In data analytics, a researcher is able to break their study’s findings into three main types of analysis: quantitative, categorical, and qualitative. The type of data an individual or a team collects is determined by the question that is sought to be answered and the resources attainable. For this discussion board, the main focus will be on the benefits and drawbacks to using qualitative data analysis, as well as, a brief comparison to the other forms of investigation. Although highly simplistic, William M.K.
Trochim from the Web Center for Social Research Methods writes that qualitative data “includes virtually any information that can be captured that is not numerical in nature” (Trochim, 2006, pg. 1); some examples include in-depth interviews, direct observation, and written documents. Below are a few examples of qualitative questions and answers:”In which country were your children born?” Germany”What is your occupation?” HR Manager”Do you work full-time or part-time?” Part-time”In which city or town is the house located?” Grand Rapids”What is the industry of the business?” Hospitality”What is the main activity of the farm?” Cattle Categorical data is when numbers are categorized in groupings and quantitative data is numeric or quantity based; whereas, qualitative data is more focused on the quality of data. A few strengths of qualitative data analysis are that it is able to pick up on slight changes in perspectives and needs of participants, it can be used to rationalize and support quantitative analysis, and it is a source of detailed, thorough information that can be used to identify patterns and trends of behavior (UM, 2018, para. 6). On the other hand, a few limitations with qualitative data methods are limited to a smaller population size which doesn’t properly represent larger demographics, more time consuming, and evaluator bias in analysis (UM, 2018, para. 6).