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Qualitative Data – Types, Analysis, Methods, Pros-Cons & Examples

Understanding & defining qualitative data

Qualitative data can be defined as the type of data that characterizes and approximates but cannot measure the properties, attributes, or characteristics of a phenomenon or a thing. The data can also be recorded and observed are generally non-numerical in value. The scientists doing any type of research implement the qualitative data to chalk out the characterizations of polling demographics. These were employed to create a strategy on how to get the required vote in certain ways.

Qualitative data are a collection of various types of information, collected via methods like one to one interview, observations, organizing a focus group, and many others like this. The other name of this data is categorical data as it can be arranged categorically depending on the properties, phenomenon or a thing.

Also read: Quantitative Data – Types, Analysis, Methods, Pros-Cons & Examples

Different Types of Qualitative Data

Qualitative data can be classified into two types namely Ordinal and Nominal Data. Let us see each of them clearly as below.

  1. Nominal Data: Nominal data is also known as the Nominal Scale.
  2. Ordinal Data: This data has order & categories but the differences or the gap between them is not very well defined.

Nominal Data

It is a classification of the categorical variables that do not offer any quantitative value. The other names of nominal data are labeled data or named data. This type of data is used to name or label different types of variables without disclosing any quantitative value.

Nominal data is not easy to manipulate by the application of mathematical operators unlike ratio or interval data. For instance, a researcher may require creating a database of the location and phone numbers of a group of people. An online survey like a name with a phone number and country code can be created. The country code will be on the closed input alternative, while the phone number should be open.

Ordinal Data

This type of qualitative data includes the variables that have ordered and natural categories. But the distance between these categories of the data is not exactly known. Ordinal data is collected when the consumer gives ratings for his/her satisfaction on a different scale like excellent, average and not good.

The best example of ordinal data is the severity of a bug in the software, spiciness of the food, speed of the runner and many more. In some cases, ordinal data is referred to as being in between the quantitative and qualitative data. This is because it can involve the attributes of both types of data.

Qualitative Data

Related: Data Presentation & Analysis, Strategic Analysis, SWOT Analysis

Importance of Qualitative Data

The importance of Qualitative data can be clearly seen below.

  • Qualitative data has a significant role in determining the frequency of the characteristics or attributes.
  • It assists the researchers or mathematicians to create different parameters via which a larger number of data sets can be formed.
  • Qualitative data offers the means to the researchers to quantify everything that revolves around them.
  • For a market research team, qualitative data offer them answers for the questions like what problems or issues are their customers facing? What type of customers do they have? Where should the company shift its attention so that all the issues with the customers are resolved?
  • Qualitative data deals with the issues efficiently and effectively by capturing the perceptions and emotions of the people.

Characteristics of the Qualitative Data

Below are a few of the characteristics of Qualitative data consists. Take a quick look at it. 

  1. Numeric Values: Qualitative data may take up numeric values but does not include any numeric properties. This is most commonly observed in the ordinal data.
  2. Analysis: Qualitative data is mainly examined employing median, frequency and mode distributions. Ordinal data implements median and frequency to obtain the result whereas nominal data utilizes the only mode to conclude the results.
  3. Types: As we already know, qualitative data are categorized into two types namely ordinal and nominal data.
  4. Order: Only ordinal data includes order and scale in it. But this scale does not follow any standard measurement theory.
  5. Visualization of Data: Qualitative data also includes some techniques for visualization of the data like pie and bar charts.

Advantages of the Qualitative Data

Qualitative data comes with a variety of advantages out of which some of them are:

  • Assists in the in-depth analysis: Qualitative data offers researchers with in-depth knowledge of the subject. The researchers investigate the participants and can collect the correct amount of data by asking the right questions at the right time to them. From a different series of questions and answers, conclusions can be drawn from the collected data.
  • Understanding the mentality of the Customers: Qualitative data helps the companies to understand the mentality of the consumers and what they think about the product or the services. The data gives a detailed insight into the organization about why the consumer will buy that product. By understanding the language of the customers helps the market research team to infer the data collected more systematically.
  • Rich Data: The qualitative data that is collected can be implemented to research in the future also. Since the questions asked during the analysis of the qualitative data are open-ended, the interviewee has full freedom to express their views that leads to the collection of additional information.

