Analyzing Qualitative Data

OUTLINE

Verbal data are classified as qualitative data. Qualitative data includes things like notes from interviews, focus group transcripts, responses to open-ended questions, video recording transcriptions, online product reviews, news stories, and the like. There are many different primary and secondary sources from which qualitative data can be gathered, including people, focus groups, business records, government publications, and online resources. Making meaningful conclusions from the frequently excessive amount of data gathered is the goal of the analysis of qualitative data.

STEPS TO REMEMBER FOR QUALITATIVE DATA ANALYSIS

Analyzing qualitative data is a challenging task. The issue is that there aren't as many defined, well recognized standards and criteria for assessing qualitative data as there are for quantitative data analysis.

But over time, a few generic methods for analyzing qualitative data have been created. Much of the methodology covered in this chapter is derived from the work of Miles and Huberman (1994). They state that the analysis of qualitative data typically consists of three steps: reduction of data, display of data, and conclusion drafting.

Data reduction is the initial step in the analysis of qualitative data. Data reduction is the act of choosing, classifying, and coding the data. Methods of presenting the data are referred to as data display. The researcher (and subsequently the reader) may find it easier to grasp the data if they have access to a collection of quotes, a matrix, a graph, or a chart that shows trends in the data. Data displays may thus assist you in making decisions based on patterns seen in the smaller set of data.

After identifying these broad phases, it is important to remember that the process of analyzing qualitative data is continuous and iterative rather than sequential. For example, data coding can assist you in coming up with concepts for how the data could be presented while also assisting you in coming to some initial findings. Preliminary findings may therefore influence how the raw data are coded, categorized and displayed.

This chapter will go into great detail on the three crucial phases in qualitative data analysis: data reduction, data display, and conclusion drawing and verification. We will use an example to demonstrate these phases in the analysis of qualitative data. Throughout the chapter, we will utilize boxes to represent the case in order to highlight important aspects of the qualitative research method.

REDUCTION OF DATA

A lot of data is produced during the collecting of qualitative data. Therefore, the reduction of data by coding and categorization is the initial stage in data analysis. The analytical process of reducing, rearranging, and integrating your collected qualitative data into a hypothesis is called coding. Coding is done to assist you in making sense of the data and drawing conclusions that are relevant. Text units that are subsequently grouped and classified are labeled with codes. In order to improve your comprehension of the data—that is, to be able to see patterns in the data, find links between the data, and arrange the data into logical categories—you might need to go back to your data several times during the iterative process of coding.

Choose your code unit before you start any coding. Numerous degrees of analysis are possible for qualitative data, in fact. Paragraphs, themes, phrases, and words are a few types of coding units. The word is the most common and smallest unit of measurement. Theme: "a single assertion about a subject" (Kassarjian, 1977, p. 12) is a more expansive and frequently more valuable unit of content analysis. The expression of an idea is what you are essentially searching for when you use the theme as a coding unit (Minichiello, Aroni, Timewell & Alexander, 1990).

For this reason, you can give a code to any size text unit as long as it focuses on a specific theme or problem. Take the subsequent crucial incident, for example:

Following the lunch, I requested the check. I anticipated receiving the check when the waitress nodded. There was still no check after three smokes. I turned to see the waitress and the bartender engaged in a spirited conversation. There are two themes in this crucial incident:

1. The waitress does not deliver the requested service at the scheduled time: "I expected to receive the check when the waitress nodded." There was still no check after three smokes.

2. The server is not attentive to the customer; she is not running late because she is overly busy; rather, she is having a lively chat with the bartender in place of delivering the check.

As a result, the previously mentioned important occurrence was classified as "personal attention" (which was not given) and "delivery promises" (which were broken).

This example shows how the codes "personal attention" and "delivery promises" aid in condensing the data to a more manageable size. Keep in mind that appropriate coding entails not only minimizing the amount of data but also ensuring that no relevant data are removed. Therefore, it's critical that the codes "personal attention" and "delivery promises" accurately convey the meaning of the text unit that has been coded.

The process of identifying, ordering, and arranging code units is known as categorization. Both deductive and inductive methods can be used to construct codes and categories. When a theory is unavailable, codes and categories must be inferred from the data inductively. This is what has been referred to as grounded theory in its most severe form (see Chapter 6).

Nonetheless, you will frequently have an initial theory that you may use as the foundation for your codes and categories. In these cases, you can create a preliminary list of codes and categories based on the theory then, if needed, modify or enhance these when additional codes and categories emerge inductively during the research process (Miles & Huberman, 1994). Adopting current codes and classifications has the advantage that you can to build on and/ or expand prevailing knowledge.

You'll start to see patterns and connections in your data as you start classifying it into main categories and subcategories. Keep in mind that as you analyze the data, your list of categories and subcategories may vary. For example, it can be necessary to define new categories, modify the definitions of existing ones, and divide categories into smaller subcategories. All of this is a part of the qualitative data analysis iterative process.

Counting the frequency of a specific topic or event or the number of respondents who mention a specific subject or event are examples of useful metrics to record at times. A general notion of the (relative) value of the categories and subcategories may be obtained by quantifying your qualitative data.

Display of Data

The second important step in your analysis of your qualitative data, according to Miles and Huberman (1994), is data display. The process of showing your reduced facts entails organizing and condensing them. To that end, you may find that using charts, matrices, diagrams, graphs, often stated phrases, and/or drawings makes it easier to arrange the data and identify patterns and linkages that will ultimately make drawing conclusions easier. A matrix was thought to be the best display in our case to bring the qualitative data together. Depending on the type of data set, the goal of the display, and the researcher's preferences, a particular data display technique may be used. In general, a matrix has a descriptive nature, as the aforementioned example illustrates. Other displays, such as networks or diagrams, allow you to present causal relationships between concepts in your data.

DRAWING CONCLUSIONS

The "final" analytical step in the examination of qualitative data is drawing conclusions. Here is where you answer your research questions by figuring out what the identified themes represent, coming up with explanations for the patterns and relationships you've noticed, or drawing comparisons and contrasts. This is the essence of data analysis.

Dan Goldin NASA - National Aeronautics and Space Administration NASA Jet Propulsion Laboratory NASA Ames Research Center NASA Marshall Space Flight Center NASA Technology Transfer Program NASA Careers ISRO ISRO - Indian Space Research Organization ISRO - Indian Space Research Organisation COVERING ISRO Humans of ISRO DRDO, Centre for Airborne Systems DRDO, Ministry of Defence, Govt. of India Defence Research and Development Laboratory (DRDL) - DRDO LinkedIn LinkedIn Guide to Creating LinkedIn Guide to Networking Google Apple Microsoft Amazon

#business? #innovation #management #digitalmarketing #technology #creativity?#futurism #startups #marketing #socialmedia #socialnetworking #management #motivation #personaldevelopment #jobinterviews #sustainability #selfhelp #leadership #personalbranding #education #productivity #travel #sales #socialentrepreneurship #fundraising #law #strategy #culture #fashion #networking #hiring #health #inspiration #er_hr_muskanchaudhary #training #hr #recruitment #engineer #linkedin #linkedinposts #linkedinconnection #linkedinjobs #instagram #facebook