Table 5-2 shows the results of the zero-order correlation study. Strong correlations
exist between aca and nca (R = -0.98) and afa and nfa (R = 0.543), respectively. Based on
the strong correlation observed between the parameters studied, it was decided to focus
the study on only two out of the four power law parameters, namely nca and nfa. The
results show a weak negative correlation between nca, nfa, and |E40*|. Further testing for
statistical significance revealed no statistically significant correlations between nca, nfa,
and E40* .
Table 5-2. Results of correlation study between power law parameters and dynamic
modulus at 400C and 1 Hz frequency
Power Law Regression Coefficients
E,,,*1 aca nca afa nfa
E ,,*11 1.000 0.414 -0.498 0.464 -0.348
aca 0.414 1.000 -0.980 0.948 0.578
nca -0.498 -0.98 1.000 -0.908 -0.536
afa 0.464 0.948 -0.908 1.000 0.543
nfa -0.348 0.578 -0.536 0.543 1.000
'Denotes the dynamic modulus at 1 Hz frequency and 400C.
In order to further evaluate the relationship between nca, nfa and |E40*|, a bivariate
partial correlation study was performed. In here, a bivariate partial correlation denotes
the correlation obtained between two variables, while controlling for a third variable. For
example, r12.3 denotes the correlation of variables 1 and 2, while controlling for variable
3. In most cases, a partial correlation of the general form r12.3 will turn out to be smaller
than the original correlation r12. In the rare cases where it turns out to be larger, the third
variable, 3, is considered to be a suppressor variable, based on the assumption that it is
suppressing the larger correlation that would appear between 1 and 2 if the effects of
variable 3 were held constant.
Table 5-3 presents the results of the bivariate partial correlation study, in which p
denotes the level of significance of a potential correlation. Hence, p < 0.01 means that