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Calculating "Effective Decision Policy Change." To calculate EDPC, regression is used to determine standardized coefficients--as weights--for each of the decision attributes employed in Part 2 for each individual.
In analyzing the conjoint data to generate the inputs for calculating EDPC, we found 91% of the individuals' decision policies are statistically significant (p < .05) in the first set of responses (prior to feedback), with a mean [R.sup.2] of .67.
This approach facilitates an investigation of the amount of variance in EDPC accounted for by the inclusion of additional explanatory variables in Steps 2, and 3, "over and above" the group of variables included in the previous regression model.
The base model does explain a significant amount of the variance in EDPC ([R.sup.2] = .089, p > .05).
The full model (Step 3) explains a significant amount of variance ([R.sup.2] = .531, p < .001) in EDPC. Further, the full model represents a significant improvement in explained variance over and above the main effects model ([DELTA][R.sup.2] = .071, p < .001).
Figure 1 indicates that moving from outcome to cognitive feedback (left to right) improves EDPC. Further, the demonstrated improvement in EDPC is more positive for those individuals high on metacognitive knowledge than those low on metacognitive knowledge (depicted by the significant and positive change in slope between low and high metacognitive knowledge).