Use the Compute function in SPSS (via syntax or pull-down menus) to create unit-weighted composite scores. Alternatively you can create (regression-weighted) composite scores via exploratory factor analysis (not shown in this tutorial).
Obtain descriptive statistics (M, SD, Skewness, and Kurtosis) e.g., via Analyze - Descriptive Statistics - Descriptives - Options - Distribution - Kurtosis and Skewness
Data file: data_15_1.sav
Allen and Bennett 15.2 [1], pp. 209-221
Create a composite satisfaction score as the average of responses to the nine items: Open1, Open3, Open7, Open9, Open2R, Open4R, Open6R, Open 8R, Open10R (where the R indicates a recoded item).
Open5 is dropped because analyses of internal consistency indicated that it didn't belong (it has negative and low correlations with other items and the Cronbach's alpha increases when it is removed). Why do you think it doesn't belong? (examine the wording of the item - "liberal" has different meanings in different contexts, e.g., in Australian politics, the Liberal Party is right-wing, whereas in North American politics, liberal means left-wing)
Open7 (carry conversation to a higher level) could be dropped or included - it has small positive correlations with the other items and the Cronbach's alpha would basically stay the same whether it was included or dropped