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Understanding Quantitative Data in Educational Research

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Understanding Quantitative Data in Educational Research

Nicoleta Gaciu - Oxford Brookes University, UK
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It also includes guidance on how to use R and RStudio, how to visualise quantitative data, and the fundamentals of inferential statistics, estimations and hypothesis testing.

Nicoleta Gaciu is Senior Lecturer in Education at Oxford Brookes University.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

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Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

Description : To provide a detailed and accurate description of a particular phenomenon or population.
Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

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Researcher, Academic Writer, Web developer

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Survey Research – Types, Methods, Examples

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In This Article Expand or collapse the "in this article" section Data Collection in Educational Research

Introduction, general overviews.

General Quantitative Overviews
Questionnaires
Quantitative Interviewing
Quantitative Observation
Technical Properties
General Qualitative Overviews
In-Depth Interviewing
Focus Groups
Qualitative Observation
Qualitative Document Analysis
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Data Collection in Educational Research by James H. McMillan , Laura P. Gogia LAST REVIEWED: 30 June 2014 LAST MODIFIED: 30 June 2014 DOI: 10.1093/obo/9780199756810-0087

Data collection methods in educational research are used to gather information that is then analyzed and interpreted. As such, data collection is a very important step in conducting research and can influence results significantly. Once the research question and sources of data are identified, appropriate methods of data collection are determined. Data collection includes a broad range of more specific techniques. Historically, much of the data collection performed in educational research depended on methods developed for studies in the field of psychology, a discipline which took what is termed a “quantitative” approach. This involves using instruments, scales, Tests , and structured observation and interviewing. By the mid- to late twentieth centuries, other disciplines such as anthropology and sociology began to influence educational researchers. Forms of data collection broadened to include what is now called “qualitative” methods, with an emphasis on narratives, participant perspectives, and less structured observation and interviewing. As contemporary educational researchers also draw from fields such as business, political science, and medicine, data collection in education has become a multidisciplinary phenomenon. Because data collection is such a broad topic, General Overviews that attempt to cover all or most techniques tend to offer introductory treatments. Few texts, however, provide comprehensive coverage of every data collection technique. Instead, some cover techniques appropriate for either quantitative or qualitative research approaches. Even more focus on one or two data collection methods within those two research contexts. Consequently, after presenting general overviews, this entry is categorized by data collection appropriate for quantitative and Qualitative Data Collection . These sections, in turn, are subdivided into the major types of quantitative and qualitative data collection techniques. While there are some data collection techniques specific to mixed method research design, which implies a combination of qualitative and quantitative research methodologies, these specific procedures are not emphasized in the present article—readers are referred to the Oxford Bibliography article Mixed Methods Research by Nancy Leech for a comprehensive treatment of mixed method data collection techniques. To locate sources for this article, extensive searches were performed using general-use Internet search engines and educational, psychological, and social science research databases. These searches included keywords around data collection and research methods, as well as specific data collection techniques such as surveys, Tests , Focus Groups , and observation. Frequently cited texts and articles, most recent editions at the time, and sources specific to educational research were given priority. Once these sources were identified, their suggested readings and reference lists were mined for other potential sources. Works or scholars found in multiple reference lists were investigated. When applicable, book reviews in peer-reviewed journals were located and taken into account when curating sources. Sources that demonstrated a high level of impact or offered unique coverage of the topic were included.

General educational research overviews typically include several chapters on data collection, organized into qualitative and quantitative approaches. As a rule they are updated frequently so that they offer timely discussions of methodological trends. Most of them are introductory in nature, written for student researchers. Because of the influence of psychology and other social sciences on the development of data collection in educational research, representative works of psychology ( Trochim 2006 ) and of general social sciences ( Robson 2011 ) are included. Available online, Trochim 2006 is a reader-friendly introduction that provides succinct explanations of most quantitative and qualitative approaches. Olsen 2012 is helpful in showing how data collection techniques used in other disciplines have implications for educational studies. Specific to education, Gall, et al. 2007 is a frequently cited text that contains most educational data collection techniques, although it tends to emphasize more traditional quantitative approaches. Johnson and Christensen 2014 offers a more balanced treatment meant for novice researchers and educational research consumers. Cohen, et al. 2011 also provides a balanced approach, but from a British perspective. Fielding, et al. 2008 offer practical advice on recently developed forms of online data collection, with special attention given to the ethical ramifications of Internet-based data collection. Finally, Arthur, et al. 2012 is unique in this section in that it is an edited work offering short overviews of data collection techniques authored by contemporary leading experts.

Arthur, James, Michael Waring, Robert Coe, and Larry Hedges, eds. 2012. Research methods and methodologies in education . London: SAGE.

A diverse edited text discussing trends in study designs, data collection, and data analysis. It includes twelve chapters devoted to different forms of data collection, written by authors who have recently published extensively on the topic. Annotated bibliographies found at the end of each chapter provide guidance for further reading.

Cohen, Louis, Lawrence Manion, and Keith Morrison. 2011. Research methods in education . 7th ed. London: Routledge.

This long-running, bestselling, comprehensive source offers practical advice with clear theoretical foundations. The newest edition has undergone significant revision. Specific to data collection, revisions include new chapters devoted to data collection via the Internet and visual media. Slides highlighting main points are available on a supplementary website.

Fielding, Nigel, Raymond Lee, and Grant Blank. 2008. The SAGE handbook of online research methods . Thousand Oaks, CA: SAGE.

This extensive handbook presents chapters on Internet research design and data collection written by leading scholars in the field. It discusses using the Internet as an archival resource and a research tool, focusing on the most recent trends in multidisciplinary Internet research.

Gall, Meredith, Joyce Gall, and Walter Borg. 2007. Educational research: An introduction . 8th ed. White Plains, NY: Pearson.

A long-standing, well-respected, nuts-and-bolts perspective on data collection meant to prepare students for conducting original research. Although it tends to emphasize quantitative research methodologies, it has a uniquely rich chapter on historical document analysis.

Johnson, Burke, and Larry Christensen. 2014. Educational research: Quantitative, qualitative, and mixed approaches . 5th ed. Thousand Oaks, CA: SAGE.

A comprehensive introductory text for the consumer and the would-be researcher, with extensive lists of additional resources for gathering all types of data. It discusses quantitative and qualitative research methodologies and data collection evenly but provides extended coverage of questionnaire construction.

Olsen, Wendy. 2012. Data collection: Key debates and methods in social research . London: SAGE.

This recently published toolkit of quantitative, qualitative, and mixed method approaches to data collection provides a more contemporary introduction for both students and research professionals. It offers a helpful overview of data collection as an integral part of research in several different fields of study.

Robson, Colin. 2011. Real world research: A resource for users of social research methods in applied settings . West Sussex, UK: Wiley

This introductory text is intended for all social science. There is an applied, integrated emphasis on contemporary quantitative and qualitative data collection techniques in a separate section of the book, including individual and focus group observations, surveys, unstructured and structured interviewing, and tests.

Trochim, William. 2006. Research methods knowledge base

A free online hypertext textbook on applied social research methods. Data collection techniques associated with qualitative and quantitative research are covered comprehensively. Foundational information appropriate for undergraduates and early graduate students is presented through a series of easy-to-navigate and intuitively ordered webpages. Printed editions are available for purchase in an edition written with James Donnelly (Atomic Dog/Cengage Learning, 2008).

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms.

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
The research aims for objectivity (i.e., without bias) and is separated from the data.
The design of the study is determined before it begins.
For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
Useful for testing and validating already constructed theories.
Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

Designing qualitative research
Methods of data collection and analysis
Introduction to quantitative and qualitative research
Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
Qualitative research in health care: Analysing qualitative data
Qualitative data analysis: the framework approach
Using the framework method for the analysis of
Qualitative data in multi-disciplinary health research
Content Analysis
Grounded Theory
Thematic Analysis

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Appraising Quantitative Research in Health Education: Guidelines for Public Health Educators

Leonard jack, jr..

Associate Dean for Research and Endowed Chair of Minority Health Disparities, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, Louisiana 70125; Telephone: 504-520-5345; Fax: 504-520-7971

Sandra C. Hayes

Central Mississippi Area Health Education Center, 350 West Woodrow Wilson, Suite 3320, Jackson, MS 39213; Telephone: 601-987-0272; Fax: 601-815-5388

Jeanfreau G. Scharalda

Louisiana State University Health Sciences Center School of Nursing, 1900 Gravier Street, New Orleans, Louisiana 70112; Telephone: 504-568-4140; Fax: 504-568-5853

Barbara Stetson

Department of Psychological and Brain Sciences, 317 Life Sciences Building, University of Louisville, Louisville, KY 40292; Telephone: 502-852-2540; Fax: 502-852-8904

Nkenge H. Jones-Jack

Epidemiologist & Evaluation Consultant, Metairie, Louisiana 70002. Telephone: 678-524-1147; Fax: 504-267-4080

Matthew Valliere

Chronic Disease Prevention and Control, Bureau of Primary Care and Rural Health, Office of the Secretary, 628 North 4th Street, Baton Rouge, LA 70821-3118; Telephone: 225-342-2655; Fax: 225-342-2652

William R. Kirchain

Division of Clinical and Administrative Sciences, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, Room 121, New Orleans, Louisiana 70125; Telephone: 504-520-5395; Fax: 504-520-7971

Michael Fagen

Co-Associate Editor for the Evaluation and Practice section of Health Promotion Practice , Department of Community Health Sciences, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., M/C 923, Chicago, IL 60608-1260, Telephone: 312-355-0647; Fax: 312-996-3551

Cris LeBlanc

Centers of Excellence Scholar, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, Louisiana 70125; Telephone: 504-520-5345; Fax: 504-520-7971

Many practicing health educators do not feel they possess the skills necessary to critically appraise quantitative research. This publication is designed to help provide practicing health educators with basic tools helpful to facilitate a better understanding of quantitative research. This article describes the major components—title, introduction, methods, analyses, results and discussion sections—of quantitative research. Readers will be introduced to information on the various types of study designs and seven key questions health educators can use to facilitate the appraisal process. Upon reading, health educators will be in a better position to determine whether research studies are well designed and executed.

Appraising the Quality of Quantitative Research in Health Education

Practicing health educators often find themselves with little time to read published research in great detail. Some health educators with limited time to read scientific papers may get frustrated as they get bogged down trying to understand research terminology, methods, and approaches. The purpose of appraising a scientific publication is to assess whether the study’s research questions (hypotheses), methods and results (findings) are sufficiently valid to produce useful information ( Fowkes and Fulton, 1991 ; Donnelly, 2004 ; Greenhalgh and Taylor, 1997 ; Johnson and Onwuegbuze, 2004 ; Greenhalgh, 1997 ; Yin, 2003; and Hennekens and Buring, 1987 ). Having the ability to deconstruct and reconstruct scientific publications is a critical skill in a results-oriented environment linked to increasing demands and expectations for improved program outcomes and strong justifications to program focus and direction. Health educators do must not solely rely on the opinions of researchers, but, rather, increase their confidence in their own abilities to discern the quality of published scientific research. Health educators with little experience reading and appraising scientific publications, may find this task less difficult if they: 1) become more familiar with the key components of a research publication, and 2) utilize questions presented in this article to critically appraise the strengths and weaknesses of published research.

