DQ 2 :Using the “Determining the Appropriate Sample Size,” Topic Material, perform a sample size calculation to determine how large your sample should be

Two clauses in the prompt are doing all the work, and they say the same thing twice: the same people take both surveys, and the analysis is a paired t-test. That makes this a within-subjects design, and the consequence is easy to state and easy to get wrong. An independent-samples calculation is powered on the variability between people, but a paired calculation is powered on the variability of the difference scores within each person, and because each person acts as their own control, that variability is usually much smaller. The practical result is that a paired design needs a substantially smaller sample than a between-groups design to detect the same change, which is precisely why quality improvement projects use it. If your calculation reports a number that looks like a two-group trial's, you have almost certainly used the wrong standard deviation and the post will say so to anyone who reads it carefully.

Set out your four inputs explicitly and defend each with a citation, because the brief asks you to justify the calculation rather than merely to produce a number. Alpha is conventionally set at 0.05 two-tailed and power at 0.80, and saying why 0.80 rather than 0.90 is worth a sentence, because it is a decision about tolerating a Type II error. The effect size is the input that actually requires judgement: state the smallest change in your survey score that would be clinically meaningful in your setting, not the largest one you hope to see, and cite a prior study or a minimal important difference if one exists for your instrument. Add an attrition allowance, since the same people must return for the second survey. Close on validity honestly: an underpowered study risks a false negative, an overpowered one detects trivial differences and wastes participant burden, and neither is an external validity problem, which is a separate question.