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Are you obsessed with finding patterns in data? Do you want to learn more about data analysis and ways to break into this interesting line of work? If you answered yes to these questions, this article is a great place to start. 

In this article, we’ll take a deep dive into the process of qualitative data analysis, learn what it is, how it works and some effective methods of analyzing data.

What is Data Analysis?

Data analysis is the process of inspecting, organizing, transforming and modeling collected data to identify patterns, connections and relationships. 
With it you can gather useful information and use it to draw constructive conclusions.

While many companies and researchers prefer analyzing numeric data (quantitative data), analyzing seemingly inconclusive non-numeric qualitative data can inform key insights and decisions that numeric data alone cannot.

What is Qualitative Data?

Qualitative data refers to non-numeric narrative or descriptive information such as interview transcripts, conversations, video and audio recordings, images and text documents: highly varied data about people’s opinions, values and behaviors. Since most of this kind of data falls easily into groups, qualitative data is also known as categorical data.

Why Qualitative Data Analysis is Useful

It isn’t always possible to obtain numerical data. With no quantitative data to fall back on, researchers must often rely on qualitative data for results. And although qualitative data sets can be huge and diverse, analyzing them can provide valuable insights impossible to produce using numerical data alone.

Qualitative data analysis can help market researchers understand the mindset of their customers and  provide insight into why a customer purchased a product, which then aids in developing better products that suit customer needs. 

Analyzing purely numerical data (sales, revenue figures) without considering variable qualitative data (past experience, client feedback, customer reviews) may lead to incomplete findings or incorrect conclusions.

Qualitative data analysis also helps identify important categories, patterns and relationships in data sets and brings into consideration nuances, emotions and behavior not suitable for numerical analysis.

How Qualitative Data Analysis Works

Analyzing qualitative data (words, images, observations, narratives and conversations) involves ingesting and organizing the data, then assigning categories to make it possible to identify patterns and relationships.

Collect data: Your data may take the form of interview transcripts, focus group logs, conversations, notes, video and audio recordings, surveys, reference materials and text documents of any kind.

Organize your data: Ideally, your data collection plan included an organizing principle from the outset. If your data consists of questionnaire results, you can organize based on the exact questions asked. Also bear in mind that it’s vital to account for possible biases or predispositions in the input so you can interpret the data accurately.

Create a numeric model: Also called coding, this step involves categorizing and labeling the collected data with properties to make it more comprehensible. A numeric model helps reveal patterns to gain deep insights into the data, leading to informed decisions based on the research.

Validate your data: For research data to be effective, you need to filter out incorrect data. Validation isn’t just one step in qualitative data analysis but a continuous process throughout the course of the research, taking into account the accuracy of your research methods.

Analyze the data: Now you can begin analyzing your data. Start by scanning the preliminary data for recurring themes, phrases and words commonly used by respondents, and unusual expressions that show up in the data. Identify aspects and topics you expected to crop up that didn’t get addressed or mentioned by the respondents. If applicable, compare these findings with the observations and inferences made by other research and highlight similarities and differences.

Present the analysis: After the analysis, you must present your findings in the form of a report, which should cover the research method you used, its objectives and limitations. Detail the inferences and suggestions obtained from your analysis that may benefit future research.

Now that we’ve learned about the steps involved in analyzing non-numeric data, let’s take a look at some commonly used methods for analyzing qualitative data.

The Qualitative Data Analysis Methods You Should Know About

Here are some of the most commonly used qualitative data analysis methods for analyzing and quantifying qualitative data:

Content analysis: In content analysis, an analyst categorizes, tabulates and summarizes verbal or behavioral data. Content analysis is usually used to analyze responses from interviews.

Narrative analysis: This method focuses on stories shared by people in interviews, field observations, or surveys based on their varied experiences to answer research questions. 

Discourse analysis: Like narrative analysis, discourse analysis addresses naturally occurring speech and text of all kinds. This kind of analysis bears on interactions between people and the social context and environment in which the researcher and respondent interacted.

Grounded theory: This method involves the use of qualitative data to understand why a certain phenomenon happened. Researchers start by studying a single case to formulate a hypothesis and then consider similar cases in different settings to examine if they fit the hypothesis. A hypothesis is altered or changed entirely as researchers study more cases until they reach an explanation fitting all cases.

Summary

In this article, we learned what qualitative data analysis is all about, why it is useful, how it works, and its most common methods of it. 

In an age where data represents an invaluable resource, tapping into all available data and extracting meaningful insights from it’s an immensely valuable skill that enterprises seek out when making key strategic decisions. 

If you aspire to become a certified data analyst and to pursue a career in a growing field with many opportunities, you should definitely check out the Data Analyst Nanodegree Program on Udacity. This program will teach you how to use Python, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions.

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