Qualitative Data

In the field of analysis, the term “qualitative data” and “quantitative data” are used frequently. Quantitative and Qualitative are the two sides of the coin named “Data in Statistics” but as many people are familiar with quantitative data (i.e., numerical data of various sorts), qualitative data is often less understood. Understanding the qualitative data is essential for researchers, analysts, decision-makers, or anyone who wants to gain deep insights into people’s behaviors, attitudes, and experiences. 

In this article, we have tried to explain Qualitative data in simple and easy-to-understand language and this article can be a comprehensive guide for Quantitative data. In this article, we will explore the world of qualitative data, with different approaches to its analysis, and also learn about the advantages and disadvantages of the same.

Types of Data in Statistics

The grouping of data can be based on the quantitative and qualitative aspects of the gathered information, hen data can be classified into the following types:

• Qualitative Data
• Quantitative Data

What is Qualitative Data?

Qualitative Data uses variables to represent labels or characteristics of entities or objects, such as movie genres or travel methods. The labels cannot be represented in numerical form, and their numerical values may not hold any significance. Qualitative data is also known as categorical data it is expressed through indicators and deals with perceptions. 

Qualitative data cannot be averaged, and aggregate methods like mean or average do not hold for non-numerical data. Qualitative data can be grouped based on categories, and it is useful in determining the frequency of traits or characteristics. For instance, the color of hair can be categorized into three main colors, being, black-brown or blonde. It deals with perceptions. Qualitative data is useful in determining the particular frequency of traits or characteristics.

Example of Qualitative Data

There are several examples of Qualitative Data in the real world, some of these examples are:

• Interview transcripts: Data collected from survey forms after the interviews can provide rich qualitative data that describes the opinions, attitudes, and experiences of participants.
• Observation notes: When observing a behavior or phenomenon, recorded data of that phenomenon is also an example of qualitative data as it can tell us about the characteristics, context, and nuances of the observed phenomenon.
• Open-ended survey responses: In a survey, there are some open-ended questions sometimes to know about the participant’s experiences, perceptions, and opinions on a given topic. This data is also an example of qualitative data.

Features of Qualitative Data

The features or characteristics of the qualitative data are as follows.

• Qualitative data is descriptive in nature i.e., it describes and explains the phenomenon in-depth, and often provides rich contextual information as well.
• Qualitative data is non-numerical in nature, i.e., it is in the form of notes, photos, and survey forms.
• As this data is descriptive and non-numerical, it can be interpreted by different people differently, thus the analysis of this kind of data varies with the researcher.
• In Qualitative data collection, collected data is not very specific. It has open-ended responses and detailed answers and experiences provided by the participants.

Types of Qualitative Data

Qualitative data can be further categorized into the following types:

• Nominal Data
• Ordinal Data

Let’s understand these types in detail as follows.

Nominal Data

Nominal data is represented using names, as indicated by their Latin origin. It includes named or labeled data and does not take numerical values into consideration. For example, different movie or series genres, such as horror, sci-fi, and rom-com, are nominal categorical data. They are labeled in different forms.

Ordinal Data

Ordinal qualitative data uses a certain scale or measure to group data into categories or groups. The data is generally ordered or measured, but the scale used to represent the data may not be standard or specific. This type of data includes numerical values and displays properties of both categorical data and numerical data. Categorical data can be analyzed by making groups, and it can be visually represented using bar graphs. Ordinal categorical data can be illustrated using surveys that use numbers to compute comparison data belonging to groups under categorical variables.

Analysis of Qualitative Data

Analysis of data is a much more crucial part than the collection of it as data in itself without analysis didn’t tell us anything about the phenomenon for which it is collected. As for the analysis of Qualitative Data, there can be two main approaches:

• Deductive Approach
• Inductive Approach

Deductive Approach

The deductive approach to qualitative data analysis starts with the preconceived ideas or concepts for which we collect data and analyze it to see if the evidence supports or nullifies these preconceived ideas. Some steps involved in using the deductive approach to qualitative data analysis:

1. The first step in this approach is to develop a theoretical framework based on thorough research, which further will be proved by the data or not.
2. After the completion of the framework or hypothesis, we collect the data using various means.
3. After the collection of data, we use programming languages to code Machine Learning models to find the patterns which are relevant to our hypothesis.
4. After all this, we analyze the results and draw a conclusion on whether our hypothesis is correct or not or if it needs much more data to conclude.

Inductive Approach

The inductive approach to qualitative data analysis starts with the collection of data and works its way towards identifying patterns, and themes. It is an approach researchers explore various different themes and concludes the results as the hypothesis with the evidence from the data. Unlike the deductive approach, here researchers always arise at a conclusion with some correct hypothesis.

The following are some steps involved in using the inductive approach to qualitative data analysis:

• In this approach, we first collect the data using various methods such as interviews, observations, and various focus groups. This data is usually in the form of audio recordings, transcripts, notes, or photos.
• After the collection of data, basic coding procedures start which helps us identify and label the segments of data which represents similar idea and concepts.
• After the labeling and identification of data are complete, some basic patterns arise in the data, which need further research and pattern finding where the Machine Learning models are used.
• After all the research data is used as evidence to publish the findings of the research. Now a hypothesis or framework is developed using all the themes and patterns to support the framework.

