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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