Pearson VS Spearman — Choosing and interpreting the test

#1
Hello,
I need to fix the problem choosing and interpreting the correct statistical test for my data.
I have data from (a) Minnesota Job Satisfaction Questionnaire short form (20 questions, answers from 1 (=very dissatisfied) to 5 (=very satisfied)) and
(b) from Social Readjustment Rating Scale (43 items, minimum of summed scores of stress events is 12 and the maximum score is 500).
Number of respondents (doctors) = 60.

My hypothesis was that the more stress events was in a person's life in the last 6 months, the less satisfied he/she will be, that is, the scores of Social Readjustment Rating Scale (which measures quantity and intensity of stress events) will be negatively associated with Job Satisfaction scores.

In my data Job Satisfaction scores are in a range of 20-60 and SRRS scores are in a range of 12-500.

First I checked Pearson's r coefficient because all the researches I met on this topic used this correlation. Results I got was: r = 0.01 which is very low and means that there is no correlation between the two variables and sig = 0.997 which is higher than 0.05 so it means I must reject the hypothesis that there is no correlation, right?

Then I thought, maybe it's because my data is of ordinal measure because the data can be ranked just like ordinal scale requires (from less stressed respondents to more stressed ones or from less satisfied people to more satisfied ones). Then I calculated Spearman' rho which was -0.45 (which seems to be logical) but significance value was 0.73.
So how can I understand anything here? Correlation coefficient tells me there is moderate negative correlation between the two variables and at the same time, the correlation isn't significant so I can't believe in it?

Where did I go wrong? (When I chose Pearson or Spearman?) and what should I do? How can I interpret these results? Is there a correlation between being stressed out from the stress events and being satisfied with your job, or not?

Sorry for my English (I'm at the intermediate level here) and my potentially inappropriate question (I'm a beginner here).

Thank you for replying in advance!
 

Karabiner

TS Contributor
#2
sig = 0.997 which is higher than 0.05 so it means I must reject the hypothesis that there is no correlation, right?
No. That means that you cannot reject the hypothesis that r=0 (in the population).
Then I thought, maybe it's because my data is of ordinal measure because the data can be ranked just like ordinal scale requires
Any interval scaled data can be ranked.
Then I calculated Spearman' rho which was -0.45 (which seems to be logical) but significance value was 0.73.
This is impossible, if n=60.

For n=60 and rho= -0.45, the resulting p-value must be < 0.01 .

Moreover, a change from r=0.01 to rho=-0.45 i.e. just by switching from Pearson to Spearman, would be very rare.

Check your sample sizes for the respective calculations, and make an X-Y-scatterplot for visual inspection.

With kind regards

K.