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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Raimo Streefkerk

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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QUALITATIVE VS. QUANTITATIVE RESEARCH DESIGN, GGGA3232 EDUCATIONAL…

• INTRODUCTION
• Survey is a method used to collect data from a predefined group of respondents to gain information and insights on various topics of interest.
• Surveys use standardized questions to ensure that every respondent is asked the same questions in the same way.
• Surveys can be designed to protect the anonymity of respondents.
• PREPARED BY: HIDAYATI ILLIANA, UMIE SHAFIEQHA, THARSHINI JEYAKRISHNAN, RAJA MUHAMMAD ARIF
• PREPARED FOR: DR. INTAN FARAHANA KAMSIN
• A focus group is a research technique used to collect data through group interaction.
• Small group of people have been carefully chosen to discuss a certain issue about a given topic.
• Focus groups provide insight on the why, what and how questions by identifying and exploring how people think and behave.
• WHEN TO APPLY
• DATA COLLECTION METHOD
• VALIDITY AND RELIABILITY
• Qualitative
• Quantitative
• In depth discussion to gather information
• Cross checking system amongst group members
• Purposely selective based on experience and relevance towards research
• Very descriptive in research context
• Clear instructions
• Peer debriefing
• Create detailed and accurate transcripts of focus group discussions to ensure that data are reliably captured.
• Audio or video documentation for proofreading and reference
• Expert review on specific matters
• Theories that construct accurately
• Factor analysis
• Hypothesis to test if the survey aligns with the research carried out
• Feedback to make sure the relevance of the survey
• Random sampling and feedbacks so there is no section bias
• Revision based on feedback received only if necessary
• A test sample based on the survey to ensure no problems occur with the survey
• Use online survey tools like Google Forms or SurveyMonkey to distribute questionnaires electronically.
• Direct interaction between the interviewer and the respondent.
• Use phone calls to administer the survey.
• Distribute printed questionnaires for respondents to fill out manually.
• Useful when internet access is limited.
• Send survey invitations via email with a link to an online questionnaire.
• Use smart phones or tablets
• Focus on non-verbal communication, group dynamics, and the overall atmosphere.
• Record the session using audio or video equipment to capture the entire discussion accurately.
• Assign a note-taker to document key points, non-verbal cues, and group interactions.
• Collected based on participants' body language, use of space, and other physical cues.

Quantitative Data

Ai generator.

Quantitative data refers to information that can be measured and expressed numerically. This type of data is crucial for performing quantitative analysis , a method used to evaluate numerical data to uncover patterns, correlations, and trends. In fields like finance, economics, and the natural sciences, quantitative risk analysis is utilized to assess potential risks by quantifying their probability and impact. The precision and objectivity of quantitative data make it essential for making data-driven decisions and forming the basis for statistical analysis.

What is Quantitative Data?

Quantitative data is numerical information that can be measured and analyzed statistically. It represents quantities and allows for objective comparison and analysis.

Examples of Quantitative Data

• Age in years
• Height in centimeters
• Weight in kilograms
• Temperature in degrees Celsius
• Number of siblings
• Annual income in dollars
• Distance in miles
• Test scores in percentages
• Number of books read in a year
• Time in minutes
• Number of employees in a company
• Population of a city
• Speed in miles per hour
• Number of students in a class
• Price of a product in dollars
• Volume of water in liters
• Number of steps taken in a day
• Daily calorie intake
• Frequency of visits to the gym per month
• Number of social media followers
• Hours of sleep per night
• Number of pages in a book
• Length of a movie in minutes
• Number of items sold per day
• Score in a game
• Number of cars in a parking lot
• Monthly utility bills in dollars
• Number of courses completed
• Quantity of rainfall in millimeters
• Number of products in inventory
• Blood pressure readings
• Number of phone calls made per day
• Distance run in a week in kilometers
• Number of website visits per month
• Number of pets owned
• Number of countries visited
• Monthly rent in dollars
• Number of clients served
• Weight of luggage in pounds
• Number of trees in a park
• Annual sales revenue in dollars
• Number of hours worked per week
• Quantity of milk produced by a cow in liters
• Number of concerts attended per year
• Number of patients treated in a hospital
• Number of goals scored in a season
• Monthly savings in dollars
• Number of chapters in a novel
• Frequency of meetings per week
• Number of assignments submitted

What is the Difference Between Quantitative and Qualitative Data?

What are the Different Types of Quantitative Data?

1. discrete data.

Discrete data represents countable items. It is often whole numbers and does not include fractions or decimals. This type of data is used in scenarios where items can only be counted in whole units.

• Number of students in a classroom
• Number of books in a library

2. Continuous Data

Continuous data can take any value within a range. This type of data includes fractions and decimals, making it suitable for measurements that require precision.

Application in Research:

• Data Analysis: Both discrete and continuous data are fundamental in data analysis , allowing researchers to perform statistical tests, create models, and derive insights from numerical information.
• Historical Research: Quantitative data in historical research helps in understanding trends over time, such as population growth, economic changes, and social developments.
• Quantitative Research: This Quantitative research method relies heavily on quantitative data to test hypotheses, establish patterns, and predict outcomes, making it vital for scientific, economic, and social research.

How is Quantitative Data Collected?

1. surveys and questionnaires.

These tools gather numerical information by asking people questions. Respondents choose from set options, making it easy to count and compare answers.

2. Experiments

Researchers conduct experiments by changing variables to see how they affect other variables. This helps in understanding cause and effect.

3. Observations

In this method, data is collected by watching and recording events or behaviors as they happen. For example, counting how many people enter a store.

4. Existing Records and Databases

Quantitative data can also be found in existing sources like government reports, academic studies, or company records. Researchers use this data for analysis.

5. Sensors and Instruments

Devices like thermometers, scales, and GPS units measure physical quantities and provide precise numerical data.

6. Structured Interviews

Interviewers ask a set list of questions to gather numerical responses from participants. This method ensures consistency in the data collected.

Interval vs. ratio data

How is quantitative data analyzed?

1. data collection.

Before analysis, ensure that data is accurately and reliably collected through methods such as surveys, experiments, or existing records.

2. Data Cleaning

Clean the data by removing any errors, duplicates, or inconsistencies. This step ensures that the data set is accurate and ready for analysis.

3. Descriptive Statistics

Use descriptive statistics to summarize and describe the main features of the data. This includes measures such as:

• Mean : The average value.
• Median : The middle value when data is ordered.
• Mode : The most frequently occurring value.
• Standard Deviation : A measure of the amount of variation or dispersion in the data.

4. Data Visualization

Visualize the data to identify patterns, trends, and outliers. Common visualization techniques include:

• Histograms : Show the distribution of data.
• Bar Charts : Compare different groups.
• Line Graphs : Show trends over time.
• Scatter Plots : Identify relationships between variables.

5. Inferential Statistics

Apply inferential statistics to make predictions or inferences about a population based on a sample of data. This involves:

• Hypothesis Testing : Determining if there is enough evidence to support a specific hypothesis.
• Confidence Intervals : Estimating the range within which a population parameter lies.
• Regression Analysis : Examining the relationship between variables.

6. Data Interpretation

Interpret the results to draw conclusions and make informed decisions. This step involves understanding the implications of the statistical findings and how they relate to the research question or problem.

7. Reporting Results

Present the findings in a clear and concise manner. This may involve writing reports, creating presentations, or publishing research papers. Ensure that the results are communicated effectively to the target audience.

What’s the Difference Between Descriptive and Inferential Analysis of Quantitative Data?

What are the Advantages and Disadvantages of Quantitative Data?

Objectivity and Reliability : Quantitative data is based on measurable values, which makes it more objective and less prone to bias. The results are replicable, allowing for consistent verification of findings.

Precision and Consistency : Quantitative data allows for precise measurement and quantification. This precision helps in making accurate comparisons and analyzing trends over time.

Statistical Analysis : The numerical nature of quantitative data enables the use of statistical analysis to identify patterns, relationships, and causal effects. Advanced statistical methods can be applied to test hypotheses and make predictions.

Generalizability : Large sample sizes and standardized data collection methods enable findings to be generalized to larger populations, enhancing the external validity of the research.

Efficient Data Collection : Quantitative data collection methods, such as surveys and experiments, can be more efficient and quicker to administer to large groups compared to qualitative methods.

Clear Data Presentation : Quantitative data can be easily presented using graphs, charts, and tables, making it easier to communicate findings clearly and effectively.

Disadvantages

Limited Flexibility : Standardized data collection methods can be rigid, not allowing for flexibility in exploring unexpected results or new avenues of inquiry.

Potential for Misinterpretation : Without proper understanding of statistical methods and the context of the data, there is a risk of misinterpreting the results. Misleading conclusions can be drawn from incorrect or incomplete analysis.

Resource Intensive : Collecting large amounts of quantitative data can be resource-intensive, requiring significant time, effort, and financial investment for surveys, experiments, and data analysis.

Measurement Errors : Errors in measurement tools or data entry can affect the accuracy and reliability of quantitative data. Small errors can lead to significant deviations in the results.

Limited Depth : Quantitative data typically does not provide in-depth insights into complex issues or human experiences, which may require qualitative data to fully understand.

Should I use Quantitative in my Research?

You Need to Measure and Quantify : If your research aims to quantify variables, measure frequencies, or make numerical comparisons, quantitative data is suitable.

Example : Measuring the average income level of a population.

You Aim for Objectivity : When you require objective data that can be statistically analyzed to test hypotheses and identify patterns, trends, or correlations.

Example : Analyzing the correlation between hours of study and exam scores.

You Want Generalizable Results : If you aim to generalize findings from a sample to a larger population, quantitative methods allow for this, provided you have a sufficiently large and representative sample.

Example : Conducting a survey to estimate the percentage of people who prefer online shopping over in-store shopping.

You Have Large Populations: When dealing with large populations where collecting and analyzing numerical data is more feasible and efficient.

Example: National health surveys to track prevalence of diseases.

You Need Statistical Analysis: When your research requires the application of statistical tests, quantitative data is essential.

Example: Using regression analysis to predict future sales based on past trends.

What are Some Common Quantitative Analysis Tools?

1. microsoft excel.

• Description : Spreadsheet software for organizing and analyzing data.
• Features : Formulas, charts, pivot tables.
• Use Case : Basic to intermediate data analysis.
• Description : Software for statistical analysis.
• Features : Descriptive statistics, regression analysis, ANOVA.
• Use Case : Social sciences and health research.
• Description : Programming language for statistics and graphics.
• Features : Statistical techniques, data manipulation, extensive packages.
• Use Case : Advanced statistical analysis and data science.
• Description : Software suite for advanced analytics.
• Features : Statistical procedures, predictive modeling, data mining.
• Use Case : Business, healthcare, government.
• Description : Software for data analysis and visualization.
• Features : Data management, statistical analysis, graphics.
• Use Case : Economics, sociology, epidemiology.
• Description : Language and environment for numerical computation.
• Features : Mathematical functions, algorithm development, data visualization.
• Use Case : Engineering, finance, scientific research.
• Description : Data visualization software.
• Features : Interactive dashboards, real-time analysis, visual analytics.
• Use Case : Business intelligence and reporting.
• Description : Statistical software for data analysis.
• Features : Descriptive statistics, hypothesis testing, control charts.
• Use Case : Manufacturing, quality improvement, Six Sigma projects.

9. Google Data Studio

• Description : Tool for creating interactive dashboards and reports.
• Features : Data integration, customizable reports, real-time updates.
• Use Case : Marketing, sales, performance tracking.

10. Python (with libraries like Pandas, NumPy, Matplotlib)

• Description : Programming language with data analysis libraries.
• Features : Data manipulation (Pandas), numerical computations (NumPy), plotting (Matplotlib).
• Use Case : Data science and machine learning.

11. IBM Watson Analytics

• Description : Cloud-based analytics service.
• Features : Automated data visualization, predictive modeling.
• Use Case : Business intelligence and data-driven decision-making.

Quantitative Data Examples for Students

Academic performance.

• Test scores (e.g., 85%, 90%)
• GPA (e.g., 3.5, 4.0)
• Number of assignments completed
• Attendance records (e.g., number of days present)
• Hours spent studying per week

Classroom Activities

• Number of books read in a semester
• Number of extracurricular activities participated in
• Number of homework problems solved
• Participation points earned in class
• Time taken to complete a test (in minutes)

Personal Life

• Age (in years)
• Height (in centimeters or inches)
• Weight (in kilograms or pounds)
• Daily screen time (in hours)
• Number of steps taken per day

Technology Usage

• Number of text messages sent per day
• Number of emails received per day
• Hours spent on social media per week
• Number of apps downloaded on a phone
• Battery life percentage at the end of the day

Health and Fitness

• Number of push-ups completed in one session
• Distance run in kilometers or miles
• Heart rate (beats per minute)

What is quantitative data?

Quantitative data is numerical information that can be measured and analyzed statistically.

How is quantitative data collected?

It is collected through surveys, experiments, observations, existing records, and sensors.

Why use quantitative data?

It provides objective, measurable, and comparable results for statistical analysis and decision-making.

What are examples of quantitative data?

Examples include test scores, height, weight, income, and temperature.

What tools analyze quantitative data?

Common tools are Microsoft Excel, SPSS, R, SAS, and Tableau.

How is quantitative data visualized?

It is visualized using charts, graphs, histograms, and scatter plots.

What is descriptive statistics?

Descriptive statistics summarize and describe data features, such as mean and standard deviation.

What is inferential statistics?

Inferential statistics make predictions or inferences about a population based on a sample.

What is the difference between interval and ratio data?

Interval data has no true zero point, while ratio data has a true zero.

What are the advantages of quantitative data?

Advantages include objectivity, reliability, precision, and the ability to perform statistical analysis.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Quantitative Research Terms

Quantitative Research

Tags and Description

IB Psychology (HL)

method of research that relies on measuring variables using a numerical system.

(quantitative = quantity)

any characteristic that is objectively registered and quantified.

theoretically defined variables. To define a construct, you muct separate it from similar constructs.

Operationalization

expressing a construct in terms of observable behavior, makes it clear what is being measured.

Independent Variable (IV)

variable that is being manipulated by researcher.

Dependent Variable (DV)

how the condition changes based on what changed in the IV.

variables that are constant and don’t change, they are controlled and make sure to not affect the IV and DV.

predicts how the IV affects the DV

Null Hypothesis: no relationship - results are just due to chance

Experimental Hypothesis: there IS a cause-and-effect relationship.

Extraneous Variables

variables that can distort relationships between the IV and the DV.

True Lab Experiment

randomlly allocated (split) participants into control groups and experimental groups.

Field Experiments

studies conducted in the real world yet researchers can still manipulate the study (is in an environment that is not a lab).

Quasi Experiments

purposefully put people of race/gender into specific groups in order to test stereotypes

Natural Experiments

beyond the control of researcher, they are just observing. Cannot establish a cause-&-effect relationship.

Demand Characteristics

when participants act differently because they know the intention of the study.

Expectancy Effect

participants aim to guess the hypothesis in order to “help” the researcher by acting in a certain way.

Screw-You Effect

participant attempts to detect the hypothesis in order to purposefully throw off the study.

Social Desireability

When a participant attempts to present themselves in a generally favorable manner, in order to conceal their true opinions.

Researcher Bias

when researchers expectations affect the result of the study, double blind control can help with this (where participant and researcher don’t know who is in what condition).

Participant Variability

when characteristics of the participant affect the dependent variable.

Correlational Studies

(ex: surveys, questionnaires, naturalistic observations) Variables are NOT manipulated, but data is still collected in order to show that there is a relationship between them.

Positive/Negative correlation: they change together or co-vary.

Correlation

Tests association between variables.

variables are ONLY observed ( no manipulation by researcher ).

Limited control allows other variables to be present.

High external validity.

Statistical Significance

the likelihood that your data has a cause-and-effect relationship. Shows how reliable you data is. If it is GREATER than 5%, it is considered to be “non-reliable”.

Independent Measures Design

random allocation of (dividing of) participants into control and experimental groups and then a comparison of the two groups. So only the IV differs in each.

Matched Pairs Design

Participants are tested on a variable, matched up based on performance & then split up between the 2 conditions (to balance both conditions out).

Example :  a researcher testing a new Alzheimer's disease drug matched up participants of the same age & intelligence, then randomly assigned a person per pair to a group receiving the drug (experimental group) and the other person from the pair to the group that will not receive it (control group).

Repeated Measures Design

where each condition of the experiment uses the same group of participants. Goal is to compare the CONDITIONS rather than the groups.

Example : in a candy taste test, the researcher would want every participant to taste and rate each type of candy.

Order Effects

occurs when the participants' responses are affected by the ORDER in which the conditions of the experiment were presented to them.

Counterbalancing

where the participant sample is divided in half - one half completing the experiment in a specific order, and the other half completing the experiment in REVERSE order.

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What Is Quantitative Data? [Overview, Examples, and Uses]

Written by: Sakshi Gupta

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As companies increasingly rely upon the field of data science to unearth actionable insights, quantitative data is likewise becoming increasingly important. In both academics and in a range of industries, the broad applications of AI-based systems and machine learning algorithms have expanded the already crucial role that quantitative data plays. There’s so much data to be analyzed that it’s been estimated that more data points have been generated in the past couple of years than the number of observable stars.

With so many emerging technologies relying on the collection and analysis of quantitative data, a strong understanding of what quantitative data is and what purpose it serves is one of the most coveted skills on the job market.

That’s why we’ve created this guide. Below, we’ll detail everything you need to know about quantitative data and how it’s used.

What Is Quantitative Data?

Quantitative data is any data that has numerical properties. One of the most important functions of quantitative data is to answer questions like “how often,” or “how many.” The only way to answer these questions is to collect data that is quantifiable, meaning it can be measured.

Why Does Quantitative Data Matter?

Quantitative data has become increasingly important because of the high demand for the predictions that it can produce. From medical to manufacturing, every company, government, and an ever-increasing number of individuals relies on some form of quantitative data regularly.

Quantitative Data Examples

You probably see examples of quantitative data every day. Here are just a few examples of quantitative data:

• Inches of rain per year
• Someone’s height, weight, or age
• Number of days in a week, month, or year
• Temperatures
• Test scores

What Are the Types of Quantitative Data?

Quantitative data is divided into two categories: discrete and continuous. While it can be easy to visualize to understand what falls under the category of quantitative, the difference between discrete data and continuous data is a little more complicated. The best way to understand this difference is to think of discrete data as being countable and continuous data as being measured.

Discrete Data

While all countable data is quantitative, not all quantitative data is countable. Discrete data is counted data. What this means is that each countable data point is not only quantitative, but also discrete. Below are some examples of countable data.

• 10,000 views on a webpage
• 2,000,000 votes in an election
• 100 likes on Instagram

Continuous Data

Wind speeds from a previous storm or the weight of the world’s largest pumpkin would be examples of data that is continuous. Continuous data can have decimals and can represent non-countable things. Using similar subjects to the discrete data above, you can see how the continuous data differs in what it measures and describes.

• Person’s age throughout a year
• 1.14 minutes spent on a webpage
• Average age of voters: 35–50 years
• Instagram reel length ranging from 30–45 seconds

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Quantitative Data Use-Cases

Quantitative data is used to make predictions, solve problems, and optimize processes and systems. Here are a few examples of what that looks like in action:

Mathematics

Mathematics heavily relies on quantitative data. Engineers and applied mathematicians have developed methods for medical research, biological research, and industrial processes, all through quantitative data analysis. For example, the mathematical usage of quantitative data could be a statistician working with a large data set to determine the weight variation for a set of people and whether or not there was a correlation between two different medical conditions. These are a few problems that could only be determined through quantitative data analysis.

Market Research

Market research is one of the most marketable uses of quantitative data. Knowing how many people would like to purchase a product can provide significant insight into how a retailer should design their marketing strategies. Using potential customer profiles, market researchers create tools like customer journey maps to better understand customer behavior. To acquire the data necessary to construct these kinds of reports, many tools like surveys and polls are used to collect quantitative data from a target population.

Weather Forecasting

Weather forecasting has evolved into a data-driven process. As methods have gotten more accurate, minute-by-minute updates can be provided to people in affected areas and save countless lives. Research centers monitor the changes in barometric pressures, wind speeds, and temperatures—each of these sources produces quantitative data that is used to make predictions and forecasts.

Traffic Engineering

As cities continue to expand, civil engineers are having to employ more sophisticated methods to maintain a consistent flow of traffic through urban environments using large amounts of quantitative data. Numbers of cars passing through an intersection, average speeds, numbers of accidents in a certain period of time—all of these metrics are examples of data that can be quantified to help engineers make predictions.

Stock Analysis

Stock indexes and market analysis all rely on quantitative data analysis. This analysis is used to make predictions about the stock market as a whole as well as individual stocks. Market indexes from around the world quantitative data that is reported by firms who analyze the markets using many data points. These data points they receive come from companies’ smaller sections of the market and quantitative models.

