introduction research methods

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Introduction to Research Methods A Hands-on Approach

• Bora Pajo - Franklin University
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“ Introduction to Research Methods is an excellent resource for students and instructors alike. With readable text in an approachable tone, it effectively covers the breadth of topics related to research methods. Its lucidity is stellar.”

“ Introduction to Research Methods: A Hands-on Approach is highly relevant to the work my undergraduate students do and is delivered at a level they can follow. It does a great job of presenting complex information in an easy-to-understand format. At the graduate level, I could see this text serving as a primary read but then supplementing with more complex articles/chapters. Accessibility of the content is a core strength of this text. It makes in-depth content easy to follow for the novice researcher, while at the same time is engaging enough to not be bland for those with research experience. Overall, this text reads as a road map for how to conduct ethical research and provides the necessary step-by-step instructions on how to be successful during the research process.”

“ Introduction to Research Methods: A Hands-on Approach is an excellent text that presents research methods in an easy-to-understand way with examples that foster critical thinking."

“The hands-on, practical approach to Pajo’s Introduction to Research Methods is a huge key strength.”

Very useful for research methods

• The new edition is available in SAGE Vantage , an intuitive learning platform that integrates quality SAGE textbook content with assignable multimedia activities and auto-graded assessments to drive student engagement and ensure accountability. Unparalleled in its ease of use and built for dynamic teaching and learning, Vantage offers customizable LMS integration and best-in-class support. Learn more.
• A new chapter on Big Data offers a window into the possibilities of what social scientists are accomplishing in the world of machine learning and the endless possibilities of data visualization. It familiarizes students with current and future opportunities in social and behavioral science.
• A completely revised chapter on qualitative designs and data collection illustrates how qualitative designs fit within scientific research methods by emphasizing similarities and identifying differences.
• Updated research examples and visuals reflect current studies in a variety of interdisciplinary fields.
• APA Style 7th Edition is used throughout the book so students see and practice the latest citation styles.
• A conversational and jargon-free writing style brings the relevance of research to life.
• Research in Action features illustrate real, annotated research examples, giving students the skills to read and interpret research they may encounter in their everyday lives.
• Research Workshop features offer step-by-step help on practical topics that extend beyond the chapter to provide hands-on tips for students conducting their own research.
• Ethical Considerations within each chapter address different aspects of research to emphasize ethics as an integral part of the research conversation.
• Chapter pedagogy includes summaries, key terms, photos, and “Taking It a Step Further” questions to extend understanding and application of text concepts.

Sample Materials & Chapters

Chapter 1: The Purpose of Research

Chapter 2: Formulating a Research Question

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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• Knowledge Base
• Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs quantitative : Will your data take the form of words or numbers?
• Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
• Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

• From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

• During an experiment.
• Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

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 .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Research Methods Guide: Introduction

• Research Design & Method
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

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• Research Design & Method
• Resources & Consultation

About This Guide

Data collection and data analysis are research methods that can be applied to many disciplines.

This research methods guide will help you choose a methodology and launch into your research project.  The focus of this guide, however, is on two of the most popular methods: survey and interviews .

Where to Begin

1. Defining a Topic & Developing a Research Question

• Pick a topic that interests you (if you don't have a topic yet).
• Carefully review your assignment to make sure your topic is appropriate and fits the scope of the assignment (if your topic was assigned to you).
• Identify what you know about your topic and the questions you still have.
• Read more tips from one of your librarians for narrowing a topic.

• Start with general background information: e.g. Google search, newspaper and magazine articles.
• Look for more specialized information: e.g. Scholarly articles and books via library databases.
• If needed, look for Library Guides or talk to your librarian (subject specialist). 
• Searching the library's " Discovery search "
• Advanced searching tips for searching using library databases
• Selecting & evaluating resources
• Next: Research Design & Method >>
• Last Updated: Aug 21, 2023 10:42 AM

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Chapter 1: Introduction to Research Methods

Learning Objectives

At the end of this chapter, you will be able to:

• Define the term “research methods”.
• List the nine steps in undertaking a research project.
• Differentiate between applied and basic research.
• Explain where research ideas come from.
• Define ontology and epistemology and explain the difference between the two.
• Identify and describe five key research paradigms in social sciences.
• Differentiate between inductive and deductive approaches to research.

