Conducting Survey Analysis

Conducting Survey Analysis

Questionnaire and sampling design in a survey research are not independent of other modules in the process like data analysis. While a survey research executive is designing research plan and questionnaire, she needs to keep in mind the type of analysis to run afterwards so as to attain the research objective. Same applies to types of conclusions to be drawn from the survey research study. The research, sampling plans and questionnaire design are totally dependent on this. If one fails to do this, you risk collecting an inappropriate form of data for your analyses, or neglecting important contextual questions. Data analysis in a survey research can be categorized into two – qualitative and quantitative analyses.

Quantitative and qualitative analysis

The more frequently deployed technique of data analysis happens to be quantitative data analysis. As the name suggests it is more about number crunching. The quantitative data analysis tries to throw light on more of ‘macro’ issues. It is often expressed in numbers. For example:

a. Almost 70% of the target population elements have expressed positive opinion towards alternate fuels

b. More than 40% the target population elements have shown interest in the new flavor of ice cream

These examples clearly illustrate the prevalence of quantitative data analysis. The terms sampling, statistical treatment, mathematical expressions, etc are the natural derivatives of quantitative data analysis. Quantitative data analysis can be enjoyed most through the difficult route of statistics application.
Quantitative data analysis is used in most of the studies except obviously for qualitative studies like focus groups or clinics. In short, any numbers are seen, make a note than it is quantitative data analysis.

Qualitative data analysis, on the other hand, is all about finding and reporting insights into a subject matter. Qualitative data analysis doesn’t at all deal with any of the statistical treatments or mathematical inference. These examples will illustrate the difference:

a. Three persons have expressed their fear about easy availability of alternate fuel

b. One person has reported too sweet taste of the new flavor ice cream

The examples (taken from live studies) in quantitative and qualitative data analyses clearly show the difference between the two. Quantitative data analysis is about correct application of a statistical or mathematical tool while qualitative data analysis is correctly picking up a stray voice and visualize its impact on the whole analysis. For example, only one person has reported sweet taste of ice cream. Now, is it true for all or only for one person? This must be validated through another set of analysis or study.

The problem with qualitative analysis is that the analytical tools are limited. The main tool is “content analysis”, which involves reading the full text and recoding it according to a number of categories determined by the researcher. This more than often proves to be time-consuming and subjective.