what is the nature of study in research

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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

Nature of Research Meaning, Characteristics and Types

Table of contents:-, nature of research.

• Meaning of Research

Research Definition

• Characteristics of Research

Criteria for Good Research

• Qualities of a Good Research
• Types of Research

Need for Research

The basic nature of research is to advance knowledge and seek solutions to problems. To do this, we start with simple questions. For example, the fundamental questions in journalistic practice are: who, what, why, where, when and how. In research, these questions are addressed more systematically, reliably, testable, and replicable. In practice, all the questions are mixed, and it is difficult to isolate one from the other when dealing with human behaviour and social phenomena.

In research, these are isolated and studied in depth – separately and together. The basic premise is that any issue/event/phenomenon can be learned and subjected to appropriate systematic, objective scientific procedures, and conclusions can be arrived at that can preferably be generalised to the population. Such results and conclusions should also be amenable to replication as the search for knowledge is conducted with a defined set of rules and procedures commonly understood and shared by all sciences.

The following points can characterise the nature of research:

1) Systematic Activity

The research follows a systematic procedure to analyse a research problem in a better way. It is essential to avoid haphazard research methods and adhere to a well-structured approach for reliable outcomes. Researchers can proceed to the next step only after successfully concluding the previous one.

2) Logical Process

The basic tenet of research is “logic”. All the assumptions and analyses undertaken are based on certain logic. Research is a scientific, systematic, and planned investigation to understand the underlying problem.

3) Iterative Process

Research is an iterative process. Sometimes it becomes necessary for the researcher to review the work of earlier stages, which makes it cyclic. Often it becomes harder for the researcher to find out the starting and ending points.

4) Based on Empirical Evidence

Research studies are empirical. Researchers employ various scientific tools and techniques at every step of the research process. Accuracy and reliance on observable experiences or empirical evidence are verified in each research step. Therefore, quantitative research is easier to validate than qualitative research, which is more conceptual.

5) Controlled in Nature

Researchers frequently manage variable effects by permitting the variation of selected variables for testing purposes. Due to this reason, controlling the variables in scientific research is much easier than controlling the factors in social research. Hence in research, it is essential to control the variables carefully.

Research Meaning

Research comprises two different words, “Re” and “Search”. ‘Re’ implies a repetitive or iterative process, whereas ‘search’ signifies conducting a comprehensive examination or looking over carefully to find something. Various researchers have defined research in different ways because of its expansive scope. In general, researchers define research as a scientific process that establishes and/or validates new facts, ideas, and theories across diverse domains of knowledge. The research aims at adding to the existing stock of knowledge for the betterment of the world.

According to Waltz and Bausell, “Research is a systematic, formal, rigorous and precise process employed to gain solutions to problems or to discover and interpret new facts and relationships”.

John Best states, “Research is a systematic activity directed towards discovery and the development of an organised body of knowledge.”

According to Clifford Woody, “Research comprises defining and redefining problems, formulating hypothesis or suggested solutions, collecting, organising and evaluating data. Making deductions and reaching conclusions to determine they fit the formulating hypothesis.”

Encyclopaedia of Social Science defines research as, “the manipulation of generalising to extend, connect or verify knowledge…” Manipulation incorporates experimentation adopted to arrive at generalisation.

Kerlinger (1973) defines “research as a systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relationship about various phenomena.”

Burns (1994) also defines “research as a systematic investigation to find answers to a problem”.

Research involves scientific and systematic analysis of a specific area of study, culminating in the formulation of findings supported by sound reasoning.

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Characteristics of Good Research

A good research should qualify in the following essential criteria:

1) Ethically Conducted

A researcher should abide by the ethical standards laid down to conduct research accurately. Researchers must thoroughly examine, explain, and document both the research data and the limiting factors. This practice ensures transparency with the readers. The data should remain unaltered to accurately reflect the findings. The researchers must document the results of the study comprehensively.

2) Reliability

Reliability refers to the repeatability of a research, tool, procedure, or instrument. The degree of reliability of a research study depends on the consistency of its findings. Researchers determine the reliability of their work by observing consistent results under similar conditions and procedures. For example, a researcher may study the effect of a course written in English on the final grades of a group of students. To ensure the reliability of the study’s findings, researchers can replicate the study with a different group of students and achieve consistent results.

3) Clearly Defined Objectives

Researchers must clearly define the objectives of a research study. Well-defined research objectives provide researchers with a clear roadmap to follow. It helps the researchers to determine the type of data required to efficiently conduct the research.

4) Accuracy

Accurate research occurs when the research process, instruments, and tools interconnect seamlessly. It verifies that researchers are appropriately selecting their research tools. For example, Observation is the recommended data collection method when researching mental patients, as it helps overcome the challenge of potential inaccuracy in questionnaires or interviews .

5) Flexibility

Research involves re-examining the data till correct findings arrive. This is possible only if the research approach is flexible. There should always be scope to add on significant data or modify existing data as needed.

6) Generalisable Results

The degree to which the result of research can be applied to a bigger population is called generalisability. While carrying a research, the researcher selects a small sample from a target population. Hence, the sample and the research findings accurately reflect the characteristics of the target population. If the research results can be applied to other samples from a similar population, then the research findings can be considered generalisable.

7) Validity

Validity is a measure of the applicability of the research. It refers to the suitability and efficiency of the research instrument or procedure regarding the research problem. Validity measures the accuracy of an instrument in measuring the problem. It is a measurement of the applicability of the research. Validity is the basis of deciding whether a research conclusion, assumption, or proposition is true or false. The validity of research is maintained by clearly defining the concepts involved.

8) Credibility of Sources

Credibility means that the research data should be taken from trustworthy sources. Although the use of secondary data in research allows the researcher to complete the research within the timeframe, he loses credibility, as the secondary data are usually manipulated and hence relying exclusively on it can lead to erroneous and faulty research conclusions. A researcher should try to use primary data to the greatest extent feasible. If primary data is not available, then a specific amount of secondary data can be used. However, conducting research completely based on secondary data can harm the credibility of the research.

Objectives of Good Research

Research aims to uncover answers to questions by applying scientific procedures. The primary goal of research is to find hidden facts that have yet to be discovered. Although each research study has its specific purpose, research objectives can be broadly categorized into the following groups:

1. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research or experimental studies).

2. To gain familiarity with a phenomenon or achieve new insights into it (studies with this objective are termed exploratory research studies).

3. To determine the frequency with which something occurs or is associated with something else (studies with this objective are known as diagnostic research studies).

4. To accurately portray the characteristics of a particular individual, situation, or group (studies with this objective are known as descriptive research studies).

Research serves as a pool of knowledge. It is a vital source of guidelines for addressing various business, personal, professional, governmental, and social problems. It is a formal training ground, enabling individuals to understand new developments in their respective fields better.

The criteria for good research are outlined as follows:

1. The validity and reliability of the data should be examined.

2. The research report should be candid enough to assess the effects of the findings.

3. The research design should be carefully planned to generate results that maintain objectivity.

4. The purpose of the research should be clearly defined, and common concepts used should be operationally defined.

5. Data analysis in the research report should be adequate to reveal its significance, and the analysis method employed should be appropriate.

6. The research procedure must be precisely planned, focused, and appropriately described to enable other researchers to conduct further studies for advancement.

Qualities of Good Research

Good research possesses certain qualities, as outlined below:

1. Empirical

Conclusions are drawn based on hardcore evidence from real-life experiences and observations. This reliance on concrete information provides a foundation for external validation of research results.

2. Develop theories and Principles

Good research contributes to developing theories and principles, aiding in accurate predictions regarding the variables under study. Through the observation and analysis of samples, researchers can make sound generalizations about entire populations, extending beyond immediate situations, objects, or groups being investigated.

Research is guided by the rules of reasoning and logical processes, including induction (general to specific) and deduction (specific to public). Logical reasoning enhances the feasibility and meaningfulness of research in decision-making.

4. Replicable

The designs, procedures, and results of scientific research should be replicable, allowing anyone other than the original researcher to assess their validity. This ensures that one researcher can use or build upon the results obtained by another, making the procedures and results both replicable and transferrable.

5. Systematic

Research is structured according to a set of rules, following specific steps in a defined sequence. Systematic research encourages creative thinking, avoiding reliance on guessing and intuition to reach conclusions.

6. Valid and Verifiable

Research involves precise observation and accurate description. Researchers select reliable and valid instruments for data collection and utilize statistical measures to portray results accurately. The conclusions drawn are correct and can be verified by the researcher and others.

The research strives to achieve the following needs:

1) Describe the Features

The research seeks to describe the features of a particular phenomenon. It is one of the core activities of research where a researcher either observes the phenomenon and records its characteristic behaviour, conducts standardised tests to measure the behaviour or describes the change in attitude or opinion of the customers. For example, a researcher can describe the behaviour of smokers by either analysing or observing their behaviour by undergoing some standard tests, such as measuring per-day consumption, the level of resistance, etc.

2) Influence Activities

The research emphasises applying the existing theories and models instead of developing new theories, for influencing various facets of the environment. Most of the research conducted in social, behavioural and educational research falls under the area of influence.

3) Explore unknown facts

One of the prime objectives of research is to explore an unknown object or phenomenon. While exploring, a researcher tries to understand the details of the situation or phenomenon for developing preliminary hypotheses and generalisations. Exploring allows the researchers to develop theories and explain the questions of how and why a phenomenon operates in a particular way.

4) Explain a Phenomenon

Another objective of the research is to explain several facts. The research aims to explain why and how a phenomenon operates in a specific way. Researchers develop theories to explain the behaviour of a particular phenomenon, these theories are prepared by determining the factors that cause the change and identifying their effects on the phenomenon. Most scientific and educational researchers have this objective for their studies. For example, if a researcher is trying to know, “Do holiday trips for employee families improve work-life balance?”. Therefore, the cause is ‘holiday trips’ and the effect is ‘work-life balance’.

5) Predict Future Activities

Research is also conducted to predict future activities. Predictions can be made based on explanations regarding a phenomenon. Hence, for making forecasts adequate prior information is essential. Forecasting activity can also be performed on the research based on explanation. Here, predictions are made based on cause-and-effect relationships in a phenomenon. A good example of this objective is the research that analysts conduct during elections to predict the winning political party based on the information that they can gather from the voting polls.

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science dissertation writing guides

Guide to Nature of the Study of a Science Dissertation

If you are new to desertion writing, you will hear people commend the nature of the study and get confused. What is the nature of a study in research? It is a simple concept once you understand it. The nature of the study is a description of the sample under study and the process you use in the collection of data. It explains the sources and instruments used for the collection of data that answers the study questions.

What is the nature of study? For a proper understanding of the nature of the study, you need to have other basic knowledge of a dissertation structure.

How do you tell a good dissertation thesis?

There are several things to look at when judging the best dissertation thesis. Your professor will pay attention to your topic when validating the quality of the paper you are presenting. The title should reflect the theme of your dissertation.

Apart from the title, other sections of the dissertation about its value are the introduction, methodology, results, and discussion. All of this section should be according to the guideline and writing format. The referencing should follow the paper referencing style.

How should you write

There is a basic format to follow in writing a dissertation paper. It should have a summary section that briefly explains what the whole paper entails. After the summary, there is an introduction section. The section describes why you are carrying out the study. The paper also contains a background information section which includes all the information you need to understand the results.

• Writing a thesis statement

A thesis statement is a summary of all central points of your thesis paper. It always comes nearly the end of your introductions. The statement will differ depending on the type of dissertation you are writing. It should, however, illustrate the main point you need to explain in your paper.

• How to write a good thesis introduction

The introduction is a crucial part of dissertation writing. It provides an impression of the entire work. It should be accurate and presented attractively to encourage the reader to go through your work to an end. Some characteristics make a good thesis introduction. Your introduction should have a general description of the topic and some background information. It should also give a literature review related to the topic and define some scope and terms used in the thesis. You should also describe the current situation and identify the gaps.

• Writing the conclusion

When writing a conclusion paragraph, ensure it contains topical, supporting, and closing sentences. The topical sentence should refresh the thesis statement. On the other hand, the supporting sentence provides a summary of the main points in the thesis and links them together. The closing sentence connects with the introduction and provides a sense of closure.

• Writing an ending sentence in the thesis paper

How should you end sentences in your thesis is paper? The best way of making an ending in a thesis is by rephrasing the topic. Review the main points in your dissertation and connect with the summaries of all the arguments in the paper.

Nature of study explains the sample under study and the methods you use to collect and analyze data. It is a general guide on how you will arrive at your findings. To understand the nature of the study, you must have the basic knowledge of the entire dissertation writing process. The content above provides some basic information you need to know on the thesis as you try to understand about the nature of the study.

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Online Guide to Writing and Research

The research process, explore more of umgc.

• Online Guide to Writing

The Nature of Research

What is “research”?

Your friend mentions that they just knitted a sweater and you have always wanted to learn how to knit.  What could you do now? You could watch a video on YouTube, look for knitting books at your local library, or ask your friend who can knit how to start.  What you are actively doing here is “researching” the topic of knitting and learning more about it.  We do this in our everyday lives, and we also do this when writing a paper for our academic coursework.   

Simply put, research is information gathering about something that’s new to you.  We research every day, utilizing the internet for most of our research gathering, whether personal or academic. 

Academic research is information gathering under parameters specified by your coursework and assignment instructions.  For instance, if you are a political science major and you need to write a research paper about the similarities and differences between socialism and capitalism, you would then perform research to learn more about these terms and your final product (your paper) will join the scholarly community when completed.   

Mailing Address: 3501 University Blvd. East, Adelphi, MD 20783 This work is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . © 2022 UMGC. All links to external sites were verified at the time of publication. UMGC is not responsible for the validity or integrity of information located at external sites.

Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

Writing Handbooks

Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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How does nature exposure make people healthier?: Evidence for the role of impulsivity and expanded space perception

Meredith a. repke.

1 Department of Psychology, University of Montana, Missoula, Montana, United States of America

Meredith S. Berry

2 Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

Lucian G. Conway, III

Alexander metcalf.

3 College of Forestry and Conservation, University of Montana, Missoula, Montana, United States of America

Reid M. Hensen

Conor phelan, associated data.

Data are available at https://osf.io/5wb7u .

Nature exposure has been linked to a plethora of health benefits, but the mechanism for this effect is not well understood. We conducted two studies to test a new model linking the health benefits of nature exposure to reduced impulsivity in decision-making (as measured by delay discounting) via psychologically expanding space perception. In study 1 we collected a nationwide U.S. sample (n = 609) to determine whether nature exposure was predictive of health outcomes and whether impulsive decision-making mediated the effect. Results indicated that Nature Accessibility and Nature Exposure From Home significantly predicted reduced scores on the Depression, Anxiety, Stress Scales (DASS) ( p < .001, p = .03, respectively) and improved general health and wellbeing ( p < .001, p < .01, respectively). Nature Accessibility also predicted reduced impulsive decision-making ( p < .01), and Nature Accessibility showed significant indirect effects through impulsive decision-making on both the DASS ( p = .02) and general health and wellbeing ( p = .04). In Study 2, a lab-based paradigm found that nature exposure expanded space perception ( p < .001), and while the indirect effect of nature exposure through space perception on impulsive decision-making did not meet conventional standards of significance ( p < .10), the pattern was consistent with hypotheses. This combination of ecologically-valid and experimental methods offers promising support for an impulsivity-focused model explaining the nature-health relationship.

Introduction

Human history evolved around an intimate connection to the natural environment (e.g., see [ 1 , 2 ]). This has changed dramatically over the last century. The recent shift to over half of the world’s population living in urban areas [ 3 ] together with advancements in technology (e.g., see [ 4 , 5 ]) has drastically reduced the amount of time many people spend in contact with nature. This separation of humans from nature may not be inconsequential. A growing body of research is dedicated to exploring how interactions with the natural environment affect human health and wellbeing [ 6 ]. To date, researchers have demonstrated that humans gain a plethora of health and wellbeing benefits from nature exposure. Here, we present two studies that test a new theory linking the health benefits of nature exposure to reduced impulsivity via psychologically expanding space perception.

Nature exposure and human health

A wealth of evidence exists linking nature exposure to improved health outcomes. From reduced recovery time following surgery [ 7 ] to reduced Attention Deficit Hyperactivity Disorder symptomology in children (e.g., [ 8 – 12 ]), health improvements in patients with cancer (e.g., increased expression of anti cancer proteins [ 13 ]) and cardiovascular disease (e.g., reduction of hypertension [ 14 , 15 ]) and many other outcomes–the abundance of evidence for this effect is striking. While much of the research exploring the effects of nature exposure has focused on psychological effects (see e.g., [ 16 – 19 ]), there is also considerable research linking nature exposure to improved physiological health markers. For example, exposure to nature images has been linked to decreased oxyhemoglobin concentrations (which is believed to be associated with psychological calming) in the right prefrontal cortex [ 20 ], and favorable changes in heart rate variability [ 21 ]. Physiological effects have also been observed in studies testing the effects of various nature exposure therapies (for review of such studies conducted in Japan see [ 22 ]). In addition to effects associated with viewing nature scenes or participating in nature exposure therapy, research has also found evidence in favor of positive physiological effects of basic environmental exposure. For example, sunlight has been linked to vitamin D production, release of nitric oxide, production of beta-endorphin, and regulation of circadian rhythms [ 23 ].

Further, research has revealed nature not only improves a wide array of health and wellbeing outcomes, but that both experimentally manipulated increases in nature exposure (e.g. [ 15 ]) and nature exposure resulting from living and/or working within proximity to ample natural space (e.g., [ 24 – 26 ]) can have these impacts on health. Given this wealth of evidence linking nature exposure to human health and wellbeing, some researchers have shifted from asking whether nature exposure improves health, to asking how nature exposure improves health.

Possible mechanisms

Before researchers had rigorously evaluated the effects of nature exposure, there was the “nature benefit assumption”[ 27 ]. This term captures the intuitive belief many held prior to recent decades–that nature exposure was generally good for humans. However, it was not until Wilson’s biophilia theory [ 28 ], which stated that humans have evolved to respond positively to unthreatening natural environments, that rigorous scientific inquiry began. Since the emergence of this theory, researchers have turned their attention to understanding the specific nature of these positive responses. As a result, a substantial body of research now confirms the basic tenet of the nature benefit assumption. With evidence that nature exposure predicts positive outcomes for humans, researchers have increasingly shifted to asking how it is that nature affects humans to bring about so many positive outcomes. As a result of this collective inquiry, evidence has been found in support of a variety of possible mechanisms underlying the positive effects of nature exposure. While many potential pathways have been studied (e.g. see [ 29 , 30 ] for reviews), much attention has been paid to restoration, social consequences, and physical activity as potential underlying mechanisms.

Restoration

Early research into the mediators of the nature exposure-health and wellness relationship revealed evidence that natural environments facilitate physiological, emotional and attention restoration [ 31 ]. From this line of research, two complementary theories have spawned: Stress Reduction Theory [ 32 ] and Attention Restoration Theory (ART [ 33 ]). ART posits that nature exposure encourages effortless brain function, which facilitates its recovery from fatigue. SRT focuses on the role of affect. In particular, SRT suggests that exposure to natural environments facilitates positively-toned emotional reactions, which in turn have a restorative effect.

Although suggesting somewhat different routes to restoration, both theories emphasize that nature is psychologically restorative. And, consistent with this, the restorative quality of nature has been identified as a mediator of the effects of nature exposure on a variety health and wellbeing outcomes, including emotional wellbeing (see e.g., [ 34 ]) and mental health (see e.g., [ 35 ]).

Social consequences

Others have considered social consequences as a potential mechanism underlying the health and wellbeing effects of nature exposure. This endeavor has revealed evidence suggesting nature exposure increases social cohesion [ 36 ], as defined by Forrest and Kerns [ 37 ] to refer to sense of community, shared norms and values, positive and friendly relationships, and feelings of being accepted and belonging. Similarly, research has also found nature exposure enhances social connections [ 38 ] and social contacts [ 39 ]. Further, there is evidence suggesting increased nature exposure predicts reduced feelings of loneliness and decreased frequency of feelings of inadequate social support, and that reduced loneliness and decreased feelings of inadequate social support in turn improve self-reported health, a number of health complaints, and mental health [ 35 ].

Physical activity

Finally, others have considered the possibility that nature encourages physical activity. Evidence has shown that nature increases engagement in physical activity [ 40 , 41 ], and that “green exercise” (physical activity that occurs while being directly exposed to nature) improves health and wellbeing outcomes [ 42 ]. Although there is evidence in its favor, in a comparison of these three potential mechanisms (restoration, social cohesion, and physical activity) conducted by de Vries and colleagues [ 36 ], support was stronger for restoration and social cohesion as mechanisms than physical activity.

The present investigation–while acknowledging that the mechanisms discussed above play an important role in understanding how nature improves health and wellbeing–seeks to consider the role of another potentially important variable not directly addressed by restoration, social cohesion or physical activity: impulsive decision-making. The role of decision-making in predicting health and wellbeing outcomes is extremely important. Lifestyle factors such as diet, exercise habits, sleep routines, and stress management are largely the result of decision-making processes, and are critical predictors of health and wellbeing. Therefore, the present study does not seek to undermine existing theories regarding the underlying mechanism(s) for the health and wellbeing benefits of nature exposure, but rather further explores an alternate, and potentially complementary emerging hypothesis: that nature exposure improves decision-making, and that decision-making in turn improves health and wellbeing outcomes. As decision-making related to health can often be conceptualized as the choice between a more impulsive option (e.g., eating unhealthy food for dinner because it is faster and easier to prepare) and a less impulsive option (e.g., spending more time and energy making a healthy dinner from scratch), the present study will focus specifically on impulsive decision-making.

Importantly, impulsive decision-making may provide additional insight into the drivers of the effects of nature exposure on health, beyond what has been learned from prior research related to underlying mechanisms. Most notably, research on impulsive decision-making and restoration-related process such as stress (e.g., [ 43 , 44 ]) and fatigue (e.g., [ 45 , 46 ]) has found mixed results, suggesting that there is value in considering impulsive decision-making as a distinct process from restoration. This is not surprising because the processes, while potentially overlapping, are conceptually distinct. For example, one may feel restored but still choose to eat unhealthy food–indeed, research on positive emotion more broadly suggests that it can sometimes make people more susceptible to immediate cues [ 47 ]. By focusing on impulsive decision-making in the present investigation, we seek to directly study the effects of nature on such decision-making processes.

Impulsivity and impulsive decision-making

Impulsive decision-making has been identified as one component of the broader construct of impulsivity–a construct that has been defined through multiple psychological lenses. As noted by Evenden [ 48 ], impulsivity has been defined as both a character trait related to quick decision-making [ 49 ], a set of actions leading to poor outcomes [ 48 ], or as a choice to prefer smaller immediate rewards over larger delayed rewards [ 50 ]. Given this diversity regarding how impulsivity ought to be defined, many have argued that impulsivity is actually comprised of multiple distinct factors (see e.g., [ 48 , 51 ]. Research by Broos and colleagues [ 51 ] into these potential factors found evidence for three distinct aspects of impulsivity: self-reported impulsivity, impulsive action, and impulsive choice.

Impulsive choice, also referred to as impulsive decision-making, is a behavioral aspect of impulsivity that is associated with the preference for smaller immediate rewards over larger delayed rewards. A common measure of impulsive decision-making is delay discounting, which can be defined as the decrease in value of an outcome or reward with the increase in delay until its receipt [ 52 ]. An impulsive choice within a delay-discounting paradigm can be operationalized as the choice of a smaller sooner (e.g., receiving $10 today) over larger later reward (e.g., receiving $50 in one month). Degree of delay discounting is predictive of a host of specific behaviors and health outcomes including smoking, obesity, alcohol use, exercise frequency, and expected longevity (see e.g., [ 53 – 56 ]). Importantly, impulsive decision-making in the form of precipitous delay discounting has been implicated as a partial cause of costly chronic illness and premature death in the US (e.g., lack of exercise/physical activity, poor diet, tobacco use, and excessive alcohol consumption; e.g., [ 57 – 64 ]). Researchers have speculated that delay discounting is the most important laboratory predictor of real world health-related behaviors currently available [ 65 ].

Nature exposure and impulsive decision-making

In addition to the abundance of research linking impulsive decision-making in delay discounting to health, Van der Wal, Schade, Krabbendam, and van Vugt [ 66 ], and Berry and colleagues [ 67 , 68 ] have also found evidence linking exposure to nature images to reduced impulsivity in delay discounting tasks. Taken together, this prior research connecting nature to impulsive decision-making and impulsive decision-making to health outcomes provides evidence for a model that impulsive decision-making mediates the nature–health relationship. However, despite this evidence connecting nature exposure to impulsive decision-making and impulsive decision-making to health in separate studies, no prior work has simultaneously tested the links between nature exposure, delay discounting and health in the same study. While prior research has suggested a link between nature exposure and increased self-discipline [ 69 ], research suggests self-discipline and impulsive decision-making should not be considered synonymous constructs (see e.g., [ 70 , 71 ]).

Understanding how nature might affect health via impulsive decision-making represents a potentially important and largely unexplored mechanism for determining nature’s beneficial effects on personal and societal-level health and wellbeing, and may offer insight into targeted nature interventions for broader public health benefits. Therefore, the present investigation explores the role of impulsive decision-making as measured by delay discounting to better understand a mechanism determining how nature improves health by potentially reducing unhealthy impulsive decision-making. Study 1 considers impulsive decision-making as a possible mechanism underlying the effect of nature exposure on health. Study 2 then considers whether an expansion of space perception might be the underlying mechanism driving nature’s effect on impulsive decision-making.

While prior research provides evidence that nature exposure reduces impulsive decision-making, and separately, that reduced impulsivity in decision-making leads to improved health outcomes, a formal evaluation of the nature→impulsive decision-making→health path has not been conducted. Therefore, Study 1 addresses this gap by evaluating the importance of impulsive decision-making in the nature–health relationship. To do so, we test the degree that nature’s relationship with health is indirectly carried through impulsivity using standard methods [ 72 , 73 ]. Using a sample of participants from across the United States, we hypothesized that: (1) increased nature exposure would predict improved health outcomes, (2) increased nature exposure would predict reduced impulsivity in decision-making, (3) reduced impulsive decision-making would predict improved health outcomes, and (4) there would be an indirect effect of nature on health through impulsive decision-making.

Study 1 methods

Participants.

Six hundred and nine participants were recruited through Mechanical Turk . Participation was limited to US residents aged 18 or over. Of the 609 participants, 60% were female. The mean age was 36.39 years ( SD = 12.08). The University of Montana Institutional Review Board approved all procedures.

Participants began by reading the informed consent. Following agreeing to participate in the study, participants completed all measures and responded to basic demographic questions. All experimental data collection was programmed via Qualtrics (Provo, Utah).

Study 1 measures

Nature exposure.

Participants responded to thirteen questions assessing the degree to which they are exposed to nature in their daily lives. A principal component analysis using a Varimax rotation with Kaiser Normalization was performed on all items assessing nature exposure. Components with eigenvalues greater than or equal to 1 were retained. Three measures of nature exposure were identified through this process and are referred to as Nature Exposure From Home, Nature Accessibility, and Presence of Blue Space. The first component (Nature Exposure from Home) contained items related to the nature visible from one’s home, access to a yard with green elements (e.g., grass, trees, etc.), and assessments of the degree that one’s neighborhood contains green elements ( n = 6, α = .90). The second component (Nature Accessibility) contained items assessing the prevalence of parks or other pleasant natural features nearby, the amount of time one spends outdoors, and how safe one feels being outdoors in the area around where they live ( n = 5, α = .73). Finally, the third component (Presence of Blue Space) contained one item assessing whether one had access to a yard with a blue element (e.g., pond, creek, etc.) and another item measuring how one’s neighborhood compared to areas in presence of blue elements ( n = 2, α = .65). Items were developed by research team and are similar to methods used in prior research (e.g. [ 74 , 75 ]). See S1 Appendix for list of items.

Geospatial nature proximity

In addition to the self-report items, we also quantified the natural land cover surrounding respondents’ home addresses using remotely sensed data, aggregated at three nested scales. Respondents were asked to provide their home address in an optional response item (61% of participants reported some portion of an address, ranging from street address to zip code). Addresses provided were geocoded using ESRI ArcMap 10.3.1. Natural land cover in the vicinity of address locations was assessed using the National Land Cover Dataset (NLCD), a spatially explicit raster database with 30-m resolution depicting land cover and land cover change for the United States [ 76 ]. In this dataset, land cover is classified into 16 categories based on spectral signature and post-processing analysis, including: open water, perennial ice/snow, developed (4 categories), barren land, forest (3 categories), grassland/herbaceous, shrub/scrub, pasture/hay, cultivated crops, and wetlands (2 categories). We considered ‘natural land cover’ to be any 30-m raster cell classified as water, perennial ice/snow, grassland/herbaceous, shrub/scrub, pasture/hay, cultivated crops, and any forest or wetland. Percent natural land cover was calculated as the number of natural land cover cells divided by total cells within three nested spatial scales: immediate vicinity, neighborhood, and broader locality. Immediate vicinity was operationalized as any cell within 90m of respondents’ home address (participants who only provided a zip code were excluded from this level of analysis). Neighborhood and broader locality were operationalized as the Census block and County intersected by respondent’s home address, respectively.

Six variables resulted from this process, which we will refer to as: (1) Immediate Vicinity Green, (2) Neighborhood Green, (3) Broader Locality Green, (4) Immediate Vicinity Blue, (5) Neighborhood Blue, and (6) Broader Locality Blue. The ‘green’ items were all positively correlated and therefore combined into a summary variable, Geospatial Green Proximity (α = .72). The ‘blue’ items were not sufficiently correlated with one another, and therefore considered as separate variables for analyses.

Health measures

Participants completed two health measures: (1) The Depression Anxiety Stress Scales, or DASS [ 77 ] (DASS scores were reverse-coded during analysis, resulting in higher scores reflecting increased mental health), and (2) items designed to measure general health and wellbeing (derived from prior research [ 78 ]; α = .88).

Impulsive decision-making measure

To measure degree of impulsive decision-making, participants completed a delay-discounting task using hypothetical monetary outcomes. Participants responded to a series of questions asking “Would you rather have [amount of money] now, or [amount of money] in [delay]?” (see [ 79 ] for a detailed description of the descending fixed order delay discounting task). Participants chose either the immediate or delayed option. Immediate amounts were presented in a fixed descending order ($100, $99, $95, $90, $85, $80, $70, $60, $50, $40, $30, $20, $15, $10, $5, and $1). The delayed amount was constant at $100. All aforementioned values were presented at each delay (delays included 1 day, 1 month, 1 year, and 5 years).

Impulsive decision-making was quantified using Area Under the Curve (AUC; [ 80 ]). AUC is derived from normalizing the indifference points in the fixed sequence procedure, which are defined as the last immediate amount chosen at each delay, as well as normalizing the delays (see [ 80 ]). AUC scores range from 0 to 1, with higher numbers representing less impulsivity in decision-making (i.e., more willingness to wait for delayed, but larger outcomes), and lower numbers representing more impulsive decision-making.

Participants were also asked to report their highest level of education achieved and household income as indicators of socioeconomic status.

Study 1 results

Nature exposure on health.

A series of simple linear regression analyses were initially used to test for an association between nature exposure and health. In all cases, to test for the additive impact of the variable above and beyond demographic factors, we further performed hierarchical regression where education and income were entered in Block 1 and the predictor of interest was entered in Block 2.

First, Nature Accessibility was significantly associated with improved mental health (β = .24, t [549] = 5.84 p < .001) and general health and wellbeing (β = .26, t [560] = 6.24, p < .001). Each of these effects showed the unique predictive value of Nature Accessibility when education and income were entered in Block 1 in hierarchical regression ( R 2 change p ’s < .001). Similarly, Nature Exposure From Home was found to be significantly associated with improved mental health (β = .10, t [547] = 2.30, p = .02) and general health and wellbeing (β = .12, t [557] = 2.80, p < .01). Each of these effects showed the unique predictive value of Nature Exposure from Home when education was entered as a covariate in Block 1 in hierarchical regression ( R 2 change p ’s < .05), while similar analysis using income as a covariate revealed non-significant added effects of Nature Exposure from Home ( R 2 change p = .15, R 2 change p = .13, respectively). Finally, Presence of Blue Space (i.e., greater blue space) was associated with a marginally lower mental health scores, although this relation was not statistically significant (β = -.08, t [564] = -1.90, p = .06). Presence of Blue Space was not found to be associated with general health (β = .02, t [574] = .46, p = .64). See Table 1 .

*p < .05.

**p < .01.

***p < .001.

Impulsive Decision-Making measured by AUC (0–1). Higher values indicate less impulsive decision-making, and lower values indicate more impulsive decision-making (see text for details).

Nature exposure on impulsive decision-making

Consistent with expectations, Nature Accessibility was significantly associated with reduced impulsivity in decision-making (β = .11, t [587] = 2.63, p < .01). The effect of Nature Accessibility when education and income were entered in Block 1 in hierarchical regression was not significant, though in the expected direction ( R 2 change p = .06). Neither Nature Exposure From Home or Presence of Blue Space were significantly associated with impulsivity in decision-making (β = .02, t[584] = .36, p = .72; β = -.03, t [603] = -.70, p = .48).

A series of simple linear regression analyses were used to test for an association between geospatial nature proximity and health. Inconsistent with expectations, none of the measures of geospatial nature proximity were associated with mental health or general health and wellbeing: Geospatial Green Proximity (β = .01, t [352] = .26, p = .79; β = -.03, t [356] = -.57, p = .57), Immediate Vicinity–Blue (β = .04, t [215] = .54, p = .59; β = -.02, t [213] = -.34, p = .73), Neighborhood–Blue (β = .05, t [352] = .90, p = .37; β = -.01, t [356] = -.12, p = .90), and Broader Locality–Blue (β = -.04, t [352] = -.79, p = .43; β = .00, t [356] = -.02, p = .99). Similarly, none of the measures of geospatial nature proximity were associated with impulsivity in decision-making: Geospatial Green Proximity (β = -.01, t [370] = -.14, p = .89), Immediate Vicinity–Blue (β = -.04, t [370] = -.61, p = .54), Neighborhood–Blue (β = -.06, t [370] = -1.09, p = .27), and Broader Locality–Blue (β = -.04, t [370] = -.75, p = .46). We return to the implications of these findings in the discussion. See Table A in S1 Appendix for correlations for all Study 1 variables.

Indirect effect nature exposure on health via impulsivity

A Sobel test [ 81 ] was conducted to determine whether there was an indirect effect of Nature Accessibility on health via impulsive decision-making. Sobel tests are used to test the significance of indirect effects, and in this case examined whether there was an indirect effect of Nature Accessibility on health via impulsive decision-making. Findings were consistent with expectations, as results revealed a significant indirect effect for both measures of health (mental health: z = 2.29, p = .02; general health and wellbeing: z = 2.02, p = .04). See Fig A in S1 Appendix for graphs.

Study 1 discussion

Results from Study 1 include two key findings. First, the results demonstrate that greater nature exposure in the form of nature visible from home and the accessibility of nature in one’s area is associated with health benefits (and that this effect generally occurs above and beyond income and education level, as indicated by hierarchical regressions accounting for those demographic factors). And second, that part of the effect of nature accessibility on health is accounted for via indirect effects through impulsive decision-making. Specifically, these results indicate that a greater degree of nature accessibility is associated with less impulsivity in decision-making, and that less impulsivity in decision-making in turn is linked to improved health. These findings both replicate prior research related to the health benefits of living in proximity to nature, as well as extend our scientific understanding of the relationship between nature and health by providing promising evidence that impulsive decision-making may act as an underlying mechanism of the relationship for certain aspects of the nature–health relationship.

This result is especially impressive given the psychological distance between the impulsive decision-making measurement and our measurements of nature and health. The impulsive decision-making measurement pertained to hypothetical monetary amounts and thus was not directly related to either nature or health. It is in fact compelling support for our guiding theory that persons who report merely subjectively experiencing more access to nature show reduced impulsive choice on a measurement not related to nature. Given the ecological validity of measuring nature exposure in this way, these results suggest a fairly pervasive link between the degree of nature accessibility in the area around where one lives and reduced impulsive decision-making and improved health outcomes.

Finally, while the geospatial nature proximity measures did not produce results consistent with expectations, this was a relatively imprecise measure of nature exposure compared to the self-report items. Where the self-report items directly measured participants’ own experiences of nature in their daily lives, the geospatial analysis estimated this experience using low-resolution land cover data based on the area where one lives. Indeed recent research by Singh, Madden, Gray, and Meentemeyer [ 82 ] highlights an important shortcoming of using the NLCD for research on developed areas. However, regardless of the quality of the measures associated with the NLCD, these results did not directly support expectations. Some prior research using mapping technology has also failed to find an association between access to green space and wellbeing (see e.g., [ 83 ]). Future research should further explore differences between self-reported access to nature and the use of geospatial mapping technology to ascertain rates of nature exposure. While evidence suggests some geospatial mapping technologies may be better than others [ 82 ] at accurately measuring nature exposure in some areas (at least in the US), it is also possible that what matters more to outcomes such as wellbeing is one’s perception of nature exposure (which would be captured more accurately via self-report methods). Overall, more research is needed on this topic.

In Study 1, we found evidence for an indirect effect of nature exposure on health via impulsive decision-making. The relationship between impulsive decision-making and health is well-established in prior research; but less is known about nature’s impulsivity-reducing effect. Therefore, Study 2 focused on the mechanism driving how nature might influence impulsive decision-making. Prior research has found that time perception may not only be important in understanding changes in impulsive decision-making, but also that it may be influenced by exposure to natural environments [ 68 ]. However, despite these connections it has yet to be tested as a possible mechanism of the nature exposure–impulsive decision-making relationship. Further, time perception is one of many possible sensory experiences that might relate to both impulsivity and health. To help fill in these gaps, Study 2 tested (a) whether time perception might help explain changes in impulsive decision-making, and (b) whether another related sensory construct–space perception–might help explain changes in impulsive decision-making.

Time, impulsive decision-making, and nature

Given the ostensible importance of time in understanding impulsive decision-making, prior research has considered how various aspects of time might relate to impulsive decision-making. Evidence has been found that modification of temporal attention to be presently biased [ 84 ], episodic future thinking [ 85 ], and expanded time perception (e.g., [ 68 , 86 ]) all reduce impulsive decision-making. Of particular interest to the present investigation is the finding related to expanded time perception, as nature exposure has also been found to expand perception of time [ 68 , 87 ].

Space expansion: The overlap of time and space

Although research ties nature exposure to expansion of time, there is little research on the link between nature and perceptual expansion at a broader level. Yet it may be that fundamental characteristics of nature contribute to natural environments eliciting expansiveness in ways that go beyond time perception. For example, natural environments may be more likely on average to prime a sense of vast scale than non-natural environments and as such, contribute to perceptual expansion. To the degree that this is true, it may be that perceptual expansion might matter in more general ways. Therefore, Study 2 also considered a second variable related to perceptual expansion; the perception of space.

Given their common conceptual overlap, it is unsurprising that time perception and space perception are linked. Not only have studies linked perceptions of time and space under a variety of conditions, but a look at everyday language also suggests these constructs do not operate independent from one another. Many studies have considered the psychological linkage of space and time (for a review of this literature and a discussion of the neurological overlap of perceptions of space and perceptions of time, see [ 88 ]). In sum, there is an abundance of evidence that suggests human perception of space and perception of time are overlapping constructs (see e.g., [ 89 – 93 ]).

Further indicators of the association between space and time can be found in the linguistic overlap of terms describing time and space frequently used in everyday conversation. For instance, is it is common to hear time described using spatial terms or in an interchangeable way (e.g. ‘the end of the term is approaching’ or ‘short meeting’). This use of spatial language to refer to time is due to a richer perceptual experience humans have for matters of space. Many believe the reason for this is an intimate linkage of spatial and temporal representations in the mind (see e.g., [ 94 – 101 ]).

Given the psychological overlap of space and time, and the evidence for time perception’s role in predicting impulsive decision-making, it follows that space might provide useful insight into impulsive decision-making behavior. Further, the richer reasoning capabilities humans have for matters of space over time [ 102 ], and the tendency to conceptualize time in terms of space [ 103 ] offers reason to believe space may actually be a more efficacious variable than time for understanding impulsive decision-making. Despite these connections, however, investigations to date have not included inquiries into the possible predictive power of space perception in relation to impulsive decision-making.

Study 2 aimed to fill this gap by considering whether changes in either perception of time or perception of space were predictive of changes in impulsive decision-making, and whether there was a significant indirect effect of nature exposure on impulsive decision-making via either of these constructs.

Study 2 hypotheses

Study 2 considered whether exposure to nature might have a similar expanding effect on perceptions of space (as has been found with time), and whether there is evidence for an indirect effect of nature on impulsive decision-making via perception of time and/or perception of space. Specifically, this study used a nature exposure manipulation and delay discounting outcome measure to test: (1) the effect of nature exposure on time perception and space perception, (2) the effect of time perception and space perception on impulsive decision-making, and (3) time perception and space perception as possible mechanisms of the nature–impulsive decision-making relationship. We hypothesized that: (1) nature exposure would expand perceptions of time and space, (2) as time and space are perceived to be more expansive, impulsive decision-making would be reduced, and (3) the nature exposure-impulsive decision making relationship would be partially carried through both time perception and space perception.

Study 2 methods

Sixty-six undergraduate students were recruited from undergraduate psychology courses. Participants provided written informed consent and received course credit for their participation. The University of Montana Institutional Review Board approved all experimental procedures.

Setting and apparatus

Participants completed the study alone in a small, quiet room. Four testing rooms were used. Each of the rooms was identical in size (115” x 64”) and layout, and each room contained two computers, two chairs, and a desk. Participants were asked to leave their personal belongings in a separate space to ensure engagement with the study and reduce distractions. Participants sat in front of a computer viewing images, reading prompts, and responding to questions that utilized E-Prime 2.0 and Qualtrics software.

Stimuli for the present study included images of nature scenes (e.g. lakes, forests, mountains) and images of built scenes (e.g., buildings, cities, roads). The built images served as a control condition for comparison to the nature scenes. These images have been used in previous investigations of attention restoration and delay discounting behavior (e.g. [ 66 , 67 , 104 ]).

All experimental procedures were identical across natural and built conditions with the exception of the condition-specific photographs viewed. Participants were randomly assigned to either the nature condition or built condition. In each condition, participants viewed a series of images (each image was displayed for 10 seconds), based on their assigned condition both before and during the delay-discounting task (described in detail below). Prior to beginning the delay discounting task participants viewed 25 randomly presented condition-specific photographs. Between each delay block of the delay-discounting task, participants viewed 5 randomly presented condition-specific photographs selected from the original 25. Following completion of the delay-discounting task, participants responded to six questions related to perception of space and time, as well as demographic items.

Delay discounting task

To measure degree of impulsive decision-making, participants completed a delay-discounting task involving the choice between a fewer number of days of improved air quality now, versus a greater number of days of improved air quality after a range of delays (see [ 105 ] for a detailed description of the task). Participants used the computer mouse to select either a smaller air quality improvement immediately, or a larger air quality improvement after a delay. The immediate amount increased or decreased based on the participant’s response as previously described (see [ 79 , 106 ] for detailed descriptions of the titration procedure). Delays tested were 1 day, 1 week, 1 month, 6 months, 1 year, 5 years, and 25 years in that order. Impulsive decision-making was quantified using the same Area Under the Curve (AUC; [ 80 ]) analysis described in Study 1.

Perception of space

Participants were asked three questions designed by the research team to assess their perception of space. For the first space perception item, participants were asked to picture themselves immersed in the images they saw on the screen during the study and rate how the space around them felt (on a 1–10 scale [1 –constricted, 10 –expanded]). For the second space perception item, participants were asked to close their eyes for a couple moments and then rate how the space around them felt (on a 1–10 scale [1 –constricted, 10 –expanded]). Results from these two self-report space perception items were significantly positively correlated and were consequently combined into a two-item summary scale (α = .46) for analysis.

Lastly, an exploratory item was developed with the goal of gaining insight into participant’s perception of space without asking about it directly. This third space perception item asked participants to use a blank piece of paper and pen (identical paper and pen were provided to each participant by the researcher) to draw a ‘medium-sized circle’ from which the circle area was then calculated.

Perception of time

Three questions designed by the research team (and similar to methods used in prior research [ 68 , 87 ]) were used to assess participant perception of time: (1) How quickly has time seemed to pass since you first arrived and signed the informed consent? (response scale: 1 –time moved really slowly to 10 –time moved really fast), (2) How many minutes would you estimate have elapsed since you signed the informed consent?, and (3) Consider how time feels at this moment (response scale: 1 –time feels constricted to 10 –time feels expanded).

Responses to the two Likert-scale items were positively correlated, while the minute estimate of elapsed time was negatively correlated with both Likert-scale items. Due to the correlation between the items, they were combined into a three-item summary scale (α = .58) for analysis (the minute estimate time perception item was reverse coded prior to combination).

Study 2 results

Of the 66 participants, 57% were female. The mean age was 23.62 years (SD = 7.79). Chi square (sex, race) and a t test (age) comparing demographics across natural and built conditions indicated no significant differences across groups for these variables (sex, p = .451; race, p = 1.00; age, p = .63).

Main effect of nature exposure on perception of space

Consistent with expectations, an independent samples t -test revealed a significant main effect of nature exposure on space perception, such that those exposed to nature images were more likely to report that the space around them felt expanded ( M = 6.19, SD = 2.09) than those who viewed images of built scenes ( M = 4.27, SD = 1.76), t (64) = 3.99, p < .001, d = 0.99. Also, given the exploratory nature of the circle-area item and its lack of significant correlation to other space items it was not included in the summary variable. However, the exclusion of this item does not change the pattern of results presented here: Testing a summary variable including the circle-area item also results in a statistically significant effect of condition on perception of space, t (64) = 2.33, p = .023, d = 0.57. See Table B in S1 Appendix for correlations between all space items.

Main effect of space perception on impulsive decision-making

A simple linear regression analysis was conducted to determine if space perception significantly predicted impulsive decision-making. Consistent with expectations, expanded space perception significantly predicted less impulsivity in decision-making, β = .31, t (1,63) = 2.57, p = .01.

Main effect of nature exposure on perception of time

An independent samples t -test revealed the main effect of condition on the time perception summary variable was not statistically significant, though it was in the expected direction. Those exposed to nature were somewhat more likely to report that time felt as though it was moving fast/expanded and that fewer minutes had elapsed than those exposed to built images, t (63) = 1.70, p = .09, d = 0.42.

Main effect of time perception on impulsive decision-making

A simple linear regression analysis was conducted to determine if time perception predicted changes in impulsive decision-making. Results approached statistical significance (β = .19, t [62] = 1.51, p = .14), such that increasingly expanded/lengthened time perception predicted reduced impulsive decision-making.

Analysis of the indirect effect of nature on impulsive decision-making through space perception

Given analysis of time perception did not reveal effects achieving conventional levels of statistical significance, only space perception was considered as a possible mechanism of the nature–impulsive decision-making relationship. Prior to conducting indirect effect analysis, an independent samples t -test was used to first establish the presence of a main effect of nature exposure on impulsive decision-making as measured by AUC (results of the discounting portion of this investigation are reported separately in Berry, Repke, & Conway [ 107 ]).

Finally, a Sobel test [ 81 ] was conducted to determine whether there was an indirect effect of nature on impulsive decision-making via space perception. Findings were somewhat consistent with expectations, though a significant indirect effect was not found, z = -1.66, p < .10. See Fig C in S1 Appendix for graph.

Perception of space—Perception of time relationship

While no hypothesis was made regarding the nature of the relationship of the perception of space and perception of time outcome measures, a correlation analysis was conducted for completeness. Summary variables of time and space perception were not found to be significantly correlated, r = .14, p = .27. Furthermore, additional analysis considering only items using the same response scale of ‘constricted’ to ‘expanded’ across the two domains were not found to be significantly correlated.

Study 2 discussion

Overall, these results offer support for the framework under consideration: Nature exposure significantly predicted expanded space perception, and expanded space perception significantly predicted decreased impulsive decision-making. Support of the indirect effect of the nature on impulsive decision-making relationship via space perception was generally consistent with hypotheses, but somewhat weaker. These findings lend support to the broader theory underlying this investigation. They further suggest that humans’ superior perceptual abilities for spatial over temporal matters combined with the psychological relatedness of these constructs makes space perception an important variable in understanding and predicting impulsive decision-making.

General discussion

The present studies are the first to offer evidence for the tenability of a model of nature’s health-boosting effects that focuses on impulsivity and perceptual expansion. Several important findings emerged. First, Study 1 provides evidence that the health benefits linked to nature exposure are partially explained by a reduction in impulsive decision-making. Second, Study 2 demonstrated that the effects of exposure to nature on decreased impulsive decision-making might be partially explained by expanded space perception. Taken together, these results specifically suggest nature exposure might expand space perception, which in turn may reduce impulsive decision-making, and that this reduction in impulsive decision-making might improve health outcomes.

The results of this study extend previous research by linking the indirect effects of nature exposure on health via impulsive decision-making and of nature exposure on impulsive decision-making via expanded space perception. These results may have important implications for our understanding of the effects of nature exposure on health outcomes by suggesting that with increased exposure to nature, impulsive decision-making, which might lead to stress, anxiety, depression, and reductions in overall wellbeing, might be reduced.

Second, these results are also the first to provide support for the involvement of space perception in the nature exposure–impulsive decision-making relationship presented in Study 2. Based on prior research, we considered time expansion to be an important variable in understanding the relationship between nature exposure and impulsive decision-making [ 68 ]. From there, we constructed a theory that since prior research in other contexts had linked the concepts of time and space, that space perception may be an important predictor of impulsive decision-making above and beyond time perception. This hypothesis was influenced by the idea that humans are more easily able to perceive matters related to space rather than time [ 108 ], so therefore we theorized that nature exposure was prompting a psychological expansion that was not isolated to time (and for which space perception might actually more acutely register).

The data from Study 2 suggest that we must qualify some of the assumptions on which this model was based. For instance, it is possible that exposure to nature influences perception of space differentially than perception of time such that even if nature exposure led to expanded perceptions of space and time, the reasons for these effects should not be considered equal, nor should the effects themselves be considered interchangeable (from a theoretical perspective). While this would not lessen the importance of the present findings, it would challenge the theoretical grounding presented here based on the overlap between space and time perception. Future research should be conducted to consider how perception of space and perception of time may or may not be related in their interactions with nature exposure and impulsive decision-making. Similarly, additional research should also test other assumptions such as potential other means for accessing psychological expansion beyond perception of space and time.

Limitations

As with all studies, limitations exist. While the present investigation focused specifically on a mechanism (impulsive decision-making) potentially underlying how nature exposure might influence health via changes to health behaviors, health behaviors were not directly measured in this investigation. Further research testing the merits of the model presented here should include assessment of health behaviors rather than only self-reported health outcomes. Additionally, both of these studies were conducted on U.S. samples. Given that socioeconomic status has been linked to both health and impulsive decision-making, findings presented here should be considered within the context of a population that is more educated and wealthy, on average, than most others.

Further, the present studies were based on a causal mediational model, yet Study 1 employed a non-experimental design that does not allow for direct causal tests. The obvious drawback of this decision is the disconnect between the causal nature of the theoretical model and the non-causal nature of the study design. Indeed, it is important at a larger level to distinguish between the theoretical model–which is based on a mediational approach that assumes causality–and the statistical tests themselves. No statistical test of mediation can directly test the causal path assumed in a mediational model [ 109 ], and thus as with any such mediational model, caution is warranted. However, there are also benefits to the dual approach used here across Studies 1 and 2, which had complementary strengths and weaknesses. The non-experimental design of Study 1 allowed for a more ecologically-valid test of the effects of nature exposure–by looking at how natural environments in real-world contexts are actually related to health–compared to an experimentally manipulated environment. Further, in Study 1 we found an association between nature exposure and impulsive decision-making, which we replicated using an experimental design in Study 2. Indeed, the effects of Nature Exposure in Study 2 are more easily interpreted as causal, due to the randomized nature of the experimental design. Through the combined use of these different methods, we have demonstrated that this effect is likely to be both ecologically-valid and consistent with our hypothesized casual theory. But of course, more research is necessary to more completely validate the causal nature of this design; at this point, it is consistent with our causal mediational model, but it is possible that other possible explanations might yet also account for the found effects.

Finally, the present results were based on a model suggesting the effects of nature exposure on health are serially mediated through perceptual expansion and impulsive decision-making. Despite the evidence generally in favor of this model with regards to space perception, this entire model itself was not directly tested in the same sample. Future research assessing the role of impulsive decision-making and space perception as mechanisms of the nature exposure–health relationship should include a test of all of these constructs in a single design. The evidence presented here indeed suggests that impulsive decision-making acts as an underlying mechanism of the nature exposure–health relationship, and that space perception might act as an underlying mechanism of the nature exposure–impulsive decision-making relationship. Yet from these studies, we are not able to draw firm conclusions on how impulsive decision-making and space perception may interact with one another to predict health outcomes. Thus, these studies provide important evidence for many of the pieces of an emerging model, and future research would do well to test these pieces simultaneously.

Prior research has linked a vast array of health effects to nature exposure. The present investigation sought to understand more about how nature might influence so many health outcomes by considering possible meditators underlying these effects. Using a combination of experimental and non-experimental methods, the studies presented here not only consider new mediators of these effects, but also lend further credence to the hypothesis that nature exposure plays a critical role in human health and wellbeing.

Future research on the health benefits of nature exposure and the underlying mechanism(s) stands to be theoretically as well as substantively important. As the evidence for the benefits of nature exposure in daily life mounts, additional opportunities for application are revealed in many fields including urban planning (e.g. [ 110 ]) and architecture/design both at home and in the workplace (e.g. [ 111 ]). These findings not only add to the abundance of evidence for this effect, but also demonstrate the diverse applications to many aspects of human life.

Finally, nature exposure may not only have important consequences for how humans treat themselves, but also how they treat nature (e.g. [ 112 ]). As our species is increasingly threatened by both anthropogenic climate change and epidemic levels of declining wellness, it is more important than ever to identify opportunities that ameliorate the effects of each as efficiently and effectively as possible. If simple nature exposure can simultaneously improve how humans choose to sustain their own health and wellbeing along with the health and wellbeing of the natural environment, it represents a unique and exceedingly important opportunity for researchers and society alike.

Supporting information

S1 appendix, acknowledgments.

The authors would like to thank Dr. Rita Berto for providing the stimuli used in Study 2.

Funding Statement

The authors received no specific funding for this work.

Data Availability