Chapter 6 Creating Composite Measures
Now that you’ve had a chance to get familiar with univariate analysis, we’re going to add a little more sophistication to that process. As you’re about to see, it is not necessary to limit your analysis to single measures of a variable. In this chapter, we’re going to create composite measures made up of multiple indicators of a single concept.
Why would a criminal justice researcher want to do that? Many of the key concepts in criminal justice are complex and can’t be indicated simply by the responses to a single variable or by a single piece of information. To take a very important example, how do we define crime among the most important concepts in criminal justice? If we were trying to measure how much crime takes place in a state, it would not be enough just to look at any one type of serious crime. The
Uniform Crime Reporting Program uses seven crime categories to establish a
“crime index” to measure the trend and distribution of crime in the United States: murder and nonnegligent manslaughter, forcible rape, robbery, aggravated assault, burglary, larceny and theft, and motor vehicle theft; the total crime index is the sum of these offenses.
Take another example: You can explore the kinds of harm binge drinking has on nonbingeing students, also known as secondhand binge effects. Suppose that you want to determine how many residential students experience any of these effects or you want to find out how many of the effects (ranging from none to all eight) any of the students on a campus experienced.
In this chapter, we will explore how SPSS can help you create composite measures such as a crime index or an index of secondhand binge effects. For purposes of this discussion, let’s look at attitudes toward abortion. Seven GSS items reflect people’s attitudes. You can ask SPSS to generate frequency tables for all seven abortion variables from the “2004GSS.SAV” data set. (This exercise is also presented on the Web site.) These tables on abortion suggest that attitudes toward abortion fall into three basic groups. A small minority of no more than 11% are opposed to abortion under any circumstance. We conclude this because 89% would support abortion if the woman’s life was seriously endangered. Another group, a little under half of the sample population (48%), would support a woman’s free choice of abortion for any reason. The remainder of the sample population would support abortion in only a few circumstances involving medical danger or rape.
To explore attitudes toward abortion in more depth, we need to use a new SPSS command: “Crosstabs.” This command provides us with a cross-classification or crosstabulation of people in terms of their answers to more than one question.
The resulting table is sometimes called a crosstab or a contingency table, the latter term indicating that the values of one variable are examined for how contingent they are on the values of another variable. Later in this book, we’ll explain how to use crosstabs to test hypotheses about two or more variables when each of the variables is measured on the nominal or ordinal scale. Here we’ll use it to help us understand how to combine variables into a composite measure. Let’s try a simple example.
The command pathway to this technique is “Analyze → Descriptive Statistics
→ Crosstabs.” Work your way through those menu selections, and you should reach a window that looks like the following.
Because the logic of a crosstab will be clearer when we have an example to look at, we ask that you follow these steps on faith, and we’ll explain it all in a moment. Let’s analyze the relationship between the answers people gave to the question about whether a woman should be able to have