Economic Predictions

When economists make predictions, large amounts of data have undergone statistical analysis in order to make accurate forecasts. This can involve data sets that span years and sometimes decades worth of quantitative data, including prices of goods, gross domestic product (GDP) of a nation, or inflation rates,

Social Media Analytics

One of the driving marketing tools in the digital economy is social media. When users consent to having their usage observed, they provide social media companies with quantitative data on their activities on those platforms. While there are many metrics that are involved in the process, the number of visitors to a website and a page’s bounce rate, the number of visitors that leave a site after only viewing one page, are two very significant pieces of data that are used to make content strategies.

What Is the Difference Between Quantitative Data and Qualitative Data?

Data is separated into two categories: quantitative and qualitative . Each category covers a broad range of data types and differs in the type of information collected. While quantitative data is based on numerical information that is both objective and measurable, qualitative data is based on strictly non-numerical data.

Numerical vs. Descriptive

An example of numerical versus descriptive data would be a graphic designer advising a brand on what color palette to use. The designer may conduct a survey and find that 50 more people clicked on an ad when it was one color than when it was another color. They might also say that one palette matches the brand’s image better because that color is known to generate a certain feeling with customers.

Measurable vs. Non-Measurable

Data can also be separated into measurable and non-measurable categories. Not every source of data can generate quantitative data. For example, many hospitals rate things like pain in a similar way by using a standardized pain scale. It would be non-measurable to have a patient say their pain is “bad” or “terrible.” Each person has a different interpretation of these words, making them difficult to measure. However, with the numerical pain scale, a patient can rate their pain on a scale from 0–10 based on a set description of 0 being no pain at all and 10 being the most severe pain that they have ever felt. Using this scale takes away the measurement bias in the data and makes it more measurable.

Objective vs. Subjective

Objective discoveries can often lead to subjective recommendations. For example, meteorologists use models that take numerical factors into account and make an objective judgment on where to expect a hurricane to pass. Using this objective data, the same meteorologists use subjective data to give advice to residents, like what kind of procedures to follow in cases of flooding or dangerous winds.

Data Collection Methods

Both qualitative and quantitative data types have similar ways of collecting data. It’s important to highlight the differences in how a survey may be designed to collect one type of data instead of the other. For example, a survey may ask 20 people to state how a flavor made them feel. This is an example of qualitative data. If the survey asks the same 20 people to select “Yes” or “No” on a form that asks them if they liked the flavor, it has now generated quantitative data. The difference is in both how information is collected and reported: 20 people with unique descriptions of a flavor versus a number of people out of the same population choosing one option or the other. One provides a variety of answers, while the other provides a count of people that selected an answer.

The Quantitative Data Collection and Analysis Process

The process of collecting and analyzing quantitative data must be conducted in a way that results in a data set that best represents the population that is being studied. Let’s break down what that looks like:

Data Collection

General overview.

Data collection methods for quantitative data involve many tools that differ depending on the industry. The most effective ways of collecting quantitative data remove possibilities for error from the equation.

Quantitative Data Collection Methods

Several methods exist for data collection. All methods require a lot of careful planning when being designed in order to avoid common method bias, an error where the method of data collection harms the quality of the data. An example of this bias could be asking the surveyed population an unclear question or a question that allows for answers that are outside of the scope of the study. Accurate measurements should be made, and the tools used in the process should only allow for the highest quality data to be generated.

• Surveys Conducting surveys is one of the most common methods of collecting quantitative data. The surveys are constructed with a uniform set of questions that are asked to the entire population that is being studied.
• Controlled Observations Oftentimes, studies need to be conducted in a controlled environment, one where the number of variables is lowered. This is where controlled observations are conducted. Usually conducted in labs, they are often used for studying behavior. Some of the most famous studies done on animal behavior were conducted in this same format.
• Sampling When data is collected through sampling, it is done in a way that every member of the population has a chance of being selected. This requires researchers to select their population and collection methods wisely, as mistakes could result in an error due to a sampling bias.
• Secondary Data Collection Secondary data collection is done with data sets that are already collected and compiled from primary sources. Often, researchers only need to search for data sets that already exist in order to find a set that meets their needs.
• Experiments Experiments are the gold standard for the scientific world. While they are very similar to controlled observations, experiments introduce a treatment to the population that is being studied. This separates the population into a control group that receives no treatment and an experimental group that receives the treatment. One of the most common uses of this method is in drug trials.

Data Validation and Clean-Up

For data analysis to be accurate, the data has to be validated as it is entered and cleaned to make sure that no errors occur while the analysis is being conducted. At this stage, a common issue that can arise is an estimate for measurement bias, where too many data points can be estimated, changing the final results significantly. As the information that is being analyzed will go on to make major business decisions, a standard of accuracy must be met.

Data Analysis

After the data has been cleaned; the next step is to conduct thorough data analysis .

Types of Quantitative Analysis

The quantitative data, once analyzed, is used to make key data-driven business and customer decisions. This is divided into descriptive and inferential analysis .

• Descriptive Analysis The process of describing the population or data set that is being analyzed is called descriptive analysis. No conclusions are made from this analysis.
• Inferential Analysis Inference is the process of making assertions or predictions from the data that is being analyzed. This is accomplished by making conclusions from the data instead of simply describing it.

Quantitative Analysis Tools

There are many tools that have been developed to help analysts . Most are formatted in a similar way that allows analysts to directly enter data and carry out analytic functions within that software.

• SPSS (Statistical Package for the Social Sciences) Developed by IBM, SPSS is a popular software suite for data analysis for businesses that utilizes a user-friendly interface.
• STATA STATA is a statistical software that is used in a number of different data analysis processes.
• Excel Excel is a staple of the data analysis process. Entry-level analysts can carry out operations that range from analysis to data visualization and presentation easily with the interface it provides.
• SAS SAS is a software suite that provides a platform for data analysis. While similar to SPSS, SAS doesn’t require any additional programming knowledge.

FAQs About Quantitative Data

We’ve got the answers to your most frequently asked questions.

Is Quantitative Data More Reliable Than Qualitative Data?

Both types of data have their appropriate use. Accurate quantitative data is more reliable for the purposes of conducting inferential analysis and making predictions.

For example, a data scientist who is trying to help optimize the cost of production for a manufacturer will need quantitative data related to the production process, not qualitative data, in order to make an accurate and effective plan.

Why Is Quantitative Data More Accurate Than Qualitative Data?

Quantitative data is more accurate because it is based on measurable data that is less subjective than the data provided through qualitative methods. Descriptions of the same event may be different, depending on the observer.

However, with quantitative data, information is reported as numerical values, which are uniform and more universally understood than a description.

How Important Is Quantitative Data for Research?

Quantitative data provides the ability to make predictions. It is for this reason that it is a necessary component of most fields of research. The level of significance of a research study is also determined using quantitative data.

How Can You Tell if a Study Is Quantitative or Qualitative?

Quantitative studies rely on numerical data. Qualitative studies rely on accounts and personal descriptions of events.

Since you’re here… Interested in a career in data analytics? You will be after scanning this data analytics salary guide . When you’re serious about getting a job, look into our 40-hour Intro to Data Analytics Course for total beginners, or our mentor-led Data Analytics Bootcamp .  

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Home » Variables in Research – Definition, Types and Examples

Variables in Research – Definition, Types and Examples

Table of Contents

Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

• Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
• Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
• Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
• Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
• Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
• Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
• Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

• Age : This is a continuous variable that represents the age of an individual in years.
• Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
• Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
• Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
• Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
• Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
• Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
• Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
• Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
• Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

• Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
• Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
• Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
• Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
• Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

• To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
• To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
• To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
• To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

• Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
• Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
• Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
• Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
• Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

• Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
• Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
• Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
• Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
• Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

• Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
• Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
• Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
• Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
• Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather survey data. What are they exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research  is all about  language, expression, body language and other forms of human communication . That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people  feel  the way they do, why do they  act  in a certain way, what  opinions  do they have and what  motivates  them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyse than quantitative data.

This qualitative data is called  unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyse this structured data to assist computers and the human brain?

Discover the 2023 Market Research Trends Transforming the industry

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to  understand  something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to  test or confirm  a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain  insights around a topic  and propose a  hypothesis.  Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted  quantitative  research could then reveal whether or not those stores selling children’s clothes achieve higher  customer satisfaction  scores  and a  rise in profits  for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey  question  options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analysing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for  quantitative data  are now used for interpreting non numerical data too.

Artificial intelligence programs can now be used to analyse open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organisations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyse than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

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Research design : qualitative, quantitative, and mixed method approaches