Key Components of a Scientific Research Publication

The key components of a research publication should provide important information that is needed to assess the strengths and weaknesses of the research. Key components typically include the: publication title , abstract , introduction , research methods used to address the research question(s) or hypothesis, statistical analysis used, results , and the researcher’s interpretation and conclusion or recommended use of results to inform future research or practice. A brief description of these components follows:

Publication Title

A general heading or description should provide immediate insight into the intent of the research. Titles may include information regarding the focus of the research, population or target audience being studied, and study design.

An abstract provides the reader with a brief description of the overall research, how it was done, statistical techniques employed, key results,and relevant implications or recommendations.

Introduction

This section elaborates on the content mentioned in the abstract and provides a better idea of what to anticipate in the manuscript. The introduction provides a succinct presentation of previously published literature, thus offering a purpose (rationale) for the study.

This component of the publication provides critical information on the type of research methods used to conduct the study. Common examples of study designs used to conduct quantitative research include cross sectional study, cohort study, case-control study, and controlled trial. The methods section should contain information on the inclusion and exclusion criteria used to identify participants in the study.

Quantitative data contains information that is quantifiable, perhaps through surveys that are analyzed using statistical tests to determine if the results happened by chance. Two types of statistical analyses are used: descriptive and inferential ( Johnson and Onwuegbuze, 2004 ). Descriptive statistics are used to describe the basic features of the study data and provide simple summaries about the sample and measures. With inferential statistics, researchers are trying to reach conclusions that extend beyond the immediate data alone. Thus, they use inferential statistics to make inferences from the data to more general conditions.

This section presents the reader with the researcher’s data and results of statistical analyses described in the method section. Thus, this section must align closely with the methods section.

Discussion (Conclusion)

This section should explain what the data means thereby summarizing main results and findings for the reader. Important limitations (such as the use of a non-random sample, the absence of a control group, and short duration of the intervention) should be discussed. Researchers should discuss how each limitation can impact the applicability and use of study results. This section also presents recommendations on ways the study can help advance future health education and practice.

Critically Appraising the Strengths and Weaknesses of Published Research

During careful reading of the analysis, results, and discussion (conclusion) sections, what key questions might you ask yourself in order to critically appraise the strengths and weaknesses of the research? Based on a careful review of the literature ( Greenhalgh and Taylor, 1997 ; Greenhalgh, 1997 ; and Hennekens and Buring, 1987 ) and our research experiences, we have identified seven key questions around which to guide your assessment of quantitative research.

1) Is a study design identified and appropriately applied?

Study designs refer to the methodology used to investigate a particular health phenomenon. Becoming familiar with the various study designs will help prepare you to critically assess whether its selection was applied adequately to answer the research questions (or hypotheses). As mentioned previously, common examples of study designs frequently used to conduct quantitative research include cross sectional study, cohort study, case-control study, and controlled trail. A brief description of each can be found in Table 1 .

Definitions of Study Designs

2) Is the study sample representative of the group from which it is drawn?

The study sample must be representative of the group from which it is drawn. The study sample must therefore be typical of the wider target audience to whom the research might apply. Addressing whether the study sample is representative of the group from which it is drawn will require the researcher to take into consideration the sampling method and sample size.

Sampling Method

Many sampling methods are used individually or in combination. Keep in mind that sampling methods are divided into two categories: probability sampling and non-probability sampling ( Last, 2001 ). Probability sampling (also called random sampling) is any sampling scheme in which the probability of choosing each individual is the same (or at least known, so it can be readjusted mathematically to be equal). Non-probability sampling is any sampling scheme in which the probability of an individual being chosen is unknown. Typically, researchers should offer a rationale for utilizing non-probability sampling, and when utilized, be aware of its limitations. For example, use of a convenience sample (choosing individuals in an unstructured manner) can be justified when collecting pilot data around which future studies employing more rigorous sampling methods will be utilized.

Sample Size

Established statistical theories and formulas are used to generate sample size calculations—the recommended number of individuals necessary in order to have sufficient power to detect meaningful results at a certain level of statistical significance. In the methods section, look for a statement or two confirming whether steps where taken to obtain the appropriate sample size.

3) In research studies using a control group, is this group adequate for the purpose of the study?

Source of controls.

In case-control and cohort studies, the source of controls should be such that the distribution of characteristics not under investigation are similar to those in the cases or study cohort.

In case-control studies both cases and controls are often matched on certain characteristics such as age, sex, income, and race. The criteria used for including and excluding study participants must be adequately described and examined carefully. Inclusion and exclusion criteria may include: ethnicity, age of diagnosis, length of time living with a health condition, geographic location, and presence or absence of complications. You should critically assess whether matching across these characteristics actually occurred.

4) What is the validity of measurements and outcomes identified in the study?

Validity is the extent to which a measurement captures what it claims to measure. This might take the form of questions contained on a survey, questionnaire or instrument. Researchers should address one or more of the following types of validity: face, content, criterion-related, and construct ( Last, 2001 ; William and Donnelly, 2008).

Face validity

Face validity assures that, upon examination, the variable of interest can measure what it intends to measure. If the researcher has chosen to study a variable that has not been studied before, he/she usually will need to start with face validity.

Content validity

Content validity involves comparing the content of the measurement technique to the known literature on the topic and validating the fact that the tool (e.g., survey, questionnaire) does represent the literature accurately.

Criterion-related validity

Criterion-related validity involves making sure the measures within a survey when tested proves to be effective in predicting criterion or indicators of a construct.

Construct validity

Construct validity deals with the validation of the construct that underlies the research. Here, researchers test the theory that underlies the hypothesis or research question.

5) To what extent is a common source of bias called blindness taken into account?

During data collection, a common source of bias is that subjects and/or those collecting the data are not blind to the purpose of the research. This can likely be the result of researchers going the extra mile to make sure those in the experimental group benefit from the intervention ( Fowkes and Fulton, 1991 ). Inadequate blindness can be a problem in studies utilizing all types of study designs. While total blindness is not possible, appraising whether steps were taken to be sure issues related to ensure blindness occurred is essential.

6) To what extent is the study considered complete with regard to drop outs and missing data?

Regardless of the study design employed, one must assess not only the proportion of drop outs in each group, but also why they dropped out. This may point to possible bias, as well as determine what efforts were taken to retain participants in the study.

Missing data

Despite the fact that missing data are a part of almost all research, it should still be appraised. There are several reasons why the data may be missing. The nature and extent to which data is missing should be explained.

7) To what extent are study results influenced by factors that negatively impact their credibility?

Contamination.

In research studies comparing the effectiveness of a structured intervention, contamination occurs when the control group makes changes based on learning what those participating in the intervention are doing. Despite the fact that researchers typically do not report the extent to which contamination occurs, you should nevertheless try to assess whether contamination negatively impacted the credibility of study results.

Confounding factors

A confounding factor in a study is a variable which is related to one or more of the measurements (measures or variables) defined in a study. A confounding factor may mask an actual association or falsely demonstrate an apparent association between the study variables where no real association between them exists. If confounding factors are not measured and considered, study results may be biased and compromised.

The guidelines and questions presented in this article are by no means exhaustive. However, when applied, they can help health education practitioners obtain a deeper understanding of the quality of published research. While no study is 100% perfect, we do encourage health education practitioners to pause before taking researchers at their word that study results are both accurate and impressive. If you find yourself answering ‘no’ to a majority of the key questions provided, then it is probably safe to say that, from your perspective, the quality of the research is questionable.

Over time, as you repeatedly apply the guidelines presented in this article, you will become more confident and interested in reading research publications from beginning to end. While this article is geared to health educators, it can help anyone interested in learning how to appraise published research. Table 2 lists additional reading resources that can help improve one’s understanding and knowledge of quantitative research. This article and the reading resources identified in Table 2 can serve as useful tools to frame informative conversations with your peers regarding the strengths and weaknesses of published quantitative research in health education.

Publications on How to Read, Write and Appraise Quantitative Research

Contributor Information

Leonard Jack, Jr., Associate Dean for Research and Endowed Chair of Minority Health Disparities, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, Louisiana 70125; Telephone: 504-520-5345; Fax: 504-520-7971.

Sandra C. Hayes, Central Mississippi Area Health Education Center, 350 West Woodrow Wilson, Suite 3320, Jackson, MS 39213; Telephone: 601-987-0272; Fax: 601-815-5388.

Jeanfreau G. Scharalda, Louisiana State University Health Sciences Center School of Nursing, 1900 Gravier Street, New Orleans, Louisiana 70112; Telephone: 504-568-4140; Fax: 504-568-5853.

Barbara Stetson, Department of Psychological and Brain Sciences, 317 Life Sciences Building, University of Louisville, Louisville, KY 40292; Telephone: 502-852-2540; Fax: 502-852-8904.

Nkenge H. Jones-Jack, Epidemiologist & Evaluation Consultant, Metairie, Louisiana 70002. Telephone: 678-524-1147; Fax: 504-267-4080.

Matthew Valliere, Chronic Disease Prevention and Control, Bureau of Primary Care and Rural Health, Office of the Secretary, 628 North 4th Street, Baton Rouge, LA 70821-3118; Telephone: 225-342-2655; Fax: 225-342-2652.

William R. Kirchain, Division of Clinical and Administrative Sciences, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, Room 121, New Orleans, Louisiana 70125; Telephone: 504-520-5395; Fax: 504-520-7971.

Michael Fagen, Co-Associate Editor for the Evaluation and Practice section of Health Promotion Practice , Department of Community Health Sciences, School of Public Health, University of Illinois at Chicago, 1603 W. Taylor St., M/C 923, Chicago, IL 60608-1260, Telephone: 312-355-0647; Fax: 312-996-3551.

Cris LeBlanc, Centers of Excellence Scholar, College of Pharmacy, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, Louisiana 70125; Telephone: 504-520-5345; Fax: 504-520-7971.

