Why is coding important in qualitative analysis

You know that asking open-ended survey questions gives you more actionable insights than asking your customers for just a numerical Net Promoter Score NPS. But when you ask open-ended, free-text questions, you end up with hundreds or even thousands of free-text responses.

By coding qualitative data. Coding is the process of labeling and organizing your qualitative data to identify different themes and the relationships between them. When coding customer feedback , you assign labels to words or phrases that represent important and recurring themes in each response. Coding qualitative research to find common themes and concepts is part of thematic analysis.

Thematic analysis extracts themes from text by analyzing the word and sentence structure. Qualitative data analysis is the process of examining and interpreting qualitative data to understand what it represents.

Qualitative data is defined as any non-numerical and unstructured data; when looking at customer feedback, qualitative data usually refers to any verbatim or text-based feedback such as reviews, open-ended responses in surveys , complaints, chat messages, customer interviews, case notes or social media posts.

For example, NPS metric can be strictly quantitative, but when you ask customers why they gave you a rating a score, you will need qualitative data analysis methods in place to understand the comments that customers leave alongside numerical responses.

While manual human analysis is still popular due to its perceived high accuracy, automating the analysis is quickly becoming the preferred choice. The most commonly used software for automated coding of qualitative data is text analytics software such as Thematic.

Coding qualitative data makes it easier to interpret customer feedback. Assigning codes to words and phrases in each response helps capture what the response is about which, in turn, helps you better analyze and summarize the results of the entire survey.

Researchers use coding and other qualitative data analysis processes to help them make data-driven decisions based on customer feedback. When you use coding to analyze your customer feedback, you can quantify the common themes in customer language. This makes it easier to accurately interpret and analyze customer satisfaction. Methods of coding qualitative data fall into two categories: automated coding and manual coding.

You can automate the coding of your qualitative data with thematic analysis software. Thematic analysis and qualitative data analysis software use machine learning, artificial intelligence AI , and natural language processing NLP to code your qualitative data and break text up into themes.

Businesses are also seeing the benefit of using thematic analysis softwares that have the capacity to act as a single data source, helping to break down data silos, unifying data across an organization. This is now being referred to as Unified Data Analytics. Thematic coding, also called thematic analysis, is a type of qualitative data analysis that finds themes in text by analyzing the meaning of words and sentence structure.

When you use thematic coding to analyze customer feedback for example, you can learn which themes are most frequent in feedback. This helps you understand what drives customer satisfaction in an accurate, actionable way. To learn more about how thematic analysis software helps you automate the data coding process, check out this article.

Different researchers have different processes, but manual coding usually looks something like this:. Deductive coding means you start with a predefined set of codes, then assign those codes to the new qualitative data. Deductive coding is also called concept-driven coding. Researchers prefer to code manually when the data to be coded is small its drawback is that it is now outdated, tedious and time consuming approach.

The researcher will make a codebook to write all the codes and the definitions and other details about the codes. The researcher however uses electronic coding when the data includes videos and audio that is not transcribed. Coding electronically allows the researcher to easily organize codes, run code frequencies, explore relationship between codes and do memoing.

Its drawback is that the researcher needs to be familiar with the functions of the software before starting the process. Tags codes and coding coding coding in qualitative research qualitative research. Your email address will not be published. Coding in Qualitative Research ResearchArticles. To read the full version of this content please select one of the options below:.

Other access options You may be able to access this content by logging in via your Emerald profile. Rent this content from DeepDyve. Rent from DeepDyve. If you think you should have access to this content, click to contact our support team. Contact us. See how to do simultaneous coding. After your first round pass at coding data, you can begin to group your codes into categories. These categories can be organized in a variety of ways. Within each category, you can group together codes that are similar to each other, or pertain to the same topics or general concept.

Iterate on these categories and move the codes around until you find a structure that makes sense for your analysis. While the first round pass at coding data was fast and loose, these rounds of coding are about reanalyzing, finding patterns, and getting closer to developing theories and concepts. In general you should be reducing the number of codes from your initial round of coding, and actively reflecting on how to best categorize the codes you have.

Here are some methods of coding data that are commonly used in second round coding and beyond. If you find a pattern within different parts of your data or see that certain excerpts point to the same underlying idea or meaning, code those excerpts with a unifying code. See how to do thematic analysis. With Pattern coding, you group of similarly coded excerpts under one overarching code to describe a pattern.

You then re-code the data according to this final code list with the intent to not deviate from it. With axial coding , you relate codes or categories to one another. See how to do open, axial, and selective coding. With theoretical coding, you conceptualize a hypothesis of a theoretical framework through sorting and organizing codes.

You structure the codes and categories that emerged from data into a theory. With elaborative coding, you apply a theory from a previous research study and observe whether or not your current codes and categories relate. You can think about it as elaborating on pre-existing theories. With longitudinal coding, you organize your existing codes and categories in a way that enables you to compare them over time.

Directed content analysis is a deductive approach to qualitative analysis where you start with an existing theory or framework and utilize data to either support or build upon that framework. See our step by step guide to content analysis. After these rounds of coding qualitative data, you take those codes and categories and use them to construct your final narrative. Depending on the purpose of your research, the final outcome of your research can take many forms: a theory, a set of findings, or a narrative.

In this phase you combine the creativity of structuring a narrative with the analytical nature of connecting your narrative to your codes and theories grounded in data.

Start writing out your theory, findings, and narrative, and reference the codes and categories that were used to inform them. Now, structure these into your final research deliverable. It is important to consider validity and reliability when conducting qualitative research. Peer briefing is the process of working with one or more neutral, independent peers to enhance the credibility of qualitative research.

See how to do peer debriefing. Reflexivity involves examining your own judgments, practices, and belief systems during the data collection process. The goal of being reflexive is to identify any personal beliefs that may have incidentally affected the research.

See how to practice reflexivity. Intercoder reliability ensures that when you have multiple researchers coding a set of data, that they come to the same conclusions. See how to do inter coder reliability. Negative case analysis involves finding and discussing contradictory data emerging from your working hypothesis or theory.

See how to do negative case analysis. Depending on the amount of data you need to analyze, and various constraints around your research, you can code by hand, using word processors and spreadsheets such as Microsoft Word and Microsoft Excel, or use Computer Assisted Qualitative Data Analysis Software such as Delve. There are pros and cons to each approach, and you should choose one based off what is most appropriate for your research.

Read more about how to code qualitative data. What you need: Printed out data, scissors, pen, collection of highlighters with varying colors. How to do it: Print out your data onto physical sheets of paper. Do your first round pass of coding by reading through your data and highlighting relevant excerpts.

Jot down the names of the codes in the columns. Do your 2nd round pass of coding by printing out your data again, this time cutting out each individual excerpt. Create piles of excerpts for each code. Pros: This is a great way to feel your data in a tactical way.

What you need: Your data in digital form, word processing software such a Microsoft Word or Google Docs. How to do it: Create a folder for all your data on your computer. Read through your data sets and highlight excerpts that are relevant. Create a separate word document for each code. Copy and paste the excerpts into this word document. Pros: Since most people are familiar with using word processors, the interaction should feel intuitive.