The mean value of a dataset represents the average value. It gives us a good idea of where the center of a dataset is located.
The standard deviation of a dataset represents how spread out the values are in a dataset. It gives us an idea of how closely the observations are clustered around the mean.
Using only these two values, we can gain a strong understanding about the distribution of values in a dataset.
The easiest way to calculate the mean and standard deviation of a dataset in SPSS is to use Analyze > Descriptive Statistics > Descriptives.
The following example shows how to do so in practice.
Example: How to Calculate Mean and Standard Deviation in SPSS
Suppose we have the following dataset in SPSS that shows the exam scores received by various students in some class:
To calculate the mean and standard deviation of exam scores, click the Analyze tab, then click Descriptive Statistics, then click Descriptives:
In the new window that appears, drag Exam_Score to the Variables panel:
Then click the Options button. In the new window that appears, make sure to check the boxes next to Mean and Std. deviation:
Then click Continue. Then click OK.
The following output will appear:
From the output we can see:
- The mean exam score is 89.67.
- The standard deviation of exam scores is 8.372.
In addition to calculating these metrics, it can be helpful to create a histogram to visualize the distribution.
To do so, click the Graphs tab, then click Histogram:
In the new window that appears, drag Exam_Score into the Variable panel:
Once you click OK, a histogram will be generated that shows the distribution of exam scores:
This histogram helps us see that although the mean exam score is 89.67, the exam scores vary quite a bit, ranging from the mid 60’s to the high 90’s.
The following tutorials explain how to perform other common tasks in SPSS: