Q1. Where else, other than your home, do you use the internet? (Check all that apply). Library School Workplace Internet on a cell phone Other

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1 Exploring Check-All Questions: Frequencies, Multiple Response, and Aggregation Target Software & Version: SPSS v19 Last Updated on May 4, 2012 Created by Laura Atkins Sometimes several responses or measurements are recorded for a single question. For example, there may be questions in a questionnaire that will allow a respondent to select each of the responses as an answer, as in the example below: Q1. Where else, other than your home, do you use the internet? (Check all that apply). Library School Workplace Internet on a cell phone Other Many people commonly refer to these as Check all that Apply or Checklist questions. These questions are often used when it is important to the researcher that the respondents consider each of the possibilities. The respondent could use the internet at all of these places, none of them, or any combination of these locations. Because all of these combinations are possible, it s necessary to make sure that the data is written in such a way that each category is written out as a separate variable in the dataset -- Essentially as a yes/no answer. If five people were asked this question, the data would look something like this: ID Q1_1 (library) Q1_2 (school) Q1_3 (workplace) Q1_4 (internet on a cell phone) Q1_5 (other) Once the data is recorded properly there are three ways to explore this variable in SPSS: running basic Frequencies, using the Multiple Response command, or using the Aggregate command. I. Frequencies The frequencies procedure generates a frequency table illustrating how cases are distributed across the values of a variable. 1. In the menu bar select: Analyze> Descriptive Statistics>Frequencies 2. Place each variable (Q1_1 to Q1_5) into the Variables box. This would treat each variable as a separate entity and would yield five separate summary tables:

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3 II. Multiple Response A simple, but limited and temporary approach is to use the Multiple Response option. This procedure creates a single summary table of counts and percents based on several variables that contain responses to one question. This would create one table that combines all five variables, rather than five separate tables. 1. First, make note of how the variables of interest are coded. For this example there are five categories (1-5). 2. Next, instruct SPSS that a set of variables represents responses to a single question of interest. In the menu bar, go to Analyze>Multiple Response>Define Variable Sets. To define a multiple response set in SPSS we must specify the list of variables that make up the set, the type of coding used, and a name. 3. Using the arrow button, place variables Q1_1 through Q1_5 in the Variables in Set box. 4. Click Categories and add 1-5 for the range. 5. Give the new collapsed variable a name (ex. Where_Internet). Next, give the variable a label and click Add. Notice that the set name now appears in the Multiple Response Sets list box. The $ prefix distinguishes the set name from an ordinary SPSS variable name. 6. Click Close. 7. Return to Analyze>Multiple Response. You will now see that two options have been activated: Frequencies and Crosstabs. Below is an example of frequency output for the Where_Internet variable: $Where_Internet Frequencies Responses N Percent Percent of Cases a Where else do you use the internet? library % 40.0% school % 80.0% workplace % 40.0% internet on a cell phone % 20.0% other % 20.0% Total % 200.0% a. Group A single table was created based on responses to the five variables (Q1_1 to Q1_5). The N column indicates how many respondents mentioned each location. School is the most commonly mentioned location. The Percent of Responses column indicates what percentage of the total number of locations mentioned is contained in each category. The Percent of Cases indicates what percentage of respondents used locations of each given type. The column for interpretation is Percent of Cases. The reason that it is possible to have over 100% is because each respondent can select more than one category. Theoretically, if everyone selected all categories this percentage would be equal to 500%. Note that the multiple response set that was created will remain active until a different data file is opened or you exit SPSS. One limitation of with this method of exploring these variables is that the chi-square test of statistical significance as well as graphs cannot be obtained using Multiple Response.

4 III. Aggregate A third option for exploring this data is to create a combination variable which would give all of the unique combinations listed in the dataset. The Aggregate Data command aggregates groups of cases in the active dataset into single cases and creates a new, aggregated file or creates new variables in the active dataset that contain aggregated data. Cases are aggregated based on the value of one or more break (grouping) variables. To do this will take some effort and requires more advanced skills. However, if you feel that you will be using this variable for statistical tests or beyond a single time period, this could be the right decision. This option will allow you to save your work and use the variable for later analysis. The steps are outlined below: 1. Data>Aggregate. Place Q1_1 to Q1_5 in the Break Variables box to find unique combinations. Break Variable(s) are cases which are grouped together based on the values of the break variables. Each unique combination of break variable values defines a group. When creating a new, aggregated data file, all break variables are saved in the new file with their existing names and information. 2. Check the Number of cases box. This will activate the N_Break command. The N_Break command tells SPSS by which variable to collapse the data. 3. Check the Create a new dataset button and give the new dataset a name. This will specify a new file into which the aggregated data will be placed. When finished, click OK.

5 4. In Data View of the new dataset, each row represents a unique response. The cases are represented as N_Break. Sorting N_break in descending order would show where the most common responses fall. Additionally, if there were rows with all missing values, these could be deleted because they do not need to be assigned a unique identifier, which is the next step. 5. To assign a unique identifier to each row, go to: Transform>Compute Variable Assign a name to the Target Variable (ex. Values ). In the Function Group box, select All and place $CASENUM into the Numeric Expression box by double-clicking it or using the arrow button. $Casenum assigns a unique identifier to each row/unique combination of responses.

6 In Data View, you will now see a new column of unique values for each row/unique combination of responses: Note that when sorting N_break in descending order, the unique identifier would be structured such that the most common response =1, the second most common response =2, etc. 6. The next step is to merge the aggregated data file into the original file. It is always necessary to sort cases in both files before merging. In both files, go to: Data>Sort Cases> Sort by: Q1_1 through Q1_5 7. In the original dataset, go to Data>Merge Files> Add Variables. Highlight the open dataset you wish to merge and click Continue. 8. In the Add Variables dialog box, Select Match cases on key variables in sorted files and select Non-active dataset is keyed table. Add Q1_1 through Q1_5 to Key Variables box. Add N-Break to Excluded Variable box. Click OK. You will get a warning that the merge will not work if the cases were not sorted first. Click OK.

7 9. The new variable Values can now be seen in Variable View of the original dataset. Change the decimals to 0, and add the missing values. 10. The final step is to add values and labels accordingly, by referencing the other data file that shows unique responses. Again, the Aggregate command is a useful option if you feel that you will be using the variable for statistical tests or beyond a single time period. Just save the changes made to your original dataset, and the new variable will be saved in your dataset.