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• Preface Purpose Audience Format Outline of Chapters 1. PRELIMINARY CONSIDERATIONS Ch 1. A Framework for Design
• Three Elements of Inquiry
• Alternative Knowledge Claims
• Strategies of Inquiry
• Research Methods
• Three Approaches to Research
• Criteria for Selecting an Approach
• Personal Experiences
• Writing Exercises
• Additional Readings Ch 2. Review of the Literature
• Identifying a Topic
• A Researchable Topic
• Purpose of the Literature Review
• Literature Reviews in Qualitative, Quantitative, and Mixed Method Research
• Design Techniques
• Example 2.1 Review of a Quantitative Study
• Example 2.2 Review of a Study Advancing a Typology
• Style Manuals
• A Model for Writing the Literature Review
• Additional Readings Ch 3 Writing Strategies and Ethical Considerations
• Writing the Proposal Central Arguments to Make Example 3.1 A Qualitative Constructivist/Interpretivist Format Example 3.2 A Qualitative Advocacy/Participatory Format Example 3.3 A Quantitative Format Example 3.4 A Mixed Methods Format
• Writing Tips Example 3.5 A Sample Passage Illustrating the Hook-and-Eye-Technique
• Ethical Issues to Anticipate
• Additional Readings Part 2 DESIGNING RESEARCH Ch 4 The Introduction
• The Importance of Introductions
• Qualitative, Quantitative, and Mixed Methods Introductions
• A Model for an Introduction Example 4.1 Deficiencies in the Literature - Needed Explorations Example 4.2 Deficiencies in the Literature - Few Studies
• Additional Readings Ch 5 The Purpose Statement
• Significance and Meaning of a Purpose Statement
• A Qualitative Purpose Statement Example 5.1 A Purpose Statement in a Qualitative Phenomenology Study Example 5.2 A Purpose Statement in a Case Study Example 5.3 A Purpose Statement in an Ethnographic Study
• A Quantitative Purpose Statement Example 5.4 A Purpose Statement in a Grounded Theory Study Example 5.5 A Purpose Statement in a Published Survey Study Example 5.6 A Purpose Statement in a Dissertation Survey Study Example 5.7 A Purpose Statement in a Experimental Study
• A Mixed Methods Purpose Statement Example 5.8 A Mixed Methods Purpose Statement, Convergent Strategy of Inquiry Example 5.9 A Mixed Methods Purpose Statement, Sequential Strategy of Inquiry
• Additional Readings Ch 6
• Research Questions and Hypotheses
• Qualitative Research Questions Example 6.1 A Qualitative Central Question From an Ethnography Example 6.2 Central Questions From a Case Study
• Quantitative Research Questions and Hypotheses Example 6.3 A Null Hypothesis Example 6.4 Directional Hypotheses Example 6.5 Nondirectional and Directional Hypotheses Example 6.6 Standard Use of Language in Hypothesis
• Mixed Method Research Questions and Hypotheses Example 6.7 Descriptive and Inferential Questions Example 6.8 Hypotheses and Research Questions in a Mixed Methods Study
• Additional Readings Ch 7 The Use of Theory
• Quantitative Theory-Use Example 7.1 A Quantitative Theory Section
• Qualitative Theory-Use Example 7.2 An Example of Theory-Use Early in a Qualitative Study Example 7.3 A Theory at the End of a Qualitative Study
• Mixed Methods Theory-Use Example 7.4 A Transformative-Emancipatory Mixed Methods Study
• Additional Readings Ch 8
• Definitions, Limitations, and Significance
• The Definition of Terms Example 8.1 Terms Defined in a Mixed Methods Dissertation Example 8.2 Terms Defined in an Independent Variables Section in a Quantitative Dissertation
• Delimitations and Limitations Example 8.3 A Delimitation and a Limitation in a Doctoral Dissertation Proposal
• Significance of the Proposed Study Example 8.4 Significance of the Study Stated in an Introduction to a Quantitative Study
• Additional Readings Ch 9 Quantitative Methods
• Defining Surveys and Experiments
• Components of a Survey Method Plan Example 9.1 An Example of a Survey Method Section
• Components of an Experimental Method Plan Example 9.2 Pre-Experimental Designs Example 9.3 Quasi-Experimental Designs Example 9.4 True Experimental Designs Example 9.5 Single-Subject Designs Threats to Validity Example 9.6 An Experimental Method Section
• Additional Readings Ch 10 Qualitative Procedures
• The Characteristics of Qualitative Research
• The Researcher's Role
• Data Collection Procedures
• Data Recording Procedures
• Data Analysis and Interpretation
• Validating the Accuracy of Findings
• The Qualitative Narrative Example 10.1 Qualitative Procedures
• Additional Readings Ch 11 Mixed Methods Procedures
• Components of Mixed Methods Procedures
• The Nature of Mixed Methods Research
• Types of Mixed Methods Strategies
• Alternative Strategies and Visual Models
• Data Analysis and Validation Procedures
• Report Presentation Structure
• Examples of Mixed Methods Procedures Example 11.1 A Sequential Strategy of Inquiry Example 11.2 A Concurrent Strategy of Inquiry
• Additional Readings References Author Index Subject Index About the Author.
• (source: Nielsen Book Data)

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Meaning of research in English

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• He has dedicated his life to scientific research.
• He emphasized that all the people taking part in the research were volunteers .
• The state of Michigan has endowed three institutes to do research for industry .
• I'd like to see the research that these recommendations are founded on.
• It took months of painstaking research to write the book .
• absorptive capacity
• dream something up
• modularization
• nanotechnology
• technologist
• the mother of something idiom
• think outside the box idiom
• think something up
• study What do you plan on studying at university?
• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• have the measure of someone/something idiom
• interpretable
• interpretive
• reinspection
• reinterpret
• reinterpretation
• reinvestigate
• reinvestigation

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to become dry, smaller, and covered with lines as if by crushing or folding, or to make something do this

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

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Article contents

Historical approaches to security/strategic studies.

• Constantinos Koliopoulos Constantinos Koliopoulos Department of International, European and Area Studies, Panteion University
• https://doi.org/10.1093/acrefore/9780190846626.013.210
• Published in print: 01 March 2010
• Published online: 22 December 2017
• This version: 25 January 2019
• Previous version

One can treat the terms “security studies” and “strategic studies” as synonymous and as pertaining to the study of the interaction of policy ends with military and other means under conditions of actual or potential conflict. This definition means that security/strategic studies can be a fairly broad field. Moreover, this broadness applies not only to the subject matter of the field, but to its time span as well. The study of strategy is arguably as old as war itself, and certainly far older than the formal establishment of strategic studies as an academic discipline in the aftermath of World War II. In this vein, one may well regard works like those of Thucydides and Clausewitz as belonging to the broad field of strategic/security studies.

Although the study of war and strategy would often go hand in hand with military history, from very early times there have appeared treatises on strategy (actually on “the art of war”) that are clearly distinguished from historical treatises and thus from the very beginning set strategic/security studies on a clearly distinct track. Be that as it may, the historical approach to strategic/security studies has always been and still remains a very powerful analytical tool—provided it is handled with the necessary care.

Beginning with Thucydides, and continuing with such luminaries as Vegetius, Clausewitz, Delbrück, and Corbett, the historical approach to strategic/security studies has provided the field with some of its most brilliant treatises. This venerable tradition continued after World War I and until well into the Cold War, including historically minded gems such as those by Fuller and Brodie. However, the advent of nuclear weapons and the consequent preoccupation of strategic/security studies with nuclear strategy led by and large to the loss of the field’s earlier historical bearings. Though never completely shelved, the historical approach was relatively subdued. It began to stage a comeback during the 1970s, aided by scholars like Howard, Luttwak, and Gray and further bolstered by the renewed interest in classical strategic theory. The end of the Cold War found the historical approach in terrific shape. Thus, not only does it once again tap the huge reservoir of ancient history, but it has also harnessed the newly available tools of quantitative research and the academic rigor of the social sciences. Since the end of the Cold War has definitely not brought about the end of history and the obsolescence of historical experience, it seems safe to conclude that the historical approach to strategic/security studies will fully retain its validity well into the 21st century.

• security/strategic studies
• military history

Updated in this version

Summary and keywords updated. References updated and expanded.

Introduction

This article treats strategic studies and security studies as synonymous and as pertaining to the study of the interaction of policy ends with military and other means under conditions of actual or potential conflict. Strategic/security studies is arguably the most historically minded branch of international relations. Although the present shape of this field has been heavily influenced by the Cold War, the study of strategy is much older. Thus, although strategic/security studies did not formally exist at the time of Clausewitz and Delbrück, it is absurd to assert that their works (Clausewitz, 1989 ; Delbrück, 1975 – 1985 ) should be excluded from the field; though it may seem anachronistic, this article will regard such works as belonging to strategic/security studies.

The study of war and strategy has often been indistinguishable from military history; Thucydides’ History of the Peloponnesian War ( 1972 ) is a primary example. However, from very early times we have treatises on strategy (actually on “the art of war”) that are clearly distinguished from historical treatises and thus from the very beginning set strategic/security studies on a clearly distinct track. The works of Sun Tzu, Kautilya, and Vegetius are cases in point. Sun Tzu’s Art of War , probably written during the 4th century bce , has only two or three very sketchy historical illustrations. Kautilya’s Arthasastra , possibly written during the late 4th century bce , has nothing. Vegetius ( 1943 ) wrote during the late 4th century ce . His work, which had a profound influence on Western military thought, uses concise historical examples to illustrate and support the author’s theoretical observations and practical recommendations; no one could mistake that book for a military history text.

The historical approach to strategic/security studies is permeated by the belief that the nature of strategy has remained essentially unchanged throughout history. Of course, this strand of thought has come to recognize, sometimes reluctantly (cf. Gat, 2001 :6–11, 313, 334), that changing conditions lead to corresponding changes in tactics. It is also recognized that changes in the nature of tactics are bound to influence strategy as well (Clausewitz, 1989 :226). Still, it is asserted that grand strategy (“the logic of conflict”), military strategy (“the nature of war”), or even operational art (“the art of the general”) are fundamentally timeless in character, hence a historical approach is essential for their understanding (Napoleon, 1943 :236; Fuller, 1970a :15; Thucydides, 1972 :I 22; Liddell Hart, 1991 :3–6; Fuller, 1998 :7; Gray, 1999 ). This was considered self-evident until the aftermath of World War II, which witnessed the emergence of a school of strategic/security studies that was almost completely divorced from history.

Desirable as it is, the study of history for purposes of strategic/security analysis must fulfill three requirements if it is to be of use: (1) it must be done in depth, so that the scholar understands “what really happened”; (2) it must cover a great time span, so that the scholar realizes what changes and what remains immutable over time; (3) it must take into account the broader political, economic, and social context (Howard, 1983 :195–197).

The historical approach to strategic/security studies is not without its potential pitfalls. The most obvious one is the facile recourse to historical analogies. Drawing inferences and analogies from history is, of course, the very essence of the historical approach. However, the golden rule here is to be aware of the limits of the analogy and not to substitute flimsy historical analogies and examples for analytic thought (Howard, 1983 :132, 191–193; Gray, 1990 :335).

Another potential pitfall is that of anachronism, namely the use of modern terms to describe past situations, institutions, etc. In the specific context of strategic/security studies this pitfall is arguably less acute, owing to the timeless nature of strategy. Nonetheless, it is occasionally doubted whether modern strategic terminology can be legitimately used to describe the actions of political leaders and military commanders of bygone eras. Still, even though one ought to be cautious in attributing modern thoughts and practices to historical political and military leaders (Howard, 1983 :191–192), it is indeed justifiable to use modern strategic terminology to explain the past and test theoretical assumptions with general application (Gray, 1990 :334); the historians may disapprove, but the aim of strategic/security studies can be well served.

Another pitfall to be avoided is excessive lack of empathy for the strategic decision makers of the past. In a sense, this is connected with the aforementioned pitfall of anachronism. Since changes in tactics are bound to affect strategy, the historically minded strategic/security analysts have to be well versed in the tactical conditions of the periods they examine, in order to accurately assess the strategic options open to historical actors. It is decidedly unfair, as it is mistaken, to castigate strategic decision makers of the past for failing to follow strategic options that may look attractive to modern analysts but were tactically unfeasible with the military instruments actually at hand. Excessive lack of empathy may also come as a result of hindsight. The analysts, lacking the real-life pressures of historical decision makers and enjoying the benefit of hindsight, may be tempted to judge overly harshly the decisions made. In this way, a historically minded strategic/security analysis could degenerate into mere argument with the figures that actually made history (Overy, 1980 :xi).

