What is quantitative data? Quantitative is defined as data that can normally be reproduced by a machine or a human being. Quantitative data is usually measured, counted, and expressed in terms of some other number. When comparing quantitative items to qualitative data, the quantitative items always lose out because they are less precise.
Qualitative information is much more descriptive than quantitative data. Many qualitative data examples come from focus groups. Participants describe their reactions to a specific instance, situation, product, etc… In a focus group discussion.
Qualitative Data is very descriptive and much more so than quantitative data. For example, if a question was “How did this encounter make you feel?” And you had two or more answers, one of which was “very much” and the other was “somewhat,” that is considered to be “qualitative information.” In this case, the outcome that was measured using hard numbers (such as dollars) would not show how the person felt about the experience.
A question like this illustrates a common problem in qualitative analysis. Most people categorize experience as either good or bad, or positive or negative, when it comes to their own experience. This is known as a “yes or no” question. Some people are able to answer with a yes, while others cannot. Qualitative analysis is different, in that it involves asking questions about the feelings an individual has, rather than the outcomes of actions.
What is Qualitative Data? Qualitative Data is rarely collected in a quantitative way. It can, however, be collected using qualitative methods. This is usually done through the use of questionnaires designed to collect data from individuals over a period of time. These can also be collected in a quantitative way, but they will tend to take longer to complete.
The biggest drawback to using quantitative data in qualitative research is the problems that can arise from using the wrong statistics. When using quantitative data, questions about what individuals “feel” or “think” about a subject may be too broad. If the question was “how does this experience make you feel,” then it may be difficult to get a true answer on whether or not a person really felt that way. On the other hand, if the question was “how do you think this experience made you feel,” then it would be more difficult to get a true answer on whether or not a person actually thought about the event in question.
With qualitative data analysis, you can simply ask people what they experienced and get a truthful answer. There is no need to go into great detail, just as there is no need to make sure that all of the responses are actually given by people. Another advantage to this type of data analysis is that you can then statistically control for these variables and eliminate the things that do not matter (such as weather). You can then focus on those factors that are important to you. This makes it much easier to analyze the data and get a true understanding of what happened during the event.
In conclusion, both quantitative and qualitative data analysis can provide a great insight into an event. The main issue is that you must choose which type is best suited to the situation. If your goal is simply to collect facts that will be used later in the case, then using statistics can often provide a quicker and more accurate answer. However, if you have intentions of using the information in a creative way, then you should consider using qualitative data. This data type allows you to explore what occurred at the event and interpret it effectively.