationship is statistically significant, and the strength of the relationship is
strong (.522), we can be confident that such a relationship can be generalized from our sample to the
general population. However, since no controls were included in the analysis, the relationship might be
spurious, unless the true cause and effect relation between the two variables holds, once other variables that
are known to influence political tolerance are introduced. In order to test whether this condition exists, a
regression analysis was performed on the relationship between education and tolerance.
A regression analysis determines if the control variables in our model influence the dependent
variable, and the magnitude and direction of that influence in respect to the independent variable,
education. The three control variables that had three values or more in that variable were coded into
dummy variables; one value in the variable was excluded from the regression, in order to facilitate the
comparison of that value to the to the original baseline variable. So, for example the “Catholic” variable is
coded so that it represents individuals expressing a religious preference for Catholicism and this is compared
to the original baseline variable which contains representations of other faiths. The sex of the respondent
only had two values, and therefore it was not recoded. (See Table E).
TABLE E
Model
Unstandardized
Coefficients
37
Standardized
Coefficients
B Std. Error Beta
t Sig.
1 (Constant) 4.259 .541 7.877 .000
educ HIGHEST YEAR OF .515 .034 .403 15.069 .000
SCHOOL COMPLETED
Cathol Catholic -.103 .317 -.011 -.326 .745