1. Limit your analysis to pre-test data only. Choose one categorical and one continuous variable from the following list: educational attainment (Degree), age (Age), previous training on IPV as part of formal education (PrevTrain_FE), index of perceived preparation to address IPV (PP_index), index of perceived knowledge to address IPV (PK_index).
Then, using the appropriate univariate statistic for your chosen variable’s level of measurement describe the dataset in terms of those variables. Include the results table and verbal description of results for both variables.
2. Limit your analysis to pre-test data only. In this exercise, we will recode a categorical variable to concentrate our responses across a smaller number of categories (often necessary for small datasets!). We will recode the variable on educational attainment (degree), which has four attributes, into a new variable, which will have two attributes.
First, run a frequency distribution on the original variable (degree) and include the results table below. For this step, you do not need to describe the results!
Next, use the Transform/Recode into Different Variables menu to recode the original variable into a simplified version using the instructions below:
Once you have created the new variable, add value labels and update missing values in variable view.
Use a crosstab to demonstrate you have recoded correctly. Include that results table below.
Run a frequency distribution on your new variable (the recoded version of DEGREE). Include the results table and a description of the results.
3. Limit your analysis to pre-test data only. In this exercise, we will create an index using items assessing different dimensions of a complex concept: organizational capacity to address IPV. There are 10 relevant survey items, each using a 4 point scale (1= No, 2= Unsure, 3=Probably, 4= Definitely). To ease creation of a composite variable, I have created recoded versions of these ten variables (e.g., Org_IPVspec Org_IPVspecREC). For the recoded versions, 0=no/unsure and 1=probably/definitely. In this exercise, use the recoded versions (Org_....REC) to create a composite variable.
Use the Transform/Compute menu to create a composite variable that is the sum of the 10 organizational capacity variables.
Once you have computed your variable, label it in variable view.
Although the original variables are categorical, we can treat the index as continuous. For the final step, use the appropriate univariate statistic to describe the organizational capacity index you just created. Include both the table and a description of the results.
4. Limit your analysis to pre-test data only. In this exercise, we will take the organizational capacity index and recode it so it is ordinal. This will allow us to divide our dataset into social workers working in settings with a low, medium, or high capacity to address IPV (we’ll use this variable in Lab III.)
First, generate a frequency distribution for the organizational capacity index. We will use information from this table to make decisions about the categories for the variable we will create.
Next, use the Transform/Recode into Different Variables menu to create a new variable with the name OrgCapacityIndexORD and label Ordinal version of organizational capacity index.
Then, using the “If” menu, assign the following values to cases on the new variable:
Old Value New Value New Label
7-10 1 Low
11-14 2 Medium
15-20 3 High
-99 -99 NR
Once you have created the new variable, add value labels and update missing values in variable view.
Last, use the appropriate univariate statistic to describe the dataset in terms of your new variable. Include the results table and brief description of what those results tell us about organizational capacity to address IPV.
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