Title: Forming and coding response categories (2)
Course: UGQuants (SSPS) > Section: Survey Design

Forming and coding response categories (2)

Coding multiple responses

The question regarding marital status which we have just considered illustrated a simple pre-coded closed question where respondents are instructed to select only one response. Such questions are usually straightforward to code. However, with both closed and open questions, respondents are often permitted to provide more than one response. Such questions are somewhat trickier to code.

An example of such a question would be:

From the following list, what do you think are the two most important qualities in a politician? Please tick TWO options.

        1. Honesty [    ]
        2. Confidence [    ]
        3. Integrity [    ]
        4. Compassion [    ]
        5. Flexibility [    ]

There are two possible approaches to coding a question of this type. Firstly, we could use the multiple dichotomy method. With this method we create a separate variable for each category of the question. Respondents will be assigned a yes code for the answers they have selected and a no code for those answers they have not selected. Each possible response category is therefore coded as a dichotomous variable indicating whether or not it had been selected. For example, the response category ‘Honesty’ could be coded as a dichotomous variable, 1 for yes (i.e. selected) and 2 for no (i.e. not selected). Table one illustrates the multiple dichotomy approach to coding.

Table 1: Multiple dichotomy method

Variable number | Is this an important quality? | Yes (code=1) | No (code=2) | Total N
1 | Honesty | 51 | 49 | 100
2 | Confidence | 29 | 71 | 100
3 | Integrity | 57 | 43 | 100
4 | Compassion | 39 | 61 | 100
5 | Flexibility | 24 | 76 | 100

Alternatively, we could code this question using the multiple response method. With this method, we do not create separate variables for each category of the question; rather, we create two variables indicating the respondent’s two choices. Each of the two variables would have five categories representing the possible choices. Table two illustrates the multiple response approach to coding.

Table 2: Multiple response method

Code | Variable 1 | Variable 2
1. Honesty | 31 | 20
2.Confidence | 16 | 13
3.Integrity | 25 | 32
4.Compassion | 18 | 21
5.Flexibility | 10 | 14
Total N | 100 | 100

As the tables illustrate, the difference between these methods of coding lies in the way in which the answers are split into variables and in the codes given to these variables. Irrespective of which method we choose, we record the same number of respondents providing each response.

Both the multiple dichotomy method and the multiple response method can be used with open and closed questions.

Coding numerical data | We have seen that coding is the process of applying numerical values to each question response. However, some responses may already be numerical, for example, age and income. In such circumstances it is not necessary for us to code these responses as the numbers themselves can act as the code. For example, the response ‘18’ to the question ‘What is your age?’ will simply be coded as 18.
Coding missing data | Even where respondents do not provide an answer to a question, we must still provide this non-response with a code. Such a code is known as a missing data code. The key objective of coding non-response is to separate it from valid responses. There are four key types of non-response, and it is usually preferable to assign different codes for these different types of missing data. In the first instance, it may be that the respondent was not required to answer a particular question and the non-response is therefore the result of a non-applicable question. In the second instance, the non-response may be due to the answer not being ascertained. In such cases the question may have been missed by the respondent or the interviewer or it may have been unclear what the respondent’s answer was. In the third instance, non-response may simply be due to a respondent’s refusal to answer a particular question. Finally, a respondent may not know the answer or have no opinion in regard to a particular question. Such responses may sometimes be treated as valid whilst other times may be coded as missing, depending on the nature of the question and the aims of the research. A fifth type of non-response results when a respondent cannot be contacted, or refuses to participate at all, and for probability samples, this is also important to code.
Creating a codebook | Once we have determined our coding frame, we need to make a clear record of it. This record is known as a ‘codebook’ and should contain a range of information. For example, it should include the specific wording of all questions, list all variable names and the type of data used for each variable, and indicate the coding scheme for all variables including both valid and missing data codes.  A codebook allows other researchers to undertake subsequent analysis of the data without having to obtain additional information from the original researcher(s).