Learn what is Qualitative Data and Quantitive Data, differences between Qualitative vs Quantitative Data Analysis and Research in this tutorial:
Quantitative vs Qualitative data come into the picture when we talk about Research data in a broader way. Unlike any ordinary information, research data is something that is generated, observed, collected, and validated.
Such data could be in the form of facts, figures, videos, images, measurements, data notebooks, records, etc. It can be in any format i.e. digital or non-digital.
In this tutorial, we will learn about the two broad categories of Research Data i.e. Quantitative Data and Qualitative Data. We will also understand their differences in detail.
What You Will Learn:
- Qualitative Vs Quantitative Data
- Data Collection And Data Analysis
- Quantitative And Qualitative Data Analysis Methods
- What Is Qualitative Data
- What Is Quantitative Data
- Key Differences Between Qualitative And Quantitative Research
- Real Life Or Practical Application
Qualitative Vs Quantitative Data
To begin with, let’s first understand the Research Data Life Cycle.
Research Data Life Cycle
Research involves a lot of steps that need to be carried out in a systematic manner. Data plays a very important role throughout the research cycle.
Check the image below for understanding the life cycle:
In the above image, the various stages of the research lifecycle are explained.
- Planning Research: In this phase, the research subject is defined and suitable data sources are identified.
- Collecting Data: All relevant and existing data are collected and all required data are gathered.
- Processing and Analysing Data: The relevant data to the research are verified and validated. Data is managed and stored. Data sources are cited as well.
- Publishing and Sharing Data: Data is promoted and suitable access to data is determined after publishing.
- Preserving Data: Data is stored and preserved in a suitable format for access.
- Re-using Data: Data can be further used for other research or learning purpose.
Types Of Data
Data is a collection of facts and figures for any survey or analysis. After understanding the importance of data in any research, let’s see the different types in which data is categorized into.
Quantitative And Qualitative Data
Quantitative data are expressed in numbers and involve statistical methods. They are concise and measurable. They help to bring conclusions to the research.
Qualitative data is based on properties or characteristics. They are exploratory and may lead to further evaluations.
Primary And Secondary Data
Primary data are those that are collected for the first time by the person who’s going to perform the research. When compared to secondary, primary data collection is a tedious process and involves more time. E.g. Telephone interview.
Secondary data are those that have been collected already by someone for any other purpose. The secondary collection process is quite easy and rapid. Eg: Library.
Descriptive And Experimental Data
Descriptive data is a result of the data gathered during the research on certain populations, events, or any situations. It is on the basis of statistical analysis.
Experimental data is the result of the manipulations that are done on data variables to gain the best output.
Data Collection And Data Analysis
As data plays a key role in any research, both data collection and data analysis techniques are equally important as well. For success in any business, the data collection and analysis results form the base.
To prove anything, you need certain data. Data forms the basis for any research and the research is successful or say, fruitful only when the data that drives it is good. So, choosing the right technique for data collection decides it all.
Data collection is the process of gathering information required for the research. It can be the raw data on which your research methods and procedures will be built upon. The different data collection techniques involve – Observation, Questionnaires, Interviews, and Focus group discussion.
Understanding the aim of the project is firstly important. Only then the relevant data that caters to the needs of the research can be collected.
Example: To begin research on a drug say Abbot A, you need to collect all the information such as drug developed pharmacy, patients already injected, dosage and usage, etc.
As the name suggests, this technique involves analyzing the data collected through various techniques and arriving at conclusions. The data gathered is inspected and is transformed to the required results. Data analysis has many facets as various patterns and models are involved.
Quantitative And Qualitative Data Analysis Methods
The two fundamental methods involved for data collection and analysis are:
Qualitative Data Analysis is mostly in the narrative form including the data collected from sources. Intangible information is used here.
Quantitative Data Analysis is measured in terms of numbers. Here, the data is presented in measurement scales which can be further considered for more statistical manipulation.
There are many analysis techniques available these days, with the development in technology. Each technique caters to its respective needs and is unique in its own way.
The top 8 Data Analysis methods are as follows:
- Exploratory Data Analysis
- Descriptive Analysis
- Regression Analysis
- Predictive Analysis
- Inferential Analysis
- Decision Tress Analysis
- Mechanistic Analysis
- Evolutionary Programming
#1) Exploratory Data Analysis
They are used to explore the previously used data and to bring answers to future studies. Exploratory data analysis was promoted by John Tukey and was defined in 1961. Data sets are usually represented by visual methods. Graphs and charts are used in this method. It cannot give exact predictions about upcoming events.
#2) Descriptive Analysis
As the name suggests this method is based on the description. To describe we need to cater to the behavior and usage of data in the past. It paves a statistical way to judge how the past behaviors are and how they will affect the future.
If the analysis has only one variable, then it is called univariate. If it uses more than one variable, then it is called bivariate or multivariate analysis.
#3) Regression Analysis
Regression simply means to determine how an outcome is affected by several input variables. It is generally measured by a chart or table. The strength of the variables and their relationship can be measured here. There are several kinds of regression like linear regression, non-linear, logistic regression, etc.
#4) Predictive Analysis
Prediction about the future trends is the main use of this analysis. The rising trends and changes of various industries can be measured.
#5) Inferential Analysis
This method can determine and predict excellent results for data analysis if a proper sampling technique is followed along with good tools.
#6) Decision Tree Analysis
A decision tree is a support system and is a tree-like structure that uses possible outcomes for a problem. Resource cost, utility, and other parameters are used here.
The steps involved are as follows:
- List all the possible decisions for the research.
- Assign the probability of risk for each occurrence.
- Assign the impact of risk as a monetary value.
- Calculate the expected monetary value for each decision path.
Further reading =>> Decision Tree Algorithm in Data Mining
#7) Mechanistic Analysis
It is a statistical way of starting an analysis. The exact changes of the variables that will affect the other variables are studied here.
#8) Evolutionary Programming
Various programming technologies are used in this analysis. It gives more accurate results and is of great help these days.
What Is Qualitative Data
Qualitative data is just used to provide information in a descriptive and non-statistical way. No specific measurements in terms of numbers or other parameters are provided.
It just gives an overall description of the physical features, rather than the basic information upon which the initial understanding of the project relies. Characteristics and basic descriptions are known from this data.
Qualitative research is exploring the human sciences in a way that describes the look and feel of the data used. Qualitative research gives an idea of the process that can be extended in many ways as it just gives an overall idea.
Forms of Qualitative Data:
- Texts and Documents: Verbal texts can be written and saved by users in pdf, excel, word, emails, messages, etc.
- Audio and Video recordings: These are available in formats such as MPEG, MOV, MP 3, MP4, etc.
- Images and Symbols: A lot of applications use symbols like logos, vectors, etc. Images in JPEG, AI, EPS format.
- Interview Transcripts and Focus Groups: Interview transcription is a written communication by the interviewer and employer. It gives clear information about the topic discussed. Transcription plays a vital role in many organizations. When the researcher directly interacts with the group of people for particular research in order to grab the details, then it is called a focus group. The whole group will share their opinions, views on the intended research and share the data.
- Observations and Notes: As the name suggests, anything that is observed is noted as useful data.
Let’s take an example of a syringe ABBOTT A. For this, the following data are collected as a part of Qualitative and Quantitative Analysis.
Qualitative Data Examples:
- The syringe is designed by xyz pharmacy.
- The shape is rectangular with a glass needle.
- The syringe is used for diabetic patients.
Quantitative Data Examples:
- The capacity of the syringe is 12 ml.
- It can be injected only into 1 person at a time.
- The validity of the syringe is 20 days.
