quantitative research in education

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Quantitative Research in Education A Primer

• Wayne K. Hoy - Ohio State University, USA
• Curt M. Adams - University of Oklahoma, USA
• Description

“ The book provides a reference point for beginning educational researchers to grasp the most pertinent elements of designing and conducting research… ”

— Megan Tschannen-Moran, The College of William & Mary

Quantitative Research in Education: A Primer, Second Edition is a brief and practical text designed to allay anxiety about quantitative research. Award-winning authors Wayne K. Hoy and Curt M. Adams first introduce readers to the nature of research and science, and then present the meaning of concepts and research problems as they dispel notions that quantitative research is too difficult, too theoretical, and not practical. Rich with concrete examples and illustrations, the Primer emphasizes conceptual understanding and the practical utility of quantitative methods while teaching strategies and techniques for developing original research hypotheses.

The Second Edition includes suggestions for empirical investigation and features a new section on self-determination theory, examples from the latest research, a concluding chapter illustrating the practical applications of quantitative research, and much more. This accessible Primer is perfect for students and researchers who want a quick understanding of the process of scientific inquiry and who want to learn how to effectively create and test ideas.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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“This text will definitely be useful in providing students with a solid orientation to research design particularly in quantitative research”

“Precision, precision, precision! I think this is a must have companion text for graduate students who have to complete a thesis or dissertation. The author does an outstanding job of cataloging and describing difficult research methods terms in a clear and concise way.”

“Greatest strength is the comprehensiveness of the treatment”

“A reference point for beginning educational researchers to grasp the most pertinent elements of designing and conducting research”

Provides all the essential information for quantitative research in a concise book.

A book on research in education but quite well can be accommodated into other social science areas. A great easy to follow publication especially if someone is new to statistical analysis.

There are two strong chapters in this publication that are clearer and more relevant that the sources presently being used by my students. Chapter 3 is particularly well written and clear and builds a progression in terms of understanding statistics. Chapter 4 is also effective however I would probably place this before Chapter 3. In terms of detail there is probably too much in Chapter 4 on Hypothesis whereas Chapter 3 could be developed perhaps by the inclusion of more examples.

Very helpful book that provides a basis for students undertaking education based research.

For those that are interested in doing research that is quantitative in nature, this book is useful, although we tend to advise a more qualitative approach. Therefore I can see myself dipping in and out of this book as it provides some good explanations and there is follow through. I would have welcomed more working examples as this would have concretised everything a lot more.

This is a good supplement to the research methods module, especially for those students who are entering into the field of education. The quantitative methods discussed are also transferrable to other subjects.

NEW TO THIS EDITION:    

• A new chapter devoted to the practical applications of education research uses the concepts of collective trust, organizational climate, and improvement science to illustrate the utility of a quantitative approach. It also offers guidelines for analyzing and improving the practice of research in education.
• New hypotheses found in a variety of research studies are available for readers to analyze and diagram.
• A new section on self-determination theory has been added to demonstrate the relation between theory and practice.
• A new section on self-regulatory climate gives readers an opportunity to explore an exciting new area that they are likely to encounter in practice.  
• A conceptual description of Hierarchical Linear Modeling (HLM) has been added to help readers understand statistical data organized at more than one level.    

KEY FEATURES:  

• Education-specific concrete examples bring concepts to life and engage readers with relevant, meaningful illustrations.
• Check Your Understanding exercises and questions assess the reader’s ability to understand, value, and apply the content of the chapter.  
• Strat egies and techniques for generating hypotheses help readers understand the process of creating their own hypotheses.
• Key Terms are highlighted in the text when they first appear and then summarized in a list at the end of the chapter to help reinforce key concepts.
• A Glossary concisely and clearly defines all the key terms in the text so readers have immediate access to ideas and concepts needing review.
• Charts throughout the text allow readers to select appropriate statistical techniques for given scenarios.
• The Diagramming Table (in Chapter 4) enables readers to diagram and dissect hypotheses by ensuring the key elements of a hypothesis are considered, analyzed, and understood.
• An Elements of a Proposal section (Appendix A) gives readers directions for developing a quantitative research plan and motivates readers to get started—the most difficult step for many.
• The A Few Writing Tips section (Appendix B) lists a number of salient writing suggestions to help readers avoid common mistakes found in formal writing.

Sample Materials & Chapters

For instructors, select a purchasing option.

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In This Article Expand or collapse the "in this article" section Quantitative Research Designs in Educational Research

Introduction, general overviews.

• Survey Research Designs
• Correlational Designs
• Other Nonexperimental Designs
• Randomized Experimental Designs
• Quasi-Experimental Designs
• Single-Case Designs
• Single-Case Analyses

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• Methodologies for Conducting Education Research
• Mixed Methods Research
• Multivariate Research Methodology
• Qualitative Data Analysis Techniques
• Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies
• Researcher Development and Skills Training within the Context of Postgraduate Programs
• Single-Subject Research Design
• Social Network Analysis
• Statistical Assumptions

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Quantitative Research Designs in Educational Research by James H. McMillan , Richard S. Mohn , Micol V. Hammack LAST REVIEWED: 24 July 2013 LAST MODIFIED: 24 July 2013 DOI: 10.1093/obo/9780199756810-0113

The field of education has embraced quantitative research designs since early in the 20th century. The foundation for these designs was based primarily in the psychological literature, and psychology and the social sciences more generally continued to have a strong influence on quantitative designs until the assimilation of qualitative designs in the 1970s and 1980s. More recently, a renewed emphasis on quasi-experimental and nonexperimental quantitative designs to infer causal conclusions has resulted in many newer sources specifically targeting these approaches to the field of education. This bibliography begins with a discussion of general introductions to all quantitative designs in the educational literature. The sources in this section tend to be textbooks or well-known sources written many years ago, though still very relevant and helpful. It should be noted that there are many other sources in the social sciences more generally that contain principles of quantitative designs that are applicable to education. This article then classifies quantitative designs primarily as either nonexperimental or experimental but also emphasizes the use of nonexperimental designs for making causal inferences. Among experimental designs the article distinguishes between those that include random assignment of subjects, those that are quasi-experimental (with no random assignment), and those that are single-case (single-subject) designs. Quasi-experimental and nonexperimental designs used for making causal inferences are becoming more popular in education given the practical difficulties and expense in conducting well-controlled experiments, particularly with the use of structural equation modeling (SEM). There have also been recent developments in statistical analyses that allow stronger causal inferences. Historically, quantitative designs have been tied closely to sampling, measurement, and statistics. In this bibliography there are important sources for newer statistical procedures that are needed for particular designs, especially single-case designs, but relatively little attention to sampling or measurement. The literature on quantitative designs in education is not well focused or comprehensively addressed in very many sources, except in general overview textbooks. Those sources that do include the range of designs are introductory in nature; more advanced designs and statistical analyses tend to be found in journal articles and other individual documents, with a couple exceptions. Another new trend in educational research designs is the use of mixed-method designs (both quantitative and qualitative), though this article does not emphasize these designs.

