Interpreting and Using Statistics in Psychological Research. Andrew N. Christopher

particularly if you need to collect and present data in your class. In this section, we will learn how to make each type of display using SPSS.

      We will use Wendt’s (2013) data in the SPSS spreadsheet file titled “Wendt’s data.sav”. Let’s open that spreadsheet and get to work on our visual displays.

      Making a Bar Graph

      Let’s first make the bar graph that appears in Figure 3.5. This figure shows the average burnout scores of first-year and senior-year students. Remember that we make a bar graph when we have a nominal (categorical) variable for which we want information. In this example, we want to see burnout scores for first-year and senior students. Let’s make this bar graph with SPSS.

      1 Click on Graphs and then on Chart Builder.

      2 At the bottom of the Chart Builder window, choose Bar, and from the visual options to the right, select (by double-clicking) the Simple Bar graph option.

      3 At the top of the Chart Builder window will be a blank graph. Drag the variable Year in College onto the x-axis. Then drag the variable Burnout onto the y-axis.

      4 Click OK, and your bar graph will appear.

      Our first bar graph displayed the average burnout for first-years and seniors. Let’s make another bar graph, with this one displaying the total number of first-years and seniors in Wendt’s (2013) research. We saw this frequency distribution graph in Figure 3.1, and now we will make it ourselves.

      1 Click on Graphs and then on Chart Builder.

      2 As in the previous example, choose Bar and then Simple Bar.

      3 Once again, put the variable Year in College on the x-axis and Burnout on the y-axis.Here’s where things get a little different.

      4 In the Element Properties window, click on the Statistic: dropdown menu in the middle of that window. Choose Valid N. By selecting Valid N, we are getting frequency counts for the number of first-years and the number of seniors in this research.

      5 Click Apply at the bottom of the Element Properties window.

      6 Click OK, and your frequency distribution bar graph will soon appear.

      Making a Scatterplot

      Let’s make the scatterplot that appears in Figure 3.7. This scatterplot displayed the relationship between role overload and burnout in Wendt’s (2013) research. Here is how we use SPSS to make a scatterplot:

      1 Click on Graphs and then on Chart Builder.

      2 Choose Scatter/Dot, and then double-click on Simple Scatter.

      3 Drag Role Overload onto the x-axis and Burnout onto the y-axis.

      4 Click OK, and your scatterplot will soon appear.

      Making a Line Graph

      To give you some experience creating an SPSS spreadsheet and entering data into a spreadsheet, we’ll use some new data and create a time plot. Table 3.6 contains the number of motor vehicle thefts per 100,000 people in United States between 1995 and 2012 (Federal Bureau of Investigation, 2015). Let’s enter these data into SPSS and create a time plot.

      You will need to start a new SPSS spreadsheet. To enter these data into SPSS, we need to first go into Variable View (in the bottom left corner of the spreadsheet). Here, we create two variables: (1) year and (2) motor vehicle thefts per 100,000 people.

Table 3.6

      In the first column, titled Name, you need to give each variable a name. I called the first variable Year and the second variable Motor_vehicle_thefts. Notice the underscores to connect the words in this second variable. SPSS does not allow you to have spaces between words in the Name column.

      In the second column, make sure your type is Numeric. Although arbitrary, the Width column can be left at its default for our purposes. For the variable Year, change Decimals to zero (0). It makes no sense to have decimal places in the year. For the variable Motor_vehicle_thefts, Table 3.6 reveals they are reported to one decimal place, so for this variable, change Decimals to one (1).

      In the Label column, we are allowed to call our variables anything we want. Given the constraints in the Name column, it is a good idea to provide some sort of label to each variable, as I have done here:

      When you have a large dataset with many variables, providing a label for each variable is helpful in staying organized, which is something that will become even more important in later chapters.

      Finally, move over to your Measure column. Here is where we tell SPSS how the data were measured. Test yourself here. Is the Year variable nominal, ordinal, or scale? How about the Motor_vehicle_thefts variable? Here’s the screenshot to answer these two questions:

      In the lower left corner of the screen, switch to Data View, and here’s what you will see:

      Now, enter your data from Table 3.6 into your spreadsheet. When you get done, here’s what you will see:

      1 Click on Graphs and then on Chart Builder.

      2 Choose Line, and then double-click on Simple Line.

      3 Drag the Year variable onto the x-axis and the Motor_vehicle_thefts variable onto the y-axis.

      4 Now move over to the Element Properties window. In the Statistic box, click on the dropdown menu, and choose Value. Doing so will allow your y-axis to be labeled correctly.

      5 Hit Apply at the bottom of the Element Properties box.

      6 Then hit OK in the Chart Builder box, and prepare to be impressed.

      Learning Check

      1 Use Wendt’s (2013) data and in SPSS, make a bar graph to illustrate sex differences in dysfunctional perfectionism.A:

      2 In the previous question, why would it not be appropriate to make a scatterplot to illustrate sex differences in dysfunctional perfectionism?A: A person’s sex is a nominal variable. That is, a person falls into one and only one category on this variable. A scatterplot is appropriate for scale data; bar graphs are appropriate for nominal data.

      3 Use Wendt’s (2013) data and in SPSS, make a scatterplot of the relationship between dysfunctional perfectionism and role overload.A:

      Notes

      1. It certainly does make logical sense to place “first-years” before “seniors” on the x-axis because students have to be first-year college students before they can become college seniors.

2. Of course, there are other pieces of data that the restaurant wanted to know, such as the types of pizzas ordered and whether customers dined in or ordered take-away. However, for the purposes of illustrating time plots, I presented only daily