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How do you Write the Rationale for Research?

• By DiscoverPhDs
• October 21, 2020

What is the Rationale of Research?

The term rationale of research means the reason for performing the research study in question. In writing your rational you should able to convey why there was a need for your study to be carried out. It’s an important part of your research paper that should explain how your research was novel and explain why it was significant; this helps the reader understand why your research question needed to be addressed in your research paper, term paper or other research report.

The rationale for research is also sometimes referred to as the justification for the study. When writing your rational, first begin by introducing and explaining what other researchers have published on within your research field.

Having explained the work of previous literature and prior research, include discussion about where the gaps in knowledge are in your field. Use these to define potential research questions that need answering and explain the importance of addressing these unanswered questions.

The rationale conveys to the reader of your publication exactly why your research topic was needed and why it was significant . Having defined your research rationale, you would then go on to define your hypothesis and your research objectives.

Final Comments

Defining the rationale research, is a key part of the research process and academic writing in any research project. You use this in your research paper to firstly explain the research problem within your dissertation topic. This gives you the research justification you need to define your research question and what the expected outcomes may be.

Statistical treatment of data is essential for all researchers, regardless of whether you’re a biologist, computer scientist or psychologist, but what exactly is it?

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In the UK, a dissertation, usually around 20,000 words is written by undergraduate and Master’s students, whilst a thesis, around 80,000 words, is written as part of a PhD.

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Scientific misconduct can be described as a deviation from the accepted standards of scientific research, study and publication ethics.

The term monotonic relationship is a statistical definition that is used to describe the link between two variables.

Amy recently entered her third and final year of her PhD at the University of Strathclyde. Her research has focussed on young people’s understanding of mental health stigma in Scotland.

Prof Raghupathi gained his PhD in Biochemistry and Molecular Biology from Virginia Commonwealth University in 1991. He is now a professor in the Department of Neurobiology & Anatomy at Drexel University College of Medicine.

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Rationale for the Study

It is important for you to be able to explain the importance of the research you are conducting by providing valid arguments. Rationale for the study, also referred to as justification for the study, is reason why you have conducted your study in the first place. This part in your paper needs to explain uniqueness and importance of your research. Rationale for the study needs to be specific and ideally, it should relate to the following points:

1. The research needs to contribute to the elimination of a gap in the literature. Elimination of gap in the present literature is one of the compulsory requirements for your study. In other words, you don’t need to ‘re-invent the wheel’ and your research aims and objectives need to focus on new topics. For example, you can choose to conduct an empirical study to assess the implications of COVID-19 pandemic on the numbers of tourists visitors in your city. This might be previously undressed topic, taking into account that COVID-19 pandemic is a relatively recent phenomenon.

Alternatively, if you cannot find a new topic to research, you can attempt to offer fresh perspectives on existing management, business or economic issues. For example, while thousands of studies have been previously conducted to study various aspects of leadership, this topic as far from being exhausted as a research area. Specifically, new studies can be conducted in the area of leadership to analyze the impacts of new communication mediums such as TikTok, and other social networking sites on leadership practices.

You can also discuss the shortcomings of previous works devoted to your research area. Shortcomings in previous studies can be divided into three groups:

a) Methodological limitations . Methodology employed in previous study may be flawed in terms of research design, research approach or sampling.

b) Contextual limitations . Relevance of previous works may be non-existent for the present because external factors have changed.

c) Conceptual limitations . Previous studies may be unjustifiably bound up to a particular model or an ideology.

While discussing the shortcomings of previous studies you should explain how you are going to correct them. This principle is true to almost all areas in business studies i.e. gaps or shortcomings in the literature can be found in relation to almost all areas of business and economics.

2. The research can be conducted to solve a specific problem. It helps if you can explain why you are the right person and in the right position to solve the problem. You have to explain the essence of the problem in a detailed manner and highlight practical benefits associated with the solution of the problem. Suppose, your dissertation topic is “a study into advantages and disadvantages of various entry strategies into Chinese market”. In this case, you can say that practical implications of your research relates to assisting businesses aiming to enter Chinese market to do more informed decision making.

Alternatively, if your research is devoted to the analysis of impacts of CSR programs and initiatives on brand image, practical contributions of your study would relate to contributing to the level of effectiveness of CSR programs of businesses.

Additional examples of studies that can assist to address specific practical problems may include the following:

• A study into the reasons of high employee turnover at Hanson Brick
• A critical analysis of employee motivation problems at Esporta, Finchley Road, London
• A research into effective succession planning at Microsoft
• A study into major differences between private and public primary education in the USA and implications of these differences on the quality of education

However, it is important to note that it is not an obligatory for a dissertation   to be associated with the solution of a specific problem. Dissertations can be purely theory-based as well. Examples of such studies include the following:

• Born or bred: revising The Great Man theory of leadership in the 21 st century
• A critical analysis of the relevance of McClelland’s Achievement theory to the US information technology industry
• Neoliberalism as a major reason behind the emergence of the global financial and economic crisis of 2007-2009
• Analysis of Lewin’s Model of Change and its relevance to pharmaceutical sector of France

3. Your study has to contribute to the level of professional development of the researcher . That is you. You have to explain in a detailed manner in what ways your research contributes to the achievement of your long-term career aspirations.

For example, you have selected a research topic of “ A critical analysis of the relevance of McClelland’s Achievement theory in the US information technology industry ”.  You may state that you associate your career aspirations with becoming an IT executive in the US, and accordingly, in-depth knowledge of employee motivation in this industry is going to contribute your chances of success in your chosen career path.

Therefore, you are in a better position if you have already identified your career objectives, so that during the research process you can get detailed knowledge about various aspects of your chosen industry.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline.

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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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• More Social Media from NIH

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How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your research topic
• Capture reader interest
• Summarize existing research
• Position your own approach
• Define your specific research problem and problem statement
• Highlight the novelty and contributions of the study
• Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

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Table of Contents

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The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

• Scope: The topic covered in the research paper
• Context: Background of your topic
• Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

• It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
• It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
• It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
• It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

• What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
• What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
• What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

• Highlight the importance of the research field or topic
• Describe the background of the topic
• Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

• Reveal a gap in existing research or oppose an existing assumption
• Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

• State the purpose of your study
• Highlight the key characteristics of your study
• Describe important results
• Highlight the novelty of the study.
• Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

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With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

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You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

• Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
• Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
• Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
• Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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

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

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

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Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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The Importance of Research Design: A Comprehensive Guide

Research design plays a crucial role in conducting scientific studies and gaining meaningful insights. A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. This comprehensive guide will provide an in-depth understanding of research design, its key components, different types, and its role in scientific inquiry. Furthermore, it will discuss the necessary steps in developing a research design and highlight some of the challenges that researchers commonly face.

Table of Contents

Understanding research design.

Research design refers to the overall plan or strategy that outlines how a study is conducted. It serves as a blueprint for researchers, guiding them in their investigation, and helps ensure that the study objectives are met. Understanding research design is essential for researchers to effectively gather and analyze data to answer research questions.

When embarking on a research study, researchers must carefully consider the design they will use. The design determines the structure of the study, including the research questions, data collection methods, and analysis techniques. It provides clarity on how the study will be conducted and helps researchers determine the best approach to achieve their research objectives. A well-designed study increases the chances of obtaining valid and reliable results.

Definition and Purpose of Research Design

Research design is the framework that outlines the structure of a study, including the research questions, data collection methods, and analysis techniques. It provides a systematic approach to conducting research and ensures that all aspects of the study are carefully planned and executed.

The purpose of research design is to provide a clear roadmap for researchers to follow. It helps them define the research questions they want to answer and identify the variables they will study. By clearly defining the purpose of the study, researchers can ensure that their research design aligns with their objectives.

Key Components of Research Design

A research design consists of several key components that influence the study’s validity and reliability. These components include the research questions, variables and operational definitions, sampling techniques, data collection methods, and statistical analysis procedures.

The research questions are the foundation of any study. They guide the entire research process and help researchers focus their efforts. By formulating clear and concise research questions, researchers can ensure that their study addresses the specific issues they want to investigate.

Variables and operational definitions are also crucial components of research design. Variables are the concepts or phenomena that researchers want to measure or study. Operational definitions provide a clear and specific description of how these variables will be measured or observed. By clearly defining variables and their operational definitions, researchers can ensure that their study is consistent and replicable.

Sampling techniques play a vital role in research design as well. Researchers must carefully select the participants or samples they will study to ensure that their findings are generalizable to the larger population. Different sampling techniques, such as random sampling or purposive sampling, can be used depending on the research objectives and constraints.

Data collection methods are another important component of research design. Researchers must decide how they will collect data, whether through surveys, interviews, observations, or experiments. The choice of data collection method depends on the research questions and the type of data needed to answer them.

Finally, statistical analysis procedures are used to analyze the collected data and draw meaningful conclusions. Researchers must determine the appropriate statistical tests or techniques to use based on the nature of their data and research questions. The choice of statistical analysis procedures ensures that the data is analyzed accurately and that the results are valid and reliable.

Types of Research Design

Research design encompasses various types that researchers can choose depending on their research goals and the nature of the phenomenon being studied. Understanding the different types of research design is essential for researchers to select the most appropriate approach for their study.

When embarking on a research project, researchers must carefully consider the design they will employ. The design chosen will shape the entire study, from the data collection process to the analysis and interpretation of results. Let’s explore some of the most common types of research design in more detail.

Experimental Design

Experimental design involves manipulating one or more variables to observe their effect on the dependent variable. This type of design allows researchers to establish cause-and-effect relationships between variables by controlling for extraneous factors. Experimental design often relies on random assignment and control groups to minimize biases.

Imagine a group of researchers interested in studying the effects of a new teaching method on student performance. They could randomly assign students to two groups: one group would receive instruction using the new teaching method, while the other group would receive instruction using the traditional method. By comparing the performance of the two groups, the researchers can determine whether the new teaching method has a significant impact on student learning.

Experimental design provides a strong foundation for making causal claims, as it allows researchers to control for confounding variables and isolate the effects of the independent variable. However, it may not always be feasible or ethical to manipulate variables, leading researchers to explore alternative designs.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Introduction to experimental methods
• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

Non-Experimental Design

Non-experimental design is used when it is not feasible or ethical to manipulate variables. This design relies on naturally occurring variations in data and focuses on observing and describing relationships between variables. Non-experimental design can be useful for exploratory research or when studying phenomena that cannot be controlled, such as human behavior.

For instance, researchers interested in studying the relationship between socioeconomic status and health outcomes may collect data from a large sample of individuals and analyze the existing differences. By examining the data, they can determine whether there is a correlation between socioeconomic status and health, without manipulating any variables.

Non-experimental design allows researchers to study real-world phenomena in their natural setting, providing valuable insights into complex social, psychological, and economic processes. However, it is important to note that non-experimental designs cannot establish causality, as there may be other variables at play that influence the observed relationships.

Quasi-Experimental Design

Quasi-experimental design resembles experimental design but lacks the element of random assignment. In situations where random assignment is not possible or practical, researchers can utilize quasi-experimental designs to gather data and make inferences. However, caution must be exercised when drawing causal conclusions from quasi-experimental studies.

Consider a scenario where researchers are interested in studying the effects of a new drug on patient recovery time. They cannot randomly assign patients to receive the drug or a placebo due to ethical considerations. Instead, they can compare the recovery times of patients who voluntarily choose to take the drug with those who do not. While this design allows for data collection and analysis, it is important to acknowledge that other factors, such as patient motivation or severity of illness, may influence the observed outcomes.

Quasi-experimental designs are valuable when experimental designs are not feasible or ethical. They provide an opportunity to explore relationships and gather data in real-world contexts. However, researchers must be cautious when interpreting the results, as causal claims may be limited due to the lack of random assignment.

By understanding the different types of research design, researchers can make informed decisions about the most appropriate approach for their study. Each design offers unique advantages and limitations, and the choice depends on the research question, available resources, and ethical considerations. Regardless of the design chosen, rigorous methodology and careful data analysis are crucial for producing reliable and valid research findings.

The Role of Research Design in Scientific Inquiry

A well-designed research study enhances the validity and reliability of the findings. Research design plays a crucial role in ensuring the scientific rigor of a study and facilitates the replication of studies by other researchers. Understanding the role of research design in scientific inquiry is vital for researchers to conduct impactful and robust research.

Ensuring Validity and Reliability

Research design plays a critical role in ensuring the validity and reliability of the study’s findings. Validity refers to the degree to which the study measures what it intends to measure, while reliability pertains to the consistency and stability of the results. Through careful consideration of the research design, researchers can minimize potential biases and increase the accuracy of their measurements.

Facilitating Replication of Studies

A robust research design allows for the replication of studies by other researchers. Replication plays a vital role in the scientific process as it helps confirm the validity and generalizability of research findings. By clearly documenting the research design, researchers enable others to reproduce the study and validate the results, thereby contributing to the cumulative knowledge in a field.

Steps in Developing a Research Design

Developing a research design involves a systematic process that includes several important steps. Researchers need to carefully consider each step to ensure that their study is well-designed and capable of addressing their research questions effectively.

Identifying Research Questions

The first step in developing a research design is to identify and define the research questions or hypotheses. Researchers need to clearly articulate what they aim to investigate and what specific information they want to gather. Clear research questions provide guidance for the subsequent steps in the research design process.

Selecting Appropriate Design Type

Once the research questions are identified, researchers need to select the most appropriate type of research design. The choice of design depends on various factors, including the research goals, the nature of the research questions, and the available resources. Careful consideration of these factors is crucial to ensure that the chosen design aligns with the study objectives.

Determining Data Collection Methods

After selecting the research design, researchers need to determine the most suitable data collection methods. Depending on the research questions and the type of data required, researchers can utilize a range of methods, such as surveys, interviews, observations, or experiments. The chosen methods should align with the research objectives and allow for the collection of high-quality data.

One of the most important considerations when designing a study in human behavior research is participant recruitment. We have written a comprehensive guide on best practices and pitfalls to be aware of when recruiting participants, which can be read here.

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

In the realm of scientific studies, especially within human cognitive-behavioral research, the deployment of advanced technologies such as iMotions software and biosensors has revolutionized research design. This chapter delves into how these technologies can be integrated into various research designs, improving the depth, accuracy, and reliability of scientific inquiries.

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

The iMotions platform stands as a pivotal tool in modern research design. It’s designed to integrate data from a plethora of biosensors, providing a comprehensive analysis of human behavior. This software facilitates the synchronizing of physiological, cognitive, and emotional responses with external stimuli, thus enriching the understanding of human behavior in various contexts.

Biosensors: Gateways to Deeper Insights

Biosensors, including eye trackers, EEG, GSR, ECG, and facial expression analysis tools, provide nuanced insights into the subconscious and conscious aspects of human behavior. These tools help researchers in capturing data that is often unattainable through traditional data collection methods like surveys and interviews.

Application in Different Research Designs

• Eye Tracking : In experimental designs, where the impact of visual stimuli is crucial, eye trackers can reveal how subjects interact with these stimuli, thereby offering insights into cognitive processes and attention.
• EEG : EEG biosensors allow researchers to monitor brain activity in response to controlled experimental manipulations, offering a window into cognitive and emotional responses.

• Facial Expression Analysis : In observational studies, analyzing facial expressions can provide objective data on emotional responses in natural settings, complementing subjective self-reports.
• GSR/EDA : These tools measure physiological arousal in real-life scenarios, giving researchers insights into emotional states without the need for intrusive measures.
• EMG : In studies where direct manipulation isn’t feasible, EMG can indicate subtle responses to stimuli, which might be overlooked in traditional observational methods.
• ECG/PPG : These sensors can be used to understand the impact of various interventions on physiological states such as stress or relaxation.

Streamlining Research Design with iMotions

The iMotions platform offers a streamlined process for integrating various biosensors into a research design. Researchers can easily design experiments, collect multimodal data, and analyze results in a unified interface. This reduces the complexity often associated with handling multiple streams of data and ensures a cohesive and comprehensive research approach.

Integrating iMotions software and biosensors into research design opens new horizons for scientific inquiry. This technology enhances the depth and breadth of data collection, paving the way for more nuanced and comprehensive findings.