For many years there have been textbooks that present the range of quantitative research designs, both in education and the social sciences more broadly. Indeed, most of the quantitative design research principles are much the same for education, psychology, and other social sciences. These sources provide an introduction to basic designs that are used within the broader context of other educational research methodologies such as qualitative and mixed-method. Examples of these textbooks written specifically for education include Johnson and Christensen 2012 ; Mertens 2010 ; Arthur, et al. 2012 ; and Creswell 2012 . An example of a similar text written for the social sciences, including education that is dedicated only to quantitative research, is Gliner, et al. 2009 . In these texts separate chapters are devoted to different types of quantitative designs. For example, Creswell 2012 contains three quantitative design chapters—experimental, which includes both randomized and quasi-experimental designs; correlational (nonexperimental); and survey (also nonexperimental). Johnson and Christensen 2012 also includes three quantitative design chapters, with greater emphasis on quasi-experimental and single-subject research. Mertens 2010 includes a chapter on causal-comparative designs (nonexperimental). Often survey research is addressed as a distinct type of quantitative research with an emphasis on sampling and measurement (how to design surveys). Green, et al. 2006 also presents introductory chapters on different types of quantitative designs, but each of the chapters has different authors. In this book chapters extend basic designs by examining in greater detail nonexperimental methodologies structured for causal inferences and scaled-up experiments. Two additional sources are noted because they represent the types of publications for the social sciences more broadly that discuss many of the same principles of quantitative design among other types of designs. Bickman and Rog 2009 uses different chapter authors to cover topics such as statistical power for designs, sampling, randomized controlled trials, and quasi-experiments, and educational researchers will find this information helpful in designing their studies. Little 2012 provides a comprehensive coverage of topics related to quantitative methods in the social, behavioral, and education fields.

Arthur, James, Michael Waring, Robert Coe, and Larry V. Hedges, eds. 2012. Research methods & methodologies in education . Thousand Oaks, CA: SAGE.

Readers will find this book more of a handbook than a textbook. Different individuals author each of the chapters, representing quantitative, qualitative, and mixed-method designs. The quantitative chapters are on the treatment of advanced statistical applications, including analysis of variance, regression, and multilevel analysis.

Bickman, Leonard, and Debra J. Rog, eds. 2009. The SAGE handbook of applied social research methods . 2d ed. Thousand Oaks, CA: SAGE.

This handbook includes quantitative design chapters that are written for the social sciences broadly. There are relatively advanced treatments of statistical power, randomized controlled trials, and sampling in quantitative designs, though the coverage of additional topics is not as complete as other sources in this section.

Creswell, John W. 2012. Educational research: Planning, conducting, and evaluating quantitative and qualitative research . 4th ed. Boston: Pearson.

Creswell presents an introduction to all major types of research designs. Three chapters cover quantitative designs—experimental, correlational, and survey research. Both the correlational and survey research chapters focus on nonexperimental designs. Overall the introductions are complete and helpful to those beginning their study of quantitative research designs.

Gliner, Jeffrey A., George A. Morgan, and Nancy L. Leech. 2009. Research methods in applied settings: An integrated approach to design and analysis . 2d ed. New York: Routledge.

This text, unlike others in this section, is devoted solely to quantitative research. As such, all aspects of quantitative designs are covered. There are separate chapters on experimental, nonexperimental, and single-subject designs and on internal validity, sampling, and data-collection techniques for quantitative studies. The content of the book is somewhat more advanced than others listed in this section and is unique in its quantitative focus.

Green, Judith L., Gregory Camilli, and Patricia B. Elmore, eds. 2006. Handbook of complementary methods in education research . Mahwah, NJ: Lawrence Erlbaum.

Green, Camilli, and Elmore edited forty-six chapters that represent many contemporary issues and topics related to quantitative designs. Written by noted researchers, the chapters cover design experiments, quasi-experimentation, randomized experiments, and survey methods. Other chapters include statistical topics that have relevance for quantitative designs.

Johnson, Burke, and Larry B. Christensen. 2012. Educational research: Quantitative, qualitative, and mixed approaches . 4th ed. Thousand Oaks, CA: SAGE.

This comprehensive textbook of educational research methods includes extensive coverage of qualitative and mixed-method designs along with quantitative designs. Three of twenty chapters focus on quantitative designs (experimental, quasi-experimental, and single-case) and nonexperimental, including longitudinal and retrospective, designs. The level of material is relatively high, and there are introductory chapters on sampling and quantitative analyses.

Little, Todd D., ed. 2012. The Oxford handbook of quantitative methods . Vol. 1, Foundations . New York: Oxford Univ. Press.

This handbook is a relatively advanced treatment of quantitative design and statistical analyses. Multiple authors are used to address strengths and weaknesses of many different issues and methods, including advanced statistical tools.

Mertens, Donna M. 2010. Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods . 3d ed. Thousand Oaks, CA: SAGE.

This textbook is an introduction to all types of educational designs and includes four chapters devoted to quantitative research—experimental and quasi-experimental, causal comparative and correlational, survey, and single-case research. The author’s treatment of some topics is somewhat more advanced than texts such as Creswell 2012 , with extensive attention to threats to internal validity for some of the designs.

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Current Issues and Trends in Special Education: Research, Technology, and Teacher Preparation

ISBN : 978-1-84950-954-1 , eISBN : 978-1-84950-955-8

Publication date: 23 April 2010

Quantitative research is based on epistemic beliefs that can be traced back to David Hume. Hume and others who followed in his wake suggested that we can never directly observe cause and effect. Rather we perceive what is called “constant conjunction” or the regularities of relationships among events. Through observing these regularities, we can develop generalizable laws that, once established, describe predictable patterns that can be replicated with reliability. This form of reasoning involves studying groups of individuals and is often called nomothetic and is contrasted with idiographic research that focuses on the uniqueness of the individual. It is clear that large-scale experiments with random assignment to treatment are based on nomothetic models, as are quasi-experimental studies where intact groups of people (e.g., students in a particular classroom) are assigned to treatments.