Fowkes FG, Fulton PM. Critical appraisal of published research: introductory guidelines. British Medical Journal. 1991; 302 :1136–40. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Donnelly RA. The Complete Idiots Guide to Statistics. Alpha Books; New York, NY: 2004. pp. 6–7. [ Google Scholar ]
Greenhalgh T, Taylor R. How to read a paper: Papers that go beyond numbers (qualitative research) British Medical Journal. 1997; 315 :740–743. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Greenhalgh T. How to read a paper: Assessing the methodological quality of published papers. British Medical Journal. 315 :305–308. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Johnson RB, Onwuegbuze AJ. Mixed methods research: A research paradigm whose time has come. Educational Researcher. 2004; 33 :14–26. [ Google Scholar ]
Hennekens CH, Buring JE. Epidemiology in Medicine. Little, Brown and Company; Boston, Massachusetts: 1987. pp. 106–108. [ Google Scholar ]
Last JM. A dictionary of epidemiology. 4. Oxford University Press, Inc; New York, New York: 2001. [ Google Scholar ]
Trochim WM, Donnelly J. Research methods knowledge base. 3. Atomic Dog; Mason, Ohio: 2008. pp. 6–8. [ Google Scholar ]

Leveraging collective action and environmental literacy to address complex sustainability challenges

Perspective
Open access
Published: 09 August 2022
Volume 52 , pages 30–44, ( 2023 )

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define quantitative research in education

Nicole M. Ardoin ORCID: orcid.org/0000-0002-3290-8211 1 ,
Alison W. Bowers 2 &
Mele Wheaton 3

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Developing and enhancing societal capacity to understand, debate elements of, and take actionable steps toward a sustainable future at a scale beyond the individual are critical when addressing sustainability challenges such as climate change, resource scarcity, biodiversity loss, and zoonotic disease. Although mounting evidence exists for how to facilitate individual action to address sustainability challenges, there is less understanding of how to foster collective action in this realm. To support research and practice promoting collective action to address sustainability issues, we define the term “collective environmental literacy” by delineating four key potent aspects: scale, dynamic processes, shared resources, and synergy. Building on existing collective constructs and thought, we highlight areas where researchers, practitioners, and policymakers can support individuals and communities as they come together to identify, develop, and implement solutions to wicked problems. We close by discussing limitations of this work and future directions in studying collective environmental literacy.

Reimagining the language of engagement in a post-stakeholder world

Can public awareness, knowledge and engagement improve climate change adaptation policies?

Leverage points for sustainability transformation

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Introduction

For socio-ecologically intertwined issues—such as climate change, land conversion, biodiversity loss, resource scarcity, and zoonotic diseases—and their associated multi-decadal timeframes, individual action is necessary, yet not sufficient, for systemic, sustained change (Amel et al. 2017 ; Bodin 2017 ; Niemiec et al. 2020 ; Spitzer and Fraser 2020 ). Instead, collective action, or individuals working together toward a common good, is essential for achieving the scope and scale of solutions to current sustainability challenges. To support communities as they engage in policy and action for socio-environmental change, communicators, land managers, policymakers, and other practitioners need an understanding of how communities coalesce and leverage their shared knowledge, skills, connections, and experiences.

Engagement efforts, such as those grounded in behavior-change approaches or community-based social marketing initiatives, that address socio-environmental issues have often emphasized individuals as the pathway to change. Such efforts address a range of domains including, but not limited to, residential energy use, personal transportation choices, and workplace recycling efforts, often doing so in a stepwise fashion, envisioning each setting or suite of behaviors as discrete spheres of action and influence (Heimlich and Ardoin 2008 ; McKenzie-Mohr 2011 ). In this way, specific actions are treated incrementally and linearly, considering first the individual barriers to be removed and then the motivations to be activated (and, sometimes, sustained; Monroe 2003 ; Gifford et al. 2011 ). Once each behavior is successfully instantiated, the next barrier is then addressed. Proceeding methodically from one action to the next, such initiatives often quite successfully alter a series of actions or group of related behaviors (at least initially) by addressing them incrementally, one at a time (Byerly et al. 2018 ). Following this aspirational logic chain, many resources have been channeled into such programs under the assumption that, by raising awareness and knowledge, such information, communication, and educational outreach efforts will shift attitudes and behaviors to an extent that, ultimately, mass-scale change will follow. (See discussion in Wals et al. 2014 .)

Numerous studies have demonstrated, however, that challenges arise with these stepwise approaches, particularly with regard to their ability to address complex issues and persist over time (Heimlich and Ardoin 2008 ; Wals et al. 2014 ). Such approaches place a tremendous—and unrealistic—burden on individuals, ignoring key aspects not only of behavioral science but also of social science more broadly, including the view that humans exist nested within socio-ecological systems and, thus, are most successful at achieving lasting change when it is meaningful, relevant, and undertaken within a supportive context (Swim et al. 2011 ; Feola 2015 ). Individualized approaches often require multiple steps or nudges (Byerly et al. 2018 ), or ongoing reminders to retain their salience (Stern et al. 2008 ). Because of the emphasis on decontextualized action, such approaches can miss, ignore, obfuscate, or minimize the importance of the bigger picture, which includes the sociocultural, biophysical, and political economic contexts (Ardoin 2006 ; Amel et al. 2017 ). Although the tightly trained focus on small, actionable steps and reliance on individual willpower may help in initially achieving success with initial habit formation (Carden and Wood 2018 ), it becomes questionable in terms of bringing about a wave of transformation on larger scales in the longer term. For those decontextualized actions to persist, they require continued prompting, constancy, and support in the social and biophysical context (Schultz 2014 ; Manfredo et al. 2016 ; Wood and Rünger 2016 ).

Less common in practice are theoretically based initiatives that embrace the holistic nature of the human experience, which occurs within complex systems spanning time and space in a multidimensional, weblike fashion (Bronfenbrenner 1979 ; Rogoff 2003 ; Barron 2006 ; DeCaro and Stokes 2008 ; Gould et al. 2019 ; Hovardas 2020 ). These systems-thinking approaches, while varying across disciplines and epistemological perspectives, envision human experiences, including learning and behavior, as occurring within a milieu that include the social, political, cultural, and historical contexts (Rogoff 2003 ; Roth and Lee 2007 ; Swim et al. 2011 ; Gordon 2019 ). In such a view, people’s everyday practices continuously reflect and grow out of past learning and experiences, not only at the individual, but also at the collective level (Lave 1991 ; Gutiérrez and Rogoff 2003 ; Nasir et al. 2020 ; Ardoin and Heimlich 2021 ). The multidimensional context in which we exist—including the broader temporal and spatial ecosystem—both facilitates and constrains our actions.

Scholars across diverse areas of study discuss the need for and power of collective thought and action, using various conceptual frames, models, and terms, such as collective action, behavior, impact, and intelligence; collaborative governance; communities of practice; crowdsourcing; and social movement theory; among many others (Table 1 ). These scholars acknowledge and explore the influence of our multidimensional context on collective thought and action. In this paper, we explore the elements and processes that constitute collective environmental literacy . We draw on the vast, relevant literature and, in so doing, we attempt to invoke the power of the collective: by reviewing and synthesizing ideas from a variety of fields, we strive to leverage existing constructs and perspectives that explore notions of the “collective” (see Table 1 for a summary of constructs and theories reviewed to develop our working definition of collective environmental literacy). A primary goal of this paper is to dialogue with other researchers and practitioners working in this arena who are eager to uncover and further explore related avenues.

First, we present a formal definition of collective environmental literacy. Next, we briefly review the dominant view of environmental literacy at the individual level and, in support of a collective take on environmental literacy, we examine various collective constructs. We then delve more deeply into the definition of collective environmental literacy by outlining four key aspects: scale, dynamic processes, shared resources, and synergy. We conclude by providing suggestions for future directions in studying collective environmental literacy.

Defining collective environmental literacy

Decades of research in political science, economics, anthropology, sociology, psychology, and the learning sciences, among other fields (Chawla and Cushing 2007 ; Ostrom 2009 ; Sawyer 2014 ; Bamberg et al. 2015 ; Chan 2016 ; Jost et al. 2017 ) repeatedly demonstrates the effectiveness, and indeed necessity of, collective action when addressing problems that are inherently social in nature. Yet theoretical frameworks and empirical documentation emphasize that such collective activities rarely arise spontaneously and, when they do, are a result of preconditions that have sown fertile ground (van Zomeren et al. 2008 ; Duncan 2018 ). Persistent and effective collective action then requires scaffolding in the form of institutional, sociocultural, and political economic structure that provides ongoing support. To facilitate discussions of how to effectively support collective action around sustainability issues, we suggest the concept of “collective environmental literacy.” We conceptualize collective environmental literacy as more than collective action; rather, we suggest that the term encapsulates action along with its various supporting structures and resources. Additionally, we employ the word “literacy” as it connotes learning, intention, and the idea that knowledge, skills, attitudes, and behaviors can be enhanced iteratively over time. By using “literacy,” we strive to highlight the efforts, often unseen, that lead to effective collective action in communities. We draw on scholarship in science and health education, areas that have begun over the past two decades to theorize about related areas of collective science literacy (Roth and Lee 2002 , 2004 ; Lee and Roth 2003 ; Feinstein 2018 ) and health literacy (Freedman et al. 2009 ; Papen 2009 ; Chinn 2011 ; Guzys et al. 2015 ). Although these evolving constructs lack consensus definitions, they illuminate affordances and constraints that exist when conceptualizing collective environmental literacy (National Academies of Sciences, Engineering, and Medicine [NASEM] 2016 ).

Some of the key necessary—but not sufficient—conditions that facilitate aligned, collective actions include a common body of decision-making information; shared attitudes, values, and beliefs toward a motivating issue or concern; and efficacy skills that facilitate change-making (Sturmer and Simon 2004 ; van Zomeren et al. 2008 ; Jagers et al. 2020 ). In addition, other contextual factors are essential, such as trust, reciprocity, collective efficacy, and communication among group members and societal-level facilitators, such as social norms, institutions, and technology (Bandura 2000 ; Ostrom 2010 ; McAdam and Boudet 2012 ; Jagers et al. 2020 ). Taken together, we term this body of knowledge, dispositions, skills, and the context in which they flourish collective environmental literacy . More formally, we define collective environmental literacy as: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig. 1 ).

Key elements of collective environmental literacy

Environmental literacy: Historically individual, increasingly collective

Over the past five decades, the term “environmental literacy” has come into increasingly frequent use. Breaking from the traditional association of “literacy” with reading and writing in formal school contexts, environmental literacy emphasizes associations with character and behavior, often in the form of responsible environmental stewardship (Roth 1992 ). Footnote 1 Such perspectives define the concept as including affective (attitudinal), cognitive (knowledge-based), and behavioral domains, emphasizing that environmental literacy is both a process and outcome that develops, builds, and morphs over time (Hollweg et al. 2011 ; Wheaton et al. 2018 ; Clark et al. 2020 ).

