Quantitative data is data that takes the numerical form or data that can be translated into numerical form (Rogers et al., 2007). Examples include the number of lines in a code, the number of days needed to complete a project, and the number of developers needed to develop a software product.
Analyzing quantitative data involves numerical methods that measure the size, amount, or magnitude of something. It can be used to quantify or measure such things as the opinion, behavior, and attributes of participants involved in a study being conducted. For example, it may be determined through a quantitative analysis that 60% of the people who purchase video games are male and that 90% of them are aged between 20 and 40.
Qualitative data, on the other hand, cannot be counted, measured, or sensibly expressed in numerical form. Unlike quantitative data that can be tangibly measured, qualitative data is more concerned with identifying or determining the nature of something where the representation of data can take the form of stories, patterns, and themes. For example, a qualitative analysis may lead to the conclusion that the average gamer is creative, artistic, and has a tendency to be introverted.
It should be kept in mind that all forms of data gathering activities can result in both qualitative and quantitative data. Moreover, any qualitative data can be represented by numbers, which can be manipulated in various ways and which can be used for interpreting the results of a study being made. Although quantitative or numerical data seems more precise and objective, it should be kept in mind that not all types of data merit getting translated into numerical form as doing so may misrepresent the data. In some cases, qualitative data is best kept as such instead of being turned into quantitative data. For example, when soliciting customer feedback about the services that a company provides, analyzing the data based on the number of feedbacks received (quantitative data) rather than on the nature of the feedbacks received (qualitative data) is not an effective or sensible manner of analyzing the data.
Data can be gathered through interviews, questionnaires, or observations. In interviews and questionnaires, closed questions such as questions about age, citizenship, and marital status, yield quantitative data. On the other hand, open questions yield qualitative data. Examples are questions on the participant’s opinion of the customer service being provided to him or her or any suggestions that he or she can give for the customer service’s improvement.
In observations, the observations and transcriptions of the observer can be considered qualitative data whereas certain elements of these notes – including data logs –can be treated as quantitative data. More particularly, examples of qualitative data gathered through observations include notes on the participants’ behavior or the manner by which a task is completed. On the other hand, examples of quantitative data gathered through observations include the time spent in the completion of a task, the number of people required to complete a task, or the demographics of the people who worked on a task.
Two of the techniques used in analyzing data are percentages and averages. Percentages are used to standardize data, particularly for the comparison of 2 or more large sets of responses. Averages, on the other hand, can be classified as mode, median, or mean. Mode refers to the number that most commonly occurs; median is the middle value of the data after the numbers are ranked; and mean pertains the interpretation of the average that is most commonly understood, that is, the addition of all the figures and the division of the sum by the number of figures involved.
When analyzing qualitative data, patterns must be identified. These patterns are usually evident during the data gathering phase and a guiding framework must be used to provide some structure to the data.
Rogers, Y., Sharp, H., & Preece, J. (2007). Chapter 8: Data analysis, interpretation, and
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