Brigham, F.J. (2010), "Chapter 1 Quantitative research in education: Impact on evidence-based instruction", Obiakor, F.E. , Bakken, J.P. and Rotatori, n.F. (Ed.) Current Issues and Trends in Special Education: Research, Technology, and Teacher Preparation ( Advances in Special Education, Vol. 20 ), Emerald Group Publishing Limited, Leeds, pp. 3-17. https://doi.org/10.1108/S0270-4013(2010)0000020004

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Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Return to: 4140 Department of Educational Policy Studies    

A Graduate Certificate in Quantitative Research in Education is available from the College of Education and Human Development to eligible students enrolled in a doctoral program at Georgia State University. To earn the certificate, students must complete a minimum of eight 3-credit-hour doctoral-level quantitative research methods courses with a collective GPA of 3.5 or higher in those courses, with no grade lower than a B in any course to be counted toward the certificate.

In addition, students must successfully defend a quantitative research dissertation. A faculty member from the Research, Measurement and Statistics (RMS) program of the Department of Educational Policy Studies is required to be on the student’s dissertation committee.

Certificate Requirements

Four of the eight courses must be the following.

• EPRS 8530 - Quantitative Methods and Analysis in Education I 3 Credit Hours
• EPRS 8540 - Quantitative Methods and Analysis in Education II 3 Credit Hours
• EPRS 8550 - Quantitative Methods and Analysis in Education III 3 Credit Hours
• EPSF 9260 - Epistemology and Learning 3 Credit Hours

Remaining Four Courses

The remaining four courses must be doctoral-level quantitative method courses, bearing a call number of 8000 or higher, from the approved list of certificate courses. The list is updated yearly and available in the Department of Educational Policy Studies. A current list of these courses is provided below:

• EPRS 8535 - Critical Quantitative Methodology 3 Credit Hours
• EPRS 8600 - Statistical Programming and Data Management 3 Credit Hours
• EPRS 8610 - Advanced Computer Methods for Educational Research 3 Credit Hours
• EPRS 8620 - Program Evaluation I 3 Credit Hours
• EPRS 8660 - Bayesian Statistics 3 Credit Hours
• EPRS 8820 - Program Evaluation and Institutional Research 3 Credit Hours
• EPRS 8830 - Survey Research, Sampling Principles and Questionnaire Design 3 Credit Hours
• EPRS 8840 - Meta-Analysis 3 Credit Hours
• EPRS 8920 - Educational Measurement 3 Credit Hours
• EPRS 9350 - Introduction to Item Response Theory 3 Credit Hours
• EPRS 9360 - Advanced Item Response Theory 3 Credit Hours
• EPRS 9550 - Multivariate Analysis 3 Credit Hours
• EPRS 9560 - Structural Equation Modeling 3 Credit Hours
• EPRS 9570 - Hierarchical Linear Modeling I 3 Credit Hours
• EPRS 9571 - Hierarchical Linear Modeling II 3 Credit Hours
• EPRS 9830 - Research Ethics in the Professional and Social Sciences 3 Credit Hours
• EPRS 9900 - Research Design 3 Credit Hours
• PSYC 8430 - Psychological Research Statistics III 3 Credit Hours
• Other Quantitative Methods courses as approved by the Certificate Coordinator and the Department Chair

Eligibility

In order to be eligible to earn the certificate, students must:

• be enrolled in a doctoral program at Georgia State University
• have completed at least three courses from the College of Education and Human Development Doctoral Research Core, with a collective GPA in those courses of 3.5 or higher
• submit the application to the Department of Educational Policy Studies, with the endorsement of an RMS faculty member, prior to defending the prospectus.

Normal Time to Complete Program

Two additional semesters with 5 courses is estimated to be additional coursework which would be included within the doctoral program timeframe because it is likely that at least 4 courses meet both the certificate and doctoral requirements. The certificate program requires 8 courses. Courses beyond the three courses in the doctoral core which meet the certificate requirements may be included in the doctoral program of study for the student based on each student’s individualized program. Typically, at least one course meeting certificate requirements beyond the three from the doctoral core would be included in the student’s doctoral program for the student’s doctoral degree.

Learning Outcomes and Assessments

The RMS faculty evaluate students on the following learning outcomes for the certificate:

• Addresses the research question(s) with appropriate methodology
• Demonstrates knowledge of previous research and/or literature in the field
• Document adheres to the standards of quality writing
• Oral presentation communicates research in a manner appropriate for the material and audience
• Potential for contribution to the discipline
• Demonstrates knowledge in the field of the certificate program in the dissertation defense

On-Time Graduation Rates

On-time graduation rate is 100% based on the College of Education and Human Development doctoral program time-frame.

Program Costs

Current information is available at sfs.gsu.edu/resources/tuition/ .

Occupations

The Quantitative Research in Education Certificate aids in the preparation of students to be employed as:

• Education Teachers, Postsecondary (OC 25-1081)
• Social Scientists and Related Workers, All Other (SOC 19-3099)
• Statistics Professors (SOC 25-1022)
• Survey Research Professors (SOC 25-1069)
• Survey Researchers or Survey Methodologists (SOC 19-3022)
• Program Analysts (SOC 13-1111)
• Research Methodology and Quantitative Methods (SOC 15-2041) for Statisticians
• Research Methodology and Quantitative Methods (SOC 11-9199) for Managers and All Others

You can find additional information on the Standard Occupational Classification (SOC) and occupational profiles on these professions at the U.S. Bureau of Labor Statistics and O*Net web sites: www.bls.gov/soc/ and www.onetcenter.org/ .

Other Certificate Information

There is no state or accrediting agency that requires tracking placement rates. Students who receive a certificate also complete the doctoral program which is a higher credentialed program; thus, calculation of median loan debt for the certificate program is not required.

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Arts, Humanities, & Social Sciences

What predicts human behavior and how to change it

In the largest quantitative synthesis to date, dolores albarracín and her team dig through years of research on the science behind behavior change to determine the best ways to promote changes in behavior..

Pandemics, global warming, and rampant gun violence are all clear lessons in the need to move large groups of people to change their behavior. When a crisis hits, researchers, policymakers, health officials, and community leaders have to know how best to encourage people to change en masse and quickly. Each crisis leads to rehashing the same strategies, even those that have not worked in the past, due to the lack of definitive science of what interventions work across the board combined with well intended but erroneous intuitions.

To produce evidence on what determines and changes behavior, Dolores Albarracín and her colleagues from Penn’s Social Action Lab undertook a review of all of the available meta-analyses—a synthesis of the results from multiple studies—to determine what interventions work best when trying to change people’s behavior. What results is a new classification of predictors of behavior and a novel, empirical model for understanding the different ways to change behavior by targeting either individual or social/structural factors.

A new paper published in Nature Reviews Psychology reports that the strategies that people assume will work—like giving people accurate information or trying to change their beliefs—do not. At the same time, others like providing social support and tapping into individuals’ behavioral skills and habits as well as removing practical obstacles to behavior (e.g., providing health insurance to encourage health behaviors) can have more sizable impacts.

“Interventions targeting knowledge, general attitudes, beliefs, administrative and legal sanctions, and trustworthiness—these factors researchers and policymakers put so much weight on—are actually quite ineffective,” says Albarracín, the Alexandra Heyman Nash University Professor in the Annenberg School for Communication and director of the Science of Science Communication Division of the Annenberg Public Policy Center, who also has an appointment in Penn’s School of Arts & Sciences. “They have negligible effects.”

Unfortunately, many policies and reports are centered around goals like increasing vaccine confidence (an attitude) or curbing misinformation. Policymakers must look at evidence to determine what factors will return the investment, Albarracín says.

Co-author Javier Granados Samayoa, the Vartan Gregorian Postdoctoral Fellow at the Annenberg Public Policy Center , has noticed researchers’ tendency to target knowledge and beliefs when creating behavior change interventions.

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A quantitative study examining the effects of sleep quality on construction workers’ performance in the city of Jeddah, Saudi Arabia

• Original Article
• Open access
• Published: 14 May 2024

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• Ahmad Baghdadi   ORCID: orcid.org/0009-0009-1734-1390 1  

Poor sleep and related sleep disorders have been linked to subpar performance in sectors like health and education. Such sleep issues in the workforce negatively affect individual and organisational productivity. Despite this, the high-stress environment of the construction sector has been largely overlooked. This study delves into the role of sleep in construction field management and human resource practices, examining how a better understanding of workers' sleep patterns could enhance job performance. The research involved an online survey of 119 construction workers in the city of Jeddah in Saudi Arabia, focusing on their sleep duration and its impact on job performance, with data analysis conducted using SPSS software. The findings indicate that inadequate sleep influenced by factors such as dietary habits is significantly related to poor performance. The majority of workers are affected by poor sleep quality. The study suggests that construction management should implement health awareness campaigns to improve workers' responsiveness and awareness regarding sleep. It emphasises the need for management to develop strategies to increase sleep awareness and education in the construction industry, aiming to improve overall job performance.

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1 Introduction

The construction sector offers a significant platform for establishing sleep quality impacts among workers. It requires both physical energy and emotional concentration to perform. Thus, any lapse in sleep quality can disrupt the performance and engagement levels among the individual workers or their respective teams [ 1 ]. Construction workers experience the challenges of sleep deprivation, owing to the nature of their workplace and the responsibilities expected of them. This study explores the relevance or impacts of quality sleep on construction workers' performance and engagement levels in the city of Jeddah, Saudi Arabia. Through the assessment of the research on related subjects, it is evident that the construction sector is briefly explored. There needs to be more in the perspective of construction field management on how leadership can improve performance by targeting the aspects of quality sleep. Therefore, this study seeks to explore the components of sleep science, its relevance, and its significance in enhancing the performances of construction field workers. An ideal literature review reveals the contemporary issues, research gaps, implications and future studies that can be bridged to achieve the core expectations and goals. Achieving the objectives requires critically assessing past scholarly views, including theoretical frameworks and the sleep science that connect the variables.

Thus, this study aims to survey construction workers in the city of Jeddah, Saudi Arabia, to understand the impact of sleep quality on their performance. Thus, the purpose is to correlate sleep quality to the performances of the construction sector workers in light of whether they experience sleep deprivation or the right quality and the interventions the management can undertake. Specifically, this is an online survey of male construction workers to assess their sleep deprivation situation, which enables policy shift and management approaches towards improving performances. Similar studies have been conducted in the health, education, and related sectors to underscore the significance of ample and quality sleep on the performance of the employees. However, the main research question is: “ What are the specific impacts of sleep quality on the performance of construction workers in Jeddah, Saudi Arabia? ” Other questions include:

What are the specific activities, addictions, and patterns in your lifestyle or behaviour that contribute to sleep deprivation?

Are you aware of the impacts of quality sleep on your workplace performance? Does quality sleep matter to you at the workplace?

What recommendations can you give management to ensure policy change and a shift in management to address sleep deprivation among employees?

2 Literature review

Correlating sleep deprivation or quality to the performance and output of the workforce has been studied in the past, especially in the health and education sectors. In these areas, the primary motivation has been the performance of healthcare providers and students when subjected to sleep deprivation or inadequate sleep. Thus, a range of literature is available that offers background information on how this research can be contextualised, including the research gaps to be addressed through the execution of this study. Therefore, relevant literature that connects to the variables is researched through suitable databases, libraries, and sites that provide the relevant information, patterns, trends, and contemporaries for achieving the expected outcomes. The expectation is that these studies can help give the correct information, address the gap, and help with discussion regarding the impacts of sleep deprivation on the performance of construction sector workers.

2.1 Sleep deprivation and performances

Sleep deprivation is an expanding problem affecting general workers and construction workers. Sleep deprivation affects a worker's alertness, attention, and other cognitive functions. Nearly 93% of Indians are sleep-deprived, 58% of the people believe that their work is affected due to lack of sleep, and 87% of Indians accept that their health is affected due to lack of sleep. In the survey, the researcher found that 70% of college workers are affected by sleep deprivation, particularly chronic sleep deprivation, which has more negative consequences. However, some studies explored the subject of sleep deprivation in the context of establishing the risk factors or the behavioural patterns that cause it. For instance, sleep deprivation includes alcohol consumption and food intake as a worldwide problem, as evident in the study on college workers [ 2 ].

