Social scientists use SPSS (Statistical Package for the Social Sciences) to analyze data with an ANOVA (Analysis of Variance) to compare the effect of independent variables on dependent variables. They report the significance, the likelihood the difference is due to chance (a significance of less than.05 means there are fewer than 5 chances out of 100 the result is due to chance). Social scientists also determine Eta Squared, the effect size or the percentage of the dependent variable explained by the independent variable.
Click on "File" at the top of the SPSS screen to pull up data from an existing data file. Select "Open" from the drop-down dialog box.
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Click on "Look in" from the directory displayed. Select "Data" from the "Type of Files."
Click on the file name of the date you wish to analyze; for example, "Jury.sav."
Click on "Statistics" at the top of the SPSS screen. Then select "General Linear Model" from the dialog box and "Simple Factorial" from the drop-down box.
Highlight your dependent variable (for example, level of guilt) from the list on the left and push the arrow pointing right to move it into the box labeled "Dependent."
Highlight your independent variables (for example, sex and race) from the list of variables on the left and click on the arrow pointing right to move each of them into the box labeled "Factors."
Click on "Define Range" and type in your minimum value for the dependent variable (for example 1) and the maximum value for the variable (for example 12).
Click on "Options" from the three buttons on the bottom of the dialog box, which consists of "Contrasts," "Post Hoc" and "Options."
Click on "Effect Size" on the drop-down menu. Click "Continue."
Review the output labeled "Tests of Between-Subjects Effects." The box to the left lists each of the independent variables and the interaction variable under the heading "Source."
Follow the row next to each variable to the column labeled "Sig." This column indicates the level of significance (the likelihood the result is due to chance). The lower the significance, the less likely the differences between the groups are due to chance and the more likely they are due to the independent variable. For example, a significance level or probability of less than .01 means there's a fewer than 1 possibility in 100 that the results are due to chance.
Follow the row next to each variable to the column labeled "Eta Squared," the most important information. Eta squared is the measure of effect size. It is the percentage of the dependent variable explained by the independent variable. The higher the percentage (the closer to 1), the more important the effect of the independent variable. For example, an Eta Squared of .65 means that 65% of the independent variable is explained by the independent variable.