Whether in experimental, non-experimental, or quasi-experimental designs, iMotions and biosensors offer invaluable tools for researchers aiming to uncover the intricate layers of human behavior and cognitive processes. The future of research design is undeniably intertwined with the advancements in these technologies, leading to more robust, reliable, and insightful scientific discoveries.

Challenges in Research Design

Research design can present several challenges that researchers need to overcome to conduct reliable and valid studies. Being aware of these challenges is essential for researchers to address them effectively and ensure the integrity of their research.

Ethical Considerations

Research design must adhere to ethical guidelines and principles to protect the rights and well-being of participants. Researchers need to obtain informed consent, ensure participant confidentiality, and minimize potential harm or discomfort. Ethical considerations should be carefully integrated into the research design to promote ethical research practices.

Practical Limitations

Researchers often face practical limitations that may impact the design and execution of their studies. Limited resources, time constraints, access to participants or data, and logistical challenges can pose obstacles during the research process. Researchers need to navigate these limitations and make thoughtful choices to ensure the feasibility and quality of their research.

Research design is a vital aspect of conducting scientific studies. It provides a structured framework for researchers to answer their research questions and obtain reliable and valid results. By understanding the different types of research design and following the necessary steps in developing a research design, researchers can enhance the rigor and impact of their studies.

However, researchers must also be mindful of the challenges they may encounter, such as ethical considerations and practical limitations, and take appropriate measures to address them. Ultimately, a well-designed research study contributes to the advancement of knowledge and promotes evidence-based decision-making in various fields.

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Home » Research Recommendations – Examples and Writing Guide

Research Recommendations – Examples and Writing Guide

Table of Contents

Research Recommendations

Definition:

Research recommendations refer to suggestions or advice given to someone who is looking to conduct research on a specific topic or area. These recommendations may include suggestions for research methods, data collection techniques, sources of information, and other factors that can help to ensure that the research is conducted in a rigorous and effective manner. Research recommendations may be provided by experts in the field, such as professors, researchers, or consultants, and are intended to help guide the researcher towards the most appropriate and effective approach to their research project.

Parts of Research Recommendations

Research recommendations can vary depending on the specific project or area of research, but typically they will include some or all of the following parts:

• Research question or objective : This is the overarching goal or purpose of the research project.
• Research methods : This includes the specific techniques and strategies that will be used to collect and analyze data. The methods will depend on the research question and the type of data being collected.
• Data collection: This refers to the process of gathering information or data that will be used to answer the research question. This can involve a range of different methods, including surveys, interviews, observations, or experiments.
• Data analysis : This involves the process of examining and interpreting the data that has been collected. This can involve statistical analysis, qualitative analysis, or a combination of both.
• Results and conclusions: This section summarizes the findings of the research and presents any conclusions or recommendations based on those findings.
• Limitations and future research: This section discusses any limitations of the study and suggests areas for future research that could build on the findings of the current project.

How to Write Research Recommendations

Writing research recommendations involves providing specific suggestions or advice to a researcher on how to conduct their study. Here are some steps to consider when writing research recommendations:

• Understand the research question: Before writing research recommendations, it is important to have a clear understanding of the research question and the objectives of the study. This will help to ensure that the recommendations are relevant and appropriate.
• Consider the research methods: Consider the most appropriate research methods that could be used to collect and analyze data that will address the research question. Identify the strengths and weaknesses of the different methods and how they might apply to the specific research question.
• Provide specific recommendations: Provide specific and actionable recommendations that the researcher can implement in their study. This can include recommendations related to sample size, data collection techniques, research instruments, data analysis methods, or other relevant factors.
• Justify recommendations : Justify why each recommendation is being made and how it will help to address the research question or objective. It is important to provide a clear rationale for each recommendation to help the researcher understand why it is important.
• Consider limitations and ethical considerations : Consider any limitations or potential ethical considerations that may arise in conducting the research. Provide recommendations for addressing these issues or mitigating their impact.
• Summarize recommendations: Provide a summary of the recommendations at the end of the report or document, highlighting the most important points and emphasizing how the recommendations will contribute to the overall success of the research project.

