Qualitative Data Analysis Methods And Techniques

As data is an invaluable source of business insight, the knowing what are the various qualitative data analysis methods and techniques has a crucial importance.

There are different approaches, types of statistical methods, strategies, and ways to analyze qualitative data.




Choosing the best way and technique depends on the nature of your data and the goals set in qualitative research.

On this page:

  • What is qualitative data analysis? Definition.
  • Techniques and methods used in qualitative data analysis: list and explanation.
  • Infographic in PDF.

What is qualitative data analysis?

Let’s define it:

Qualitative Data Analysis (QDA) involves the process and procedures for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data.

Just to recall that qualitative data is data that can’t be expressed as a number. Qualitative data consist of words, pictures, observations, and symbols, not numbers. For more details see our post qualitative vs quantitative data.

Generally, QDA has 4 main steps:

Qualitative Data Analysis Process - steps

  • Becoming familiar with your data. You need to read and re-read the data, write down detailed notes and impressions, and deciding which pieces of data possess value.
  • Coding into themes – this means to create categories and subcategories. Each category is an example of some thematic idea. The goal is to group data associated with some thematic idea which allows them to be examined together.  The list of build themes represents your first set of codes.
  • Searching for patterns and connections – looking for the relative importance of data and identifying relationships between data sets or themes.
  • Interpretation of the data and drawing conclusions.

Qualitative Data Analysis Methods And Techniques

There are a wide variety of qualitative data analysis methods and techniques and the most popular and best known of them are:

1. Grounded Theory Analysis

The grounded analysis is a method and approach that involves generating a theory through the collection and analysis of data. That theory explains how an event or aspect of the social world “works”.

Grounded theory is one of the most popular qualitative strategies that leads to a general, abstract theory of a process or action grounded in the views of people that participate in a study.

The main feature of the grounded theory, which differentiates it from other qualitative data analysis methods is its focus on theory development.

Grounded theory technique is very suitable to use, especially when you have to analyze large quantities of unstructured qualitative data. (to know what unstructured data mean see our post unstructured data examples).

Key data collection methods and techniques that Grounded theory uses:

  • Interview with open-ended questions
  • Focus groups
  • Participant observation
  • Study of texts

Note: data analysis in Grounded theory happens at the same time as data collection. It means you shouldn’t wait until all your data is collected and then analyze it. The methods have to be flexible and change when the data show a new theory or direction.

2. Content Analysis

Content analysis is one of the most widely used qualitative data techniques for interpreting meaning from text data and thus identify important aspects of the content.

It includes processes and procedures for the categorization of text data for the purpose of classification and summarization.

The text can be various formats such as documents, pictures, video, audio, and etc.




The most popular application of content analysis nowadays is in analyzing the social media field. It is used to quantify patterns and trends in social media communication.

You will be surprised with the broad range of content analysis software solutions today. They help you to examine almost any type of unstructured text data such as business documents, emails, social media, chats, comments, news, blogs, and much more.

In addition, the machine learning classification algorithms can significantly increase the amount of text which can be labeled and classified.

Key purposes of content analysis are:

  • To find out important aspects of the content.
  • To present them in a clear and effective way.
  • To support some argument.

In fact, content analysis is not a single method. It has popular techniques and approaches: conventional, directed, or summative. The main differences among the techniques are origins of codes and coding schemes.

3. Narrative analysis

Narrative analysis is also one of the best-known qualitative data analysis methods and techniques.

Narrative analysis is an approach to analyzing different stories people create. The purpose is to understand how people form meaning in their lives as narratives. Click To Tweet

It uses various types of stories such as autobiography, family stories, photos, life experience, as the units of analysis. It focuses on the ways in which people create and use different stories to interpret and explain the daily life and the world.

What can we discover from the narrative analysis?

  • An interesting knowledge and understandings about people, their culture and how they are changing.
  • It helps us making sense of the past and create meanings of it.
  • Helps in organizing information about how humans interpret events.
  • Show us the values and beliefs that guide those interpretations.

A long number of data collection methods can be performed. Data can be in the form of journal records, interview transcripts, observations, autobiographical writing, documents, storytelling, personal accounts, letter writing, and etc.

4. Discourse Analysis

Discourse analysis is about analyzing the natural occurring talk – spoken communication and all other types of text.

It studies the meanings that the use of language and spoken interactions produce and the contexts of these meanings.

This method not only analyses communications but also takes into consideration the social context in which the communications happen.

This technique can also include analysis of written texts, such as emails or the body language.

Discourse analysis is a field of linguistics which focuses on the role of language in the building of social reality.

Typically, discourse analysis brings together the analysis of language use and the analysis of situations.

5. Framework Analysis

Framework is one of the qualitative data analysis methods that are gaining popularity across multiple areas, including health, social policy, and psychology.

This approach is used to organize and manage data with the help of the process of summarization. It results in a powerful and flexible matrix that allows you to analyze data both by theme and case.

The matrix consists of rows (cases), columns (codes) and ‘cells’ of summarised data. Thus it provides a structure into which you can systematically cut the data, to analyze it by case and by code.

Using theme display, framework technique lets you analyze your data thematically and to perform exploratory analysis of the entire dataset.

Framework analysis identifies similarities and differences in qualitative data, thereby aims to draw descriptive or explanatory conclusions grouped in themes.

Framework analysis is one of the easiest qualitative data analysis techniques.

It has very clear steps you can follow and makes highly structured outputs of summarised data.

Download the following infographic in PDF for free

Qualitative Data Analysis Methods - infographic

Conclusion

You have a good range of qualitative data analysis methods to choose from, in order to achieve the main purpose of qualitative analysis – to explain, understand, and interpret data.

There are many software solutions (such as Atlas.ti) designed to assist you with the analysis of qualitative data.

Moreover, for the advanced users, there is a free qualitative data analysis software able to analyze a large amount of text.

They have enhanced people’s ability to perform data analysis by handling multiple means and ways of collecting, sorting, presenting, and analyzing raw data.



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