No abstract
Sometimes, in the criminal justice research, we will need to analyze a single concept not only from one indicator, but from multiple indicators' point of view. Occasionally, analyzing just one variable at a time will not yield the desirable and complete result. Therefore, we will need to measure a few pieces of information together. One classical example is measuring up what's called the-crime index‖, which is made up of seven crime categories: murder and nonnegligent manslaughter, forcible rape, robbery, aggravated assault, burglary, larceny and theft, and motor-vehicle theft. Each of these crimes may have a separate variable in a data set and in order to measure them, we may need to sum all of them up and create a new variable,-crime index‖. This lecture will cover creating composite measures of different variables of interest. Creating Composite Measures Using COMPUTE INTRODUCTION We have observed in the 2004gss data set that there are seven variables regarding abortion (the original GSS file has more than 7 variables regarding abortion). The General Social Survey asks a question regarding a woman's right to obtain a legal abortion under various situations. The PreQuestion Text is:-Please tell me whether or not you think it should be possible for a pregnant woman to obtain a legal abortion if:‖. The literal questions make the variables listed below (circumstances are in parentheses): ABANY (when woman wants abortion for any reason) ABDEFECT (when there is strong chance of serious defect in the baby) ABHLTH (woman's health seriously endangered) ABNOMORE (woman wants no more children) ABPOOR (woman is poor and can't afford more children) ABRAPE (woman wants abortion when is pregnant as result of rape) ABSINGLE (woman is not married). Each variable has two valid answers: Yes coded as 1 and No coded as 2. The missing values are: 0-NAP (Not APplicable), 8-DK (Don't Know), and 9-NA (No Answer). We can run frequency reports on all seven individual variables regarding abortion and come up with seven different tables, which would not make it fun at all to get a summarized analysis on them. What would make it fun, or at least easy? …Having only one frequency table for all seven variables! Luckily for us, SPSS has a command, Compute, that allows us to sum all of these seven variables into one. The way compute works is by adding the values (remember: values are codes/numbers and SPSS is thrilled to have to work with numbers) of different variables together. Since all of the-seven‖ have the same set of answers (1 for Yes and 2 for No), when we add them together we will get various values. Let's say that a participant answered "Yes" to all seven questions. In this case, the respondent's sum (or new value) would be 7 (=7 Yes answers times 1, which is the code for Yes). In case all answers would have been "No" the new value will equal 14 (=7*2). And of course there would be new values in between, all of these answering differently for each question. The missing values would be transferred as-missing‖ in the newly computed var...
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