Example of Research Recommendations

Example of Research Recommendations sample for students:

• Further investigate the effects of X on Y by conducting a larger-scale randomized controlled trial with a diverse population.
• Explore the relationship between A and B by conducting qualitative interviews with individuals who have experience with both.
• Investigate the long-term effects of intervention C by conducting a follow-up study with participants one year after completion.
• Examine the effectiveness of intervention D in a real-world setting by conducting a field study in a naturalistic environment.
• Compare and contrast the results of this study with those of previous research on the same topic to identify any discrepancies or inconsistencies in the findings.
• Expand upon the limitations of this study by addressing potential confounding variables and conducting further analyses to control for them.
• Investigate the relationship between E and F by conducting a meta-analysis of existing literature on the topic.
• Explore the potential moderating effects of variable G on the relationship between H and I by conducting subgroup analyses.
• Identify potential areas for future research based on the gaps in current literature and the findings of this study.
• Conduct a replication study to validate the results of this study and further establish the generalizability of the findings.

Applications of Research Recommendations

Research recommendations are important as they provide guidance on how to improve or solve a problem. The applications of research recommendations are numerous and can be used in various fields. Some of the applications of research recommendations include:

• Policy-making: Research recommendations can be used to develop policies that address specific issues. For example, recommendations from research on climate change can be used to develop policies that reduce carbon emissions and promote sustainability.
• Program development: Research recommendations can guide the development of programs that address specific issues. For example, recommendations from research on education can be used to develop programs that improve student achievement.
• Product development : Research recommendations can guide the development of products that meet specific needs. For example, recommendations from research on consumer behavior can be used to develop products that appeal to consumers.
• Marketing strategies: Research recommendations can be used to develop effective marketing strategies. For example, recommendations from research on target audiences can be used to develop marketing strategies that effectively reach specific demographic groups.
• Medical practice : Research recommendations can guide medical practitioners in providing the best possible care to patients. For example, recommendations from research on treatments for specific conditions can be used to improve patient outcomes.
• Scientific research: Research recommendations can guide future research in a specific field. For example, recommendations from research on a specific disease can be used to guide future research on treatments and cures for that disease.

Purpose of Research Recommendations

The purpose of research recommendations is to provide guidance on how to improve or solve a problem based on the findings of research. Research recommendations are typically made at the end of a research study and are based on the conclusions drawn from the research data. The purpose of research recommendations is to provide actionable advice to individuals or organizations that can help them make informed decisions, develop effective strategies, or implement changes that address the issues identified in the research.

The main purpose of research recommendations is to facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings. Recommendations can help bridge the gap between research and practice by providing specific actions that can be taken based on the research results. By providing clear and actionable recommendations, researchers can help ensure that their findings are put into practice, leading to improvements in various fields, such as healthcare, education, business, and public policy.

Characteristics of Research Recommendations

Research recommendations are a key component of research studies and are intended to provide practical guidance on how to apply research findings to real-world problems. The following are some of the key characteristics of research recommendations:

• Actionable : Research recommendations should be specific and actionable, providing clear guidance on what actions should be taken to address the problem identified in the research.
• Evidence-based: Research recommendations should be based on the findings of the research study, supported by the data collected and analyzed.
• Contextual: Research recommendations should be tailored to the specific context in which they will be implemented, taking into account the unique circumstances and constraints of the situation.
• Feasible : Research recommendations should be realistic and feasible, taking into account the available resources, time constraints, and other factors that may impact their implementation.
• Prioritized: Research recommendations should be prioritized based on their potential impact and feasibility, with the most important recommendations given the highest priority.
• Communicated effectively: Research recommendations should be communicated clearly and effectively, using language that is understandable to the target audience.
• Evaluated : Research recommendations should be evaluated to determine their effectiveness in addressing the problem identified in the research, and to identify opportunities for improvement.

Advantages of Research Recommendations

Research recommendations have several advantages, including:

• Providing practical guidance: Research recommendations provide practical guidance on how to apply research findings to real-world problems, helping to bridge the gap between research and practice.
• Improving decision-making: Research recommendations help decision-makers make informed decisions based on the findings of research, leading to better outcomes and improved performance.
• Enhancing accountability : Research recommendations can help enhance accountability by providing clear guidance on what actions should be taken, and by providing a basis for evaluating progress and outcomes.
• Informing policy development : Research recommendations can inform the development of policies that are evidence-based and tailored to the specific needs of a given situation.
• Enhancing knowledge transfer: Research recommendations help facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings.
• Encouraging further research : Research recommendations can help identify gaps in knowledge and areas for further research, encouraging continued exploration and discovery.
• Promoting innovation: Research recommendations can help identify innovative solutions to complex problems, leading to new ideas and approaches.

