# Qualitative Data: A Comprehensive Guide For 2021

## Introduction

The process of qualitative data collection can be assessed using two different viewpoints. This is of the respondent and the questionnaire. A respondent is not bothered about the classification of the data that he is inputting. However, this very answer is of importance to the questionnaire to help determine the analysis method that will be used. According to the data being investigated, there are different qualitative data analysis methods. Statistics uses two main data types which are quantitative and qualitative data. This article will be focusing on qualitative data.

## 1) Definition

Qualitative data is a data type that describes the information. This is investigative and is also open-ended that lets the respondent express himself completely. Qualitative data can also be referred to as categorical data which is something that cannot be measured in numbers. Qualitative data is based on attributes, properties, and labels. Phone numbers and national identification numbers are examples of qualitative data. This is unique and is specific for an individual. The male or female sex, state of origin, name, and citizenship are also kinds of qualitative data.

## 2) Types

Qualitative data is divided into two kinds. These are the nominal and ordinal data.

• Nominal data in statistics is a categorical variable classification that does not have any quantitative value. It is referred to as names and labeled data as well. There is no quantitative value attached to it. The quantitative value does not have any numeric characteristics here. This means that the nominal data cannot get manipulated making use of any mathematical operator. Like for example, if a researcher wants to generate a phone number database then an online survey could be conducted as this includes closed open-ended questions.
• Ordinal data is a kind of qualitative data where the variable has ordered and natural categories and there is no known distance between the categories. Like for example when the respondent will respond with a satisfied or an unsatisfied option. Ordinal data usually is a collection of an ordinal variable. This could be used by a company in the exit questionnaire that is filled by an employee who is leaving the organization.

In some cases, it may be possible to classify ordinal data into quantitative or between qualitative and quantitative data. This is because it consists of both characteristics.

## 3) Examples

Here are some qualitative data examples:

• It is used in voting. In the process of voting nominal data of the vote is taken and the frequency at which these votes occur gets measured. The candidate who has the higher number of votes is declared the winner. This is called the mode in statistics.
• Qualitative data is also used to find out the ex-pat population by an embassy. An embassy maintains the data of immigrants who come to the country. The qualitative data helps them to maintain their database.
• Nominal data is also used in events when the host may ask for the attendees’ names and phone numbers etc. They may also have to answer questions about where they came to know about this event. This helps in marketing.

## 4) Analysis

The qualitative data analysis is done in two stages. In the first stage, raw data gets converted to something readable and meaningful. This is done by developing codes which is a major step to analyze the qualitative data. The data is then ceded where the developer categories are reviewed closely. The third stage is where patterns, themes, and relationships are found out in the data. Here the analyst will check for any differences or similarities in the data. The data then gets summarized in the last step.

## 5) Approach

The data then goes through two approaches. This is done after the first stage is complete. The data analysis goes through the deductive and the inductive approach.

• Deductive Approach- This is used to analyze qualitative data that is based on an existing hypothesis or stricture. The researcher will pick any theory and test what its implications are with the data. This is an easy approach since the researcher here has already come up with an idea about the result of the analysis before he conducts the research. This approach is used in scientific investigations.
• Inductive Approach– In the inductive approach of qualitative data analysis, a new theory is developed or a hypothesis is developed for analyzing data. The researcher will find patterns, themes, and relationships in the data and will work to develop the theory that can help him to explain the data. The inductive approach takes more time and is also more difficult than the former method of data approach.

• It offers detail and depth to the data as it goes further to record the emotions
• With open-ended questions, new conversations are parked that expands the research scope more ta what the researcher may have expected
• It helps to stimulate differences in behaviour in individuals. It lets researchers know why someone acts in a particular manner
• It is also used to explain qualitative data if used along with quantitative data it gives details about how each number is got and lets the researcher make better assumptions

• It takes more effort and time and is also more expensive than quantitative data
• The researcher needs to deal with a smaller sample size because of the huge effort to process the qualitative data
• It is not easy to generalize the data based on the result of the analysis. Thus when there are assumptions made it could lead to a wrong conclusion
• It makes comparisons difficult because the responses are varied which could be unrelated to one another
• There could be loads of irrelevant data that the researcher may have to deal with

## Conclusion

There are tools used to collect qualitative data that make the research easy and simple. The tools also help to record data in real-time. Various techniques are used to collect qualitative data. The researcher will be interested in the occurrence of the event and the perspective of what he needs to collect data.

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