Spearman VS Pearson, test choosing and interpreting) (Statistics & SPSS)

This forum made possible through the generous support of SDN members, donors, and sponsors. Thank you.


New Member
7+ Year Member
Jun 29, 2016
Reaction score

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!

Members don't see this ad.


Full Member
15+ Year Member
Feb 19, 2007
Reaction score
Pearson was probably the better choice. Any continuous variable could be described as you did, it doesn't make it ordinal. Tradition is looking at single-items we treat as ordinal but the scales are continuous. Its not quite that black & white, but it seems to work well. In most cases they converge, though apparently not terribly well in your scenario.

60 is a pretty small sample size. I have no way of confirming if you are actually running the analysis correctly, but assuming you are the interpretation is pretty simple. There is no correlation between those two variables. Doesn't mean they can't possibly be related, just means they weren't in this context.
  • Like
Reactions: 1 user
Dec 4, 2014
Reaction score
I agree w/ Ollie's comment above. But you might also check out the Laerd statistics guides (laerd.com) if you have any concerns about whether you set up data correctly or met all assumptions. I think it is 6$ a month for the extended version (and in my view well worth it) but they also have some great content for free. I use it often to walk me through how to appropriately check all of the assumptions to make sure I'm not missing any, and potential options to consider when certain assumptions are violated. I'm not sure what level you are in your stats experience but specially when I was beginning in stats and SPSS the visuals of "here's what this box looks like on SPSS" and how to interpret the output saved me some time.
  • Like
Reactions: 1 user
Members don't see this ad :)


There's no special rule that your data can only be ordinal to use a Spearman correlation. If your data are continuous but you have a small sample size with an odd distribution, I'd favor Spearman's correlation despite the (small) sacrifice of power.

Another nice online reference is StatSoft's Electronic Statistics Textbook: http://www.statsoft.com/Textbook