Pearson's or Spearman's? We are doing a lab report comparing attachment scores and their effect on trust scores.
We have been told to do correlations followed by linear regression to get rid of confounders. 
However, we have found that trust, the dependent variable, strongly deviates from normality kurtosis = 2.02 and q-q plot looks abnormal but the KS test does not suggest abnormality.
I am not sure whether to report Pearson's or Spearman's considering the independent variable is normally distributed and confused as to why we have been asked to do regression if trust is not normally distributed.
Spearman's is not significant whereas Pearson's is.
A simplified answer would be preferred as I am an undergraduate doing my first research project. 
 A: Focusing on the correlation issue (Pearson vs. Spearman), based on what you've told us, it sounds like the Pearson correlation will be adequate for this situation.
The KS test tends to be overly sensitive to departures from normality, so that non-significant KS test, along with the modest kurtosis, suggests that normality isn't grossly violated.  The Pearson correlation will suffer from increased Type I and II errors, but only with much more extreme departures from normality.  For example, some simulation studies find inflated Type I and II errors when excess kurtosis is as high as 12, but I have not seen such results for excess kurtosis of 2.  See this previous post for a reference and more details.
Some caveats and other considerations:
1.  The traditional significance test of the Pearson correlation (the one you probably learned in intro stats) assumes that both variables are approximately normal.  In contrast, the typical significance tests for regression assume normal residuals (that is, that the datapoints' vertical distances relative to the regression line are normally distributed). 
2.  The fact that you have very different results for the Pearson versus Spearman correlation suggests that you should examine a scatterplot carefully.  Ask yourself whether a single outlier is driving the pattern.  If so, the Spearman correlation might actually make more sense, as it is less sensitive to outliers.