The emphasis on defining, measuring, and developing interventions to bring about environmental literacy has primarily remained at the individual scale, as evidenced by frequent descriptions of an environmentally literate person (Roth 1992 ; Hollweg et al. 2011 among others) rather than community or community member. In most understandings, discussions, and manifestations of environmental literacy, the implicit assumption remains that the unit of action, intervention, and therefore analysis occurs at the individual level. Yet instinctively and perhaps by nature, community members often seek information and, as a result, take action collectively, sharing what some scholars call “the hive mind” or “group mind,” relying on each other for distributed knowledge, expertise, motivation, and support (Surowiecki 2005 ; Sunstein 2008 ; Sloman and Fernbach 2017 ; Paul 2021 ).

As with the proverbial elephant (Saxe, n.d.), each person, household, or neighborhood group may understand or “see” a different part of an issue or challenge, bring a novel understanding to the table, and have a certain perspective or skill to contribute. Although some environmental literacy discussions allude to a collective lens (e.g., Hollweg et al. 2011 ; Ardoin et al. 2013 ; Wheaton et al. 2018 ; Bey et al. 2020 ), defining, developing frameworks, and creating measures to assess the efficacy of such collective-scale sustainability-related endeavors has remained elusive. Footnote 2 Looking to related fields and disciplines—such as ecosystem theory, epidemiology and public health, sociology, network theory, and urban planning, among others—can provide insight, theoretical frames, and empirical examples to assist in such conceptualizations (McAdam and Boudet 2012 ; National Research Council 2015 ) (See Table 1 for an overview of some of the many areas of study that informed our conceptualization of collective environmental literacy).

Seeking the essence of the collective: Looking to and learning from others

The social sciences have long focused on “the kinds of activities engaged in by sizable but loosely organized groups of people” (Turner et al. 2020 , para. 1) and addressed various collective constructs, such as collective behavior, action, intelligence, and memory (Table 1 ). Although related constructs in both the social and natural sciences—such as communities of practice (Wenger and Snyder 2000 ), collaborative governance (Ansell and Gash 2008 ; Emerson et al. 2012 ), and the collaboration–coordination continuum (Sadoff and Grey 2005 ; Prager 2015 ), as well as those from social movement theory and related areas (McAdam and Boudet 2012 ; de Moor and Wahlström 2019 )—lack the word “collective” in name, they too leverage the benefits of collectivity. A central tenet connects all of these areas: powerful processes, actions, and outcomes can arise when individuals coalesce around a common purpose or cause. This notion of a dynamic, potent force transcending the individual to enhance the efficacy of outcomes motivates the application of a collective lens to the environmental literacy concept.

Dating to the 1800s, discussions of collective behavior have explored connections to social order, structures, and norms (Park 1927 ; Smelser 2011 /1962; Turner and Killian 1987 ). Initially, the focus emphasized spontaneous, often violent crowd behaviors, such as riots, mobs, and rebellions. More contemporarily, sociologists, political scientists, and others who study social movements and collective behaviors acknowledge that such phenomena may take many forms, including those occurring in natural ecosystems, such as ant colonies, bird flocks, and even the human brain (Gordon 2019 ). In sociology, collective action represents a paradigm shift highlighting coordinated, purposeful pro-social movements, while de-emphasizing aroused emotions and crowd behavior (Miller 2014 ). In political science, Ostrom’s ( 1990 , 2000 , 2010 ) theory of collective action in the context of the management of shared resources extends the concept’s reach to economics and other fields. In education and the learning sciences, social learning and sociocultural theories tap into the idea of learning as a social-cognitive-cultural endeavor (Vygotsky 1980 ; Lave and Wenger 1991 ; Tudge and Winterhoff 1993 ; Rogoff 2003 ; Reed et al. 2010 ).

Collective action, specifically, and collective constructs, generally, have found their way into the research and practice in the fields of conservation, natural resources, and environmental management. Collective action theory has been applied in a range of settings and scenarios, including agriculture (Mills et al. 2011 ), invasive species management (Marshall et al. 2016 ; Sullivan et al. 2017 ; Lubeck et al. 2019 ; Clarke et al. 2021 ), fire management (Canadas et al. 2016 ; Charnley et al. 2020 ), habitat conservation (Raymond 2006 ; Niemiec et al. 2020 ), and water governance (Lopez-Gunn 2003 ; Baldwin et al. 2018 ), among others. Frameworks and methods that emphasize other collective-related ideas—like collaboration, co-production, and group learning—are also ubiquitous in natural resource and environmental management. These constructs include community-based conservation (DeCaro and Stokes 2008 ; Niemiec et al. 2016 ), community natural resource management (Kellert et al. 2000 ; Dale et al. 2020 ), collaboration/coordination (Sadoff and Grey 2005 ; Prager 2015 ), polycentricity (Galaz et al. 2012 ; Heikkila et al. 2018 ), knowledge co-production (Armitage et al. 2011 ; Singh et al. 2021 ), and social learning (Reed et al. 2010 ; Hovardas 2020 ). Many writings on collective efforts in the social sciences broadly, and applied in the area of environment specifically, provide insights into collective action’s necessary preconditions, which prove invaluable to further defining and later operationalizing collective environmental literacy.

Unpacking the definition of collective environmental literacy: Anchoring principles

As described, we propose the following working definition of collective environmental literacy drawing on our analysis of related literatures and informed by scholarly and professional experience in the sustainability and conservation fields: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig. 1 ). This definition centers on four core, intertwined ideas: the scale of the group involved; the dynamic nature of the process; shared resources brought by, available to, and needed by the group; and the synergy that arises from group interaction.

Multi-scalar

When transitioning from the focus on individual to collective actions—and, herein, principles of environmental literacy—the most obvious and primary requisite shift is one of scale. Yet, moving to a collective scale does not mean abandoning action at the individual scale; rather, success at the collective level is intrinsically tied to what occurs at an individual level. Such collective-scale impacts leverage the power of the hive, harnessing people’s willingness, ability, and motivation to take action alongside others, share their ideas and resources to build collective ideas and resources, contribute to making a difference in an impactful way, and participate communally in pro-social activities.

Collective environmental literacy is likely dynamic in its orientation to scale, incorporating place-based notions, such as ecoregional or community-level environmental literacy (with an emphasis on geographic boundaries). On the other hand, it may encapsulate environmental literacy of a group or organization united by a common identity (e.g., organizational membership) or cause (e.g., old-growth forests, coastal protection), rather than solely or even primarily by geography. Although shifting scales can make measuring collective environmental literacy more difficult, dynamic levels may be a benefit when addressing planetary boundary issues such as climate change, biodiversity, and ocean acidification (Galaz et al. 2012 ). Some scholars have called for a polycentric approach to these large-scale issues in response to a perceived failure of global-wide, top-down solutions (Ostrom 2010 , 2012 ; Jordan et al. 2018 ). Conceptualizing and consequently supporting collective environmental literacy at multiple scales can facilitate such desired polycentricity.

Rather than representing a static outcome, environmental literacy is a dynamic process that is fluctuating and complex, reflective of iterative interactions among community members, whose discussions and negotiations reflect the changing context of sustainability issues. Footnote 3 Such open-minded processes allow for, and indeed welcome, adaptation in a way that builds social-ecological resilience (Berkes and Jolly 2002 ; Adger et al. 2005 ; Berkes 2007 ). Additionally, this dynamism allows for collective development and maturation, supporting community growth in collective knowledge, attitudes, skills, and actions via new experiences, interactions, and efforts (Berkman et al. 2010 ). With this mindset, and within a sociocultural perspective, collective environmental literacy evolves through drawing on and contributing to the community’s funds of knowledge (González et al. 2006 ). Movement and actions within and among groups impact collective literacy, as members share knowledge and other resources, shifting individuals and the group in the course of their shared practices (Samerski 2019 ).

In a collective mode, effectiveness is heightened as shared resources are streamlined, waste is minimized, and innovation maximized. Rather than each group member developing individual expertise in every matter of concern, the shared knowledge, skills, and behaviors can be distributed, pursued, and amplified among group members efficiently and effectively, with collective literacy emerging from the process of pooling diverse forms of capital and aggregating resources. This perspective builds on ideas of social capital as a collective good (Ostrom 1990 ; Putnam 2020 ), wherein relationships of trust and reciprocity are both inputs and outcomes (Pretty and Ward 2001 ). The shared resources then catalyze and sustain action as they are reassembled and coalesced at the group level for collective impact.

The pooled resources—likely vast—may include, but are not limited to, physical and human resources, funding, time, energy, and space and place (physical or digital). Shared resources may also include forms of theorized capital, such as intellectual and social (Putnam 2020 ). Also of note is the recognition that these resources extend far beyond information and knowledge. Of particular interest when building collective environmental literacy are resources previously ignored or overlooked by those in power in prior sustainability efforts. For example, collective environmental literacy can draw strength from shared resources unique to the community or even subgroups within the larger community. Discussions of Indigenous knowledge (Gadgil et al. 1993 ) and funds of knowledge (González et al. 2006 ; Cruz et al. 2018 ) suggest critical, shared resources that highlight strengths of an individual community and its members. Another dimension of shared resources relates to the strength of institutional connections, such as the benefits that accrue from leveraging the collective knowledge, expertise, and resources of organizational collaborators working in adjacent areas to further and amplify each other’s impact (Wojcik et al. 2021 ).

Synergistic

Finally, given the inherent complexities related to defining, deploying, implementing, and measuring these dynamic, at-times ephemeral processes, resources, and outcomes at a collective scale, working in such a manner must be clearly advantageous to pressing sustainability issues at hand. Numerous related constructs and approaches from a range of fields emphasize the benefits of diverse collaboration to collective thought and action, including improved solutions, more effective and fair processes, and more socioculturally just outcomes (Klein 1990 ; Jörg 2011 ; Wenger and Snyder 2000 ; Djenontin and Meadow 2018 ). These benefits go beyond efficient aggregation and distribution of resources, invoking an almost magical quality that defines synergy, resulting in robust processes and outcomes that are more than the sum of the parts.

This synergy relies on the diversity of a group across various dimensions, bringing power, strength, and insight to a decision-making process (Bear and Woolley 2011 ; Curşeu and Pluut 2013 ; Freeman and Huang 2015 ; Lu et al. 2017 ; Bendor and Page 2019 ). Individuals are limited not only to singular knowledge-perspectives and skillsets, but also to their own experiences, which influence their self-affirming viewpoints and tendencies to seek out confirmatory information for existing beliefs (Kahan et al. 2011 ). Although the coming together of those from different racial, cultural, social, and economic backgrounds facilitates a collective literacy process that draws on a wider range of resources and equips a gestalt, it also sets up the need to consider issues of power, privilege, voice, and representation (Bäckstrand 2006 ) and the role of social capital, leading to questions related to trust and reciprocity in effective collectives (Pretty and Ward 2001 ; Folke et al. 2005 ).