Welcome to Introduction to Research Methods. In this textbook, you will learn why research is done and, more importantly, about the methods researchers use to conduct research. Research comes in many forms and, although you may feel that it has no relevance to you and/ or that you know nothing about it, you are exposed to research multiple times a day. You also undertake research yourself, perhaps without even realizing it. This course will help you to understand the research you are exposed to on a daily basis, and how to be more critical of the research you read and use in your own life and career.

This text is intended as an introduction. A plethora of resources exists related to more detailed aspects of conducting research; it is not our intention to replace any of these more comprehensive resources. Feedback helps to improve this open-source textbook, and is appreciated in the development of the resource.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Methods

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This guide serves as an access point to an array of materials on research methods and ethics available in the Library and on the Web. Resources in various formats, including print and online, are selected to accommodate different learning preferences. Key resources include the Emerald's "How to ... Guides for Researchers" and “Research Methods Knowledge Base”.

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Introduction to Research Methods: A Hands-On Approach

Student resources, welcome to the companion website.

Welcome to the SAGE edge site for Introduction to Research Methods, First Edition .

The SAGE edge site for Introduction to Research Methods by Bora Pajo offers a robust online environment you can access anytime, anywhere, and features an impressive array of free tools and resources to keep you on the cutting edge of your learning experience.

Introduction to Research Methods: A Hands-On Approach makes learning research methods easy for students by giving them activities they can experience and do on their own. With clear, simple, and even humorous prose, this text offers students a straightforward introduction to an exciting new world of social and behavioral science research. Rather than making research methods seem intimidating, author Bora Pajo shows students how social science and behavioral research can be an easy, ongoing conversation on topics that matter in their lives. Each chapter includes real research examples that illustrate specific topics that the chapter covers, guides that help students explore actual research challenges in more depth, and ethical considerations relating to specific chapter topics.

Acknowledgements

We gratefully acknowledge Bora Pajo for writing an excellent text. Special thanks are also due to Douglas Cooper, Brenda Gill, and Shalini Tendulkar for developing the ancillaries on this site.

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Introduction to Research Methods

Eric van Holm, PhD

1 Introduction to the Book

“The true path to wisdom can be identified … it has to have practical application in your life. Otherwise, wisdom becomes a useless thing and deteriorates, like a sword that is never used.” - Paulo Coelho, “The Pilgrimage”

This book is intended as a practical introduction to research methods in the social sciences. If you pursue research academically or professionally, it will probably not be the last book you need to read on the subject. This is intended as something of a gentle introduction with a focus on the applications of the information and examples.

There are a lot of terms that many such textbooks would include that are left out of this edition. I believe that learning is like pouring water into a cup. Once the cup is full, you can keep pouring but the cup wont hold more water. What you should do is drink the water before you refill it. The analogy gets a little bit stretched there, but “drinking the water” stands in for using the information. Once you actually understand the basics in this text, you’ll be ready to read a more advanced book that fills you back up.

So how did I decide what to leave out and what to include? I gave preference to the terms and information I need in my working life as someone who does research. Terms like inductive and deductive research are left out, not because they are unimportant, but because I rarely encounter them. Their definitions might be good material for a test on the terms in this book, but I don’t believe they are fundamentally important to your ability to engage with published research or conduct basic research yourself.

Research is best learned through practice. Like many classes, it is hard to really understand the difference between the different terms and ideas unless you’re actually using them for yourself. This book is written with the understanding that the subject is hard to internalize, and so real world examples are offered where they can be. But that’s not enough to make you an expert. And in fact, it’s hard for anyone to be an expert - there are always new methods and techniques to learn that can improve the ability of researchers to answer important questions. But this book is a good place to start, or at least I hope it is.

With that, I have to give a warning. If you take research methods from another teacher in the future, things might be described differently. That means I’ve sometimes ignored esoteric terms that you wont encounter outside a research methods class. And sometimes I define the terms I use slightly differently than others would. The goal is for this book to be approachable for students who have no background in the subject, aren’t interested in methods, and don’t like statistics.

