EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Qualitative vs Quantitative Data
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Qualitative vs Quantitative Data

By Priya PedamkarPriya Pedamkar

Qualitative vs Quantitative Data

Difference Between Qualitative vs Quantitative Data

The analysis in any research project involves summarizing the mass of information that has been collected and presenting the end results in such a way that it communicates the foremost necessary findings or options. For example, if a vesture complete is making an attempt to spot the most recent trends among young girls, the complete can initially reach young girls and raise their queries relevant to the analysis objective. Once collecting this information, the vesture can analyze the data to spot patterns – for example, it should discover that almost all young girls would really like to examine additional sort of jeans. There are many alternative data analysis ways, but the two most commonly and majorly used are Qualitative and Quantitative Analysis.

Head to Head Comparison between Qualitative vs Quantitative Data (Infographics)

Below are the top 8 differences between Qualitative vs Quantitative Data:

Qualitative vs Quantitative Data (Infographics)

Key Differences between Qualitative vs Quantitative Data

One variety of data is objective, up-to-the-point, and conclusive. The other variety of data is subjective, interpretive, and explained easily. Quantitative data can be counted, measured, and expressed using numbers. Qualitative data is descriptive and abstract and may be classified on traits and characteristics. The key variations between Qualitative and Quantitative data are as prescribed below:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

  • The data type, in which the classification of objects is based on attributes (quality) is called qualitative data. The type of information that might be counted and expressed in numbers and values is called quantitative data.
  • Quantitative data relies on numbers. Simple arithmetic or additional advanced applied mathematics analysis is employed to get commonalities or patterns within the information. The results are usually seen in graphs and tables. Applications like Excel, SPSS, or R can be accustomed to calculate things like Average scores, range of times a specific answer was given, the correlation between two or additional variables, dependability, and validity of the results.
  • The approach to the inquiry within the case of qualitative data is subjective and holistic, whereas quantitative information has an associative objective and targeted approach.
  • Qualitative data determines the depth of understanding, whereas quantitative data ascertain the amount of prevalence.
  • In qualitative data, the sample size is small and is drawn from non-representative samples. Conversely, the sample size is massive in quantitative data drawn from the representative sample.
  • Qualitative data develops initial understanding, i.e. it defines the matter. In contrast to quantitative data, which recommends the ultimate course of action.
  • In the Qualitative kind, verbal data is collected. Conversely, in the quantitative kind, measurable data is gathered.
  • Qualitative analysis and data is conducted with the aim of exploring and discovering concepts utilized in the continuous processes. As hostile quantitative analysis data, the aim is to look at cause and result relationships between variables.
  • Elements utilized in the analysis of qualitative research are words, pictures, and objects whereas that quantitative analysis are of numerical information.
  • Lastly, qualitative data develops the initial understanding whereas, quantitative data recommends a final course of action.

Qualitative VS Quantitative Data

Criteria

Qualitative Data

Quantitative Data

Meaning/Definition This type of data analysis is a technique of inquiry that develops an understanding of human and social sciences, to seek out the means individuals think and feel. This type of data analysis is a technique that is used to generate numerical information and hard facts, by using applied mathematics, logical, and mathematical technique.
Approach Qualitative may be a variety of subjective analysis that is more involved with non-statistical data that cannot be computed. Quantitative may be a variety of objective analysis that quantifies data.
Sample Sample is small and is non-representative of the whole population The sample is massive and can be generalized to hide the whole population.
Data Typical data embrace color, gender, nationality, religion, and plenty of additional. Typical data embrace measurable quantities like length, size, weight, mass, and plenty of additional.
Analysis The analysis is employed to grasp why an exact development happens. The analysis is bothered by what number or what quantity an exact development happens.
Data Type Qualitative data is text-based. Quantitative data is number-based.
Collection Method Collected using interviews, written documents, observations. Collected using surveys, observations, experiments, and interviews.
Results Results are simply aggregated for analysis and simply conferred. Understanding of what individual variation means; deepening understanding, insights.
Perceived Quality It can be perceived as biased, inevitable, or lateen to get sure results. Offers credibility of an associate outsider creating an assessment.

Qualitative vs Quantitative Data Comparison Table

Let us discuss the top comparison between Qualitative vs Quantitative Data:

Purpose

Qualitative Data

Quantitative Data

Hypothesis Tentative, Evolving, supports a specific study. Specific, testable, explicit before a specific study.
Sampling Purposive: Intent to pick out “small”, not essentially representative, sample so as to induce in-depth understanding. Random: Intent to pick out “large”, representative sample so as to generalize results to a population.
Research Setting Controlled setting not as necessary. Controlled to the degree potential.
Approach to Inquiry Subjective, holistic, process-oriented Objective, focused, outcome-oriented.
Data Interpretation Conclusions are tentative (can change), reviewed on an ongoing basis, conclusions are generalizations. The validity of the interferences/generalizations is the reader’s responsibility. Conclusions and generalizations formulated at the end of the study stated with a pre-determined degree of certainty. Interferences/generalizations are the researcher’s responsibility.
Design and Method Flexible, specified only in general terms in advance of study Non-intervention, minimal disturbance, all descriptive – History, Biography, Ethnography, Phenomenology, Grounded Theory, Case Study. Consider many variables, small groups. Structured, inflexible, specified in detail in advance of study Intervention, manipulation, and control Descriptive Correlation, Casual-Comparative, Experimental. Consider a few variables, large group.
Measurement Non-standardized, narrative (written word), ongoing Standardized, numerical (measurements, numbers), at the end.
Statistical Analysis Statistical Analysis in Qualitative data is a bit difficult to achieve than Quantitative data. Statistical analysis in Quantitative data is easier to achieve than Qualitative data.

The main difference between qualitative and quantitative data is that qualitative data is descriptive, while quantitative data is numerical. Usually, statistical analysis is easier with quantitative data than qualitative data. Statistics, social sciences, computing are some disciplines that use this type of data. You must consider that there are qualitative shades in the quantitative instrument, but not to be confused with qualitative, precisely for the reasons that have been specified so far.

Recommended Articles

This is a guide to the top differences between Qualitative vs Quantitative Data. Here we also discuss the key differences with infographics and comparison tables. You may also have a look at the following articles to learn more –

  1. Coherence vs Cohesion
  2. What is Qualitative Data Analysis
  3. Fundamental Analysis vs Technical Analysis
  4. CFA vs CAIA
Popular Course in this category
Data Scientist Training (85 Courses, 67+ Projects)
  85 Online Courses |  67 Hands-on Projects |  660+ Hours |  Verifiable Certificate of Completion
4.8
Price

View Course

Related Courses

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,354 ratings)
Tableau Training (8 Courses, 8+ Projects)4.9
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.8
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
0 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more