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

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#### Alanky

##### New Member
7+ Year Member
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).

#### Ollie123

##### Full Member
15+ Year Member
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.

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#### singasongofjoy

##### Full Member
7+ Year Member
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.

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#### Alanky

##### New Member
7+ Year Member
Thank you so much!!!

#### WisNeuro

##### Board Certified in Clinical Neuropsychology
10+ Year Member
Small sample size and a variable that likely has a HUGE standard deviation = difficulty in finding relationships.

1 user

##### Counseling Psychologist
10+ Year Member
Agreed with WisNeuro, I'm always skeptical of correlations under 200 people as being near stable enough

D

#### deleted343839

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

#### WisNeuro

##### Board Certified in Clinical Neuropsychology
10+ Year Member
Agreed with WisNeuro, I'm always skeptical of correlations under 200 people as being near stable enough

Especially considering the range of the SRRS. I'd want to see the distribution on that one.