Signs and symptoms of sleep deprivation are often manifested in the workplace. For instance, the imminent results of sleep deprivation are biased feelings of fatigue [ 3 ]. There are connections between these symptoms and poor performance, based on the science of sleep, which is explored in many studies. For instance, a comparative analysis of the research findings in [ 4 , 5 ] shows the similarities in the context of alcohol consumption and how they impact the concentration levels among workers and create sleep deprivation. The two authors appreciate that alcoholism and substance and drug use are indeed risk factors and have the relevant symptoms to ascertain the claims. However, authors in [ 6 ] prioritised the behaviours such as taking stimulants to ensure people stay awake.

Sleep quality is directly correlated to the performances and well-being of workers, irrespective of their sectorial affiliations [ 7 ]. For instance, studies conducted in China and South Korea established that about 30% and 63% of the construction workers in these countries struggle with sleep quality [ 7 ]. Besides, the impacts of these trends on the scope of sleep routines, including the higher chances of workplace injuries and under-performances, are correlated. Work-related juries and possible fatalities are attributed to the poor or inadequate sleep quality that characterises workers in the construction sector. Similarly, construction workers and the relationship between sleep quality and their performances can be connected to their mental, emotional, and behavioural routines. Critical components of such studies demonstrate that construction workers who experience low or poor sleep quality are more likely to experience cognitive behaviour and rational abilities challenges resulting from continued sleep deprivation [ 8 ]. These outcomes underline the holistic impacts of sleep quality on performance and the health and wellness of the construction workers, which translate to their commitment and output.

2.2 Theoretical framework

This study is anchored on the significance of ample and quality sleep on workers' performance. Thus, sleep science is a crucial part of the study when exploring the available literature. For instance, Oswald's Restoration Theory of Sleep underlines the significance of getting the right amount of sleep in the workforce. This individual approach is widely applied in various sectors to underline the need for the consciousness of sleep routines and the science and studies that support the assumptions. For instance, Adeyanju explored the relevance of Oswald's theoretical framework in ascertaining students' academic performance [ 9 ]. The author used the theory's core tenets and characteristics, including that sleep is crucial for restoring the mind, body, and physical and emotional wellness. Sleep serves as a reset session, hence refreshing and restarting the mind to achieve the expected goals and objectives of learning and performance [ 9 ]. Moreover, Himashree et al. [ 10 ] correlated sleep and performance to understand the trends and how the workers are impacted [ 10 ]. The outcomes included a direct correlation, in which those with sleep deprivation showed little interest, physical synergy, and mental fatigue related to the loss of psychological capital to perform.

Restoration theory underlines the key activities individuals should take in a personal capacity to ensure optimal sleep routines. Oswald's view was that any disruption of the restoration process could have overarching mental and emotional health implications on the workers, impacting their engagement levels and performances [ 9 ]. Oswald demonstrated that disruptions to the restoration process when sleeping could have negative implications, including interference with people's ability to sleep, workers becoming more impulsive and emotional in decisions and behaviours, and having a health risk hazard as part of this strategic failure [ 10 ]. Oswald's view of sleep forms an integral part of this study as it explores how the disruptions to the restorative roles of sleep can impact the holistic nature of humans and their ultimate performances in the workplace.

2.3 Sleep deprivation and health risks

Poor sleep quality is a risk factor for mental fatigue. Studies have connected these variables to establish how inadequate sleep leads to low daytime job performance [ 11 ]. Such studies demonstrate the significance of quality sleep in daytime performances, including alertness, memory, learning, critical thinking, and effectiveness in task execution. A secondary data analysis in [ 12 ] demonstrates the factors impacting sleep quality among construction workers and how they generally lead to low performance, commitment, and motivation in their daytime commitments. Other factors explored include occupational injuries, decline in job competencies, and poor intergroup and team dynamics to accomplish tasks. This is because the affected workforce uses specific and personal methods to address the sleepiness they experience. These studies were evident in the findings across Southern India, which demonstrated the severity of inadequate sleep in the construction sector and their implications on the workforce [ 1 ]. As mentioned in other studies, quality sleep leads to higher employee self-efficacy, which translates into confidence, commitment, and performance. These factors interrelate to achieve the expected results and outcomes that directly impact the employees' commitment and the possible solutions for the workforce.

Sleep deprivation is a serious health risk factor that cuts across many sectors and areas of wellness. In the context of performances, some of the risk factors that lead to sleep deprivation and the low-quality trends beyond optimal are excessive alcohol and drug intakes, which continue to hurt the concentration and physical and mental abilities of the employees. These elements make it challenging for the workforce to gain the right mindset and physical abilities to complete construction tasks [ 4 ]. Besides, the nature of the construction workplace, including associated noise and disturbances from machine operations, can impact mental peace, which leads to sleep deprivation. According to a study on this subject matter among college workers in China, the devastating impacts of the sound environment in the construction fields can deter achieving proper sleep routines [ 2 ].

In some studies, sleep deprivation is associated with severe instances of worker frustration, limitations, and absenteeism [ 13 ]. These research works significantly impact the understanding of sleep quality and how they align with commitment and performance. Some studies have also explored the impact of overtime work routines on standard sleep patterns. For instance, Parkes' article established the implications of the overtime work model among offshore day workers, including those having sleep disorders that have long-term impacts [ 14 ]. The findings demonstrate the challenges in adjusting to the overtime work routines for either day or night-shift employees and how the administrators can address the implications on the employees' performances.

3 Methodology

Researching sleep and its implications in construction field management requires assessing workers' responsiveness to tasks. This research employs an online descriptive survey method to collect data on sleep deprivation or the quality of workers' performance in construction. Previous applications in similar aspects of the sleep domain and the project management or health contexts inform the choice and rationale for this method.

In this context, the online descriptive survey methodology is crucial in correlating sleep patterns and quality to the workers' performance or output in construction projects. This is relevant for project management and human resources to understand how to design training and awareness routines for employees to improve their sleep. In this regard, the construction workers within the city of Jeddah are sampled using stratified random sampling. They participated in this online survey to assess their responses to the correlation between the two variables. These methods are consistent and help assess the correlation based on the behaviour and responses of the construction workers.

3.1 Questionnaire and sample size

The data collected from this survey contains six sections, which the participants fill in. The initial part of the survey gathers basic respondent details such as gender, location, and age to validate the study's credibility. The second part collects demographic data on trends, behaviours, and personal characteristics, including height, weight, and lifestyle habits, aiding in categorising participants for sleep disorder research. The third section examines socio-demographic factors affecting bedtime, including time spent on social media and online activities. The fourth section queries educational background to assess awareness of sleep's impact. The fifth section probes health issues, including insomnia and mental and physical health status. Finally, the sixth section explores respondents' understanding of sleep quality, patterns, and quantity to identify trends.

