By M. Landaluce-Calvo, Ignacio García-Lautre, Vidal Díaz de Rada, & Elena Abascal
The aim of much sociological research is to assess public opinion, and the data are often collected by the survey method. This enables the detection of different response, behaviour or opinion profiles and the characterization of groups of respondents with similar views on a certain topic or set of questions. As is widely known, however, different types of question not only yield different qualities of response, but also require different methods of analysis.
Any attempt to classify survey question types require consideration of five criteria: 1) degree of freedom in the response; 2) type of content, 3) level of sensitivity/threat; 4) level of measurement; and 5) number of response options per question. The last classification (with respect to the number of responses) first differentiates between single response and multiple response questions. Here is the main objective of our article in IJSRM: How to extract maximum information from multiple response questions.
There are two broad types of multiple-response questions. One is the categorical response question, where the respondent is instructed to “check-all-that-apply” (the categories are exhaustive, but not mutually exclusive.). The other is the binary response question, where the respondent is required to check yes or no to each response option. Respondents find “check-all-that-apply” questions more difficult to answer because the multiple options require more use of memory. Under the binary-response format the respondent must consider pairs of options, one by one, and check one option in each case. Each pair of options requires an answer, so only a minimal demand is placed on memory. This procedure yields more responses, in both telephone and online surveys and requires less effort on the part of the respondent, although it may lengthen the questionnaire.
Those admitting various response options can be further classified into grid or check-all-that-apply questions. In the case of the latter, the categories are exhaustive, but not mutually exclusive. This multiple-response question format is its widespread use both in the field of opinion polling and in sociological and marketing research. International research project such as the European Social Survey and the Word Values Survey, for example, contain large numbers of multiple responses questions.
All the above considerations relate to the stages of data collection and participant opinion retrieval, but what about the analysis? A review of the specialist literature reveals a lack of attention to the specific data-processing treatment, and the failure to use a multidimensional exploratory approach that would enable the maximum amount of information to be extracted from the response options. The analysis is limited mainly to calculating one-dimensional frequencies (the frequency with which a given response occurs over the total number of respondents or total number of responses) or two-dimensional frequencies resulting from crossing the chosen response option with other socio-demographic or socio-economic characteristics, etc; in other words, a partial approach in either case.
Our article in IJSRM present a multidimensional analysis protocol that provides the researcher with tools to identify more and better profiles about “who says what”. The underlying philosophy in this approach is to “let the data speak for themselves”, and to learn from them. The strategy begins by coding the response options as a set of metric binary variables (presence/absence). The ideal methodological duo for the exploration of the resulting data is Principal Component Analysis coupled with an Ascending Hierarchical Cluster Analysis, incorporating, in addition, supplementary variables (gender, age, marital status, educational attainment, etc.).
This protocol applies to the analysis of three different multiple-response questions included in a Spanish National Sociological Survey (CIS- Centro de Investigaciones Sociológicas):
- “How do you usually spend your free time?”, the respondent has 17 options and can select as many as desired; no order of preference is required and the categories are not mutually exclusive.
- “During 2017, how have you spent or do you intend spending your leisure periods?”, with 10 options, there is no limit on the number of them that can be checked, but there are two which automatically exclude the rest: “I haven’t thought about it yet” and “I have no leisure periods”.
- “When deciding how to spend your days off, what are your top three priorities?”, there is alimit of three options, out of 10 possible, no order of preference required.
This empirical analysis provides evidence not only of the interpretation potential of the coding/analysis protocol, but also of the limitations of some multiple-response question formats. Specifically, it is shown that multi-response with limited options is not a suitable format for detecting response patterns or overall tendencies leading to the identification of global respondent profiles. In addition, this study corroborates that in the “forced choice” and “check all that apply” the respondents are more likely to choose from the options presented at the beginning of a list (primacy effect). Early theories attributing the phenomenon to such questions requiring deeper cognitive processing.
Read the full article in IJSRM here.