Making a business research questionnaire is no big deal, but making one that successfully derives the information that is actually required by the business to improve its practices is something not every person can do. One of the things that help make an online survey more effective is the understanding of the different types of data that is required out of survey respondents and of the different ways to measure the response. Different situations demand the use of different types of data. Remember, different scales have to be designed to derive these different types of data.
The feedback of survey respondents can be categorized into two basic forms of data – non-parametric and parametric.
The sort of data that does not have any direction and cannot be divided is called non-parametric data. Usually histograms are used to analyze non-parametric data. There are two basic types of non-parametric data.
Nominal data refers to alphabetical or numeric data that is used to name people or objects for symbolic purposes and has no mathematical value.
For example, a questionnaire may ask the respondents to name the brand of shampoo they use. Numeric data too can also be included in the category of nominal data such as numbers written on the backs of sportsmen.
Ordinal data refers to numeric data that indicates only the relative ranking of different items, without representing the intensity of the mathematical value or the distance between the values. For example, respondents to an online survey may be asked to rank different brands of shampoo. Hence, the ranking of different shampoo brands will inform researchers the relative preference of survey respondents but will not inform them about the intensity of the difference of preference of one shampoo brand from another.
Numeric data that has direction is called parametric data. It can be used to analyze the difference the different responses and can also be at times divided. There are two different types of parametric data.
The collection of internal data is done on a scale on which all points are equidistant from the ones next to them. Scales measuring interval data do not have zero because of the nature of thing being measured. For example, respondents can be asked to rate their happiness on a scale of 1 through 10. Interval data cannot be divided because of the non-absolute nature of the data.
Ratio data is the most absolute form of numeric data collected from respondents. It can be divided and altered in different ways to derive more meaning. All absolute mathematical values can be called ratio data such as income, age, sales, market share, etc.
Although ratio data may seem the most usable form of data and researchers may feel tempted to ask their survey respondents to answer all the questions in ratio form, it is either not practically possible to do so or isn’t the best form of data because of the objective of the research being conducted. An effective online survey questionnaire contains questions that derive the sort of data that will come handy in getting better insight into the respondents’ minds.
The QuestionPro and Survey Analytics online survey platforms allow you to collect all of these forms of data. The trick is knowing which type to choose to get the best information that you can use in order to make a decision.