It is quite appropriate to use a Pearson correlation when you have specific hypotheses about the zero-order correlations between to variables. When you have hypothesized a direction of the correlation (as in Hy 1 & 2), use a one-tailed significance value. When no direction is specified, as in Hy 3, then two-tailed should be used. If you incorporate age, approach it in an exploratory manner using either partial correlation (one exploratory variable ) or multiple regression (two or more exploratory variables).

If you have mentioned the direction of correlation (if you already know), it is better to go with one-tailed sig. To answer your question related to what stat. test is purely dependent on the data distribution. The Normality test needs to be performed to select either Pearson and spearmen correlation.

Kolmogorov-Smirnov's test can be used to test normal distribution. I would do a regression analysis and not simple correlations bit by bit. What is MMQ here, there are many MMQ scales.

Rhianon Allen thank you. Is it possible to do a partial correlation for age (as the one exploratory variable) with NFC and all 3 MMQ sub-components or would I have to complete 4 individual partial correlations?

Nimantha Karunathilaka thank you, I'll be sure to include the normality test assumption as a caveat for the Pearsons!

S. Béatrice Marianne Ewalds-Kvist thank you. Do you mean multiple regression of the 3 MMQ components against NFC or partial regression of each individually? MMQ in this case is Troyer & Rich's multifactorial memory questionnaire

Partial correlation will be only for estimating the linear association between two variables with a third variable partialled out. If you want to test all the variables simultaneously or in steps, use a linear multiple regression supplemented with examination of the residuals to check for nonlinear associations.