Having outlined the main methodological issues involved, we now move on to the survey of the relevant literature.

The Early Literature Up to Clausewitz

Thucydides’ History of the Peloponnesian War ( 1972 ), apart from its acclaimed status as a historical work and an international relations textbook, has also every right to be regarded as a classic essay on strategy. To start with, Thucydides provided the first recorded outline of two opposing grand strategic designs. Both sides in the Peloponnesian War understood that victory demanded not only military prowess, but also economic resources, domestic legitimacy, and a favorable diplomatic environment. Consequently, each of them tried to secure these desirables while simultaneously negating them to the other side. Moreover, Thucydides ( 1972 :I 82, II 18, II 62) offered a sophisticated treatment of many strategic concepts such as coercion and command of the sea. Last, but not least, Thucydides, in presenting the strategy employed by Athens during the initial phase of the Peloponnesian War, outlined the archetype of what Hans Delbrück ( 1975 – 1985 ) would call the “strategy of exhaustion.”

Thucydides ( 1972 :I 22) based his historical approach on the supposed immutability of human nature. His argument was that a thorough, accurate, and objective examination of a seminal historical event is bound to increase one’s understanding of human nature and consequently of political and strategic affairs. However, since Thucydides was keen on getting as precise a knowledge of his subject matter as possible, he believed ( 1972 :I 1, I 21) that the historical approach ought to deal with contemporary or nearly contemporary events.

The belief in the utility of history, contemporary or more remote, for the study of war permeates the ancient tradition of political and military history, as exemplified in the works of Xenophon ( 1918 – 1921 , 1998 ) and Caesar ( 1914 , 1917 ). Although they lack the depth of Thucydides, these works do contain nuggets of wisdom. Opinions about their value for posterity vary; Frederick the Great was dissatisfied by Caesar’s work (Gat, 2001 :60), but Thomas Schelling ( 1966 :vii) found it useful. A somewhat special case is the Greek historian and statesman Polybius ( 1922 – 1927 ). His account of the rise of Rome is still an important textbook of international relations and contains quite a few useful pieces of strategic analysis.

The Imperial Roman military treatises also followed a historical approach. The work of Vegetius has already been dealt with, whereas one cannot help mentioning the works of Frontinus ( 1925 ) and Polyaenus ( 1994 ). Written in the 1st and the 2nd century ce , respectively, these works are compilations of instances of deception and surprise in war. Although some mythological examples do creep in, the approach is basically historical; it was believed that the stratagems of the past could give inspiration to contemporary Roman commanders.

The historical approach to the study of military affairs was not an exclusively Western phenomenon. Abstract, apophthegmatic texts do occupy a prominent place in Asian military tradition, as was also the case in Byzantium. However, from quite early on, China also produced a considerable corpus of military literature that combined theoretical insights on tactics and strategy with historical examples. This literature grew from the Tang dynasty (618–907 ce ) onward (Black, 2004 :90). The work of Ralph Sawyer ( 1998 , 2002 ) has made much of this corpus accessible to Western readers.

The historical approach was the cornerstone of Machiavelli’s work ( 2003 , 2005 , 2008 ). The Florentine statesman drew upon a wealth of examples from ancient Roman and contemporary Italian and European history in order to come up with useful generalizations in political and military matters. However, although his historical approach served his analysis extremely well in matters of politics and grand strategy, it seems that his admiration of the Roman Republic and his desire to promote the civic and republican spirit among his fellow citizens led him astray with regard to tactical matters. In order to justify his advocacy of a tactical formation that resembled the Roman legion, Machiavelli felt compelled to claim historical continuity at the tactical level of war and thus downgrade the impact of contemporary developments in military technology; the quality of his analysis suffered as a result (Gat, 2001 :6–11). This should be a cautionary tale for every advocate of the historical approach to strategic/security studies.

The military writers of the 17th and 18th centuries used the historical approach as a matter of course, with ancient history being quite popular among them (Gat, 2001 :35–80). Clausewitz ( 1989 :172–174), a man noted for his critical attitude, commends the attempt of one of those writers, the French Marquis de Feuquières, to “teach the art of war entirely by historical examples” and expresses gratitude for the results of Feuquières’s historical research; still, Clausewitz claimed that the Frenchman ultimately failed in his mission. Even Maurice de Saxe ( 1943 ), who pointed out that historically minded treatises on war make for pleasant reading but often neglect the nuts and bolts of the military profession, used his fair share of historical examples. The spirit of the times is well captured by the celebrated dictum of Napoleon ( 1943 :236): “Read over and over again the campaigns of Alexander, Hannibal, Caesar, Gustavus, Turenne, Eugene, and Frederick. Make them your models. This is the only way to become a great general and to master the secrets of the art of war. With your own genius enlightened by this study you will reject all maxims opposed to those of these great commanders.”

It may be said that the trend of historical approach that began with Machiavelli, or even with Vegetius, reached its culmination with the work of the Swiss baron Antoine Henri de Jomini. Jomini enjoyed a long life ( 1779–1869 ) and his prolific pen earned him world fame. In his Précis de l’ art de la guerre ( The Art of War ) ( 1992 ), his most renowned treatise, first published in 1838 , Jomini purported to provide a comprehensive analysis of war. The work is steeped in history. The Napoleonic Wars are the chief sources of examples, of course, but Jomini also draws from ancient, medieval, and early modern history. Incidentally, Jomini pays due attention to the various episodes of the expansion of the Ottoman Empire; he was no ethnocentricist, although his historical knowledge of non-Western civilizations was indeed confined to their contacts with the West. Jomini explicitly vouched for the continuing validity of the historical approach to the study of war, despite the great technological developments that were taking place in his lifetime. Even though Jomini ( 1992 :347) conceded that the recent improvement of firearms “would probably have an influence upon the details of tactics,” the immutable principles of war would continue to apply in the realms of strategy and operational art.

Be that as it may, Jomini’s work demonstrates that command of a vast expanse of historical examples does not guarantee an accurate grasp of the broader sociopolitical context and consequently may result in failure to link military conditions and developments with the prevailing sociopolitical milieu. For instance, instead of attributing the cumbersome logistics system of the 18th century to the particular conditions of the period (Clausewitz, 1989 :516), Jomini simply viewed it as an error induced by inadequate thinking and prejudice (Gat, 2001 :124).

Clausewitz was a strong advocate of the historical approach. However, following the teachings of his mentor Scharnhorst (Gat, 2001 :162–168, 188–191), he came up with profound refinements of the historical approach in strategic/security analysis. He laid down the relevant principles in two chapters in Book 2 of On War ( 1989 :156–174) that dealt with critical analysis and historical examples. Clausewitz defined critical analysis as the application of theoretical truths to actual events. Critical analysis consists of three intellectual activities: (1) discovery and interpretation of equivocal facts, (2) tracing of effects back to their causes, and (3) investigation and evaluation of the means employed (Clausewitz, 1989 :156). A working theory is the cornerstone of criticism, but there is more to criticism than the mechanical application of theory (e.g., General X divided his forces in the presence of the enemy, therefore he lost). On the other hand, Clausewitz ( 1989 :157–158) is quick to point out that a theoretical assertion is not necessarily proved incorrect merely because a contrary example has been found.

Clausewitz ( 1989 :164–167) makes a unique analysis of the issue of hindsight. To begin with, he gives the customary advice that historically minded analysts should try to discard the benefit of hindsight. However, Clausewitz goes deeper than that. The benefit of hindsight is often a true benefit because it may be the only way for the critics to rise above the situation and pinpoint mistakes (which they themselves would undoubtedly have committed, too). Actually, the outcome of an action often affords the most important proof of the soundness (or unsoundness) of that action.

As regards the use of historical examples, Clausewitz thinks they are very useful provided they are used judiciously. According to Clausewitz ( 1989 :171), historical examples can be used in four possible ways: (1) to explain an idea (abstract exposition being too dreary), (2) to show the application of an idea, (3) to support a statement (in this case, they merely have to prove that some phenomena or effects are indeed possible), and (4) to deduce a doctrine (by a detailed presentation of a historical event). He did not make particularly harsh demands for the first three uses of examples but would brook no compromise concerning the fourth use. Echoing Scharnhorst, Clausewitz ( 1989 :173) declared that “where a new or debatable view is concerned, a single thoroughly detailed event is more instructive than ten that are only touched upon.”

After that, he went on to claim that modern historical examples are more useful than remote ones. This is because in modern historical examples conditions remain similar to the present ones, and also because modern history has still retained some important minor elements and details that are always bound to be lost with the passage of time (Clausewitz, 1989 :173). Using this kind of reasoning, Clausewitz claimed that any historical examples older than the War of the Austrian Succession ( 1740–1748 ) would be inadequate for analysts of his own era (early 19th century ) due to profound technical, tactical, and operational changes that had meanwhile taken place. Pursuing this line of thought, Clausewitz ( 1989 :173) stated that the ancient examples are the most useless of all. However, he immediately ( 1989 :173–174) went on to qualify this assertion by pointing out that remote historical examples may still prove useful if their analysis does not depend on detailed knowledge. In fact, Clausewitz ( 1989 :174) cites as a case in point the example of the Roman military strategy of horizontal escalation to Spain and Africa while Hannibal was still undefeated in Italy: “we still know enough about the general situation of the states and armies that enabled such a roundabout method of resistance to succeed.”

Finally, Clausewitz ( 1989 :582–594) provides a sweeping historical sociology of war from ancient times to his own era, not neglecting to include the Eurasian nomads. The idea is simple: every age and every political unit has its own kind of war, conditioned by historical circumstances. In one of his celebrated passages, Clausewitz ( 1989 :583) laments that the Austrians and Prussians of 1805 , 1806 , and 1809 , blissfully unaware of the profound transformation of the war that had taken place at the onset of the 19th century , prepared for typical 18th-century wars of maneuver and were only too surprised to face “the God of War himself.” Clausewitz’s historical sociology of war remains absolutely valid both as a piece of historical observation and as a tool for strategic/security analysis.

One can see that the historical approach represented a powerful strand of thought in the early literature of the subject. It was sometimes misused, but was deemed indispensable for the study of the tactical and operational (and occasionally the strategic and the grand strategic) levels of war. Clausewitz did introduce some strict methodological rules and important qualifications for the use of the historical approach but, as will be seen in the section “ From Clausewitz to World War II ,” these were quite often honored in the breach.

