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It stands to reason that a person who did not answer the question must have missing values on all variables in the response set. This person clearly answered the question, despite having 'missing values' on some of the variables in the set. For example, someone who responds that they own a phone will still have missing values for laptop and tablet and other. That approach will not work with multiple-response questions, because the answers are spread across multiple variables, and can be selected independently. This task is not as straightforward as it is with single-choice multiple-choice questions, where we can simply count the number of missing values in a single column. Cases 3, 4, 6, and 8 own a laptop and a phone.Īn important task when working with check-all-that-apply questions is being able to say how many people did not answer the question.Cases 1, 2, and 5 own a laptop and a phone and a tablet.In plain language, the data used in both examples: In these examples, the columns represent the answers to a check-all-that-apply question, 'Which of the following devices do you own?', with four answer options: laptop, phone, tablet, or 'other'. The following two examples demonstrate both schemes using the same underlying data. Numeric codes representing present and not present (such as 0=Absent, 1=Present).

#EXCEL DATA ANALYSIS REGRESSION EXCLUDE CODE#

Numeric code (typically 1) if present, blank (missing) if not present.The data values should follow one of these two schemes:.If they are string, you will need to convert them to numeric codes (see the Automatic Recode procedure). The multiple response variables should be numeric.For a given multiple response question, each answer option should be represented in a separate column (variable).Each row (case) should represent one subject, survey response, or experimental unit.To properly analyze multiple response questions in SPSS, your dataset should have the following structure: If a person selected that option, they are assigned a '1' for that variable if they did not select that option, their data value is left blank (or is assigned a number code indicating non-selection). Data for this question is recorded in multiple columns, with one column per answer option.The person's selections are written as text, with commas (or other delimiter characters) between their choices. Data for this question is recorded in a single column.In practice, there are two basic data structures for this type of data, but one of them is much easier to work with than the other. To properly analyze these responses, our data must be structured correctly. (There will be as many binary variables as there are 'selectable' options.) Additionally, we are not just concerned about how many individuals selected a given choice we may also care about how many of the options were selected, and what combinations of the options were most common (i.e., are the selections correlated). This means that one multiple-response question is actually composed of several binary variables.

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For example, we could restructure this question into a series of single-choice, 'Yes or No' questions:

However, this is not the case for multiple response questions: each checkbox functions like a 'Yes or No' question. On its surface, it looks similar to 'single-choice' multiple choice questions, which can be summarized using (univariate) frequency tables. This particular question type is deceptively simple. Selects 'phone' and 'other' types 'mp3 player' in the write-in box We might create a survey question like this one:Īs individual users complete the survey, their selections might look like this: For example, suppose we are interested in surveying a group about what types of electronic devices they own, and suppose we are especially interested in the three most common types of mobile computing devices: laptops, phones, and tablets. In this tutorial, we will focus on a specific type of multiple response set: multiple response (or 'check-all-that-apply') questionnaire items.Ī multiple response question presents a list of possible answer options, and the respondent selects all options that are true for them. Multiple response sets occur when you have a set of related choices or characteristics in which a subject or experimental unit can possess one or more of those characteristics.

Example: Multiple Response Frequency Tables.

Defining Multiple Response Sets in SPSS.

Counting the Number of Selected Options using Count Values Within Cases.

Introduction: About Multiple Response Set Variables.

#EXCEL DATA ANALYSIS REGRESSION EXCLUDE HOW TO#

This tutorial is a primer on how to work with data from multiple choice, multiple-response (or 'check all that apply') questions in SPSS Statistics.

Excel data analysis regression exclude