My primary goal is to impart some of my excitement for research and statistics to you. If I fail in that, I hope I can at least make you better able to engage with the copious amounts of research that will shape policy and your behaviors during your life.

This book is copyrighted under Creative Commons Attribution-NonCommercial 4.0 International Public License, which means that it’s free for you and anyone to use. The Fall 2020 edition is a substantial expansion of another edition written in the Fall of 2019, and will continue to be updated in the future.

1.1 The Plan for this book

This book encompasses 1) the development of a research project and 2) the analysis of the resulting data you might collect.

The first half of the book concentrates on the development of a research project. That half of the book is written for a student that has to write a class research paper or thesis and doesn’t know where to start. It’ll walk the student through the development and identification of an idea through the collection of data.

But just collecting data isn’t enough if you’re going to do your own research. And with the growth of data that is available online, it isn’t even a necessary place to start (although understanding the first half is important), So the second half is written with the idea that the data is collected, and now it must be analyzed. In those chapters the book will walk the reader through basic steps of presenting and analyzing data that are common to many research projects. In those chapters we’ll also learn how to do all of that in R, a free software that is commonly used in data science. We’ll talk about that more later though.

Scientific Method

1. the practice of science: an introduction to research methods.

When some people think of science, they think of formulas and facts to memorize. Many of us probably studied for a test in a science class by memorizing the names of the four nucleotides in DNA (adenine, cytosine, guanine, and thymine) or by practicing with one of Newton ‘s laws of motion, like f = ma (force equals mass times acceleration). While this knowledge is an important part of science, it is not all of science. In addition to a body of knowledge that includes formulas and facts, science is a practice by which we pursue answers to questions that can be approached scientifically. This practice is referred to collectively as scientific research , and while the techniques that scientists use to conduct research may differ between disciplines, the underlying principles and objectives are similar. Whether you are talking about biology, chemistry, geology, physics, or any other scientific field, the body of knowledge that is built through these disciplines is based on the collection of data that are then analyzed and interpreted in light of other research findings. How do we know about adenine, cytosine, guanine, and thymine? These were not revealed by chance, but through the work of many scientists collecting data, evaluating the results, and putting together a comprehensive theory that explained their observations .

A brief history of scientific practice

The recorded roots of formal scientific research lie in the collective work of a number of individuals in ancient Greek, Persian, Arab, Indian, Chinese, and European cultures, rather than from a single person or event. The Greek mathematician Pythagoras is regarded as the first person to promote a scientific hypothesis when, based on his descriptive study of the movement of stars in the sky in the 5 th century BCE , he proposed that the Earth was round. The Indian mathematician and astronomer Aryabhata used descriptive records regarding the movement of objects in the night sky to propose in the 6 th century CE that the sun was the center of the solar system. In the 9 th century, Chinese alchemists invented gunpowder while performing experiments attempting to make gold from other substances. And the Middle Eastern scientist Alhazen is credited with devising the concept of the scientific experiment while researching properties related to vision and light around 1000 CE.

These and other events demonstrate that a scientific approach to addressing questions about the natural world has long been present in many cultures. The roots of modern scientific research methods , however, are considered by many historians to lie in the Scientific Revolution that occurred in Europe in the 16 th and 17 th centuries. Most historians cite the beginning of the Scientific Revolution as the publication of De Revolutionibus Orbium Coelestium (On the Revolutions of the Heavenly Spheres) in 1543 by the Polish astronomer Nicolaus Copernicus . Copernicus’s careful observation and description of the movement of planets in relation to the Earth led him to hypothesize that the sun was the center of the solar system and the planets revolved around the sun in progressively larger orbits in the following order: Mercury, Venus, Earth, Mars, Jupiter, and Saturn (Figure 1). Though Copernicus was not the first person to propose a heliocentric view of the solar system, his systematic gathering of data provided a rigorous argument that challenged the commonly held belief that Earth was the center of the universe .

The Scientific Revolution was subsequently fueled by the work of Galileo Galilei , Johannes Kepler , Isaac Newton (Figure 2), and others, who not only challenged the traditional geocentric view of the universe , but explicitly rejected the older philosophical approaches to natural science popularized by Aristotle . A key event marking the rejection of the philosophical method was the publication of Novum Organum: New Directions Concerning the Interpretation of Nature by Francis Bacon in 1620. Bacon was not a scientist, but rather an English philosopher and essayist, and Novum is a work on logic. In it, Bacon presented an inductive method of reasoning that he argued was superior to the philosophical approach of Aristotle. The Baconian method involved a repeating cycle of observation , hypothesis , experimentation, and the need for independent verification. Bacon’s work championed a method that was objective, logical, and empirical and provided a basis for the development of scientific research methodology .

Bacon’s method of scientific reasoning was further refined by the publication of Philosophiæ Naturalis Principia Mathematica (Mathematical Principles of Natural Philosophy) by the English physicist and mathematician Isaac Newton in 1686. Principia established four rules (described in more detail here ) that have become the basis of modern approaches to science. In brief, Newton ‘s rules proposed that the simplest explanation of natural phenomena is often the best, countering the practice that was common in his day of assigning complicated explanations derived from belief systems , the occult, and observations of natural events. And Principia maintained that special explanations of new data should not be used when a reasonable explanation already exists, specifically criticizing the tendency of many of Newton ‘s contemporaries to embellish the significance of their findings with exotic new explanations.

Bacon and Newton laid the foundation that has been built upon by modern scientists and researchers in developing a rigorous methodology for investigating natural phenomena. In particular, the English statisticians Karl Pearson and Ronald Fisher significantly refined scientific research in the 20 th century by developing statistical techniques for data analysis and research design (see our Statistics in Science module). And the practice of science continues to evolve today, as new tools and technologies become available and our knowledge about the natural world grows. The practice of science is commonly misrepresented as a simple, four- or five-step path to answering a scientific question, called “The Scientific Method.” In reality, scientists rarely follow such a straightforward path through their research. Instead, scientific research includes many possible paths, not all of which lead to unequivocal answers. The real scientific method , or practice of science, is much more dynamic and interesting.

Comprehension Checkpoint

Scientific research, if done correctly, follows a straightforward five-step path and leads to definite answers.

More than one Scientific Method

The typical presentation of the Scientific Method (Figure 3) suggests that scientific research follows a linear path, proceeding from a question through observation , hypothesis formation, experimentation, and finally producing results and a conclusion. However, scientific research does not always proceed linearly. For example, prior to the mid 1800s, a popular scientific hypothesis held that maggots and microorganisms could be spontaneously generated from the inherent life-force that existed in some foods. Louis Pasteur doubted this hypothesis, and this led him to conduct a series of experiments that would eventually disprove the theory of spontaneous generation (see our Experimentation in Scientific Research module). Pasteur’s work would be difficult to characterize using Figure 3 – while it did involve experimentation, he did not develop a hypothesis prior to his experiments. Instead he was motivated to disprove an existing hypothesis. Or consider the work of Grove Karl Gilbert , who conducted research on the Henry Mountains in Utah in the late 1800s (see our Description in Scientific Research module). Gilbert was not drawn to the area by a pressing scientific question, but rather he was sent there by the US government to explore the region. Further, Gilbert did not perform a single experiment in the Henry Mountains; his work was based solely on observation and description, yet no one would dispute that Gilbert was practicing science. The traditional and simplistic Scientific Method presented in Figure 3 does not begin to reflect the richness or diversity of scientific research, let alone the diversity of scientists themselves.

Scientific research methods

Scientific research is a robust and dynamic practice that employs multiple methods toward investigating phenomena, including experimentation, description, comparison, and modeling. Though these methods are described separately both here and in more detail in subsequent modules, many of these methods overlap or are used in combination. For example, when NASA scientists purposefully slammed a 370 kg spacecraft named Deep Impact into a passing comet in 2005, the study had some aspects of descriptive research and some aspects of experimental research (see our Experimentation in Scientific Research module). Many scientific investigations largely employ one method, but different methods may be combined in a single study, or a single study may have characteristics of more than one method. The choice of which research method to use is personal and depends on the experiences of the scientists conducting the research and the nature of the question they are seeking to address. Despite the overlap and interconnectedness of these research methods, it is useful to discuss them separately to understand the principal characteristics of each and the ways they can be used to investigate a question.

Experimentation: Experimental methods are used to investigate the relationship(s) between two or more variables when at least one of those variables can be intentionally controlled or manipulated. The resulting effect of that manipulation (often called a treatment) can then be measured on another variable or variables. The work of the French scientist Louis Pasteur is a classic example. Pasteur put soup broth in a series of flasks, some open to the atmosphere and others sealed. He then measured the effect that the flask type had on the appearance of microorganisms in the soup broth in an effort to study the source of those microorganisms (see our Experimentation in Science module). Description: Description is used to gather data regarding natural phenomena and natural relationships and includes observations and measurements of behaviors. A classic example of a descriptive study is Copernicus’s observations and sketches of the movement of planets in the sky in an effort to determine if Earth or the sun is the orbital center of those objects (see our Description in Scientific Research module). Comparison: Comparison is used to determine and quantify relationships between two or more variables by observing different groups that either by choice or circumstance are exposed to different treatments. Examples of comparative research are the studies that were initiated in the 1950s to investigate the relationship between cigarette smoking and lung cancer in which scientists compared individuals who had chosen to smoke of their own accord with nonsmokers and correlated the decision to smoke (the treatment) with various health problems including lung cancer (see our Comparison in Scientific Research module). Modeling: Both physical and computer-based models are built to mimic natural systems and then used to conduct experiments or make observations. Weather forecasts are an example of scientific modeling that we see every day, where data collected on temperature, wind speed, and direction are used in combination with known physics of atmospheric circulation to predict the path of storms and other weather patterns (see our Modeling in Scientific Research module).

These methods are interconnected and are often used in combination to fully understand complex phenomenon. Modeling and experimentation are ways of simplifying systems toward understanding causality and future events. However, both rely on assumptions and knowledge of existing systems that can be provided by descriptive studies or other experiments . Description and comparison are used to understand existing systems and are used to examine the application of experimental and modeling results in real-world systems. Results from descriptive and comparative studies are often used to confirm causal relationships identified by models and experiments. While some questions lend themselves to one or another strategy due to the scope or nature of the problem under investigation, most areas of scientific research employ all of these methods as a means of complementing one another toward clarifying a specific hypothesis , theory , or idea in science.

Scientific research methods, such as experimentation , description , comparison , and modeling ,

• are interconnected and are often used in combination.
• are more effective if used alone.

Research methods in practice: The investigation of stratospheric ozone depletion

Scientific theories are clarified and strengthened through the collection of data from more than one method that generate multiple lines of evidence . Take, for example, the various research methods used to investigate what came to be known as the “ozone hole.”

Early descriptive and comparative studies point to problem: In 1957, the British Antarctic Survey (BAS) began a descriptive study of stratospheric ozone levels in an effort to better understand the role that ozone plays in absorbing solar energy (MacDowall & Sutcliffe, 1960). For the next 20 years, the BAS recorded ozone levels and observed seasonal shifts in ozone levels, which they attributed to natural fluctuations. In the mid-1970s, however, the BAS began to note a dramatic drop in ozone levels that they correlated with the change of seasons in the Antarctic. Within a decade, they noted that a seasonal “ozone hole” (Figure 4) had begun to appear over the South Pole (Farman et al., 1985).

The development of new technology opens novel research paths: Concurrent with the early BAS studies, the British scientist James Lovelock was working on developing new technology for the detection of trace concentrations of gases and vapors in the atmosphere (Lovelock, 1960). One instrument that Lovelock invented was a sensitive electron capture detector that could quantify atmospheric levels of chlorofluorocarbons (CFCs). At the time, CFCs were widely used as refrigerants and as propellants in aerosol cans and they were thought to be stable in the atmosphere and thus harmless chemicals. In 1970, Lovelock began an observational study of atmospheric CFCs and found that the chemicals were indeed very stable and could be carried long distances from major urban air pollution sources by prevailing winds. Under the impression that CFCs were chemically inert, Lovelock proposed that the chemicals could be used as benign atmospheric tracers of large air mass movements (Lovelock, 1971).

Modeling and experimental research are used to draw causal connections: In 1972, F. Sherwood Rowland, a chemist at the University of California at Irvine, attended a lecture on Lovelock’s work. Rowland became interested in CFCs and began studying the subject with a colleague at Irvine, Mario Molina. Molina and Rowland were familiar with modeling research by Paul Crutzen, a researcher at the National Center for Atmospheric Research in Colorado, that had previously shown that nitrogen oxides are involved in chemical reactions in the stratosphere and can influence upper atmosphere ozone levels (Crutzen, 1970). They were also familiar with modeling research by Harold Johnston, an atmospheric chemist at the University of California at Berkeley, which suggested that nitrogen oxide emissions from supersonic jets could reduce stratospheric ozone levels (Johnston, 1971). With these studies in mind, they consulted experimental research published by Michael Clyne and Ronald Walker, two British chemists, regarding the reaction rates of several chlorine-containing compounds (Clyne & Walker, 1973). In 1974, Molina and Rowland published a landmark study in the journal Nature in which they modeled chemical kinetics to show that CFCs were not completely inert, and that they could be transported to high altitudes where they would break apart in strong sunlight and release chlorine radicals (Molina & Rowland, 1974). Molina and Rowland’s model predicted that the chlorine radicals, which are reactive, would cause the destruction of significant amounts of ozone in the stratosphere.

Descriptive and comparative research provide real-world confirmation : In 1976, a group of scientists led by Allan Lazrus at the National Center for Atmospheric Research in Boulder, Colorado, used balloons to carry instruments aloft that could sample air at high altitudes. In these samples, they were able to detect the presence of CFCs above the troposphere – confirming that CFCs did indeed reach the stratosphere and that once there, they could decompose in light (Lazrus et al., 1976). Further research conducted using balloons and high-atmosphere aircraft in the 1980s confirmed that chlorine and chlorine oxide radicals contribute to the loss of ozone over the Antarctic (McElroy et al., 1986). By the late 1980s, scientists began to examine the possible link between ozone loss and skin cancer because high levels of ultraviolet light, as would exist under an ozone hole, can cause skin cancer. In areas such as Southern Chile, where the Antarctic ozone hole overlaps with a populated land mass, a significant correlation was indeed found between the growing ozone hole and increasing rates of skin cancer (Abarca & Casiccia, 2002).

As a result of this collection of diverse yet complementary scientific evidence , the world community began to limit the use of CFCs and ratified the Montreal Protocol in 1988, which imposed strict international limits on CFC use. In 1995, Molina, Rowland, and Crutzen shared the Nobel Prize in chemistry for their research that contributed to our understanding of ozone chemistry.

The ozone story (further detailed in our Resources for this module; see The Ozone Depletion Phenomenon under Research) highlights an important point: Scientific research is multi-dimensional, non-linear, and often leads down unexpected pathways. James Lovelock had no intention of contributing to the ozone depletion story; his work was directed at quantifying atmospheric CFC levels. Although gaining an understanding of the ozone hole may appear as a linear progression of events when viewed in hindsight, this was not the case at the time. While each researcher or research team built on previous work, it is more accurate to portray the relationships between their studies as a web of networked events, not as a linear series. Lovelock’s work led Molina and Rowland to their ozone depletion models , but Lovelock’s work is also widely cited by researchers developing improved electron capture detectors. Molina and Rowland not only used Lovelock’s work, but they drew on the research of Crutzen, Johnston, Clyne, Walker, and many others. Any single research advance was subsequently pursued in a number of different directions that complemented and reinforced one another – a common phenomenon in science. The entire ozone story required modeling, experiments , comparative research, and descriptive studies to develop a coherent theory about the role of ozone in the atmosphere , how we as humans are affecting it, and how we are also affected by it.

The ozone research story shows that, in practice, scientific research is

• a linear step-by-step procedure.
• an interconnected web of related studies.

The real practice of science

Scientific research methods are part of the practice through which questions can be addressed scientifically. These methods all produce data that are subject to analysis and interpretation and lead to ideas in science such as hypotheses , theories , and laws . Scientific ideas are developed and disseminated through the literature, where individuals and groups may debate the interpretations and significance of the results. Eventually, as multiple lines of evidence add weight to an idea, it becomes an integral part of the body of knowledge that exists in science and feeds back into the research process . Figure 5 provides a graphical overview of the materials we have developed to explain the real practice of science, and the key elements are described below.

The Scientific Community: Scientists (see our Scientists and the Scientific Community module) draw on their background, experiences, and even prejudices in deciding on the types of questions they pursue and the research methods that they employ, and they are supported in their efforts by the scientific institutions and the community in which they work (see our Scientific Institutions and Societies module). Human nature makes it impossible for any scientist to be completely objective, but an important aspect of scientific research is that scientists are open to any potential result. Science emphasizes the use of multiple lines of evidence as a check on the objectivity of both individual scientists and the community at large. Research is repeated, multiple methods are used to investigate the same phenomenon, and scientists report these methods and their interpretations when publishing their work. Assuring the objectivity of data and interpretation is built into the culture of science. These common practices unite a community of science made up of individuals and institutions that are dedicated to advancing science. Rowland, Molina, Lovelock, and Crutzen each were guided by their personal interests and supported by their respective institutions. For example, in addition to his work with CFCs, James Lovelock is credited with proposing the Gaia hypothesis that all living and non-living things on the planet interact with one another much like a large, single organism. This perspective influenced his interest in looking at the movement of large air masses across the globe, work that was supported by funding from the National Aeronautics and Space Administration (NASA).

Data: Science is a way of understanding the world around us that is founded on the principal of gathering and analyzing data (see our Data Analysis and Interpretation module). In contrast, before the popularization of science, philosophical explanations of natural phenomena based on reasoning rather than data were common, and these led to a host of unsupported ideas, many of which have proven incorrect. For example, in addition to his ideas on vision, the Greek philosopher Empedocles also reasoned that because most animals are warm to the touch, they must contain fire inside of them (see our States of Matter module). In contrast, the initial conclusion of the presence of a hole in the stratospheric ozone layer was based on years of data collected by scientists at the British Antarctic Survey. The amount of uncertainty and error (see our Uncertainty, Error, and Confidence module) associated with these data was critical to record as well – a small error in Dobson units would have made the hole seemingly disappear. Using statistical methods (see our Statistics in Science module) and data visualization techniques (see our Using Graphs and Visual Data in Science module) to analyze data, the scientists at the BAS drew on their own experience and knowledge to interpret those data, demonstrating that the “hole” was more than a seasonal, natural shift in ozone levels.

Ideas in science: Scientific research contributes to the body of scientific knowledge, held in record in the scientific literature (see our Utilizing the Scientific Literature module) so that future scientists can learn from past work. The literature does not simply hold a record of all of the data that scientists have collected: It also includes scientists’ interpretations of those data. To express their ideas, scientists propose hypotheses to explain observations. For example, after observing, collecting, and interpreting data, Lovelock hypothesized that CFCs could be used by meteorologists as benign tracers of the movement of large air masses. While Lovelock was correct in his prediction that CFCs could be used to trace air movement, later research showed that they are not benign. This hypothesis was just one piece of evidence that Molina and Rowland used to form their theory of ozone depletion. Scientific theories (see our Theories, Hypotheses, and Laws module) are ideas that have held up under scrutiny and are supported by multiple lines of evidence. The ozone depletion theory is based on results from all of the studies described above, not just Lovelock’s work. Unlike hypotheses, which can be tenuous in nature, theories rely on multiple lines of evidence and so are durable. Still, theories may change and be refined as new evidence and analyses come to light. For example, in 2007, a group of NASA scientists reported experimental results showing that chlorine peroxide, a compound formed when CFCs are transported to the stratosphere and which participates in the destruction of ozone, has a slower reaction rate in the presence of ultraviolet light than previously thought (Pope et al., 2007). The work by Pope and his colleagues does not dispute the theory of ozone destruction; rather, it does suggest that some modifications may be necessary in terms of the reaction rates used in atmospheric chemistry models.

Despite the fact that different scientists use different methods , they can easily share results and communicate with one another because of the common language that has developed to present and interpret data and construct ideas. These shared characteristics allow studies as disparate as atmospheric chemistry, plant biology, and paleontology to be grouped together under the heading of “science.” Although a practicing scientist in any one of those disciplines will require very specialized factual knowledge to conduct their research , the broad similarities in methodology allow that knowledge to be shared across many disciplines.