From Clausewitz to World War II

During this period, practitioners of the historical approach steadily moved away from the tactical level of war, as it increasingly became evident that constant technological changes rendered obsolete the tactical experiences of previous eras. At the same time, the whole field kept broadening its focus to cover overall military strategy and grand strategy, that is, returning to the Thucydidean archetype that had been rarely heard of after Polybius. New vistas were opened, and the historical approach had plenty to offer. Finally, the study of war was progressively institutionalized, chiefly in military academies and general staffs but also in universities, thus sowing the seeds for the spectacular growth of strategic/security studies after World War II. In either case, military history was considered an integral part of the curriculum (Black, 2004 :186–188, 190–192).

A brave attempt to harness the historical experience to the study of war was made by the French colonel Charles Ardant du Picq ( 1946 ), whose collected works were published posthumously in 1880 . Ardant du Picq dealt with the tactical level of war, drawing heavily from ancient battles and sending detailed questionnaires to his colleagues with a view to preserving and distilling their war experience. The endeavor to ascertain contemporary tactical conditions and come up with useful tactical and organizational recommendations for the French army was highly commendable. However, deducing tactical lessons from ancient battles was a potentially disastrous exercise. Ardant du Picq attributed primary value to morale as a means to success in battle; moral superiority could overcome greater destructive power. However, it is only fair to say that the increasing rapidity and volume of battlefield fire rendered his analysis progressively obsolete and misleading (Fuller, 1970b :297).

Ardant du Picq cannot be held responsible for the disastrous French offensives during World War I. At any rate, his honest research and lack of obvious personal agenda (apart from the improvement of the French army), plus the fact that he died when the machine gun was still in its infancy, should incline one to give him the benefit of the doubt. This is by no means the case with Ardant du Picq’s far more famous compatriot, Marshal Ferdinand Foch, whose Principles of War ( 1918 ) provides a hard-to-surpass example of what a practitioner of the historical approach to strategic/security studies must avoid. Analytical and historical accuracy were willingly sacrificed on the altar of Foch’s exaltation of the offensive and morale—an exaltation closely linked to French domestic politics (Snyder, 1984 :41–106, 201–203). He asserted that a battle can only be lost (or won) morally and not physically, hence a battle won is one in which a commander does not acknowledge defeat. This kind of sophistry extended to contorted mathematical calculations—“mathematical abracadabra” (Fuller, 1972 :123)—intended to prove that the improved infantry firepower actually favored the offensive, and culminated in outright misleading historical illustrations where suicidal blunders were presented as brilliant pieces of generalship (Foch, 1918 ; Brodie, 1959 :47–50; Fuller, 1972 :122–128).

The historical approach fared much better at the hands of the American captain (eventually rear admiral) Alfred Thayer Mahan. In his two most famous works, The Influence of Sea Power Upon History, 1660–1783 ( 1957 ) and The Influence of Sea Power Upon the French Revolution and Empire, 1793–1812 ( 2002 ), he tried to provide an analysis of the workings of sea power as a tool of grand strategy and a passport to world dominance, as well as a theory of naval strategy based on the precepts of Jomini and the conduct of outstanding practitioners such as Nelson. English/British history offered Mahan a ready-made case of the benefits of sea power. Sea power, in the form of a very strong and consistently victorious navy plus a chain of overseas bases, enabled the British to secure their homeland; dominate international trade; acquire colonies, markets, and money; sustain their Industrial Revolution; and become the most powerful nation on earth. For Mahan, this historical analysis bore a clear message for the turn-of- 19th-century United States: imitate Great Britain, in the sense of fostering sea power and acquiring overseas bases. Although sea power is clearly not the only road to international greatness, Mahan’s historically minded analysis of the subject has generally been vindicated.

Things are less clear as regards Mahan’s teachings on naval strategy. His analysis of naval battles, while eminently readable, is more often than not an example of the mechanical application of theory to historical examples that we have already seen Clausewitz warning against. In Mahan’s analysis, there is almost always a “clever” admiral who dutifully applies Jomini’s principles and wins, and a “stupid” admiral who duly commits every conceivable mistake (divides his fleet, lacks offensive spirit, etc.) and loses. Of course, one should expect to find at least some correlation between victory and “cleverness” or between defeat and “stupidity,” but in reality things are rarely present in such black-and-white terms. Apart from that, Mahan exaggerated the economic impact of the various British naval blockades. In other words, Mahan’s historical approach to naval strategy was rather crude and doctrinaire, although it must be said that the problem was mostly one of Mahan overstating his case rather than presenting a completely groundless case. All in all, Mahan came up with a brilliant conception that he fortified with a historical approach that, despite its defects, was basically sound.

Julian Corbett, the other great naval strategist of the period, was also an unambiguous case of a historically conscious strategic/security analyst. Corbett was both influential and controversial during his own time, but nowadays his star is deservedly in the ascendant (Gat, 2001 :480–493; Handel, 2001 ). Among his numerous works, arguably the most important are Some Principles of Maritime Strategy ( 1972 ) and England in the Seven Years’ War ( 1992 ). In the first, Corbett gave precise definitions and made a systematic analysis of several key concepts of naval strategy. His analysis rests on two powerful cornerstones, namely his historical learning and his study of Clausewitz. The book draws heavily on English/British historical experience; this makes for quite lively reading. As to Corbett’s book on the Seven Years’ War, this is a historical case study in much the same vein as the studies prescribed by Clausewitz: concrete, thorough, and theoretically informed, although admittedly too remote in time to fulfil Clausewitz’s contemporaneousness criteria.

One of the greatest achievements of the historical approach to strategic/security studies is the work of Hans Delbrück. In his multivolume History of the Art of War ( 1975–1985 ) Delbrück covered the period from the Persian Wars (490–479 bce ) to the Napoleonic Wars. Delbrück began by applying the new scientific historical method to military history. This was essential for doing away with much legendary and mythological material that had accumulated through the ages and kept clouding and distorting the factual background of military history. In this sense, Delbrück’s conclusive demolition of Herodotus’s account that had more than five million Persians invading Greece was a great service to both history and strategic/security studies. First, it cultivated an attitude of healthy skepticism toward ancient sources. Second, by suitably repairing the ancient accounts, it salvaged a number of ancient incidents from the morass of legend and rendered them usable by modern analysts. Consequently, Delbrück could come up with an important theoretical contribution, outlining two basic forms of strategy, namely the strategy of annihilation and the strategy of exhaustion.

A typical example of the institutionalization of the study of war that took place during the second half of the 19th century was the work of Field Marshal Alfred von Schlieffen ( 1931 ), chief of the German General Staff from 1891 until 1905 . Schlieffen became fascinated by the double envelopment effected by Hannibal’s Carthaginians against the numerically superior Romans at the battle of Cannae (216 bce ). As a result, he tried to demonstrate that enveloping attacks had been the recipe for victory throughout history. It has been correctly pointed out that Schlieffen’s analysis often did “violence to the facts” (van Creveld, 1989 :5), thus demonstrating once again the difficulty of finding a single compelling “meta-narrative” in military history and strategic/security analysis. Be that as it may, powerful enveloping attacks against the enemy flanks or rear have indeed been the primary tools of decision in land battle.

After the convulsions of World War I, many a strategist tried to figure out what to make of the new weapons unleashed by that conflict. Giulio Douhet ( 1972 ), the Italian pioneer theorist of airpower, believed that an independent air force consisting solely of bombers was the key to victory in future wars. Due to the novelty of airpower and his own futurist leanings, Douhet consciously rejected the utility of history for analyzing the new weapon (Gat, 2001 :576). One would have to wait until after World War II to encounter analyses of airpower that would feature the historical approach.

This disdain of the historical approach was by no means characteristic of all the strategic/security analysts that emerged during the interwar years. For instance, J. F. C. Fuller demonstrated that the historical approach could be profitably combined with unconventional and pioneering thinking. Although Fuller became known during the interwar years as a leading theorist of tank warfare, he arguably left an even more lasting legacy with his historically informed analyses written after World War II. His Armament and History ( 1946 ) was a trailblazing attempt to trace the impact of weapons technology on war, from the ancient Greeks to World War II. Among Fuller’s quasi-biographical works, the most important is probably The Generalship of Alexander the Great ( 1998 ). In examining the extraordinary career of Alexander the Great, Fuller showed himself to be equally at home in subjects ranging from the philosophical milieu of the ancient Greek world in the 4th century bce to Alexander’s commando operations.

Even more ambitious were Fuller’s The Decisive Battles of the Western World ( 1970a , 1970b ) and The Conduct of War, 1789–1961 ( 1972 ). The Decisive Battles is a tour de force of strategic/security analysis, tracing the rise and fall of great powers from ancient Egypt to Nazi Germany, the interplay of the security policies of international actors, the changes in the nature of war, and the evolution of military strategy and operational art, all the way down to the tactical details of a number of important battles. As to The Conduct of War , it was a profound exercise in the historical sociology of war from the 18th century onward, demonstrating how the phenomenon of war was influenced by the French, Industrial, and Russian Revolutions.

Another major historically conscious analyst that emerged during the interwar years was Basil Liddell Hart. Liddell Hart’s work has been characterized by his belief in the so-called indirect approach, which generally denotes the sidestepping of the enemy strong points and the avoidance of attrition warfare. The historical approach was one of his preferred tools. His Strategy ( 1991 ), the work for which Liddell Hart is chiefly remembered nowadays, is an outline of military history from ancient Greece to World War II—with an appendix on the Israeli War of Independence—purporting to demonstrate the superiority of the indirect approach. The book is not without its shrewd remarks, particularly with respect to grand strategy and guerrilla warfare. However, Liddell Hart was not at all rigorous or consistent in his conceptualization of the indirect approach; the result is that virtually every victory in history is somehow attributed to the victor’s “indirect approach,” while the term itself is stretched amorphous. Strategy is still a pleasant read but ultimately is another failed attempt to impose a universal framework on strategic experience.

The Cold War Years

The first modern textbook on strategy was published during World War II (Earle, 1944 ). It featured a healthy dose of historical approach, dealing with figures such as Vauban and Frederick the Great. This book presaged the emergence of strategic studies as an academic discipline, by and large independent from the earlier domination of military institutions.