Disadvantages of the Qualitative Data

Along with innumerable advantages, qualitative data includes some disadvantages also which are:

  • Very Difficult to Generalize: Since the opinions of a few people are taken into account during the analysis of the data, it becomes very much difficult to generalize the results of the population.
  • Time taking Method: A collection of qualitative data is time-consuming. If budget and time are not allowed, then only the opinions of a smaller number of people can be recorded.
  • Full dependent on the skill of the Researcher: Most of the collection methods like one to one interviews focus groups, and observations completely rely on the skill and experience of the researcher. If he/she does not know what to ask, then the analysis may remain inconclusive.

Other Pros of Qualitative Data

Let us see what are the other pros that can be found in the qualitative data as below.

  • Open-ended questions might begin a new conversation that can expect the scope of the research in the future.
  • Qualitative data assists the researchers to make accurate assumptions on the subject
  • It offers in-depth detail as qualitative data also records the emotion of the participant.
  • This type of data also simulates the differences in the behavior of the individuals.

Thus we can conclude that qualitative data is vital because it allows the researchers and the statisticians to create some parameters to observe large data sets. Qualitative data offers the means through which the observers can quantify the atmosphere surrounding them. This type of data plays a key role in analyzing the frequency of the characteristics or traits, size, dimension of the objects, and many more like this. Qualitative data forms an integral part of statistical analysis, especially when combined with quantitative data.

Also Read: Competitor Analysis, Cluster Analysis, Information Processing Cycle

Methods of data collection in qualitative research

Qualitative data is investigative in nature and requires in-depth research and analysis. The collection methods of the qualitative data are mainly dependent on the reasoning, insights, motivation to go deep into the research. All the data collection methods or tools are structured to a limited event as qualitative data cannot exactly be measured. The different collection methods of the qualitative data are as follows.

Focus groups: A Focus group is a type of setting for group discussion. It contains almost 6 to 10 people and a moderator is also present to organize the ongoing topic discussion. Depending on the type of data that is sorted, the group members might have a common element. For instance, a researcher who is conducting a study on the track runners will include who are or were track runners or have enough knowledge on the subject.

Observation Procedure: In this process of qualitative data collection method, the researcher keeps an enthusiastic eye on the correspondents and takes down all the notes. This process is called observation. Moreover, the researcher can also include various methods for recording the documentation other than jotting down notes like audio and video recording, photography and typing.

Case Studies: Qualitative data is collected by an in-depth analysis of the case studies. This method can be implemented to examine both complex and simple subjects. Case studies judiciously utilize the combination of one or more than one qualitative data collection methods to conclude the inferences.

One to One Interviews: This is the most commonly used method for the collection of qualitative data. The researcher questions the person on one to one basis and collects the necessary data. The interview can be conversational, unstructured, formal or informal. Most of the questions asked in these types of interviews are spontaneous and open-minded.

Keeping the Records: In this method, a person takes advantage of the already available documents and other data sources. This existing data can be included in the new research. This is exactly like visiting a library for collecting information. A person can go through various reference materials and books for the collection of relevant data.

Longitudinal studies: In this type of collection method, a similar source of data is continuously repeated over an extended time. Longitudinal is an observational method for research that can continue for a few years or even for decades. The main motive of this method for qualitative data collection is to identify the correlations through a comparative study of the subjects with the most common attributes.

Questionnaires or Surveys: This is the most effective method to collect qualitative data from a group of participants. Traditional questionnaires or surveys are printed on paper and are handed to the respondents to be filled by them and submitted back to the researcher manually. But now most of the researchers create an online survey or a questionnaire and send the link to the participants. This is much more convenient and time-saving than the traditional way. This is because a large amount of data can be collected within a short time and without any delay.