These are meant to include as much reliable information as possible for the qualitative and quantitative analysis. The expectation is that the data collected directly answers the question of the sleep routines and performance of the construction workers at their sites based on the project management analytics and metrics.

3.2 Survey details and participants

The study was conducted in 2023 in Jeddah, Saudi Arabia. A stratified random sampling method was used to ensure that only the eligible participants were selected. This included the construction workers in the identified site, who were male members of this workforce. The rationale for choosing this sampling method is to ensure that only the eligible participants are selected and that the workers are included across age, gender, related variables and stratification elements. A total of 119 male participants working at the identified site were selected. The researcher then distributed the forms through the online platforms. The recruitment process of the participants included approaching their workplace, which included the human resource department for voluntary participation.

Consequently, the willing participants were informed, and their emails were given for the engagement. Despite the progressive and effective engagement with the employers, trust was challenged, especially with the job emails they considered private. However, this was a voluntary participation process, and upon explanation of the reasons for the job email, it was possible to convince the 119 to participate voluntarily.

The data is collected through the survey questionnaires that are administered online to enhance the efficiency of reaching all the participants. Besides, it gives them the flexibility of taking the study within their comfort before sending it back. The collected primary data across the six questionnaire sections are analysed through logistics regression to link sleep deprivation and construction worker performance outcomes. Besides, the socio-economic lines are used as the parameters of sleep that improve their responsiveness and suitability in understanding the outcomes. These methods are justified within the context of online surveys and how they apply to this study that investigates sleep patterns and construction worker performances. Significant themes are identified from the patterns of the responses, including how the mean, median, and regressions are contextualised to make sense of the variables and correlation.

Ethical considerations, including consent, voluntary participation, practical elaboration and explanation of the scope and methodologies of the study, and privacy and confidentiality elements are considered for this study. Moreover, the data collected from the participants were secured, especially as they were stored in the personal computer using unique passwords and access keys. Besides, an assurance of not using the responses for any other purpose rather than the study and deleting the emails after the completion was implemented. The data collection process, including filling and delivering the survey questionnaire, responded to the needs of the ethical considerations, including safety, privacy, and security.

4 Results and findings

4.1 data analysis.

This research aimed to investigate the undulations of construction workers' performance due to sleep deprivation or varying elements of sleep routines. Besides, the study sought the risk factors that impact sleep, especially among construction workers. Consequently, this research collected primary data from the participants, including those aligned to critical variables. Data analysis was carried out on demographic, socio-demographic, and sleep data. The statistical analysis used a statistical package for the social science software. Thus, the study employed SPSS (Statistical Package for the Social Sciences) based on its effectiveness as a software for social science and strategic statistical analysis methods. Consequently, the choice is based on the convenience of the tool in advancing data management, performing analysis, and improving data visualisation for comparative review.

The statistical analysis enables the identification of the relevant themes that connect the two variables, sleep deprivation and the performance of the construction workers. The statistical analysis began with descriptive analysis. All the regressions were started with demographic data and then gradually added up with socio-demographic data and sleeping problems before exams. The diversity of approaches employed in this study demonstrates the specificity of the subject matter in correlating the study's variables to understand performance and other aspects of construction worker's engagement.

4.2 Descriptive statistics

Table 1 shows the descriptive results of age and time spent on social media. This smoking habit is considered as a baseline for the results. The regression technique is used to identify the link between sleep deprivation and worker performance. The co-relation technique was used before regression analysis to filter the factors, as shown in Fig.  1 . The binary logistic regression was carried out in this study to identify the specific relationship between two variables. The descriptive statistics in Table  1 demonstrate the essential aspects, including the respondents' age, behaviour, browsing before sleeping routines, missing the workplace, substances use such as smoking, social media, and stress. The data collected are analysed in the context of statistics, standard error, and related contexts to get the right co-efficient for decision-making routines on sleep and construction workers' performances. These are represented in the statistical components, including the mean, standard deviation, and variance.

Relation between food intake and sleep timing

4.3 Regression analysis results

Table 2 represents the constructed demographic variables. The comparison between sleep factors and workers' performance includes coefficients, standard errors, Wald statistics, and degrees of freedom for the factors filtered in the co-relation technique. Where B  = constant, S.E  = standard error, W  = Wald Statistics, p  = Significance (P < 0.05). Table 2 summarises these outcomes to showcase the patterns, findings, and responses in the context of demographic variables applied in the study. These include age, gender, height, weight, smoking, and food intake time. These variables help in understanding the correlation between sleep routines and the performance of employees, especially in areas considered deprived.

Food intake is numbered in binary logistic regression. Figure  1 represents the relation between food intake and sleep: S1—food intake timing, S2 —sleeping time. S1 is coded as [(1) before 8 pm, (2) 8 pm–9 pm, (3) 9 pm–10 pm], and S2 as [(1) 4h–5h, (2) 6h–7h, (3) > 8h]. Table 3 shows that sleep deprivation was associated with worker outcomes. It also demonstrates the sleep deprivation variables, which inform crucial determinants of sleep deprivation as used in the study. However, Fig.  1 showcases the respondents' views on food intake time and sleeping routines. This is relevant in how variables relate to eating habits and their impacts on sleep time.

5 Discussion

The research findings and the literature review attempt to correlate sleep deprivation to the worker's performance in construction projects. The primary motivation is that this information is crucial for the project managers and construction personnel administrators to inform and influence the workers to adopt healthy sleep patterns. In this regard, the findings demonstrate the consistency with most of the literature reviewed in this study to establish the patterns. For instance, the descriptive statistics in Table  1 indicate the investigated variables to showcase the correlation. Consequently, the participant's age, behaviour, browsing before sleep patterns, missed workplace, smoking, social media, and stress are all responsible for an employee's performance. Generally, those with sleep challenges or deprivation tend to have negative connotations that align with their state of physical, mental, and emotional health. From the results, the study established that improper food intake and social media networking, impact construction workers' performances. These are the risk factors for creating the right environment for understanding sleep routines and their impacts on performance [ 15 ]. These results demonstrate the factors that can lead to sleep deprivation among construction workers, with the participants showcasing their implications on sleep quality.

