132


 as both unstandardized and standardized, though for this analysis, the standardized value

 of # is of importance. The standardized values are values for each of the different

 variables that have been converted to the same scale so as to compare them. The variable

 that has the largest f value makes the strongest unique contribution to explaining the

 dependent variable, when the variances explained by all the other variables in the model

 are controlled for (Pallant, 2001).

 The level of significance needs to be examined as this explains whether the

 variable is making a significant unique contribution to the multiple regression equation.

 If the significance value is less than .05 (p<.05), then the variable is making a significant

 unique contribution to the prediction of the dependent variable, if it is greater than .05

 (p>.05), then the variable is not making a significant contribution to the prediction of the

 dependent variable (Field, 2000; Pallant, 2001).

 The other variables used to report the findings for this objective are the R-

 squared, adjusted R-squared, degrees of freedom (df) and the t-statistic. The R-square

 value is a measure of how much the variability in the outcome model is accounted for by

 the predictors used. The adjusted R-square value is used when a small sample is used

 and the R-square value tends to be an overestimation of the true value. Both the R-square

 and adjusted R-square variables are reported as a decimal number, for example, 0.6665,

 and used as a percentage, 66.5%, to indicate how well the model generalizes to the

 population (Pallant, 2001). Degrees of freedom are the number of observations free to

 vary around a constant parameter and used to determine the appropriate critical values in

statistical tables for evaluating the statistic (Ary et al., 1996, p.566). The t-statistic, or