I came a cross a definition which I cannot understand the logic. is there a way to explain what is the problem ?
a longitudinal design for an intervention study in 10 participants where the researchers are interested in evaluating whether there is a correlation between their main measure and a clinical condition using a simple regression analysis. Their unit of analysis should be the number of data points (1 per participant, 10 in total), resulting in 8 df. For df = 8, the critical R value (with an alpha level of. 05) for achieving significance is 0.63. That is, any correlation above the critical value will be significant (p≤0.05). If the researchers combine the pre and post measures across participants, they will end up with df = 18, the critical R value is now 0.44, rendering it easier to observe a statistically significant effect. This is inappropriate because they are mixing within- and between- analysis units, resulting in dependencies between their measures – the pre-score of a given subject cannot be varied without impacting their post-score, meaning they only truly have 8 independent df. This often results in interpretation of the results as significant when in fact the evidence is insufficient to reject the possibility that there is no effect.
My questions are these
1- if the data point is 10 then how comes that the degree of freedom is 8? 2- how to calculate the critical value ? 3- how is it 18 when one does pre and post measures calculation?