5.1 Risk factors to sleep deprivation among workers

The findings highlight the impacts of issues with mental and emotional health risk factors, including substance use such as smoking, browsing and social media activities, and other patterns. Table 1 indicates the descriptive statistics on the key variables and the coefficient values on the implications of these aspects regarding the standard deviation, variance, and mean for each variable. Ideally, people's sleep challenges emerge from the behavioural patterns mentioned here, which hinder their sleep quality. This result underscores the increased sleep health promotion patterns that most projects and institutions use in their management practices to optimise the performances of their employees [ 16 ]. The primary motivation for using these aspects is the high illiteracy of the risk factors that impact quality sleep among the employees. Besides, Oswald's Restoration theory of sleep underscores its significance, informing the project management best practices [ 17 ]. The findings in Table  1 showcase statistical coefficients and their values that connect these variables and how the critical tenets of sleep theories can improve awareness and knowledge among construction workers. Similarly, the theoretical frameworks and the necessity for improving the rationale for knowledge and the significance of sleep are embedded here [ 10 ].

In the era of social media, online gaming, and other addictive mobile and digital apps, most people spend large chunks of their time engaged actively in these entertaining products. Consequently, most of them play till midnight or beyond, establishing the addictive nature of social media use. Thus, the socio-demographic variables indicate that most participants cited that social media use takes their time for sleeping, resulting in deprivation. These are also indicated in Table  3 , showing the impacts of social media use and related activities such as online gaming. These findings are unsurprising in the context of the literature reviewed on the subject. Sleep quality versus the quality of life domains has been studied, and these aspects are evident, including how the higher-education workers respond to these dynamics [ 18 ]. Only a tiny segment of the participants (P < 0.05) demonstrates the implications of social media use, mobile phones, and gaming as not impacting their lives and sleep routines. However, the construction human and personnel management team must develop the right educational tools to give the employees the right and necessary awareness of how their social media use at home or work might impact their sleep and restoration phases.

5.2 Behavioural and lifestyle patterns and their implications to sleep deprivation

In the context of binary logistics regression, Fig.  1 demonstrates how eating patterns can lead to sleeping challenges and the eventual performance implications. The primary purpose of this question on diet and eating habits and routines is to establish the correlation, including whether it impacts sleeping time and quality. For instance, the food intake timing can be early or late, which gives varying time to responsiveness and suitability. The correlation in these variables demonstrates that participants could take their food before 8 pm, 8–9 pm, or 9–10 pm, while S2 includes sleeping for 4–5 h, 6–7 h, or more than 8 h. These findings, as evident in Fig.  1 , demonstrate that the food intake timing, especially those who take their food late at night, have less sleep and are thus deprived. These aspects are further explored in assessing sleep deprivation in the context of curriculum exam performances and outcomes in Indian society [ 19 ]. The results show the implications of eating routines and timing of the workers in the construction sector.

The human resource management practices in the construction sector should devise a mechanism for monitoring the sleep patterns among the workers. This will help create the learning manuals and training guides to eradicate the risk factors, including the sleeping routines and associated outcomes. Specifically, Fig.  1 shows the implications of late eating patterns and engaging in disruptive activities such as social media use. Thus, despite the presence of workers or lack of absenteeism, they still show the aspects of daytime sleepiness, which hurt their engagement, concentration, and participation [ 20 ]. Performances are brain-based. Thus, any factor that impedes these aspects can easily ruin the performance of the construction workers in their respective responsibilities. The labour force in the present situation is often submerged in disruptive behaviours, especially the youths, which limits their productivity. This study demonstrates these factors' alignment with long-term sleepiness and implications [ 21 ]. The age, gender, and experience are crucial in defining their sleep routines and the understanding of the risk factors that characterise such situations. They are anticipated to have the adaptive mechanisms of ample and quality time that freshens their minds before they report to their respective workplaces.

5.3 Workplace environment as a risk to quality sleep

The nature of the workplace can impact sleep. Typically, operating the machines in construction work and the risk involved can be demanding and exhausting, which subjects the employees to being tired even with the available sleep time. Despite the evident significance of technology in improving workers' performances, there can be impediments. It takes time to learn, while the addictive trends of some apps, including mobile phones, can impact youthful employees. These components lead to sleep disorders and deprivation, while people tend to align with the technological needs and innovations in their areas. These include operating the machines and having safety patterns, which can impact the time found for resting [ 21 ]. The expectation is that the construction workers are impacted by the technological exposure that they have, their eating habits and patterns, and substances use such as smoking that directly impact their sleep times and cause deprivation [ 22 ].

5.4 Impacts of sleep and interventions by individual employs to overcome sleepiness

The findings show disparities in the appreciation of sleeping risk factors that impact individual work routines. Thus, most people appreciate the presence of such risk factors differently, calling for standardised mechanisms for human resource practitioners in the construction sector to embrace. There are varying degrees of alertness, woke, mood swings, and related manifestations of sleep disorder that different employees experience, which underline the need to use practical solutions. The analytics shown in Table  3 show these differences, showing the need for the stakeholders to have the right metrics and approaches to control the manifestation of sleep disorder in the workers and how it impacts their performances. These indicate the glaring challenges in having a uniform method of appreciating the presence of deprivation as a performance hindrance factor and developing an organisational level or sectorial approach to overcoming the outcomes. Dong and Zhang [ 23 ] defines these issues in the context of how each participant had their approaches to appreciating sleep disorders, even when working in the same sector or industry.

6 Conclusion and recommendations

This study primarily investigates the relationship between sleep deprivation and the performance of construction sector workers in Jeddah, Saudi Arabia. Utilising a sample of 119 construction workers, the study employs. Participants were surveyed online and divided into five categories to collect comprehensive data regarding their sleep patterns and their impact on work performance. This method aligns with established research standards and policies, ensuring the study's conclusions are robust and reliable. The study provides a comprehensive analysis of the impact of sleep quality on the performance of construction workers in Jeddah, Saudi Arabia. The main findings from the research indicate a significant correlation between poor sleep quality and decreased job performance among construction workers. The data analysis, particularly through binary logistic regression, highlighted specific lifestyle and behavioural patterns that contribute to sleep deprivation, such as irregular dietary habits, excessive use of social media, and engagement in shift work. These factors not only disrupt sleep patterns but also affect the workers' daily functioning and safety on the job site. The results underscore the critical need for targeted interventions that address these modifiable risk factors to enhance sleep quality among construction workers. The findings confirm a clear correlation between sleep deprivation and reduced performance among construction workers. The study underscores the importance of management strategies that assess and educate workers on optimising sleep routines to enhance overall job performance. Future research should focus on developing and implementing these management strategies effectively.

