quantitative and qualitative research activities in knowledge-based and developed societies have become permanent and continuous. Variety in research methods requires a new approach to qualitative data analysis. The use of qualitative data analysis, along with other ways, is a new movement in the field of data analytics. International research shows that qualitative data analysis has expanded in recent decades and is gaining popularity today. Increasing attention to qualitative methods requires familiarity with the analytical procedures of these methods and their use. In this smart strategy blog article, we will talk about the process of qualitative data analysis, analysis units, and the relationships between the data. Stay with us until the end of the article.
What is qualitative research?
Qualitative research is a set of activities such as observation, interviewing, and participation in social activities that help the researcher to obtain information. This information is mainly verbal, textual, descriptive, analytical, perceptual, and classified. Qualitative research uses data from interviews, documents, direct observation, questionnaires, etc., to understand social issues. In addition, qualitative research requires collecting experimental materials that describe challenging moments in people’s lives. The following figure shows the components of qualitative research.
Qualitative data analysis methods
there are different methods of qualitative data analysis:
- Case study
- Grounded Theory
- Historical method
- Action Research
- Discourse analysis
strengths and weaknesses of Qualitative data analysis
Qualitative methods, like other methods, have some strengths and weaknesses, which are as follows:
- Through his direct presence, the researcher achieves people’s views on the issue. In this way, he can find lost issues.
- Qualitative descriptions are essential in predicting relationships, causes, effects, and dynamic processes.
- A qualitative researcher engages in social analysis with all his being.
- Events and circumstances do not always recur and are not widespread.
- Collecting, analyzing, and interpreting data is too time-consuming.
- Collecting, analyzing, and interpreting data is too time-consuming.
- The presence of the researcher has a profound effect on the subjects studied.
Eleven methods of qualitative data analysis
Ethnographic Thematic Analysis
This type of analysis is one of the most commonly used qualitative data analysis methods, especially in ethnography. This method has been widely used in other approaches to qualitative analysis. Thematic Analysis is almost the basis of most qualitative methods. Thematic analysis is the coding and analysis of data to understand what information the data conveys.
This type of analysis, in the first place, seeks to model the data. Once a data pattern has been obtained, it should be thematically supported. The steps of thematic analysis are as follows:
Data management: This step is dedicated to setting up and organizing the data. Some actions performed in this step are Isolating existing data types, placing data in chronological order, organizing by title, data type, or document type, and preparing a list of actions.
Conflict with data: At this stage, the researcher tries to prepare the ground for data analysis by performing a series of coding steps:
- Open coding is the first and most crucial step in engaging and understanding data. One of the most critical open coding strategies is to pay attention to summaries.
- Theme Extensions: Themes are a collection of duplicate codes identified in open coding.
- Centralized coding: This type of coding requires line-by-line movement. This time the researcher focuses on the themes he has found in open coding.
Data mapping: At this stage, the researcher depicts the themes and patterns found in the form of concept maps.
Development of analysis: Thee researcher begins a theme-based investigation at this pointn.
Classification: This is the last step of the analysis. At this stage, the researcher categorizes the patterns according to differences and similarities.
Grounded Theory in qualitative data analysis
This method of analysis is used for theorizing in areas that are difficult to prove by quantitative methods. This type of analysis seeks to theorize through the data collected. The data used in grounded Theory must be empirical. This means that the researcher is personally, objectively, and empirically involved in data collection operations. This Theory encourages creativity and freedom of action and makes the research process very flexible. In the process of making grounded Theory, the collected data is converted into coded concepts. These concepts are interconnected in three stages open coding, axial coding, and selective coding.
The researcher begins the analysis by examining the data and classifying them as textual and observational data. It then starts the coding process by separating the research questions. When a set of basic concepts is extracted, common ideas fall into more significant categories based on similarities and differences. This process is called axial coding. These principal codes are then categorized based on conditional, interactive / strategy, and consequential dimensions and linked together in a storyline. The storyline should explain the main categories logically and analytically one after the other and establish a one-way and two-way relationship between them. This process helps analyze the data analytically and logically and provides the basis for selective coding and extracting the kernel category. The primary type or core is removed at the end of the storyline. At this stage, the researcher outlines the conditions that affect this significant phenomenon, the intervening context and conditions that shape these conditions, and the consequences of carrying out these strategies in the form of a three-dimensional semantic pattern. This step is called selective coding. At this point, the researcher encodes a paradigm or provides a theoretical model that illustrates the interrelationships of these main categories.
The data in this analysis is like a narrative. This style is so distinctive in its attention to the history and experiential dimensions of personal stories and anecdotes told in a regular chain. From the beginning, narrative analysis has a holistic approach to qualitative data. A lot of qualitative data is naturally said in the form of a story, like open interviews where people tell their answers as stories. Narrative analysis advises researchers to take people’s stories seriously and consider their place in the production of the individual’s social world.
The essential characteristics and standard components between all narrations are:
- Beginning: This section shows the introduction and signs of the beginning of a story. People tell their stories in different ways. For example, “was one, was not one” is the beginning of fairy tales and legends.
- Introduction: This section provides basic information. For example, “Who was involved?”, “What happened?”, “When and where?”
- Complexity: Complexity indicates the complexity of the narrative: “What happened next?”, “How are the events related to each other?”
- Evaluation: Evaluation is formed in the face of these questions: “For what?”, “Why is the story important?”
- Results: This section tells the listener or reader of the story what happens in the end.
- End: This section tells the listener that the story is ending.
In this article on smart strategy, we have a comprehensive introduction to qualitative data analysis. You can deep dive into the details of qualitative data analysis methods in our comprehensive introduction to methods of qualitative data analysis article from the smart strategy blog , and if there is any question about smart strategy services, contact us.
Also, you can visit our LinkedIn profile for more digital marketing infographics and summarized articles.