Qualitative Data Analysis

Analysis of Qualitative Data

A full analysis of the data is extremely vital since both time and money are invested in collecting it. There are no strict rules in analyzing the qualitative data. Two main approaches are implemented in Qualitative data analysis. They are:

Deductive Approach

The deductive approach for the analysis of qualitative analysis is based on a predetermined structure by a researcher. The researcher can use the guidelines or questions for proper examination of the data. The deductive approach is easy and simple to implement as the user has a good idea about what type of reactions can he/she expect from the population.

Inductive approach

There is no predetermined framework or structure for an Inductive approach that the user can follow. This approach is more tiring, time- consuming and requires thorough research for the analysis. The researcher uses this approach when there is little or no idea on the subject.

What are the steps for conducting a Qualitative Analysis?

By following the below steps, you can easily carry out a robust analysis of the qualitative data.

  1. Arrangement of the Data: Once all the data are collected, they are mostly in the unstructured form. They make no sense at all. Thus all the data that has been collected needs to be transcribed at first. The data have to be arranged systematically. All the data should be converted into text format. This can be done manually by typing everything into a spreadsheet or any software can be used to carry out the analysis of the qualitative data.
  2. Organizing the Data: After the complete transformation and arrangement of the data, the next step in the analysis is to organize the data. There might be still a large quantity of information that needs to be organized in a systematic way to carry out the analysis without any difficulty. The data can be organized based on the objectives of the research or the questions asked. Always arrange the data in the table format so that everything remains visually clear in front of the eyes. Do not start the analysis without organizing the data, or else you will end up spending more time without obtaining any conclusive result.
  3. Set up a code System for the Collected Data: Coding is the most effective way to compress large information in an orderly way. Assignment of patterns and properties, and categorizing it based on questions are the two important steps in setting up a coding system for the qualitative data. Moreover, coding helps the researcher to derive necessary theories from the relevant findings of the research. After the assignment of the codes to the research, the user can move forward by building the patterns to have an in-depth insight into the data. This will help him/her to make informed decisions.
  4. Validation of the Data: Correct validation of the data is one of the significant steps in the analysis of the qualitative data. Data is prototypical for the research. Thus it is absolutely vital to ensure that the data is not tampered or flawed. Validation of the data is not just a one-step procedure. It is a repetitive process that needs to be executed throughout the analysis of the qualitative data. There are two ways to validate the data and they are:
    1. The accuracy of the design and models
    2. The extent of reliability to which the methods can create consistent and accurate data
Conclusion of the analysis of the qualitative data

The final step is to present a report of the analysis that can be readily read. The report should mention the type of methods used to research the entire positive and negative aspects, research studies and limitations of the study. The suggestions and finding references should also need to be mentioned in the report along with a scope of future research.

How to Share a Qualitative Data Analysis?

The findings through the qualitative analysis are mainly shared by the researchers in the framework of mathematics. Some of the most creative ways to share the outcomes of the qualitative data are:

  • Work Clouds are utilized to understate or emphasize the outcome frequency in the sample of data.
  • Graph database demonstrates the relationship between different data attributes. A graph database, also known as concept maps implements various color, size, and placement to determine the value of the elements in the data sample.
  • Once the coding of the data is finished, the researchers also employ numerous visualizations like bar or pie charts, histograms and many more.
  • Qualitative data is also shared in the form of an example, clip or quote to support the results drawn from the analysis of the data.  

Tools for Qualitative Data Analysis

Analysis of qualitative data depends on the tools for organizing, systemizing and analyzing the non-numerical data. Some of the most commonly used tools for Qualitative analyses are as below.

SWOT analysis: SWOT analysis stands for Strength, Weakness, Opportunities, and Threats. SWOT analysis is the foundation for the examination and identification of the external and internal factors that may affect the viability of the product or services, place, person, or project. It is useful to understand the dynamics of the qualitative data that may have an impact on the business.