Shortly after that, the world witnessed the advent of nuclear weapons. This would both establish strategic studies as an academic discipline and bring about a radical shift in its focus. For more than two decades strategic studies became preoccupied with nuclear strategy, and to a great extent lost its earlier historical bearings. During the so-called golden age of strategic studies, namely the 1950s and 1960s, the historical approach was relatively subdued. The novelty of nuclear weapons was arguably a valid reason for that, although the fascination with the technical aspects of those weapons and the tendency to resort to abstract thinking was carried too far (Trachtenberg, 1991 :3–46).

Still, the historical approach was never completely shelved. Among the leading theorists of nuclear strategy, Bernard Brodie was a wholehearted exponent of the historical approach. His Strategy in the Missile Age ( 1959 ) offers an overview of strategic history in an effort to gain an understanding of air and nuclear strategy; as Brodie ( 1959 :19) put it, “while air power, in which we must now include long-range missiles as well as aircraft, is of recent origin, ideas about war and how to fight it are not.” His War and Politics ( 1973 ) is even more sweeping in outlook, drawing heavily upon history in order to explore and develop the Clausewitzian idea that war is a political act aiming at the achievement of political objectives and therefore ought to be subordinated to policy. Finally, in From Crossbow to H-Bomb (Brodie & Brodie, 1973 ) Brodie and his wife, Fawn, traced the evolution of military technology and tactics.

As to other “golden age” strategists, Thomas Schelling ( 1966 ), a typical example of that era, did use illustrative historical examples in his analysis and openly recommended the reading of Thucydides and Caesar to those in search of ideas. Moreover, George Quester ( 1966 ) produced one of the first works to treat air power and deterrence in a historically conscious manner. Still, the overall picture was fairly disappointing: the historical approach of the “golden age” nuclear strategists basically amounted to invocation of hackneyed analogies of the outbreak of World War I and the Pearl Harbor attack.

It is important to note that outside the realm of nuclear strategy the historical approach continued to hold its own. For instance, it all but dominated the study of civil–military relations. The classic works on that subject followed the tradition of Alfred Vagts’s earlier work ( 1937 ) and dealt with historical cases as a matter of course (Huntington, 1957 ; Finer, 1962 ).

The same applied to the study of strategic surprise. Beginning with Roberta Wohlstetter’s ( 1962 ) seminal work on the Pearl Harbor attack, the literature drew extensively from an ever-expanding range of historical cases. This trend continued well into the 1980s (Betts, 1982 ; Levite, 1987 ). Guerrilla warfare was another subject featuring fine works using the historical approach (Laqueur, 1998 ). Michael Walzer ( 1992 ) used a huge number of historical examples to highlight and analyze the moral issues of war.

Michael Howard, for years the doyen of the strategic/security studies community, has been a strong advocate of the historical approach. His work may sometimes have more of a military historical bent but never lacks strategic analysis as, for instance, in his classic book on The Franco-Prussian War ( 1988 ). Howard’s scholarly output ( 1976 , 1983 , 1988 , 2004 ) demonstrates that the historical approach to strategic/security studies fully retained its vitality during—and after—the Cold War.

The 1970s witnessed the emergence of three new trends in the employment of the historical approach to strategic/security studies. The first of these was the return of ancient history. The use of ancient history in strategic/security analysis had had a very long pedigree but eventually all but faded away. However, the work of Edward Luttwak ( 1976 ) on the grand strategy of the Roman Empire demonstrated that strategic/security analysts could expand the range of their inquiries and profitably tap ancient history as well. Classical historians joined the new trend, and, although still a minority interest, ancient history was definitely back insofar as strategic/security analysis was concerned (Ferrill, 1986 ; Strauss & Ober, 1990 ).

The second trend was the introduction of quantitative research. Following the pioneering works of Lewis Richardson ( 1960a , 1960b ) and Quincy Wright ( 1965 ), the use of quantitative historical data entered the field of strategic/security studies. The most immediate results were a valuable work on military deception (Whaley, 1969 ) and the Correlates of War project (Singer, 1979 – 1980 ). The use of quantitative data featured prominently in Bueno de Mesquita’s expected utility theory ( 1981 ) and to this day enjoys a substantial following, as any reader of International Studies Quarterly knows.

The third trend was the increasing linkage of strategic/security literature with the social sciences. This trend, benefiting from advances in the technique of using case studies for theory development, involved greater theoretical sophistication, more rigorous terminology, and explicit testing of theoretical hypotheses. In this vein, Robert Art ( 1973 ) examined the naval strategy of Wilhelmine Germany; Alexander George and Richard Smoke ( 1974 ) dealt with the application of deterrence in American foreign policy; Klaus Knorr ( 1976 ) edited a volume containing historical illustrations of various strategic concepts (e.g., war-limiting); George Quester ( 1977 ) analyzed the relationship between offense and defense through history; and Richard Smoke ( 1978 ) conducted a detailed study of escalation, using among others the cases of the Seven Years’ and the Crimean Wars. These works were soon followed by treatises on concepts such as crises, military doctrine, and security policy (Lebow, 1981 ; Mearsheimer, 1983 ; Posen, 1984 ; Snyder, 1984 ).

Classical strategic theory began steadily to return to the forefront in the 1970s and was definitely there by the 1980s. Clausewitz got the lion’s share of scholarly attention (Paret, 1976 ; Handel, 1986 ) and Earle’s Makers of Modern Strategy was suitably updated (Paret, 1986 ). Edward Luttwak ( 2002 ) came up with an ingenious and historically minded work on the logic of strategy, Archer Jones ( 1987 ) produced an impressive meta-narrative arguing that the art of war reduces essentially to a choice between persisting and raiding strategies, while Colin Gray ( 1990 ) attempted to set the post–Cold War strategic agenda by producing a work on strategic theory that drew heavily from historical examples.

By the 1980s, strategic/security studies had recovered much of its original holistic approach. Among others, this was reflected in the appearance of historically informed works on grand strategy (Kaiser, 1990 ; Kennedy, 1991 ). As had previously happened with airpower, nuclear strategy had now sufficiently come of age to be examined in a historical light (Freedman, 1989 ).

The last decade of the Cold War witnessed a renewed interest in conventional warfare, which in turn boosted the employment of the historical approach on strategic/security studies. The two World Wars provided an inexhaustible pool of cases for analysis (Overy, 1980 ; Millett & Murray, 1988 ); sea power never ceased to command interest (Reynolds, 1983 ; Modelski & Thompson, 1988 ), and historically minded treatises on various aspects of conventional warfare kept appearing (McInnes & Sheffield, 1988 ). Martin van Creveld used the historical approach to produce highly readable, informative, and occasionally controversial books on command, military technology, and the future of war (van Creveld, 1987 , 1989 , 1991 ). As to the evolution of military technology, Trevor Dupuy ( 1980 ) continued along the lines of Fuller and the Brodies, while also attempting to quantify the impact of various weapons.

The end of the Cold War found the historical approach in the ascendancy. Clear lines of development had been established—that is, those pertaining to ancient history, quantitative approach, social science methodology, and nonnuclear strategic theory and practice—and would be duly followed during the post–Cold War period.

From the Aftermath of the Cold War to the Present

As the Cold War was drawing to its close and the Soviet Union was collapsing, it was widely proclaimed that a new era had arrived in domestic and international political affairs, rendering historical experience obsolete. It did not take long for the naiveté of such views to be exposed; for better or for worse, the historical approach to strategic/security studies would fully retain its validity in the post–Cold War era.

Indeed, this approach has been in excellent shape recently. One of its greatest current aficionados is Colin Gray. Gray ( 1990 , 1999 , 2002 ) has used the historical approach in a way similar to that of Vegetius: theory and analysis, interspersed with historical illustrations from virtually any historical period. Especially his Modern Strategy ( 1999 ) reads like a manifesto for the historical approach to strategic/security studies: Gray strongly asserts his belief in the eternal and universal nature of strategy, openly declares himself to be a representative of the “historical school of strategic thought” (which he distinguishes from the “materialistic” school), and, among others, proudly recalls one of his “finer moments” as an adviser to the U.S. government when he treated the Defense Nuclear Agency to a commentary on Byzantine strategy, using the walls of Constantinople as an example of strategic defense.

The use of ancient history in strategic/security analysis is now considered more or less normal. Apart from its widespread use in illustrative examples, there is a growing literature dealing with ancient historical case studies. These are mostly drawn from ancient Greek and Roman history (Starr, 1995 ; Mattern, 1999 ; Hanson, 2010 ), although a more inclusive approach to non-Western cases has also been attempted (Gabriel & Boose, 1994 ; Sawyer, 2007 ; Olsen & Gray, 2012 ).

The historical approach has fitted well with attempts at comprehensive analysis of the phenomenon of war (Weltman, 1995 ; Mueller, 2004 ) and strategy in general (Freedman, 2013 ). In the same vein, the historical approach has been used to good effect in studies dealing with the causes of war. Among the causes pinpointed by these studies are perceptions of threat or of military advantage, power considerations, and more mundane concerns such as territorial disputes (Van Evera, 1999 ; Senese & Vasquez, 2008 ). Attention has also been paid to the problem of creating stable and peaceful postwar international orders (Kennedy & Hitchcock, 2000 ).

The study of grand strategy from a historical perspective has also proved very popular. It is not unusual to encounter works with a broad historical sweep, organized either as separate case studies (Murray, Knox, & Bernstein, 1994 ) or around a grand theme, such as the interplay between victory and defeat (Bond, 1996 ). As regards the subjects of specific case studies, it is no accident that powerful historical actors command the greatest attention. To start with, Western scholars have begun to tap the huge reservoir of Chinese strategic experience (Johnston, 1998 ). Due attention has been paid to imperial states like Spain (Parker, 2000 ). The grand strategy of Israel, a state that arguably makes a highly efficient use of its resources, has also been analyzed with a historical approach (Maoz, 2006 ).

However, by far the most popular subject of historically minded treatises of grand strategy is the United States, with scholarly attention being overwhelmingly focused on American grand strategy during the Cold War. Much of this literature is critical (Lebow & Gross Stein, 1994 ; Payne, 2001 ), perhaps overly so, given that the actual decision makers had to operate in a novel and highly uncertain environment, especially during the early Cold War years. The Cuban missile crisis retains its appeal (Allison & Zelikow, 1999 ), whereas the Vietnam War occupies a special place within the strategic/security analyses of the Cold War (Walton, 2002 ). Finally, the historical approach is being employed in order to explore the future of the United States’ grand and military strategy; this discussion is often framed in terms of preserving America’s advantages (Herman, Gorman, Gallina, MacDonald, & Ryer, 2002 ).