The research also delves into how personal behaviours, such as excessive engagement with social media and online gaming, can adversely affect sleep patterns. Management often unrecognises these factors, but they significantly affect worker performance. The study's results are significant for the construction sector's human resource management (HRM). They provide insights into how HRM can integrate sleep consciousness into their practices, enhancing worker performance through better sleep habits. Thus, recommendations can be applied to ensure that the study's main aspects and implications are introduced at the construction sector sites to achieve the expected outcomes, come as follows:

Improving sleep awareness among employees

Most employees are victims of their behavioural and lifestyle routines, which subject them to sleeping disorders without their awareness. This lack of appreciation for the significance of sleep is a significant risk factor for their performance, as established in the study's findings. Consequently, management should devise effective mechanisms that create a dedicated learning niche for the employees. This includes training them on the core aspects of sleep awareness and consciousness, enhancing their overall well-being and productivity.

Promote personal behaviour changes towards sleep quality

These elements directly impact the employees' performances, yet it is often considered a private matter whether they sleep late or early. Thus, despite its controversial appeal, this recommendation seeks to identify the individual challenges among the workforce and enforce the behavioural or lifestyle changes that will make them appreciate the outcomes. By addressing these personal habits, the initiative aims to enhance overall sleep quality and improve work performance and health.

Implementing regular health and safety audits

Regular health and safety audits should be conducted to enhance workplace productivity further and ensure the health and safety of construction workers. These audits will help identify potential hazards that could lead to accidents or exacerbate sleep deprivation issues among workers. By systematically assessing the work environment and practices, management can implement necessary changes to mitigate risks. This proactive approach ensures compliance with safety regulations and demonstrates a commitment to worker welfare, which can boost morale and productivity.

Development of a comprehensive wellness program

Another recommendation is developing and implementing a comprehensive wellness program focusing on sleep health. This program should offer resources and support for various aspects of health, including mental health, physical fitness, nutritional advice, and sleep education. Workshops and seminars can be organised to educate workers on the importance of maintaining a healthy lifestyle and how it impacts their work performance. Regular health screenings can also help detect any underlying issues affecting sleep, such as sleep apnea or chronic stress. By investing in employees' overall well-being, companies can improve productivity and reduce absenteeism and turnover rates.

6.1 Limitations and recommendations for future research

The study's main limitation is using male-only participants, creating a discriminative framework. A men-only survey gives insights from their perspective in an industry with female workers and administrators. These limitations can adversely affect the study's generalisation and its long-term implications. The reason behind the selection of male workers is that most of the construction in Saudi Arabia are men. This is particularly true for the projects selected for this study. However, future studies should address the roles of HRM in influencing sleep consciousness and awareness through organisational learning to impact performances. This should be a priority area for the construction work administrators to ensure they inform and educate the workers on the various aspects of sleep science and its importance.

Availability of data and materials

All data and materials are available as required.

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Baghdadi, A. A quantitative study examining the effects of sleep quality on construction workers’ performance in the city of Jeddah, Saudi Arabia. J. Umm Al-Qura Univ. Eng.Archit. (2024). https://doi.org/10.1007/s43995-024-00065-1

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Received : 19 April 2024

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Published : 14 May 2024

DOI : https://doi.org/10.1007/s43995-024-00065-1

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How do the indices based on the EAT-Lancet recommendations measure adherence to healthy and sustainable diets? A comparison of measurement performance in adults from a French national survey

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Measuring adherence to EAT-Lancet recommendations for healthy and sustainable diets is challenging, leading to diverse methods and a lack of consensus on standardized metrics. Available indices vary mainly in scoring systems, food components, units, energy adjustments, and cut-off points. We aimed to evaluate and compare the measurement performance of six dietary indices for assessing adherence to EAT-Lancet reference diet. Food consumption data of 1,723 adults were obtained from the French Third Individual and National Study on Food Consumption Survey (INCA3, 2014-2015). Sociodemographic, nutritional, and environmental data were used to assess the validity and reliability of dietary indices. Results showed that the four indices assessing their food components with quantitative scoring captured dietary variability, were less dependent on energy intake and converged to a large extent with nutritional indicators. While the two binary indices showed a stronger correlation with environmental indicators, one quantitative index converged with both domains. Indices had valid unidimensional structures, meaning that the combination of food components within each index accurately reflects the same construct and supporting the use of total scores. Furthermore, the indices differed between sociodemographic groups, demonstrating concurrent criterion validity. Higher scores were associated with higher nutritional quality and lower environmental impact, but with unfavourable results for zinc intake, vitamin B12 and water use. A low concordance rate (from 32% to 43%) indicated that indices categorized individuals differently. Researchers must align study objectives with the applicability, assumptions, and functional significance of chosen indices. Indices using quantitative scoring allow a global understanding of dietary health and sustainability, being advantageous in precision-focused research, such as clinical trials or epidemiological research. Conversely, indices based on binary scoring offer a simplified perspective, being valuable tools for surveys, observational studies, and public health. Recognizing their strengths and limitations is crucial for a comprehensive assessment of diets and understanding their implications.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study is part of the FEAST (Food systems that support transitions to healthy and sustainable diets) project funded by the European Union's Horizon Europe research and innovation program under grant agreement number 101060536 and by Innovate UK under grant number 10041509. Swiss participant in FEAST is supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 22.00156. More details in https://www.feast2030.eu/.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The INCA3 study was carried out in accordance with the Declaration of Helsinki and received approval from the French Data Protection Authority (Decision DR 2013-228) on May 2, 2013, following a favorable opinion from the Advisory Committee on Information Processing in Health Research on January 30, 2013 (Opinion 13.055). Verbal informed consent was obtained from all participants before their voluntary inclusion in the study. Verbal consent was witnessed and formally recorded.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

Data from the Third French Individual and National Food Consumption Survey (INCA3) in available on the data.gouv.fr platform. Data on the environmental impacts of foods consumed in France is available on the agribalyse.ademe.fr platform.

https://www.data.gouv.fr/en/datasets/donnees-de-consommations-et-habitudes-alimentaires-de-letude-inca-3/

https://agribalyse.ademe.fr/

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