Porter’s Five Forces:  This tool was first discovered by Michael E. Porter to assist and improve the SWOT analysis.

Examples of Qualitative Data in Research

The best example of qualitative data is a group of students reading the same paragraph from the same textbooks one by one during a class. A teacher who is carefully listening to the reading gives feedback in return on how each of the students is doing. The teacher can return feedback based on the intonation, pronunciation clarity, fluency, and throw of words. Without any gradation on marks, this type of feedback is known as the qualitative data.

Qualitative data do not involve any numerical numbers in its definition of attributes. Examples of Qualitative data are the cake is blue, purple, and red in color, or the females have red, burgundy and black hair color.

Qualitative data can be referred to as any type of quantifiable information that can be implemented in any statistical analysis or mathematical calculation. Qualitative data assists in making real-life decisions derived from mathematical calculations. This data helps to answer questions like how often? How many, or how much? Qualitative data can be validated and verified. In other words, qualitative data mainly deals with the characteristics that cannot be quantified but can be observed subjectively like taste, texture, color, smell, and attractiveness. Let us see some more examples of qualitative data in the research field.

Qualification – When asking the questions for job application forms, the application usually needs to enter his/] or her qualification. The qualification of a person can be collected in two ways through

  1. Open-ended questions like what is the highest qualification. _______________________________________
  2. A closed-ended question like what is the highest qualification?
    1. B. Sc
    2. B.Com
    3. B.tech
    4. Others

Questionnaires and Surveys – Researchers implement numerous questionnaires and surveys in the process of collection and investigation of the data. This is simple and the easiest one to fill out.

Likert Scale – Likert Scale is a type of point scale mainly utilized by the researchers to accomplish the surveys and get the opinion of the participants on the corresponding subject. For instance, how is the food?

  • Very good
  • Good
  • Average
  • Bad
  • Worst

Competitive Analysis – Companies sent out a variety of questionnaires to the targeted market during the process of competitive analysis. They implement this method to scrutinize the popularity of the competitor for a similar kind of products or services. For instance, which of the following payment platforms have you recently used?

  • Paystack
  • Paypal
  • Flutterwave
  • Stripe

You can even ask the participants by adding an extra question like how frequently do they use the platform? This will definitely provide some more information to the researcher.

The severity of the Bug – Researchers give a thorough run of the software or a website to check for the severity of the bugs. The extent to which the bug can cause damage to the website or software is known as severity. Severity can be rated according to high, medium and low. The data related to the measurement of the bug severity is collected either on an ordinal or nominal scale.

Examples of Qualitative Data in Statistics

Some prominent examples of qualitative data in Statistics are as follows:

  • Expat Population: The Embassy of every country consists of the database of the total number of immigrants coming to the country. For instance, the embassy of Nigeria in the USA has a database of African citizens who have legally migrated to America. In this way, the USA authorities will have an estimation of the total number of the African population in the country. Personal details like gender, place of birth, age and address are also included in the expat population database.
  •  Median: Ordinal data includes an order or scale to it. This scale or order is employed to estimate the middle point of the set of qualitative data.
  • Voting: During voting, only the nominal data of the participant is counted. The frequency of every incurred vote is calculated and measured and the participant with the highest votes is declared as the winner. In statistics, this method is known as the mode.
  • Inclusion and Diversity: Qualitative data is implemented while building a database with diversified backgrounds based on races, genders, classes, countries and many more. For instance, many organizations take out statistics on the total number of employees. This is done to create a balance between male and female workers by employing employees of that gender which is less in number in the office.
  • Event Attendees: On the day of the event, the organizers take nominal data of the attendees that may include sex, name, phone number, and age. You may even ask the attendees about where they have heard about this event. This will add more information to the research of the marketing team.

Further reading about: Quantitative vs. Qualitative Data