Leveraging the ‘Hive’: Proceeding with collective environmental literacy

This paper presents one conceptualization of collective environmental literacy, with the understanding that numerous ways exist to envision its definition, formation, deployment, and measurement. Characterized by a collective effort, such literacies at scale offer a way to imagine, measure, and support the synergy that occurs when the emphasis moves from an individual to a larger whole. By expanding the scale and focusing on shared responsibility among actors at the systems level, opportunities arise for inspiring and enabling a broader contribution to a sustainable future. These evolving notions serve to invite ongoing conversation, both in research and practice, about how to enact our collective responsibility toward, as well as vision of, a thriving future.

Emerging from the many discussions of shared and collaborative efforts to address socio-environmental issues, our conceptualization of collective environmental literacy is a first step toward supporting communities as they work to identify, address, and solve sustainability problems. We urge continued discussions on this topic, with the goal of understanding the concept of collective environmental literacy, how to measure it, and the implications of this work for practitioners. The conceptual roots of collective environmental literacy reach into countless fields of study and, as such, a transdisciplinary approach, which includes an eye toward practice, is necessary to fully capture and maximize the tremendous amount of knowledge, wisdom, and experience around this topic. Specifically, next steps to evolve the concept include engaging sustainability researchers and practitioners in discussions of the saliency of the presented definition of collective environmental literacy. These discussions include verifying the completeness of the definition and ensuring a thorough review of relevant research: Are parts of the definition missing or unclear? What are the “blank, blind, bald, and bright spots” in the literature (Reid 2019 p. 158)? Additionally, recognizing and leveraging literacy at a collective scale most certainly is not unique to environmental work, nor is adopting literacy-related language to conceptualize and measure process outcomes, although the former has consistently proven more challenging. Moreover, although we (the authors) appreciate the connotations and structures gained by using a literacy framework, we struggle with whether “environmental literacy” is the most appropriate and useful term for the conceptualizations as described herein; we, thus, welcome lively discussions about the need for new terminology.

Even at this early stage of conceptualization, this work has implications for practitioners. For scientists, communicators, policymakers, land managers, and other professionals desiring to work with communities to address sustainability issues, a primary take-away message concerns the holistic nature of what is needed for effective collective action in the environmental realm. Many previous efforts have focused on conveying information and, while a lack of knowledge and awareness may be a barrier to action in some cases, the need for a more holistic lens is increasingly clear. This move beyond an individually focused, information-deficit model is essential for effective impact (Bolderdijk et al. 2013 ; van der Linden 2014 ; Geiger et al. 2019 ). The concept of collective environmental literacy suggests a role for developing shared resources that can foster effective collective action. When working with communities, a critical early step includes some form of needs assessment—a systematic, in-depth process that allows for meaningfully gauging gaps in shared resources required to tackle sustainability issues (Braus 2011). Following this initial, evaluative step, an understanding of the components of collective environmental literacy, as outlined in this paper, can be used to guide the development of interventions to support communities in their efforts to address those issues.

Growing discussion of collective literacy constructs, and related areas, suggests researchers, practitioners, and policymakers working in pro-social areas recognize and value collective efforts, despite the need for clearer definitions and effective measures. This definitional and measurement work, in both research and practice, is not easy. The ever-changing, dynamic contexts in which collective environmental literacy exists make defining the concept a moving target, compounded by a need to draw upon work in countless, often distinct academic fields of study. Furthermore, the hard-to-see, inner workings of collective constructs make measurement difficult. Yet, the “power of the hive” is intriguing, as the synergism that arises from communities working in an aligned manner toward a unified vision suggests a potency and wave of motivated action essential to coalescing and leveraging individual goodwill, harnessing its power and potential toward effective sustainability solutions.

See Stables and Bishop’s ( 2001 ) idea of defining environmental literacy by viewing the environment as “text.”

The climate change education literature also includes a nascent, but growing, discussion of collective-lens thinking and literacy. See, for example, Waldron et al. ( 2019 ), Mochizuki and Bryan ( 2015 ), and Kopnina ( 2016 ).

This conceptualization is similar to how some scholars describe collective health literacy (Berkman et al., 2010 ; Mårtensson and Hensing, 2012 ).

Adger, W.N. 2003. Social capital, collective action, and adaptation to climate change. Economic Geography 79: 387–404.

Article Google Scholar

Adger, W.N., T.P. Hughes, C. Folke, S.R. Carpenter, and J. Rockström. 2005. Social-ecological resilience to coastal disasters. Science 309: 1036–1039. https://doi.org/10.1126/science.1112122 .

Article CAS Google Scholar

Adler, P.S., and S.-W. Kwon. 2002. Social capital: Prospects for a new concept. Academy of Management Review 27: 17–40. https://doi.org/10.5465/amr.2002.5922314 .

Agrawal, A. 1995. Dismantling the divide between Indigenous and scientific knowledge. Development and Change 26: 413–439. https://doi.org/10.1111/j.1467-7660.1995.tb00560.x .

Aguilar, O.M. 2018. Examining the literature to reveal the nature of community EE/ESD programs and research. Environmental Education Research 24: 26–49. https://doi.org/10.1080/13504622.2016.1244658 .

Aguilar, O., A. Price, and M. Krasny. 2015. Perspectives on community environmental education. In M.C. Monroe & M.E. Krasny (Eds.), Across the spectrum: Resources for environmental educators (3rd edn., pp. 235–249). North American Association for Environmental Education.

Aldrich, D.P., and M.A. Meyer. 2015. Social capital and community resilience. American Behavioral Scientist 59: 254–269. https://doi.org/10.1177/0002764214550299 .

Amel, E., C. Manning, B. Scott, and S. Koger. 2017. Beyond the roots of human inaction: Fostering collective effort toward ecosystem conservation. Science 356: 275–279. https://doi.org/10.1126/science.aal1931 .

Ansell, C., and A. Gash. 2008. Collaborative governance in theory and practice. Journal of Public Administration Research and Theory 18: 543–571. https://doi.org/10.1093/jopart/mum032 .

Ardoin, N.M. 2006. Toward an interdisciplinary understanding of place: Lessons for environmental education. Canadian Journal of Environmental Education 11: 112–126.

Google Scholar

Ardoin, N.M., and J.E. Heimlich. 2021. Environmental learning in everyday life: Foundations of meaning and a context for change. Environmental Education Research 27: 1681–1699. https://doi.org/10.1080/13504622.2021.1992354 .

Ardoin, N.M., C. Clark, and E. Kelsey. 2013. An exploration of future trends in environmental education research. Environmental Education Research 19: 499–520. https://doi.org/10.1080/13504622.2012.709823 .

Armitage, D., F. Berkes, A. Dale, E. Kocho-Schellenberg, and E. Patton. 2011. Co-management and the co-production of knowledge: Learning to adapt in Canada’s Arctic. Global Environmental Change 21: 995–1004. https://doi.org/10.1016/j.gloenvcha.2011.04.006 .

Assis Neto, F.R., and C.A.S. Santos. 2018. Understanding crowdsourcing projects: A systematic review of tendencies, workflow, and quality management. Information Processing & Management 54: 490–506. https://doi.org/10.1016/j.ipm.2018.03.006 .

Bäckstrand, K. 2006. Multi-stakeholder partnerships for sustainable development: Rethinking legitimacy, accountability and effectiveness. European Environment 16: 290–306. https://doi.org/10.1002/eet.425 .

Baldwin, E., P. McCord, J. Dell’Angelo, and T. Evans. 2018. Collective action in a polycentric water governance system. Environmental Policy and Governance 28: 212–222. https://doi.org/10.1002/eet.1810 .

Bamberg, S., J. Rees, and S. Seebauer. 2015. Collective climate action: Determinants of participation intention in community-based pro-environmental initiatives. Journal of Environmental Psychology 43: 155–165. https://doi.org/10.1016/j.jenvp.2015.06.006 .

Bandura, A. 1977. Social learning theory . Englewood Cliffs: Prentice Hall.

Bandura, A. 2000. Exercise of human agency through collective efficacy. Current Directions in Psychological Science 9: 75–78. https://doi.org/10.1111/1467-8721.00064 .

Barron, B. 2006. Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development 49: 193–224. https://doi.org/10.1159/000094368 .

Barry, M.M., M. D’Eath, and J. Sixsmith. 2013. Interventions for improving population health literacy: Insights from a rapid review of the evidence. Journal of Health Communication 18: 1507–1522. https://doi.org/10.1080/10810730.2013.840699 .

Barton, A.C., and E. Tan. 2009. Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching 46: 50–73. https://doi.org/10.1002/tea.20269 .

Bear, J.B., and A.W. Woolley. 2011. The role of gender in team collaboration and performance. Interdisciplinary Science Reviews 36: 146–153. https://doi.org/10.1179/030801811X13013181961473 .

Bendor, J., and S.E. Page. 2019. Optimal team composition for tool-based problem solving. Journal of Economics & Management Strategy 28: 734–764. https://doi.org/10.1111/jems.12295 .

Berkes, F. 2007. Understanding uncertainty and reducing vulnerability: Lessons from resilience thinking. Natural Hazards 41: 283–295. https://doi.org/10.1007/s11069-006-9036-7 .

Berkes, F., and D. Jolly. 2002. Adapting to climate change: Social-ecological resilience in a Canadian western Arctic community. Conservation Ecology 5: 45.

Berkes, F., and H. Ross. 2013. Community resilience: Toward an integrated approach. Society & Natural Resources 26: 5–20. https://doi.org/10.1080/08941920.2012.736605 .

Berkes, F., M.K. Berkes, and H. Fast. 2007. Collaborative integrated management in Canada’s north: The role of local and traditional knowledge and community-based monitoring. Coastal Management 35: 143–162.

Berkman, N.D., T.C. Davis, and L. McCormack. 2010. Health literacy: What is it? Journal of Health Communication 15: 9–19. https://doi.org/10.1080/10810730.2010.499985 .

Bey, G., C. McDougall, and S. Schoedinger. 2020. Report on the NOAA office of education environmental literacy program community resilience education theory of change. National Oceanic and Atmospheric Administration . https://doi.org/10.25923/mh0g-5q69 .

Blumer, H. 1971. Social problems as collective behavior. Social Problems 18: 298–306.

Bodin, Ö. 2017. Collaborative environmental governance: Achieving collective action in social-ecological systems. Science . https://doi.org/10.1126/science.aan1114 .

Bolderdijk, J.W., M. Gorsira, K. Keizer, and L. Steg. 2013. Values determine the (in)effectiveness of informational interventions in promoting pro-environmental behavior. PLoS ONE 8: e83911. https://doi.org/10.1371/journal.pone.0083911 .

Brabham, D.C. 2013. Crowdsourcing . Cambridge: MIT Press.

Book Google Scholar

Braus, J. (Ed.). 2011. Tools of engagement: A toolkit for engaging people in conservation. NAAEE/Audubon. https://cdn.naaee.org/sites/default/files/eepro/resource/files/toolsofengagement.pdf .

