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The procedure that we use if we want to isolate the effect of multiple independent variables on these types of outcomes is called logistic regression.
As with regular regression, as you learn to use this statistical procedure and interpret its results, i t is critically important to keep in mind that regression procedures rely on a number of basic assumptions about the data you are analyzing. All of the basic assumptions for regular regression also hold true for logistic regression. A dvanced statistics courses can show you how to manipulate procedures to deal with most violations of regression's basic assumptions, but a strong understanding of these manipulations typically requires at least one semester-long course beyond introductory statistics. The primary goal of this class is to give you hands-on experience using regression so that you will be able to understand and critique one of the most common and prevalent
techniques of data analysis used in the social sciences. If you want to be a user of this procedure rather than a consumer, I can not encourage you enough to take a statistics class or two down the road. Enough warnings, let's analyze:
Step one. Begin your analysis by opening the SPSS dataset you want to analyze. If you don't have your data already entered into SPSS, you will want to read a chapter on how data is entered for analysis in SPSS. When you open an SPSS dataset, will see a datamatrix (spreadsheet) that lists your cases (in the rows) and your variables (in the columns). If you look at the two tabs at the bottom of this spread sheet, you will see that you can toggle back and forth between the actual entered data and summary information/labels for your variables.
Starting at the top of the data matrix, go down through the hierarchical menus selecting: