Qualitative vs Quantitative data: Difference, Methods & Analysis

“Qualitative vs. quantitative data: Which one is better?”

“What is the difference between the two?”

“Which type of data will give the best result for my research?”

These are the questions that come to mind whenever you undertake a research project.  Especially when you are involved in the data science or statistics field, it becomes essential to understand the difference between qualitative and quantitative data to perform data analysis to make informed business decisions.

Knowing both data analysis approaches can help you collect customer feedback to understand them better and make decisions responsible for your business’s success.

Read this blog to learn about what makes them different, the different methods for collecting qualitative and quantitative data, and different methods of analyzing data.

Let’s Begin.

What Is the Difference Between Qualitative and Quantitative Data?

In a nutshell, qualitative data is the numerical data that produces information that can be converted into numbers. On the other hand, qualitative data is expressed in words and used to understand thoughts, opinions, and experiences. Now that we have covered the fundamental difference between the two, let’s dive deep into quantitative data vs. qualitative data and their types with examples.

What Is Qualitative Data?

Qualitative Data is non-statistical data that describes the information that cannot be measured in numbers. It is descriptive in nature. Mainly expressed in words, this type of data collects people’s thoughts, opinions, and experiences about a particular statement or subject. The data collection techniques of qualitative data range from unstructured to semi-structured techniques.

Researchers use this data to ask questions like “why” and “how.” It is often used to analyze open-ended studies, allowing respondents to show their true feelings and actions or dig deeper into the problem.

Example of Qualitative data:

Suppose qualitative research tells you that 3 out 5 employees in your company are not satisfied with their job. To determine the reasons behind your employees’ unhappiness, you will conduct a quantitative study to provide insight into how an employee feels about working in your company. In this case, you are not just looking at “numbers”; you are asking employees to express their feelings in “words.”

Qualitative Data Types With Examples

When considering the difference between quantitative vs. qualitative data, it becomes crucial to understand data types with examples. Let’s first look into the quantitative data.

It can be divided into two types: nominal and ordinal

• Nominal data is used to categorize variables without assigning a numerical value to them. For instance, if you gather information on your target audience, you will want to know where they live. Are they based in America, Asia, or Europe? These geographical classifications fall into the category of nominal data.

• Ordinal data is used to classify your qualitative data into the hierarchy or natural order categories. For instance, while conducting customer satisfaction surveys, you might ask the customers to rate their experience with your product or services as “poor,” “satisfactory,” or “good.” Here, it is clear that good is better than good, but there’s no way of quantifying the distance between the two categories.

What Is Quantitative Data?

Contrary to qualitative data, quantitative data describes the information that is quantified – easily measured and counted and can be given a numerical value. Researchers use quantitative data to ask “how much,” “how many,” and “how often” questions. To analyze and examine quantitative data, one must conduct statistical analysis.

Compared with qualitative data, quantitative data is more structured and concise. Qualitative data describes conclusive information that is not open for exploration. Using different statistical methods, it establishes the cause and effect relationship between two variables.

Examples of quantitative data include:

• How many employees attended the training program?
• How much profit did the company generate in the last quarter?
• How often does the customer use an online shopping app?

Types of Quantitative Data and Examples

Quantitative data can be further divided into discrete and continuous data.

• Discrete data is data that cannot be divided or broken down into subparts. This data is finite and consists of integers (positive and negative numbers like 2, 100, -50, -25, etc.) Examples of discrete data would include how many individuals are there in the class and how many Android phones were sold in the last quarter.

• Continuous Data is data that can be broken down infinitely into subparts. It can have almost any numeric value and be measured on a scale or continuum. A few examples of continuous data would include your height, weight, or the time you take in the morning to commute.

Qualitative vs Quantitative Research

One of the key differences between qualitative vs. quantitative data lies in their research methods or how they are collected. 

Let’s look at some of the qualitative vs. quantitative data collection methods in detail:

Types of Qualitative Research Methods

Qualitative data is gathered through interviews, surveys, focus groups, case studies, and observations. Let’s take a look at these methods in more detail:

• Surveys: Here, open-ended questionnaires are shared with the target audience to collect qualitative data from people about their characteristics, attitudes, knowledge, behaviors, experiences, and opinions on a particular subject. Survey questions example include: “Is there anything you would like to change about our services?”

• Interviews: This qualitative research method works best when you want to obtain in-depth insight into people’s experiences and opinions. Usually, interviews are of 2 types: One-on-one interviews and Group interviews. In group interviews, fewer questions are asked since everyone has to have a chance to answer, and there is a time limitation on how long people are willing to stay.