Brieger, S.A. 2019. Social identity and environmental concern: The importance of contextual effects. Environment and Behavior 51: 828–855. https://doi.org/10.1177/0013916518756988 .

Briggs, J. 2005. The use of Indigenous knowledge in development: Problems and challenges. Progress in Development Studies 5: 99–114. https://doi.org/10.1191/1464993405ps105oa .

Briggs, J., and J. Sharp. 2004. Indigenous knowledges and development: A postcolonial caution. Third World Quarterly 25: 661–676. https://doi.org/10.1080/01436590410001678915 .

Bronfenbrenner, U. 1979. The ecology of human development: Experiments by nature and design . Cambridge: Harvard University Press.

Bruce, C., and P. Chesterton. 2002. Constituting collective consciousness: Information literacy in university curricula. International Journal for Academic Development 7: 31–40. https://doi.org/10.1080/13601440210156457 .

Byerly, H., A. Balmford, P.J. Ferraro, C.H. Wagner, E. Palchak, S. Polasky, T.H. Ricketts, A.J. Schwartz, et al. 2018. Nudging pro-environmental behavior: Evidence and opportunities. Frontiers in Ecology and the Environment 16: 159–168. https://doi.org/10.1002/fee.1777 .

Canadas, M.J., A. Novais, and M. Marques. 2016. Wildfires, forest management and landowners’ collective action: A comparative approach at the local level. Land Use Policy 56: 179–188. https://doi.org/10.1016/j.landusepol.2016.04.035 .

Carden, L., and W. Wood. 2018. Habit formation and change. Current Opinion in Behavioral Sciences 20: 117–122. https://doi.org/10.1016/j.cobeha.2017.12.009 .

Chan, M. 2016. Psychological antecedents and motivational models of collective action: Examining the role of perceived effectiveness in political protest participation. Social Movement Studies 15: 305–321. https://doi.org/10.1080/14742837.2015.1096192 .

Charnley, S., E.C. Kelly, and A.P. Fischer. 2020. Fostering collective action to reduce wildfire risk across property boundaries in the American West. Environmental Research Letters 15: 025007. https://doi.org/10.1088/1748-9326/ab639a .

Chawla, L., and D.F. Cushing. 2007. Education for strategic environmental behavior. Environmental Education Research 13: 437–452. https://doi.org/10.1080/13504620701581539 .

Chinn, D. 2011. Critical health literacy: A review and critical analysis. Social Science & Medicine 73: 60–67. https://doi.org/10.1016/j.socscimed.2011.04.004 .

Clark, C.R., J.E. Heimlich, N.M. Ardoin, and J. Braus. 2020. Using a Delphi study to clarify the landscape and core outcomes in environmental education. Environmental Education Research 26: 381–399. https://doi.org/10.1080/13504622.2020.1727859 .

Clarke, M., Z. Ma, S.A. Snyder, and K. Floress. 2021. Factors influencing family forest owners’ interest in community-led collective invasive plant management. Environmental Management 67: 1088–1099. https://doi.org/10.1007/s00267-021-01454-1 .

Cruz, A.R., S.T. Selby, and W.H. Durham. 2018. Place-based education for environmental behavior: A ‘funds of knowledge’ and social capital approach. Environmental Education Research 24: 627–647. https://doi.org/10.1080/13504622.2017.1311842 .

Curşeu, P.L., and H. Pluut. 2013. Student groups as learning entities: The effect of group diversity and teamwork quality on groups’ cognitive complexity. Studies in Higher Education 38: 87–103. https://doi.org/10.1080/03075079.2011.565122 .

Cutter, S.L., L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb. 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change 18: 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013 .

Dale, A., K. Vella, S. Ryan, K. Broderick, R. Hill, R. Potts, and T. Brewer. 2020. Governing community-based natural resource management in Australia: International implications. Land 9: 234. https://doi.org/10.3390/land9070234 .

de Moor, J., and M. Wahlström. 2019. Narrating political opportunities: Explaining strategic adaptation in the climate movement. Theory and Society 48: 419–451. https://doi.org/10.1007/s11186-019-09347-3 .

DeCaro, D., and M. Stokes. 2008. Social-psychological principles of community-based conservation and conservancy motivation: Attaining goals within an autonomy-supportive environment. Conservation Biology 22: 1443–1451.

Djenontin, I.N.S., and A.M. Meadow. 2018. The art of co-production of knowledge in environmental sciences and management: Lessons from international practice. Environmental Management 61: 885–903. https://doi.org/10.1007/s00267-018-1028-3 .

Duncan, L.E. 2018. The psychology of collective action. In The Oxford handbook of personality and social psychology , ed. K. Deaux and M. Snyder. Oxford: Oxford University Press.

Edwards, M., F. Wood, M. Davies, and A. Edwards. 2015. ‘Distributed health literacy’: Longitudinal qualitative analysis of the roles of health literacy mediators and social networks of people living with a long-term health condition. Health Expectations 18: 1180–1193. https://doi.org/10.1111/hex.12093 .

Emerson, K., T. Nabatchi, and S. Balogh. 2012. An integrative framework for collaborative governance. Journal of Public Administration Research and Theory 22: 1–29.

Engeström, Y. 2001. Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work 14: 133–156. https://doi.org/10.1080/13639080020028747 .

Ensor, J., and B. Harvey. 2015. Social learning and climate change adaptation: Evidence for international development practice. Wires Climate Change 6: 509–522. https://doi.org/10.1002/wcc.348 .

Fanta, V., M. Šálek, and P. Sklenicka. 2019. How long do floods throughout the millennium remain in the collective memory? Nature Communications 10: 1105. https://doi.org/10.1038/s41467-019-09102-3 .

Feinstein, N.W. 2018. Collective science literacy: A key to community science capacity [Conference session]. American Association for the Advancement of Science Annual Meeting, Austin, TX, USA https://d32ogoqmya1dw8.cloudfront.net/files/earthconnections/collective_science_literacy_key.pdf .

Feola, G. 2015. Societal transformation in response to global environmental change: A review of emerging concepts. Ambio 44: 376–390. https://doi.org/10.2139/ssrn.2689741 .

Fernandez-Gimenez, M.E., H.L. Ballard, and V.E. Sturtevant. 2008. Adaptive management and social learning in collaborative and community-based monitoring: A study of five community-based forestry organizations in the western USA. Ecology and Society 13: 15.

Folke, C., T. Hahn, P. Olsson, and J. Norberg. 2005. Adaptive governance of social-ecological systems. Annual Review of Environment and Resources 30: 441–473. https://doi.org/10.1146/annurev.energy.30.050504.144511 .

Freedman, D.A., K.D. Bess, H.A. Tucker, D.L. Boyd, A.M. Tuchman, and K.A. Wallston. 2009. Public health literacy defined. American Journal of Preventive Medicine 36: 446–451. https://doi.org/10.1016/j.amepre.2009.02.001 .

Freeman, R.B., and W. Huang. 2015. Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics 33: S289–S318.

Gadgil, M., F. Berkes, and C. Folke. 1993. Indigenous knowledge for biodiversity conservation. Ambio 22: 151–156.

Galaz, V., B. Crona, H. Österblom, P. Olsson, and C. Folke. 2012. Polycentric systems and interacting planetary boundaries—Emerging governance of climate change–ocean acidification–marine biodiversity. Ecological Economics 81: 21–32. https://doi.org/10.1016/j.ecolecon.2011.11.012 .

Geiger, S.M., M. Geiger, and O. Wilhelm. 2019. Environment-specific vs general knowledge and their role in pro-environmental behavior. Frontiers in Psychology 10: 718. https://doi.org/10.3389/fpsyg.2019.00718 .

Gifford, R., C. Kormos, and A. McIntyre. 2011. Behavioral dimensions of climate change: Drivers, responses, barriers, and interventions. Wires Climate Change 2: 801–827. https://doi.org/10.1002/wcc.143 .

González, N., L.C. Moll, and C. Amanti. 2006. Funds of knowledge: Theorizing practices in households, communities, and classrooms . New York: Routledge.

Gordon, D.M. 2019. Measuring collective behavior: An ecological approach. Theory in Biosciences . https://doi.org/10.1007/s12064-019-00302-5 .

Gould, R.K., N.M. Ardoin, J.M. Thomsen, and N. Wyman Roth. 2019. Exploring connections between environmental learning and behavior through four everyday-life case studies. Environmental Education Research 25: 314–340.

Graham, S., A.L. Metcalf, N. Gill, R. Niemiec, C. Moreno, T. Bach, V. Ikutegbe, L. Hallstrom, et al. 2019. Opportunities for better use of collective action theory in research and governance for invasive species management. Conservation Biology 33: 275–287. https://doi.org/10.1111/cobi.13266 .

Granovetter, M. 1978. Threshold models of collective behavior. American Journal of Sociology 83: 1420–1443.

Groulx, M., M.C. Brisbois, C.J. Lemieux, A. Winegardner, and L. Fishback. 2017. A role for nature-based citizen science in promoting individual and collective climate change action? A systematic review of learning outcomes. Science Communication 39: 45–76. https://doi.org/10.1177/1075547016688324 .

Gutiérrez, K.D., and B. Rogoff. 2003. Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher 32: 19–25. https://doi.org/10.3102/0013189X032005019 .

Guzys, D., A. Kenny, V. Dickson-Swift, and G. Threlkeld. 2015. A critical review of population health literacy assessment. BMC Public Health 15: 1–7. https://doi.org/10.1186/s12889-015-1551-6 .

Halbwachs, M. 1992. On collective memory (L. A. Coser, Ed. & Trans.). University of Chicago Press. (Original works published 1941 and 1952).

Heikkila, T., S. Villamayor-Tomas, and D. Garrick. 2018. Bringing polycentric systems into focus for environmental governance. Environmental Policy and Governance 28: 207–211. https://doi.org/10.1002/eet.1809 .

Heimlich, J.E., and N.M. Ardoin. 2008. Understanding behavior to understand behavior change: A literature review. Environmental Education Research 14: 215–237. https://doi.org/10.1080/13504620802148881 .

Hill, R., F.J. Walsh, J. Davies, A. Sparrow, M. Mooney, R.M. Wise, and M. Tengö. 2020. Knowledge co-production for Indigenous adaptation pathways: Transform post-colonial articulation complexes to empower local decision-making. Global Environmental Change 65: 102161. https://doi.org/10.1016/j.gloenvcha.2020.102161 .

Hollweg, K.S., J. Taylor, R.W. Bybee, T.J. Marcinkowski, W.C. McBeth, and P. Zoido. 2011. Developing a framework for assessing environmental literacy: Executive summary . North American Association for Environmental Education. https://cdn.naaee.org/sites/default/files/envliteracyexesummary.pdf .

Hovardas, T. 2020. A social learning approach for stakeholder engagement in large carnivore conservation and management. Frontiers in Ecology and Evolution 8: 436. https://doi.org/10.3389/fevo.2020.525278 .

Jagers, S.C., N. Harring, Å. Löfgren, M. Sjöstedt, F. Alpizar, B. Brülde, D. Langlet, A. Nilsson, et al. 2020. On the preconditions for large-scale collective action. Ambio 49: 1282–1296. https://doi.org/10.1007/s13280-019-01284-w .

Jordan, A., D. Huitema, H. van Asselt, and J. Forster. 2018. Governing climate change: Polycentricity in action? Cambridge: Cambridge University Press.

Jörg, T. 2011. New thinking in complexity for the social sciences and humanities: A generative, transdisciplinary approach . New York: Springer Science & Business Media.

Jost, J.T., J. Becker, D. Osborne, and V. Badaan. 2017. Missing in (collective) action: Ideology, system justification, and the motivational antecedents of two types of protest behavior. Current Directions in Psychological Science 26: 99–108. https://doi.org/10.1177/0963721417690633 .

Jull, J., A. Giles, and I.D. Graham. 2017. Community-based participatory research and integrated knowledge translation: Advancing the co-creation of knowledge. Implementation Science 12: 150. https://doi.org/10.1186/s13012-017-0696-3 .

Kahan, D.M., H. Jenkins-Smith, and D. Braman. 2011. Cultural cognition of scientific consensus. Journal of Risk Research 14: 147–174. https://doi.org/10.1080/13669877.2010.511246 .

Kania, J., and M. Kramer. 2011. Collective impact. Stanford Social Innovation Review 9: 36–41.

Karachiwalla, R., and F. Pinkow. 2021. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and Innovation Management 30: 563–584. https://doi.org/10.1111/caim.12454 .

Kellert, S.R., J.N. Mehta, S.A. Ebbin, and L.L. Lichtenfeld. 2000. Community natural resource management: Promise, rhetoric, and reality. Society & Natural Resources 13: 705–715.

Klein, J.T. 1990. Interdisciplinarity: History, theory, and practice . Detroit: Wayne State University Press.

Knapp, C.N., R.S. Reid, M.E. Fernández-Giménez, J.A. Klein, and K.A. Galvin. 2019. Placing transdisciplinarity in context: A review of approaches to connect scholars, society and action. Sustainability 11: 4899. https://doi.org/10.3390/su11184899 .

Koliou, M., J.W. van de Lindt, T.P. McAllister, B.R. Ellingwood, M. Dillard, and H. Cutler. 2020. State of the research in community resilience: Progress and challenges. Sustainable and Resilient Infrastructure 5: 131–151. https://doi.org/10.1080/23789689.2017.1418547 .

Kopnina, H. 2016. Of big hegemonies and little tigers: Ecocentrism and environmental justice. The Journal of Environmental Education 47: 139–150. https://doi.org/10.1080/00958964.2015.1048502 .

Krasny, M.E., M. Mukute, O. Aguilar, M.P. Masilela, and L. Olvitt. 2017. Community environmental education. In Urban environmental education review , ed. A. Russ and M.E. Krasny, 124–132. Ithaca: Cornell University Press.

Chapter Google Scholar

Lave, J. 1991. Situating learning in communities of practice.

Lave, J., and E. Wenger. 1991. Situated learning: Legitimate peripheral participation . Cambridge: Cambridge University Press.

Lee, S., and W.-M. Roth. 2003. Science and the “good citizen”: Community-based scientific literacy. Science, Technology, & Human Values 28: 403–424. https://doi.org/10.1177/0162243903028003003 .

Lévy, P., and R. Bononno. 1997. Collective intelligence: Mankind’s emerging world in cyberspace . New York: Perseus Books.

Lloyd, A. 2005. No man (or woman) is an island: Information literacy, affordances and communities of practice. The Australian Library Journal 54: 230–237. https://doi.org/10.1080/00049670.2005.10721760 .

Lopez-Gunn, E. 2003. The role of collective action in water governance: A comparative study of groundwater user associations in La Mancha aquifers in Spain. Water International 28: 367–378. https://doi.org/10.1080/02508060308691711 .

Lu, J.G., A.C. Hafenbrack, P.W. Eastwick, D.J. Wang, W.W. Maddux, and A.D. Galinsky. 2017. “Going out” of the box: Close intercultural friendships and romantic relationships spark creativity, workplace innovation, and entrepreneurship. Journal of Applied Psychology 102: 1091–1108. https://doi.org/10.1037/apl0000212 .

Lubeck, A., A. Metcalf, C. Beckman, L. Yung, and J. Angle. 2019. Collective factors drive individual invasive species control behaviors: Evidence from private lands in Montana, USA. Ecology and Society . https://doi.org/10.5751/ES-10897-240232 .

Mackay, C.M.L., M.T. Schmitt, A.E. Lutz, and J. Mendel. 2021. Recent developments in the social identity approach to the psychology of climate change. Current Opinion in Psychology 42: 95–101. https://doi.org/10.1016/j.copsyc.2021.04.009 .

Magis, K. 2010. Community resilience: An indicator of social sustainability. Society & Natural Resources 23: 401–416. https://doi.org/10.1080/08941920903305674 .

Manfredo, M.J., T.L. Teel, and A.M. Dietsch. 2016. Implications of human value shift and persistence for biodiversity conservation. Conservation Biology 30: 287–296. https://doi.org/10.1111/cobi.12619 .

Marshall, G.R., M.J. Coleman, B.M. Sindel, I.J. Reeve, and P.J. Berney. 2016. Collective action in invasive species control, and prospects for community-based governance: The case of serrated tussock ( Nassella trichotoma ) in New South Wales, Australia. Land Use Policy 56: 100–111. https://doi.org/10.1016/j.landusepol.2016.04.028 .

Mårtensson, L., and G. Hensing. 2012. Health literacy: A heterogeneous phenomenon: A literature review. Scandinavian Journal of Caring Sciences 26: 151–160. https://doi.org/10.1111/j.1471-6712.2011.00900.x .

Martin, C., and C. Steinkuehler. 2010. Collective information literacy in massively multiplayer online games. E-Learning and Digital Media 7: 355–365. https://doi.org/10.2304/elea.2010.7.4.355 .

Masson, T., and I. Fritsche. 2021. We need climate change mitigation and climate change mitigation needs the ‘We’: A state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences 42: 89–96. https://doi.org/10.1016/j.cobeha.2021.04.006 .

Massung, E., D. Coyle, K.F. Cater, M. Jay, and C. Preist. 2013. Using crowdsourcing to support pro-environmental community activism. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . https://doi.org/10.1145/2470654.2470708 .

McAdam, D. 2017. Social movement theory and the prospects for climate change activism in the United States. Annual Review of Political Science 20: 189–208. https://doi.org/10.1146/annurev-polisci-052615-025801 .

McAdam, D., and H. Boudet. 2012. Putting social movements in their place: Explaining opposition to energy projects in the United States, 2000–2005 . Cambridge University Press.

McKenzie-Mohr, D. 2011. Fostering sustainable behavior: An introduction to community-based social marketing (3rd edn.). New Society Publishers.

McKinley, D.C., A.J. Miller-Rushing, H.L. Ballard, R. Bonney, H. Brown, S.C. Cook-Patton, D.M. Evans, R.A. French, et al. 2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208: 15–28.

Miller, D.L. 2014. Introduction to collective behavior and collective action (3rd ed.). Waveland Press.

Mills, J., D. Gibbon, J. Ingram, M. Reed, C. Short, and J. Dwyer. 2011. Organising collective action for effective environmental management and social learning in Wales. The Journal of Agricultural Education and Extension 17: 69–83. https://doi.org/10.1080/1389224X.2011.536356 .

Mistry, J., and A. Berardi. 2016. Bridging Indigenous and scientific knowledge. Science 352: 1274–1275. https://doi.org/10.1126/science.aaf1160 .

Mochizuki, Y., and A. Bryan. 2015. Climate change education in the context of education for sustainable development: Rationale and principles. Journal of Education for Sustainable Development 9: 4–26. https://doi.org/10.1177/0973408215569109 .

Monroe, M.C. 2003. Two avenues for encouraging conservation behaviors. Human Ecology Review 10: 113–125.

Nasir, N.S., M.M. de Royston, B. Barron, P. Bell, R. Pea, R. Stevens, and S. Goldman. 2020. Learning pathways: How learning is culturally organized. In Handbook of the cultural foundations of learning , ed. N.S. Nasir, C.D. Lee, R. Pea, and M.M. de Royston, 195–211. Routledge.

National Academies of Sciences, Engineering, and Medicine. 2016. Science literacy: Concepts, contexts, and consequences . https://doi.org/10.17226/23595

National Research Council. 2015. Collective behavior: From cells to societies: Interdisciplinary research team summaries . National Academies Press. https://doi.org/10.17226/21737

Niemiec, R.M., N.M. Ardoin, C.B. Wharton, and G.P. Asner G.P. 2016. Motivating residents to combat invasive species on private lands: Social norms and community reciprocity. Ecology and Society , 21. https://doi.org/10.5751/ES-08362-210230

Niemiec, R.M., S. McCaffrey, and M.S. Jones. 2020. Clarifying the degree and type of public good collective action problem posed by natural resource management challenges. Ecology and Society 25: 30. https://doi.org/10.5751/ES-11483-250130 .

Norström, A.V., C. Cvitanovic, M.F. Löf, S. West, C. Wyborn, P. Balvanera, A.T. Bednarek, E.M. Bennett, et al. 2020. Principles for knowledge co-production in sustainability research. Nature Sustainability 3: 182–190. https://doi.org/10.1038/s41893-019-0448-2 .

Olick, J.K. 1999. Collective memory: The two cultures. Sociological Theory 17: 333–348. https://doi.org/10.1111/0735-2751.00083 .

Ostrom, E. 1990. Governing the commons: The evolution of institutions for collective action . Cambridge University Press.

Ostrom, E. 2000. Collective action and the evolution of social norms. Journal of Economic Perspectives 14: 137–158. https://doi.org/10.1257/jep.14.3.137 .

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325: 419–422. https://doi.org/10.1126/science.1172133 .

Ostrom, E. 2010. Polycentric systems for coping with collective action and global environmental change. Global Environmental Change 20: 550–557. https://doi.org/10.1016/j.gloenvcha.2010.07.004 .

Ostrom, E. 2012. Nested externalities and polycentric institutions: Must we wait for global solutions to climate change before taking actions at other scales? Economic Theory 49: 353–369. https://doi.org/10.1007/s00199-010-0558-6 .

Ostrom, E., and T.K. Ahn. 2009. The meaning of social capital and its link to collective action. In Handbook of social capital: The troika of sociology, political science and economics , ed. G.T. Svendsen and G.L.H. Svendsen, 17–35. Edward Elgar Publishing.

Papen, U. 2009. Literacy, learning and health: A social practices view of health literacy. Literacy and Numeracy Studies . https://doi.org/10.5130/lns.v0i0.1275 .

Park, R.E. 1927. Human nature and collective behavior. American Journal of Sociology 32: 733–741.

Paul, A.M. 2021. The extended mind: The power of thinking outside the brain . Boston: Mariner Books.

Pawilen, G.T. 2021. Integrating Indigenous knowledge in the Philippine elementary science curriculum: Integrating Indigenous knowledge. International Journal of Curriculum and Instruction 13: 1148–1160.

Prager, K. 2015. Agri-environmental collaboratives for landscape management in Europe. Current Opinion in Environmental Sustainability 12: 59–66. https://doi.org/10.1016/j.cosust.2014.10.009 .

Pretty, J., and H. Ward. 2001. Social capital and the environment. World Development 29: 209–227. https://doi.org/10.1016/S0305-750X(00)00098-X .

Putnam, R.D. 2020. Bowling alone: Revised and updated: The collapse and revival of American community . Anniversary. New York: Simon & Schuster.

Raymond, L. 2006. Cooperation without trust: Overcoming collective action barriers to endangered species protection. Policy Studies Journal 34: 37–57. https://doi.org/10.1111/j.1541-0072.2006.00144.x .

Reed, M.S., A.C. Evely, G. Cundill, I. Fazey, J. Glass, A. Laing, J. Newig, B. Parrish, et al. 2010. What is social learning? Ecology and Society 15: 12.

Reicher, S., R. Spears, and S.A. Haslam. 2010. The social identity approach in social psychology. In The SAGE handbook of identities (pp. 45–62). SAGE. https://doi.org/10.4135/9781446200889

Reid, A. 2019. Blank, blind, bald and bright spots in environmental education research. Environmental Education Research 25: 157–171. https://doi.org/10.1080/13504622.2019.1615735 .

Rogoff, B. 2003. The cultural nature of human development (Reprint edition) . Oxford: Oxford University Press.

Roth, C.E. 1992. Environmental literacy: Its roots, evolution and directions in the 1990s . http://eric.ed.gov/?id=ED348235

Roth, W.-M. 2003. Scientific literacy as an emergent feature of collective human praxis. Journal of Curriculum Studies 35: 9–23. https://doi.org/10.1080/00220270210134600 .

Roth, W.-M., and A.C. Barton. 2004. Rethinking scientific literacy . London: Psychology Press.

Roth, W.-M., and S. Lee. 2002. Scientific literacy as collective praxis. Public Understanding of Science 11: 33–56. https://doi.org/10.1088/0963-6625/11/1/302 .

Roth, W.-M., and S. Lee. 2004. Science education as/for participation in the community. Science Education 88: 263–291.

Roth, W.-M., and Y.-J. Lee. 2007. “Vygotsky’s neglected legacy”: Cultural-historical activity theory. Review of Educational Research 77: 186–232.

Sadoff, C.W., and D. Grey. 2005. Cooperation on international rivers: A continuum for securing and sharing benefits. Water International 30: 420–427.

Samerski, S. 2019. Health literacy as a social practice: Social and empirical dimensions of knowledge on health and healthcare. Social Science & Medicine 226: 1–8. https://doi.org/10.1016/j.socscimed.2019.02.024 .

Sawyer, R.K. 2014. The future of learning: Grounding educational innovation in the learning sciences. In The Cambridge handbook of the learning sciences , ed. R.K. Sawyer, 726–746. Cambridge: Cambridge University Press.

Saxe, J.G. n.d.. The blind man and the elephant . All Poetry. Retrieved October 6, 2020, from https://allpoetry.com/The-Blind-Man-And-The-Elephant .

Scheepers, D., and N. Ellemers. 2019. Social identity theory. In Social psychology in action: Evidence-based interventions from theory to practice , ed. K. Sassenberg and M.L.W. Vliek, 129–143. New York: Springer International Publishing.

Schipper, E.L.F., N.K. Dubash, and Y. Mulugetta. 2021. Climate change research and the search for solutions: Rethinking interdisciplinarity. Climatic Change 168: 18. https://doi.org/10.1007/s10584-021-03237-3 .

Schoerning, E. 2018. A no-conflict approach to informal science education increases community science literacy and engagement. Journal of Science Communication, Doi 10: 17030205.

Schultz, P.W. 2014. Strategies for promoting proenvironmental behavior: Lots of tools but few instructions. European Psychologist 19: 107–117. https://doi.org/10.1027/1016-9040/a000163 .

Sharifi, A. 2016. A critical review of selected tools for assessing community resilience. Ecological Indicators 69: 629–647. https://doi.org/10.1016/j.ecolind.2016.05.023 .

Sherrieb, K., F.H. Norris, and S. Galea. 2010. Measuring capacities for community resilience. Social Indicators Research 99: 227–247. https://doi.org/10.1007/s11205-010-9576-9 .

Singh, R.K., A. Singh, K.K. Zander, S. Mathew, and A. Kumar. 2021. Measuring successful processes of knowledge co-production for managing climate change and associated environmental stressors: Adaptation policies and practices to support Indian farmers. Journal of Environmental Management 282: 111679. https://doi.org/10.1016/j.jenvman.2020.111679 .

Sloman, S., and P. Fernbach. 2017. The knowledge illusion: Why we never think alone . New York: Riverhead Books.

Smelser, N.J. 2011. Theory of collective behavior . Quid Pro Books. (Original work published 1962).

Sørensen, K., S. Van den Broucke, J. Fullam, G. Doyle, J. Pelikan, Z. Slonska, H. Brand, and (HLS-EU) Consortium Health Literacy Project European. 2012. Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 12: 80. https://doi.org/10.1186/1471-2458-12-80 .

Spitzer, W., and J. Fraser. 2020. Advancing community science literacy. Journal of Museum Education 45: 5–15. https://doi.org/10.1080/10598650.2020.1720403 .

Stables, A., and K. Bishop. 2001. Weak and strong conceptions of environmental literacy: Implications for environmental education. Environmental Education Research 7: 89. https://doi.org/10.1080/13504620125643 .

Stern, M.J., R.B. Powell, and N.M. Ardoin. 2008. What difference does it make? Assessing outcomes from participation in a residential environmental education program. The Journal of Environmental Education 39: 31–43. https://doi.org/10.3200/JOEE.39.4.31-43 .

Stets, J.E., and P.J. Burke. 2000. Identity theory and social identity theory. Social Psychology Quarterly 63: 224–237. https://doi.org/10.2307/2695870 .

Sturmer, S., and B. Simon. 2004. Collective action: Towards a dual-pathway model. European Review of Social Psychology 15: 59–99. https://doi.org/10.1080/10463280340000117 .

Sullivan, A., A. York, D. White, S. Hall, and S. Yabiku. 2017. De jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). International Journal of the Commons 11: 171–199. https://doi.org/10.18352/ijc.676 .

Sunstein, C.R. 2008. Infotopia: How many minds produce knowledge . Oxford: Oxford University Press.

Surowiecki, J. 2005. The wisdom of crowds . New York: Anchor.

Swim, J.K., S. Clayton, and G.S. Howard. 2011. Human behavioral contributions to climate change: Psychological and contextual drivers. American Psychologist 66: 251–264.

Thaker, J., P. Howe, A. Leiserowitz, and E. Maibach. 2019. Perceived collective efficacy and trust in government influence public engagement with climate change-related water conservation policies. Environmental Communication 13: 681–699. https://doi.org/10.1080/17524032.2018.1438302 .

Tudge, J.R.H., and P.A. Winterhoff. 1993. Vygotsky, Piaget, and Bandura: Perspectives on the relations between the social world and cognitive development. Human Development 36: 61–81. https://doi.org/10.1159/000277297 .

Turner, R.H., and L.M. Killian. 1987. Collective behavior , 3rd ed. Englewood Cliffs: Prentice Hall.

Turner, R.H., N.J. Smelser, and L.M. Killian. 2020. Collective behaviour. In Encyclopedia Britannica . Encyclopedia Britannica, Inc. https://www.britannica.com/science/collective-behaviour .

van der Linden, S. 2014. Towards a new model for communicating climate change. In Understanding and governing sustainable tourism mobility , ed. S. Cohen, J. Higham, P. Peeters, and S. Gössling, 263–295. Milton Park: Routledge.

van Zomeren, M., T. Postmes, and R. Spears. 2008. Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin 134: 504–535. https://doi.org/10.1037/0033-2909.134.4.504 .

Vygotsky, L.S. 1980. Mind in society: The development of higher psychological processes . Cambridge: Harvard University Press.

Waldron, F., B. Ruane, R. Oberman, and S. Morris. 2019. Geographical process or global injustice? Contrasting educational perspectives on climate change. Environmental Education Research 25: 895–911. https://doi.org/10.1080/13504622.2016.1255876 .

Wals, A.E.J., M. Brody, J. Dillon, and R.B. Stevenson. 2014. Convergence between science and environmental education. Science 344: 583–584.

Wenger, E.C., and W.M. Snyder. 2000. Communities of practice: The organizational frontier. Harvard Business Review 78: 139–146.

Weschsler, D. 1971. Concept of collective intelligence. American Psychologist 26: 904–907. https://doi.org/10.1037/h0032223 .

Wheaton, M., A. Kannan, and N.M. Ardoin. 2018. Environmental literacy: Setting the stage (Environmental Literacy Brief, Vol. 1). Social Ecology Lab, Stanford University. https://ed.stanford.edu/sites/default/files/news/images/stanfordsocialecologylab-brief-1.pdf .

Wojcik, D.J., N.M. Ardoin, and R.K. Gould. 2021. Using social network analysis to explore and expand our understanding of a robust environmental learning landscape. Environmental Education Research 27: 1263–1283.

Wood, W., and D. Rünger. 2016. Psychology of habit. Annual Review of Psychology 67: 289–314. https://doi.org/10.1146/annurev-psych-122414-033417 .

Woolley, A.W., C.F. Chabris, A. Pentland, N. Hashmi, and T.W. Malone. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science 330: 686–688. https://doi.org/10.1126/science.1193147 .

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Acknowledgements

We are grateful to Maria DiGiano, Anna Lee, and Becca Shareff for their feedback and contributions to early drafts of this paper. We appreciate the research and writing assistance supporting this paper provided by various members of the Stanford Social Ecology Lab, especially: Brennecke Gale, Pari Ghorbani, Regina Kong, Naomi Ray, and Austin Stack.

This work was supported by a grant from the Pisces Foundation.

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Ardoin, N.M., Bowers, A.W. & Wheaton, M. Leveraging collective action and environmental literacy to address complex sustainability challenges. Ambio 52 , 30–44 (2023). https://doi.org/10.1007/s13280-022-01764-6

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