Sociology Essay

4

Lysvette Rodriguez

Soc-303

Dr. Daniel Boudon

10/24/2020 

 

Reflection Paper

‘A survey of qualitative data analytics’ is a document that describes ways and methods in which researchers can achieve qualitative data. The author acknowledges that data analytics is an area that is given short and brief descriptions in introductory textbooks. For this reason, he takes his time to explain data analytics broadly, focusing more on qualitative data analytics. He has explained the concept of data analytics as an essential element in research. Good research requires good data analytics. Therefore, the paper has been of great importance in understanding data analytics and its usefulness in research. Also, it has revealed that data analytics requires systematic approaches, just like any other research. 

There are various elements of qualitative data analytics—the first element in pattern construction. Human beings are pattern-oriented because they create patterns in their activities. For instance, while preparing flowers in the garden, humans make patterns. In the same way, human beings follow patterns in qualitative data analysis. Humans get the urge to collect data by following a certain pattern, for instance, through conducting surveys. This pattern is included as the first process of data analysis. Besides, the element of category construction involves grouping things in a particular order. Human beings have this characteristic of grouping things.In the same way, they group data into various categories based on various characteristics. Pattern and category construction are essential features of data analysis. The paper has described that both elements are intrinsic characteristics of human beings, necessary for data analysis.

On the other hand, there are different uses of qualitative data analysis. Data analysis displays how human patterns and categories Interplay and interact. In other words, it reveals the interrelationship between two things. Most researches are conducted to reveal the cause-effect relationship. For example, does the change of one element affect the results of another element?

Similarly, Interplay describes the structural nature of groups. Through Interplay, researchers can explain and discover whether a certain sequence or pattern exists (Saldaña, 2011). However, the author has made it clear that it is easy to make predictions on interaction and Interplay between categories. However, interrelationship can only be explained by data analysis.

Another interesting fact about qualitative analysis is that it does not involve specific methods. In quantitative analysis, statistical means are applied in data analysis. Qualitative data analysis is thus inductive, deductive, and requires abductive reasoning. These three methods explain how data be analyzed. The inductive method is a general means of analyzing data without looking for specific characteristics. It is simply observing a group of objects to understand their pattern. Abductive reasoning is a process of looking for explanations out of a range of possibilities. Lastly, deductive means of analyzing data implies concluding without considering other possibilities.

The part on the deduction, abductive reasoning, and induction was the most interesting. I got to understand how data can be analyzed in different ways. However, I realized that from the explanations, the methods could be faulty when used alone. Using abductive reasoning to analyze data can lead to a faulty conclusion. For example, if one notices a group of people wearing red socks and concludes that it is the uniform, he could be wrong because the people could be visitors. Therefore, the three methods of data analysis can only work best when used together.

Besides, qualitative data analysis has some standards and guidelines that make it effective. For example, think the display is a concept that describes how the data should be presented. Data can be presented in diagrams, charts, and graphs to make it easy to understand. Visual representations also help in coming up with conclusions. Researchers need to focus not only on written data but also on visualized data. Visualizing data helps in clarifying complex phenomena.

Also, credibility and trustworthiness are key elements in analyzing and presenting qualitative data. Researchers must be certain that the data used is credible and worthy to serve its purpose. Credibility is ensured when researchers acknowledge the data sources, for example, by citing their works. Through this, I have realized that researchers show credibility when they also highlight their processes to collect the data. Credibility also enhances trustworthiness, which reflects the integrity of the researcher.

Lastly, some theories explain qualitative data analysis. The grounded theory explains how to use small elements and features to develop the bigger picture; it involves a step by step process of evaluating small units to achieve the greater conclusion. Although complex, this theory helps in developing qualitative data.

In conclusion, qualitative data analysis is a process that makes qualitative research effective. Although it is part of the research, it requires careful handling because it contains details and processes, just like research. The standards of quality data analysis must be maintained to ensure effective and quality data. Also, this will ensure credibility and trustworthiness. The article has identified the fine details of qualitative data analysis. 

References

Saldaña, J. (2011). Fundamentals of qualitative research. Oxford University Press.