• Focus group: This research method is appropriate for interviewing a small group of people, usually 6-12 people, to discuss specific issues related to the subject being studied. A moderator introduces the topic and uses a prepared interview guide to direct the discussion and elicit ideas, opinions, and reactions but can ask additional questions as per the situation.

• Case Study: It is a detailed examination of people, organizations, projects, events, or communities. A case study is beneficial in examining complex situations and exploring the qualitative impact because it entails data collection from numerous sources. A case study is typically presented in written form, but it can also be shown in the form of images, films, or videos.

• Observation: It is an effective research method for gathering qualitative data on behavioral patterns, physical surroundings, activities, and processes since it involves capturing what observers see and hear in a particular situation.
  Observation is of 2 types: Obtrusive and Unobtrusive. 
  Obtrusive observation is when observations are made with the participant’s knowledge, and it is ‘unobtrusive’ when observations are made without the participant’s knowledge.

• Ethnography: It involves observing and analyzing research topics in a particular geographic place in order to understand better cultures, behaviors, trends, patterns, and problems of individuals or a group in their natural environment. To interact with the group being researched as a participant in their community, researchers must spend significant time with them, usually weeks or months. This makes it a time-consuming research method.

Also Read: Qualitative Research Questions

Types of Quantitative Research Methods

Quantitative data is collected using structured interviews, polls, close-ended surveys, and analytics tools. Let’s take a look at these methods in more detail:

• Closed-ended Interview:  It consists of close-ended questions that allow only a limited answer range like rating questions, multiple-choice questions, or “Yes/ No” Questions. To be effective, quantitative interviews must ask each respondent the same questions in the same way, with no input from the interviewer’s side.

• Closed-ended Surveys and Questionnaires: This qualitative research method is suitable when you want simple, quick feedback that can be measured. Surveys are structured questionnaires with a limited number of closed-ended questions and rating scales used to create numerical data. The data collected is analyzed using various statistical models to obtain insights and make conclusions.

• Experiments: Experiment is another quantitative research method that involves a control group and an experiment group. It is a situation where the variables are controlled and manipulated to establish cause-and-effect relationships. Experiments yield rich and reliable data that is further tested and replicated.

• Analytics Tools: Data scientists and other professionals use special analytics tools to collect quantitative data from multiple sources. For example, Google Analytics allows you to see all the crucial metrics of your website, like the number of visitors and pageviews, at a glance.

Qualitative vs Quantitative Data: Analysis

Qualitative or quantitative data do not provide any insights unless they are analyzed. So, it becomes crucial to understand the difference between qualitative vs. quantitative data analysis.

Let’s have a look at how qualitative and quantitative data are analyzed:

Quantitative Data Analysis

Because quantitative data is based on numbers, some form of mathematical analysis is required to derive patterns in data, and the results are reported through tables and graphs. The methods range from simple mathematical formulas like calculating means and median to more advanced statistical analysis like calculating the statistical significance of your results. Tools like Excel and SPSS are used to calculate:

• The average score of data
• The frequency of a particular answer
• The correlation between variables
• Statistical significance of your results

Also Read: How to Analyze Survey Data

Qualitative Data Analysis

Because qualitative data consists of words, photos, or videos rather than statistics, it is more difficult to analyze than quantitative data. Some of the most prevalent ways to analyze qualitative data are as follows:

• Qualitative Content Analysis: It is a research tool used to track the position, meaning, and occurrence of words, phrases, concepts, or themes within the given qualitative data.
• Discourse Analysis: It involves analyzing the use of language or communication within a social context. It is used to understand the world by investigating the underlying meaning of what people say and how they say it.
• Thematic analysis: It involves carefully examining the qualitative data to identify the key themes and patterns.

Which Type Is Better for Data Analysis?

Now that we have discussed qualitative vs. quantitative data in detail, it is time to determine which data type is better for data analysis. 

If we talk about qualitative data, it is usually unstructured in nature and is known for being subjective. It helps the researcher to gather in-depth insights into particular topics. Since it is expressed in words, it becomes difficult to collect and analyze qualitative data using conventional methods. However, it is still considered valuable data for obtaining qualitative insights.

On the other hand, quantitative data is structured data that includes numerical information that can be easily collected and analyzed using statistical methods. Since this data is more concrete, it is usually preferred for data analysis. 

However, the use of quantitative or qualitative data entirely depends on your research and what you are trying to measure. Using the combination of both qualitative as well as quantitative data also yields the best result for statistical analysis.

Hence, mixing qualitative and quantitative methods and using different data collection techniques is recommended as it offers a more holistic understanding of the subject. It will also provide researchers with more coherent, reliable, and valuable business conclusions.