RSCH FPX 7868 Assessment 3 Data Analysis Strategies for Qualitative Research

Name

Capella university

RSCH-FPX 7868 Qualitative Design and Analysis

Prof. Name

Date

Introduction

Making sense of enormous amounts of data is the challenge of qualitative analysis. Reducing the amount of raw data, separating the vital information from the unimportant, identifying significant patterns, and creating a framework for communicating the key findings revealed by the data are all necessary steps in this process (Patton, 2014). To understand what qualitative data represents, qualitative data analysis involves gathering, organizing, and interpreting the data. Non-numerical and unstructured data are considered qualitative. Although audio, photos, and video are also regarded as qualitative data, text is the most common form, such as in-depth answers to survey questions or user interviews. A person’s specific research goals and the type of data gathered will influence the analysis technique they choose from among the many that are available. My research seeks to understand how texting affects teen literacy in America, examining the negative and positive effects. As such, the two methodological approaches I have considered employing are ethnography and case study.

Data collection process

Despite there being numerous differences between the case study and ethnography methodological approaches, for both the case study and ethnography approach, the data will be collected primarily through interviews and questionnaires. The self-administered questionnaire is one of the most common research instruments. Although it is principally used to collect quantitative data, it can also be employed when asking open-ended questions for qualitative data (Williamson, 2002). In this case, questionnaires will be used to collect both quantitative and qualitative data that will be used to supplement the interviews. However, since questions on a questionnaire must be simple and straightforward, questionnaires cannot be used for complex questions.

That is where interviewing will come in. According to Bow (2002), interviews are used if the information sought is complicated and it is difficult to simply ask questions in a self-administered questionnaire. Since personal contact is mandatory with interviews, it is also often easier to obtain a higher response rate using this technique. However, one thing to note is that, unlike a case study that employs a structured interview, a combination of semi-formal and informal interviewing will be used in ethnography. Typically, exploratory interviews, which lack predetermined questions, are used in informal or unstructured interviewing. The semi-formal interview helps get information that the researcher has been considering before the interview, and typically the questions have been carefully worded. The semi-formal interview has predetermined questions, but it also gives the interviewer flexibility by allowing them to ask unscripted questions and arrange them in a way that best suits each respondent’s train of thought (Darke and Shanks, 2002).

Key elements of data analysis

To conduct an in-depth analysis and comparison, case study data analysis involves grouping the data by particular cases. Holistic or embedded case studies are a sign of good construction. While embedded approaches view a single unit as the sum of its parts, holistic studies examine a unit as a single, global phenomenon (Patton, 2014). I will use an embedded approach for my case study since literacy comprises different segments, such as reading, writing, speaking, and listening. I believe that these parts of literacy will reveal relevant information for the study. Three key steps are involved in the analysis of case study data. The first is data reduction to select, simplify, abstract, and transform the raw case data. The second is data display to organize the assembled information to enable the drawing of conclusions. The final step is conclusion drawing/verification to draw meaning from data and build a logical chain of evidence.

Thematic analysis is essential to the analysis of ethnographic data. The thematic analysis looks for patterns of meaning in a data set, such as a collection of transcripts from focus groups or interviews. It groups large, frequently quite large data sets into categories based on similarities or themes (Williamson and Bow, 2002). With the aid of these themes, one is able to understand and extrapolate meaning from the content. The ethnographic methodology employs an unstructured, iterative approach to data analysis. The description, analysis, and interpretation of data constitute its three components. It is common practice to recount and describe data while treating it as fact when using the term “description.” The process of looking at connections, influences, and links between the data points is called analysis. Finally, data interpretation creates a more profound understanding or justification for the data than just the individual data points and analysis.

Conclusion

The most crucial part of research is probably the data analysis. Poor analysis yields inaccurate results that undermine the study’s validity and render the conclusions useless. One must carefully select appropriate data analysis techniques to ensure that the findings are insightful and valuable.

References

Bow, A. (2002). Ethnographic techniques. In: Williamson, K. Research methods for students, academics and professionals: Information management and systems. (pp. 265-279). Elsevier.

Darke, P., & Shanks, G. (2002). Case study research. In: Williamson, K. Research methods for students, academics and professionals: Information management and systems. (pp. 111-124). Elsevier.

Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Sage Publications.

RSCH FPX 7868 Assessment 3 Data Analysis Strategies for Qualitative Research

Williamson, K. (2002). Research techniques: Questionnaires and interviews. In: Williamson, K. Research methods for students, academics and professionals: Information management and systems. (pp. 235-249). Elsevier.

Williamson, K., & Bow, A. (2002). Analysis of quantitative and qualitative data. In: Williamson, K. Research methods for students, academics and professionals: Information management and systems. (pp. 285-303). Elsevier.