Difference between Nominal and Ordinal Data

Some key differences between both types of Qualitative Data can be listed in the following table:

Feature	Nominal Data	Ordinal Data
Definition	Data that is not ranked or ordered in any way.	Data that is ranked or ordered in a specific way,
Examples	Gender, Color, Marital Status, Nationality	Education Level, Income Range, Satisfaction Level
Arithmetic operations	Cannot perform any arithmetic operations.	Can perform basic arithmetic operations such as  addition and subtraction, but not multiplication or division
Measures of Central Tendency	Mode	Mode, Median
Measures of Dispersion	None	Range, Interquartile Range

Advantages and Disadvantages of Qualitative Data

There are advantages and disadvantages to using Qualitative Data, as data is very rich in nature so a collection of this type of data is very useful for many cases, but there are some disadvantages of it as well. Let’s dive into the advantages and disadvantages of Qualitative Data in detail.

Advantages of Qualitative Data

Some advantages of Qualitative Data are as follows:

• Richness and depth of data: Qualitative data provides a rich and in-depth understanding of the phenomenon being studied and can also reveal complex relationships, social norms, and cultural practices.
• Flexibility: Qualitative research is flexible, which means that researchers can adapt their methods of collection, amount and type of data collected, and analysis to the specific needs of the study. 
• Participant perspectives: Qualitative data is often collected through interviews, which provide the researcher with the opportunity to understand participants’ perspectives. 
• Uncovering hidden phenomena: Qualitative data is particularly useful when researchers want to explore new or under-researched topics. This type of data can reveal previously unknown phenomena or provide insight into existing topics.

Disadvantages of Qualitative Data

Some disadvantages of Qualitative Data are as follows:

• Subjectivity: As qualitative data is often collected through interviews or observations, that’s why the researcher’s own biases and beliefs can influence the data and that can lead to subjective interpretations of the data.
• Small sample sizes: Qualitative research typically involves small sample sizes, which can limit the generalizability of the findings. 
• Time-consuming: Qualitative research can be time-consuming, particularly when compared to quantitative research as all the steps in this type of data are time-consuming from collection to analysis.
• Difficulty in data analysis: Qualitative data is often complex and difficult to analyze. Researchers need to be skilled in data analysis and interpretation to ensure that their findings are accurate and reliable.

Read More,

• Different Types of Data
• Difference Between Qualitative and Quantitative Data

Sample Questions on Qualitative Data

Question 1: To which category, the game data for the game “name, place animal or thing” will belong?

Solution: 

Qualitative data will be used to illustrate the type of data used to represent the names for the places, animals, things. 

Question 2: Which type of data is used by the evaluator to grade the students using a range of marks?

Solution:

The marks are expressed in the range, or using perfect integrals. Ordinal data is used to represent the range of data distribution used by the evaluator.  

Question 3: The following table depicts the percentage of people who prefer a certain movie genre. Can you represent this categorical data using a pie chart?

Sports	Percentage of Students
Cricket	25%
Table Tennis	35%
Football	40%

Solution:

Sports	Percentage of students	Calculation of Angle [Angle = (Percentage / 100) x 360°]	Angle
Cricket	25%	Angle = (25/100) x 360°	90°
Table Tennis	35%	Angle = (35/100) x 360°	126°
Football	40%	Angle = (40/100) x 360°	144°

Thus, pie chart of the given qualitative data is as follows:

Question 4: The following bar graph depicts the ordinal categorical data of the mobile phone company according to price range.

Answer the following questions according to the bar graph

• What is the total price if someone buys all three brands of phones?
• Find the average price of all the phones.

Solution:

Based on the observations made from the bar graph:

• Total number of students = 30k + 55k + 45k = 130k
• Average of the price of all the Phones = (30 + 55 + 45)/3 = 130/3 = 46.67 k

FAQs on Qualitative Data

Q1: What is Qualitative or Categorical Data?

Answer:

Qualitative data is the non-numerical data which describes the qualities, characteristics, and other descriptive information about the phenomenon or the subject for which data is collected.

Q2: What are Some Examples of Qualitative Data?

Answer:

Some examples of qualitative data include survey forms of interviews or focus groups, observational notes, photographs, and other forms of non-numerical data.

Q3: What are Some Common Methods for Collecting Qualitative Data?

Answer:

Qualitative Data is often collected through observation, interviews, focus groups, and other forms of subjective data collection methods.

Q4: How is Qualitative Data Analyzed?

Answer:

Qualitative data is typically analyzed using two approaches which are covered in detail in this article.

• Deductive Approach
• Inductive Approach

Q5: What are the Advantages of Qualitative Data?

Answer:

Some advantages of use of qualitative data are:

• Qualitative data provides in-depth information about a subject or phenomenon. 
• It can also provide rich descriptions of the context and social interactions surrounding the subject or phenomenon. 
• Qualitative data can be more flexible in terms of data collection and analysis methods, allowing for more creative and iterative approaches to research.

Q6: What are the Disadvantages of Qualitative Data?

Answer:

Some disadvantages of use of qualitative data are:

• Qualitative data can be time-consuming and resource-intensive to collect and analyze. 
• As qualitative data is subjective and interpretive, there may be concerns about the reliability and validity of the data. 
• Qualitative data may also be less generalizable than quantitative data, as it is often focused on specific contexts and perspectives.