What Is Quantitative Data
Easy to be distinguished – quantitative data comes in a more structured way. It clearly defines the quantity. It is more clearly formatted and is easy to handle.
All the basic measurements, parameters like numbers, charts are used. Appropriate information pertaining to research is mentioned in this type of data. This data plays an important role in quantitative research.
When you research on a topic with few characteristical facts where the outcomes are not predicted accurately and are open-handed, then a qualitative approach is used. Meanwhile, when you research a topic with predefined criteria using specific numerical measurements that lead to structured techniques to bring an accurate result.
Forms of Quantitative Data:
Quantitative data is mainly classified into two types i.e. discrete and continuous data.
#1) Discrete means are distinct or different. Hence discrete data are those that are countable and have separate values. They can’t be further subdivided.
Quantitative Data Example:
- The number of customers who bought iPhone.
- The number of medicines used in a week by a patient.
#2) Continuous data are those that are generally measured within a range. They can be divided into further ratios.
Quantitative Data Example:
- The height of a person.
- Weight of a baby.
Other forms of Quantitative Data include:
- Measurements: The measurement of any physical quantity. For example, a weighing machine that weighs people.
- Sensors: Sensors automatically detect the data that can be used and converted. Example: Ritcher scale
- Counts: Literal counting of the data. Example: Patient count by the hospital.
- Calculations: Mathematical calculation of data using various mathematical operations. Example: Salary
- Estimates and Prediction: Creating estimates about the calculations.
Key Differences Between Qualitative And Quantitative Research
|Qualitative Research||Quantitative Research|
|Aim of the project||Develop new research ideas based on data. A holistic interdependent system to welcome new perspectives.||A specific or particular hypothesis is used that needs to be fairly concluded.|
|Scale of research||Entire area under context and finding various patterns.||Few specific facts and situations.|
|Methods of data collection||Samples from various individual sources are collected. Case studies content and pattern used.|
It is semi-structured.
|Specific methods that are used to identify samples. Statistical analysis used. Highly structured data is used.|
|Methods of data analysis||Non-statistical and non-numeric|
Concentrates on images and concepts
Includes content analysis, observation.
|Statistical and numeric.|
|Outcome||Directional in nature. The outcome is not projectable to all the audience. It can be used as reference in future researches.||Reports are specific to the target audience. Provides guidance for business actions.|
Real Life Or Practical Application
Having known about both quantitative and qualitative data, any good business will collect the combination of both data to ensure the balance of the strength and limitations of one another. Well, it is also important to determine the approaches for the combination of qualitative and quantitative data for a successful business.
Take a business, say interior designing. Initially, for any project, the business first needs an overall idea of qualitative data such as what features the customer wants, the changes to be made, color combinations, etc.
But this data, by itself, is not sufficient. It is also necessary to define the quantity of each item in the space to make the look simple and elegant.
The below image is of a room depicting the color combination of walls, curtains, and other items.
Qualitative Observation: Walls of the room are painted in light green color with green floral curtains on the windows, a brown desk with a brown chair finished with green fabric. An emerald green block lamp resting on the desk. Wall art hangs on the light green wall between the windows.
Quantitative Observation: Painted two walls in each room, added 120″ drapes, placed brown desk of Width: 47 1/4 “, Depth: 18 1/2 “, Height: 29 1/2 “. Max load/shelf: 33 lb. Emerald green block lamp of height 51cm and width 20cm. Wall art of size 20×16”.
Through this tutorial, we learned that both Qualitative as well as Quantitative data, have their own strengths and limitations and we discussed their key differences and similarities too.
For a clear and better understanding of these two forms, you can think of a few ways from your daily life where both qualitative and quantitative data can be applied.
Take your own example, your hair color, your clothes color, your nose type, etc can be considered as qualitative observation whereas, your height, weight, age, etc can be considered as quantitative data.
According to researchers, quantitative data remains the most popular data type when compared to qualitative data. However, both have their own importance in data analysis.