An aspect of the current strategic/security studies literature is the use of historical cases to shed light on particular grand strategic choices. The grand strategic choices of foreign intervention, conquest, and occupation have received special attention (Taliaferro, 2004 ; Edelstein, 2008 ). In view of the present Western entanglement in Afghanistan and Iraq, this attention is not likely to dwindle anytime soon.

Nuclear weapons and strategy are nowadays clearly within the fold of the historical approach (Solingen, 2007 ). In an attempt to understand the dynamics of nuclear proliferation, many studies have dealt with the actual acquisition process of today’s nuclear states, with Israel and India receiving much attention in this respect (Cohen, 1998 ; Perkovich, 1999 ).

As regards conventional warfare, the trend that began in the 1980s continues unabated. The two World Wars remain popular subjects of study (Murray & Millett, 2000 ). Strategic bombing and the targeting of civilians in general have been given detailed attention (Pape, 1996 ). The same applies to the overall problems of leadership and command (McMaster, 1997 ; Cohen, 2002 ).

The subject of civil–military relations has once again been happily married with the historical approach. A number of studies have examined from a historical perspective the civil–military relations in various countries, often linking these relations to the choices of these countries at the levels of grand and military strategy (Brooks, 2008 ). The historical approach has also continued to reign supreme in the study of intelligence and strategic surprises (Mahnken, 2002 ). The same applies to the study of guerrilla warfare and terrorism. A number of works examine historical instances of guerrilla warfare, often in an attempt to find the key to victory (or defeat) for the insurgents or their opponents (Record, 2007 ; Gentile, 2013 ). A similar approach is followed regarding terrorism and counterterrorism (Alexander, 2006 ). Tackling mercenarism with a historical approach fits well with the increased tendency toward privatization of military force (Percy, 2007 ). An earlier tradition of historically minded studies of deterrence and coercion is still living on (Press, 2007 ). Arms control has also been approached from a historical angle (Croft, 1996 ). Even the concept of revolution in military affairs has been profitably examined in a historically conscious way: past revolutions have been identified, though not without disagreement, and the relevant evidence has been used for drawing pertinent conclusions for the present and the future (Rogers, 1995 ; Knox & Murray, 2001 ).

Interest in classical strategic theory remains lively. Among others, Gérard Chaliand ( 1994 ) produced a valuable anthology ranging from ancient Egypt to the end of the 20th century ce ; the relevant books of Azar Gat ( 2001 ) and Michael Handel ( 2001 ) have become standard reference works on the subject; and Beatrice Heuser ( 2010 ) has profitably delved into the works of long forgotten 16th- to early-19th-century theorists.

Finally, journals like Journal of Strategic Studies and Security Studies display a strong aptitude for the historical approach to strategic/security studies. As mentioned, this approach is currently in excellent shape. Indeed, current literature does testify to the diachronic value of this approach.

Acknowledgments

I would like to thank Marios Evriviades and an anonymous reviewer for their valuable comments. The usual disclaimer applies.

Links to Digital Materials

• Air University . A website of the US Air University, containing works of and Internet resources on great practitioners and theoreticians of war. Far from exhaustive, but useful.
• Clausewitz.com .The Clausewitz website. Images, links, bibliographies, etc., on Clausewitz.
• Correlates of War .The Correlates of War Project website. The mecca of quantitative data on the study of war.
• Alexander, Y. (2006). Counterterrorism strategies: Successes and failures of six nations . Washington, DC: Potomac Books.
• Allison, G. T. , & Zelikow, P. (1999). Essence of decision: Explaining the Cuban missile crisis (2nd ed.). New York, NY: Longman. (Originally published in 1971.)
• Ardant du Picq, C. (1946). Battle studies . Harrisburg, PA: Stackpole. (Originally published in 1880.)
• Art, R. (1973). The influence of foreign policy on seapower . Sage Professional Papers in International Studies, 2, 02-019. Beverly Hills, CA: SAGE.
• Betts, R. K. (1982). Surprise attack: Lessons for defense planning . Washington, DC: Brookings Institution.
• Black, J. (2004) Rethinking military history . London, UK: Routledge.
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What does I was able to engage directly in qualitative research that complemented the published quantitative research I drew from. mean? See a translation

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I was able to do research that aims to describe people's feelings and reasons (定性調査), which matched well with the research that measures things mathematically (数量調査) that was in the journals and books I used as references.

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I believe "qualitative research" is 定性的研究, and "quantitative research" is 定量的研究. A more simple way of saying it would be, "I performed qualitative research that related well with the existing quantitative research that I used as a reference."

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• DOI: 10.30591/monex.v12i1.3760
• Corpus ID: 268773520

Analisis Bibliometrik Good Government Governance di Indonesia dari Tahun 2015-2020

• Dri Asmawanti-S , Melin Soya
• Published in Monex Journal Research… 31 January 2023
• Political Science

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Digital government dalam menciptakan transparansi pelayanan publik: suatu analisis berdasarkan perspektif bibliometrik, related papers.

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Denver Basic Income Project Releases Year One Research Report

Note: The use of the term Basic Income in this article does not conform to BIEN’s definition. Denver Basic Income Project (DBIP) has released the results its Year One quantitative and quantitative findings. What the research has discovered supports what DBIP always believed – that guaranteed income gives families and individuals financial tools, and a cushion to cover their most basic needs per their circumstances. DBIP’s research shows:

You can review the Year One Research Report Executive Summary for an in-depth look at the research design, cost analysis due to reductions in public service utilization, and notable findings from both the quantitative and qualitative reports. You can read the full reports on the  research page  of DBIP’s website.

READ THE SUMMARY

Given that Denver annually spends over $40,000 on shelter and medical costs per person experiencing homelessness and is also dealing with the humanitarian and fiscal crisis of people arriving from the borders, cost-effective programs like this are extremely valuable. As the first and largest project of its kind studying the impact of guaranteed income on homelessness, the research and results of the Denver Basic Income Project have the potential to be replicated and scaled across the U.S. The Year One report is a monumental milestone for the Denver Basic Income Project, and we would not be here without the support of the community and our generous funders, including the City and County of Denver, The Colorado Trust, the Denver Foundation, and the Wend Collective.

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• Volume 83, Issue Suppl 1
• POS0889 PATIENT RESEARCH PARTNER INVOLVEMENT IN RHEUMATOLOGY RESEARCH: A SYSTEMATIC LITERATURE REVIEW INFORMING THE 2023 UPDATED EULAR RECOMMENDATIONS FOR THE INVOLVEMENT OF PATIENT RESEARCH PARTNERS
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• K. Aouad 1 ,
• M. De Wit 2 ,
• M. Elhai 3 ,
• D. Benavent 4 ,
• H. Bertheussen 5 ,
• J. Primdahl 6 ,
• C. Zabalan 7 ,
• P. Studenic 8 , 9 ,
• L. Gossec 10
• 1 Saint George Hospital University Medical Center, Saint George University of Beirut, Rheumatology, Beirut, Lebanon
• 2 EULAR, Patient Research Partner, Amsterdam, Netherlands
• 3 University Hospital Zurich, University of Zurich, Rheumatology, Zurich, Switzerland
• 4 Hospital Universitari de Bellvitge, Rheumatology, Barcelona, Spain
• 5 EULAR, Patient Research Partner, Oslo, Norway
• 6 University Hospital of Southern Denmark, Danish Hospital for Rheumatic Diseases, Sønderborg, Denmark
• 7 EULAR, Patient Research Partner, Bukarest, Romania
• 8 Karolinska Institutet, Division of Rheumatology, Department of Medicine (Solna), Stockholm, Sweden
• 9 Medical University of Vienna, Department of Internal Medicine III,Division of Rheumatology, Vienna, Austria
• 10 Sorbonne Université, AP-HP, Pitié-Salpêtrière hospital, Rheumatology, Paris, France

Background: Patient research partners (PRPs) are people with a disease who collaborate in a research team as partners. Their integration as equal partners is recommended [1]. However, PRP involvement still faces significant challenges that need to be addressed [2].

Objectives: The aim of this systematic literature review (SLR) was to assess PRPs’ roles, identify barriers to their involvement, and propose strategies to improve PRP involvement.

Methods: The SLR was conducted in PubMed/Medline, focusing on studies reporting PRP involvement in rheumatology research published between 2017 and January 2023. Websites were also searched in rheumatology and other specialties. Keywords such as “patient research partner,” “patient expert,” “patient and public involvement (PPI)”, and relevant acronyms (PRP, PPI) were used. Data were extracted regarding the definition of PRPs, their role and added value, as well as barriers and facilitators to PRP involvement. The quality of the articles was assessed. Quantitative data were analyzed descriptively, and principles of thematic content analysis was applied to qualitative data.

Results: Of 1016 publications, 53 articles were included; the majority of these studies were qualitative studies (26%), opinion articles (21%), meeting reports (17%) and mixed methods studies (11%). 60% of articles reported a definition of PRPs identifying terms such as “equal partnership”, “active engagement”, and “collaboration with researchers”. Roles of PRPs ranged from research partners to patient advocates, advisors, and patient reviewers. PRPs were reported/advised to be involved early in the project (32% of articles) and in all research phases (30%), from the conception stage to the implementation of research findings. The main barriers were challenges in communication and support for both PRPs and researchers (Table 1). Facilitators of PRP involvement included more than one PRP per project, training of PRPs and researchers, a supportive environment for PRPs (including adequate communication, acknowledgement and compensation of PRPs), and the presence of a PRP coordinator (Table 1).

Conclusion: This SLR identified barriers and facilitators to PRP involvement in rheumatology research. Addressing these barriers and implementing effective strategies is crucial for meaningful PRP involvement. This SLR was key to update the EULAR recommendations for PRP-researcher collaboration based on scientific evidence.

REFERENCES: [1] de Wit MP, et al. European League Against Rheumatism recommendations for the inclusion of patient representatives in scientific projects. Ann Rheum Dis. 2011.

[2] Studenic P et al. Unmet need for patient involvement in rheumatology registries and observational studies: a mixed methods study. RMD open. 2019.

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Acknowledgements: Funded by EULAR (RES005)

Disclosure of Interests: None declared.

• Interdisciplinary research
• Public health
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• Patient-led research

https://doi.org/10.1136/annrheumdis-2024-eular.3445

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hypothetical

Definition of hypothetical

• academical
• conjectural
• speculative
• suppositional
• theoretical
• theoretic

Examples of hypothetical in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothetical.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1588, in the meaning defined above

Dictionary Entries Near hypothetical

hypothesize

hypothetical imperative

Cite this Entry

“Hypothetical.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothetical. Accessed 21 Jun. 2024.

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