Find the mean and standard deviation in SPSS separately for groups; for example, get a separate mean for males and females (using the compare means procedure in spss).

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Obtain the mean and standard deviation separately for different groups of a variable in SPSS.

Video Transcript: In this tutorial we're going to take a look at how to obtain means for different groups of a variable on some other variable of interest. Let me show you what I mean here. So, for example, in this data set, we have two variables, play golf and satisfaction. Now play golf is coded, if I click on my Value Labels button here, I've already coded it, and you can see that we have either yes (coded 1), those who do play golf, or no (coded 2), those who do not play golf. And I want a mean for just the yes group, the 1s, and a mean for just the 2, the No's. So now we know what we want, we want the means broken down separately by the different groups. To do that we need to go to Analyze, Compare Means, Means. OK so select that and then here in our Dependent List, this is where the satisfaction variable would go, so move that over. And the Independent List will always be the categorical variable, in this case the variable, play golf. It's categorical and it happens to be dichotomous because there are only two options, a 1 or a 2, or a Yes or No. OK so play golf will go to the Independent List and then go to Options, so we can see what options are selected. I see a mean and I see a standard deviation by default and number of cases as well. We can keep number of cases, that could be helpful, but let's drag that up to the top here and have that present first in our output. OK so everything looks good here. Let's click Continue and then click OK. Now our output opens we're going to scroll down here, actually I'm going to delete a little bit of this. I'll delete this by selecting it, pressing the Delete key. I'm going to delete this here, so we can see these two tables at the same time. Here's the output of my Means procedure, the procedure we just ran. I'm just going to briefly mention this Case Processing Summary table, just to say it shows that all 7 observations were included, and none were excluded, so everything was analyzed, that's all that means. Zeroing in on this table, the Report table, this is what we want to see. Notice here I have the play golf variable and I have analyses for the Yes group the 5 people who were in the Yes group, and the No group, the two people in that group. And notice now I have means broken down separately for these two groups, which is exactly what I wanted. So the yes group, those who play golf, had an average satisfaction score of 8.20, whereas those who did not play golf had an average satisfaction score of 7.00. So you should be able to see, at least in the sample here, that those who played golf did have a higher average satisfaction score, or higher mean satisfaction score, than those who did not play golf. OK and we see the standard deviations here as well. Just as a side note this standard deviation is 0 because our two people who were in the No group both had a score of 7. If they both have a score 7, there's no variability at all in the data set for this group. So that's why we have a standard deviation of 0. One last note here. Notice the Total. See where it says 7 here, and then we have a mean of 7.86 standard deviation .90? If we go back up here to our original analysis, the analysis using Descriptive Statistics and then using the Descriptives option, notice we have the same thing: 7.86 for the mean .90 for the standard deviation. So what the Means procedure does down Here it breaks it down by the different groups, and it also gives you the overall analysis down here, which is what we got the first time by Descriptive Statistics. But anytime you want to get a mean, a descriptive analysis, but of means separately for different groups, you want to go to Analyze, Compare Means and then the Means procedure.

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Channel Description: For step by step help with statistics, with a focus on statistics and SPSS. Both descriptive and inferential statistics covered.

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