Computing a Pearson correlation in SPSS is a straightforward procedure, although
a single correlation itself is not worthy of a publication.
So let's learn how to conduct a simple correlation, how to interpret it, and how to write it up in APA style.
We will be using the data set Job Satisfaction.sav.
These are data from 200 mental health counselors the variables in this data set are job satisfaction score,
burnout score, compassion fatigue, turnover intention, gender (male and female), and compassion satisfaction.
Compassion fatigue is the negative emotional consequence of listening to traumatic reports, such as
helping clients who have been traumatized, and
compassion satisfaction is the positive emotional consequence of helping people who have been traumatized.
So a therapist may become burned out from the traumatic stories but might also feel a sense of accomplishment and pride
from helping those same people.
So both compassion fatigue and compassion satisfaction
affect the job satisfaction of mental health counselors.
Burnout is a loss of enthusiasm for a job that can be experienced in any job setting, not just therapy,
but compassion fatigue is a specific form of burnout that's unique to counselors.
We also have gender and turnover intention which is the intention to leave the field of doing therapy,
presumably because of either burnout or compassion fatigue.
Our research question would be, “What are the relationships between job satisfaction
burnout and compassion fatigue?"
If a therapist has high levels of compassion fatigue will burnout likely be high or low?
Probably high.
Because compassion fatigue is theoretically a form of burnout, they should be positively related.
If a therapist has high levels of compassion fatigue,
will job satisfaction likely be high or low?
Probably low. If you are burned out on a job, you tend not to be happy with that job,
so they should be negatively related.
Let's find out.
We will walk through the five steps of hypothesis testing.
I covered the assumptions of correlation and how to determine the null and alternative
hypotheses in the previous video about doing correlation by hand.
I assume that you have watched that video, so you are somewhat familiar with the degrees of freedom and rho.
If not, then watching that video will help you understand more about each of these elements of hypothesis testing.
So step number one: Select the appropriate statistic.
We are measuring the strength and direction of a relationship between two variables, so we will use the Pearson correlation.
Step 2: State the null and alternative hypothesis.
The null hypothesis Is that the population correlation is 0 for each pair.
So we would write: h-sub 0: rho = 0, where rho is the Greek letter for r.
The alternative hypothesis would be: h-sub 1: rho does not equal 0.
Step 3: Select a level of significance
For a two-tailed test, alpha = .05, degrees of freedom 198,
which is the number of pairs minus 2. The critical value Is + or - 1.98,
and this is calculated from the t-table. You will notice that this critical value Is very close to 1.96,
the critical value For a z-test, and that is because our sample Size Is so large.
Step 4: Calculate the statistics
For that we will turn to SPSS.
Here we are in SPSS using the data set Job Satisfaction.sav.
To do a Pearson's correlation in SPSS, begin by clicking 'Analyze'
'Correlate'
'Bivariate' and that opens our dialog box.
Move the variables Job Satisfaction,
Burnout, and
Compassion Fatigue into the variables window.
Notice below that the option for Pearson correlation Is set by default.
So is the choice for a two-tailed test, and the option to flag significant correlations.
Leave all of these settings as they are.
Now click on 'Options'
Check 'Means and standard deviations'
so that we can get the descriptive statistics, and
then click on 'Continue' and OK.
The output window will open up.
Let's take a look at that output.
In the Descriptive Statistics box
we see the means standard deviations and sample size for each variable.
We will use this Information for the write-up.
Remember, that this is a cleaned, data set with no missing values if we had a
data set with missing values the N here could be different for each variable.
Only vriables for which there was a pair of matched values would be included in our Pearson correlation.
Scroll down and we see the Correlations box.
Here is where we find the correlational coefficient for each pair of variables. There are significance values and
the sample size for the number of pairs of variables used in that correlation.
This is called a correlation matrix.
Notice that each variable
correlates with itself at a +1.
This is not very interesting because
every variable correlates with itself at +1, so we might just leave this out of our final correlation table.
The off diagonals are identical.
like we saw with the matrix scatterplot, so
we will not need all of them. If a correlation is statistically significant,
SPSS will flag the correlation and notate it with 1 or 2
asterisks. It will also include a note under the table as to what those asterisks mean.
In this case two asterisks means that the correlation is significant at the .01 level for a two-tailed test.
It is quite possible that, we would have some correlations that were significant at less than .05 but greater than .01.
Those correlations would have one asterisk and be notated correlation as significant at the .05 level
Two-tailed test.
I wanted to show you one more thing about these off-diagonal correlations. Just like with the matrix scatterplot
these correlations are the same,
so we really only need the information in the upper right portion of this matrix.
The same information is being duplicated in two places.
Returning to our five steps of hypothesis testing, we are ready to calculate the statistics, but because
we have so many correlations these will go better in a table
Step five is to make the decision,
But again some correlations May be significant while others are not.
So we will want to write up the correlations that are most Important for our research question.
We would report the results in APA style like this:
we would mention the type of test and for each correlation that we report, we would include the our
degrees of freedom, the coefficient, and
its significance value.
We are going to use a correlation table, so we should refer to that table in the text, and
we should include a statement about the effect size, or the coefficient of determination.
And this Is what our APA Style correlation table should look like.
Because every variable correlates with itself at a +1 use a 1 or a
dash in those spaces, or as I did here just leave them blank.
Also report each correlation only once. That is why the boxes in the lower left of this table are all blank.
When naming variables I used abbreviations
for the variables in the columns, but I defined them with the variable names.
You could also simply repeat the names of the variables in the columns and the row headers, or you could use numbers, or use abbreviations
and define them with a note below the table.
Finally, I used the SPSS convention of two asterisks for correlations significant at the .01 level,
and included the meaning of those asterisks in a note below the table.