Qualitative Data Analysis

Analysis of data is a much more crucial part than the collection of it as data in itself without analysis didn’t tell us anything about the phenomenon for which it is collected. As for the analysis of Qualitative Data, there can be two main approaches:

• Deductive Approach
• Inductive Approach

Deductive Approach

The deductive approach to qualitative data analysis starts with the preconceived ideas or concepts for which we collect data and analyze it to see if the evidence supports or nullifies these preconceived ideas. Some steps involved in using the deductive approach to qualitative data analysis:

1. The first step in this approach is to develop a theoretical framework based on thorough research, which further will be proved by the data or not.
2. After the completion of the framework or hypothesis, we collect the data using various means.
3. After the collection of data, we use programming languages to code Machine Learning models to find the patterns which are relevant to our hypothesis.
4. After all this, we analyze the results and draw a conclusion on whether our hypothesis is correct or not or if it needs much more data to conclude.

Inductive Approach

The inductive approach to qualitative data analysis starts with the collection of data and works its way towards identifying patterns, and themes. It is an approach researchers explore various different themes and concludes the results as the hypothesis with the evidence from the data. Unlike the deductive approach, here researchers always arise at a conclusion with some correct hypothesis.

The following are some steps involved in using the inductive approach to qualitative data analysis:

• In this approach, we first collect the data using various methods such as interviews, observations, and various focus groups. This data is usually in the form of audio recordings, transcripts, notes, or photos.
• After the collection of data, basic coding procedures start which helps us identify and label the segments of data which represents similar idea and concepts.
• After the labeling and identification of data are complete, some basic patterns arise in the data, which need further research and pattern finding where the Machine Learning models are used.
• After all the research data is used as evidence to publish the findings of the research. Now a hypothesis or framework is developed using all the themes and patterns to support the framework.

Qualitative data is defined as data that approximates and characterizes. Qualitative data can be observed and recorded. In the field of analysis, the terms “qualitative data” and “quantitative data” are used frequently. Quantitative and Qualitative are the two sides of the coin named “Data in Statistics” but as many people are familiar with quantitative data (i.e., numerical data of various sorts), qualitative data is often less understood. Understanding the qualitative data is essential for researchers, analysts, decision-makers, or anyone who wants to gain deep insights into people’s behaviors, attitudes, and experiences.

Qualitative data represents information and concepts that are not quantified numerically. They are typically acquired through sources like interviews, focus groups, personal diaries, lab notebooks, maps, photographs, and other observational or printed materials.

In this article, we have tried to explain Qualitative data with different approaches to its analysis, and also learn about the advantages and disadvantages of Qualitative Data.