LibGuides: Research methods: Quantitative research

What is quantitative research?

Quantitative data refers to information expressed in numerical form, anything that can be counted, measured, or assigned a numerical value. This type of data is commonly analyzed using statistical methods

Quantitative data can be categorized into several types:

Categorical data: Represents groups or categories, such as countries or types of occupations.
Discrete data: Consists of countable values, often whole numbers, like the number of students in a classroom.
Continuous data: Includes measurements that fall within a range, such as height, weight, or temperature.

This type of data is frequently used in fields such as: Accounting and Finance / Economics / Cybersecurity / Data Science

Choosing a Method for Collecting Quantitative Data

Method	Purpose	Key Strengths	Limitations	Example Use
Structured Surveys	Collect standardized data from large groups for statistical analysis.	• Easy to administer at scale • Facilitates quantitative comparisons • Can use digital tools for automation	• Rigid format limits nuance • Respondents may not fully engage • Response bias possible	Distributing a Likert-scale survey to measure employee job satisfaction
Experiments	Test cause-effect relationships by manipulating variables under controlled conditions.	• High internal validity • Control over variables • Strong evidence for causation	• May lack real-world applicability • Can raise ethical issues • Complex setup and monitoring	Testing how different layouts affect user click behavior on a website
Observational Checklists	Quantify behaviors or actions observed in natural or semi-structured settings.	• Real-time, behavior-based data • Consistent and repeatable • Can be tallied and statistically analyzed	• Limited context or explanation • Observer training required • May influence observed behavior	Counting how many times students raise their hands during a lesson
Secondary Data Analysis	Reanalyze data collected for another purpose using statistical methods.	• Cost-effective and time-saving • Access to large, validated datasets • Enables longitudinal analysis	• No control over data quality • May not match research goals • Lacks contextual detail	Analyzing national census data to study income disparities
Structured Content Analysis	Systematically code and quantify features in text, images, or media.	• Can handle large text volumes • Enables frequency-based insights • Useful in media, marketing, and politics	• Misses deeper meaning or tone • Depends on coding consistency • Time-intensive to prepare categories	Counting specific keywords in social media posts to analyze trends

Quantitative Data Analysis Tools

Excel / Google Sheets Excel / Google Sheets: While not designed specifically for qualitative work, spreadsheets are often used to manually code and organize qualitative data before transferring it to more specialized software like NVivo or MAXQDA.
R R is incredibly versatile and, with packages like RQDA or tm (for text mining), it can be used for text analysis, thematic coding, and natural language processing, bridging qualitative and quantitative approaches.
SPSS While SPSS is widely used for statistical analysis, it can handle coded qualitative data (e.g., turning interview themes into categorical variables for statistical analysis). It's useful when qualitative data has been quantified.
Stata Like SPSS, Stata excels at statistical analysis, but qualitative researchers may use it for analyzing quantified qualitative data, especially in mixed methods studies.

Data Collection Tools

Google Forms A free, user-friendly tool for creating online surveys and quizzes with automatic data collection into Google Sheets
Qualtrics A powerful research suite for advanced survey design, ideal for academic, marketing, and social science research
REDCap A secure, web-based application for building and managing online surveys and databases, commonly used in health research.
SurveyMonkey: A widely used platform for creating professional surveys, offering features like branching logic, analytics, and export options.