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General Comments concerning inferential statistics in SPSS.

Many analyses are available in the Analyze menu from simple correlations to multivariate design and more that are available through syntax. 

However, one should not let SPSS's options dictate what analyses are performed. SPSS does not provide much in the way of statistical analysis post-1975 and so other packages may be necessary to accomplish one's tasks with more statistical power. But what SPSS may lack in more modern analysis it makes up for in performing the most rudimentary forms of analyses available with relative ease (if you prefer clicking 15 things versus typing one line of code in some packages; I personally find the latter more rewarding). 

For example, load the Cars data set which is available with every installation of SPSS in the Samples directory. Once loaded, one might suspect there is a relationship between the cars' weights and their mile per gallon gasoline consumption. To run the linear regression is rather easy, simply click on Analyze, then Regression, then Linear....

Next, select weight as the independent variable and mpg as the dependent variable. Then click the OK button.

You should see output similar to what is below.

We would also like to take a look at the relationship graphically. SPSS has always been fairly weak with regard to graphical display relative to other programs, with some of the graphs bordering on laughable (e.g. the default 3d scatterplots). The Graphs menu is easy to use until you want to tweak and tailor the graph to your own liking, in which case you're likely in for a headache (and bugs). One doesn't have a whole lot of control of the initial output nor can one manipulate it very easily. However, an example is given below of a simple scatterplot from the above examination.  A couple of things stick out. One is that there appears to be a curvilinear relationship rather than strictly linear one (there are actually subgroups in this data with linear relationships of varying degrees), and secondly, one case appears to be an extreme data point (lower left) that will require some action. In this case, the data point is a miskey of some kind (4 cu inch engine?) and has missing data on several of the other variables and so we would not want to include it in the analysis. 

At any rate, here is how to get a standard scatter plot.

First, go to Graphs in the tool bar, then Legacy Dialogs, then Scatter/Dot...

Next, specify Simple Scatter (default). Then move "Miles per Gallon [mpg]" to the Y Axis box and move "Vehicle Weight (lbs.) [weight]" to the X Axis box. Then click the OK button.


You should now see something similar to what is below in the output window; which is all well and good -- a basic scatter plot showing the relationship between two variables.

However, it is often desirable to have an actual line of best fit superimposed on the data, or you may prefer different colors or scale values, or tick marks, etc. Just about anything can be manipulated in a graphical display by right-clicking on the graph and entering the chart editor (In Separate Window).

In the chart editor, just about anything in the graph can be clicked on and altered. For instance, right click on the data points displayed in the scatter plot (in the chart editor). When you do, the points should be highlighted in yellow. You can then select "Add Fit Line at Total".

Once the fit line has been specified, you should see it in the chart editor. You will also notice, a linear fit line is not the only type of line which can be specified. Looking at the Properties box (right) shows different types of fit methods available.

Now, if we simply click (left-click) outside the chart editor, somewhere in the white-space of the output window; you'll see the line has been applied to the actual scatter plot.

Other elements of the graph can be changed; color and type of data points, as well as scale by using the chart editor in similar fashion as what was done above.

Below, we have the same graph, with changes applied (including a Loess fit line).

Oneway ANOVA after using recode function to select only cases with 4, 6, & 8 cylinders. The boxplot below was produced using the Explore function with the newly recoded filter variable for number of cylinders.

To conduct the oneway ANOVA, simply go to Analyze, Compare Means, One-Way ANOVA...

Next, specify your Dependent variable(s) and Factor.

Next, click on the Post Hoc... button to specify which post hoc test(s) and which equal variances not assumed test(s) you would like. Then click the Continue button.

Next, click on Options... and select all desired options. Then click the Continue button, then click the OK button.

The output should look similar to that displayed below.

The Means Plot displayed above was specified when selecting options for the ANOVA. It is displayed here as a comparison to the boxplot produced above as part of the Explore function. Clearly, the boxplot is better at conveying the same information.


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Jon Starkweather, PhD


Richard Herrington, PhD


Last updated: 01/21/14 by Jon Starkweather.

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