Scientists use multiple methods to investigate the natural world and these interconnect and overlap, often with unexpected results. This module gives an overview of scientific research methods, data processing, and the practice of science. It discusses myths that many people believe about the scientific method and provides an introduction to our Research Methods series.

Key Concepts

• The practice of science involves many possible pathways. The classic description of the scientific method as a linear or circular process does not adequately capture the dynamic yet rigorous nature of the practice.
• Scientists use multiple research methods to gather data and develop hypotheses. These methods include experimentation, description, comparison, and modeling.
• Scientific research methods are complementary; when multiple lines of evidence independently support one another, hypotheses are strengthened and confidence in scientific conclusions improves.

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Article Contents

• Introduction
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• Declarations

Xylitol is prothrombotic and associated with cardiovascular risk

• Article contents
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Marco Witkowski, Ina Nemet, Xinmin S Li, Jennifer Wilcox, Marc Ferrell, Hassan Alamri, Nilaksh Gupta, Zeneng Wang, Wai Hong Wilson Tang, Stanley L Hazen, Xylitol is prothrombotic and associated with cardiovascular risk, European Heart Journal , 2024;, ehae244, https://doi.org/10.1093/eurheartj/ehae244

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The pathways and metabolites that contribute to residual cardiovascular disease risks are unclear. Low-calorie sweeteners are widely used sugar substitutes in processed foods with presumed health benefits. Many low-calorie sweeteners are sugar alcohols that also are produced endogenously, albeit at levels over 1000-fold lower than observed following consumption as a sugar substitute.

Untargeted metabolomics studies were performed on overnight fasting plasma samples in a discovery cohort ( n = 1157) of sequential stable subjects undergoing elective diagnostic cardiac evaluations; subsequent stable isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses were performed on an independent, non-overlapping validation cohort ( n = 2149). Complementary isolated human platelet, platelet-rich plasma, whole blood, and animal model studies examined the effect of xylitol on platelet responsiveness and thrombus formation in vivo . Finally, an intervention study was performed to assess the effects of xylitol consumption on platelet function in healthy volunteers ( n = 10).

In initial untargeted metabolomics studies (discovery cohort), circulating levels of a polyol tentatively assigned as xylitol were associated with incident (3-year) major adverse cardiovascular event (MACE) risk. Subsequent stable isotope dilution LC-MS/MS analyses (validation cohort) specific for xylitol (and not its structural isomers) confirmed its association with incident MACE risk [third vs. first tertile adjusted hazard ratio (95% confidence interval), 1.57 (1.12–2.21), P < .01]. Complementary mechanistic studies showed xylitol-enhanced multiple indices of platelet reactivity and in vivo thrombosis formation at levels observed in fasting plasma. In interventional studies, consumption of a xylitol-sweetened drink markedly raised plasma levels and enhanced multiple functional measures of platelet responsiveness in all subjects.

Xylitol is associated with incident MACE risk. Moreover, xylitol both enhanced platelet reactivity and thrombosis potential in vivo . Further studies examining the cardiovascular safety of xylitol are warranted.

Role of the artificial sweetener xylitol in cardiovascular event risk. In initial untargeted metabolomics studies (discovery cohort) and subsequent stable isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/MS) studies (validation cohort), fasting levels of xylitol are associated with incident major adverse cardiovascular events (MACE). Using human whole blood, platelet-rich plasma, and washed platelets, xylitol enhances multiple indices of platelet reactivity in vitro . Xylitol also was shown to enhance thrombosis formation in a murine arterial injury model in vivo . In human intervention studies, when subjects ingested a typical dietary amount of xylitol in an artificially sweetened food, multiple functional measures of platelet responsiveness were significantly increased. Xylitol is both clinically associated with cardiovascular event risks and mechanistically linked to enhanced platelet responsiveness and thrombosis potential in vivo . ADP, adenosine diphosphate; MI, myocardial infarction.

• cardiovascular diseases
• blood platelets
• sugar alcohols
• sweetening agents
• whole blood

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