Limitations of Research Recommendations

While research recommendations have several advantages, there are also some limitations to consider. These limitations include:

• Context-specific: Research recommendations may be context-specific and may not be applicable in all situations. Recommendations developed in one context may not be suitable for another context, requiring adaptation or modification.
• I mplementation challenges: Implementation of research recommendations may face challenges, such as lack of resources, resistance to change, or lack of buy-in from stakeholders.
• Limited scope: Research recommendations may be limited in scope, focusing only on a specific issue or aspect of a problem, while other important factors may be overlooked.
• Uncertainty : Research recommendations may be uncertain, particularly when the research findings are inconclusive or when the recommendations are based on limited data.
• Bias : Research recommendations may be influenced by researcher bias or conflicts of interest, leading to recommendations that are not in the best interests of stakeholders.
• Timing : Research recommendations may be time-sensitive, requiring timely action to be effective. Delayed action may result in missed opportunities or reduced effectiveness.
• Lack of evaluation: Research recommendations may not be evaluated to determine their effectiveness or impact, making it difficult to assess whether they are successful or not.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Present a Research Study’s Limitations

All studies have imperfections, but how to present them without diminishing the value of the work can be tricky..

Nathan Ni holds a PhD from Queens University. He is a science editor for The Scientist’s Creative Services Team who strives to better understand and communicate the relationships between health and disease.

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Scientists work with many different limitations. First and foremost, they navigate informational limitations, work around knowledge gaps when designing studies, formulating hypotheses, and analyzing data. They also handle technical limitations, making the most of what their hands, equipment, and instruments can achieve. Finally, researchers must also manage logistical limitations. Scientists will often experience sample scarcity, financial issues, or simply be unable to access the technology or materials that they want.

All scientific studies have limitations, and no study is perfect. Researchers should not run from this reality, but engage it directly. It is better to directly address the specific limitations of the work in question, and doing so is actually a way to demonstrate an author’s proficiency and aptitude.

Do: Be Transparent

From a practical perspective, being transparent is the main key to directly addressing the specific limitations of a study. Was there an experiment that the researchers wanted to perform but could not, or a sample that existed that the scientists could not obtain? Was there a piece of knowledge that would explain a question raised by the data presented within the current study? If the answer is yes, the authors should mention this and elaborate upon it within the discussion section.

Asking and addressing these questions demonstrates that the authors have knowledge, understanding, and expertise of the subject area beyond what the study directly investigated. It further demonstrates a solid grasp of the existing literature—which means a solid grasp of what others are doing, what techniques they are using, and what limitations impede their own studies. This information helps the authors contextualize where their study fits within what others have discovered, thereby mitigating the perceived effect of a given limitation on the study’s legitimacy. In essence, this strategy turns limitations, often considered weaknesses, into strengths.

For example, in their 2021 Cell Reports study on macrophage polarization mechanisms, dermatologist Alexander Marneros and colleagues wrote the following. 1

A limitation of studying macrophage polarization in vitro is that this approach only partially captures the tissue microenvironment context in which many different factors affect macrophage polarization. However, it is likely that the identified signaling mechanisms that promote polarization in vitro are also critical for polarization mechanisms that occur in vivo. This is supported by our observation that trametinib and panobinostat inhibited M2-type macrophage polarization not only in vitro but also in skin wounds and laser-induced CNV lesions.

This is a very effective structure. In the first sentence ( yellow ), the authors outlined the limitation. In the next sentence ( green ), they offered a rationalization that mitigates the effect of the limitation. Finally, they provided the evidence ( blue ) for this rationalization, using not just information from the literature, but also data that they obtained in their study specifically for this purpose. 

Don't: Be Defensive

It can feel natural to avoid talking about a study’s limitations. Scientists may believe that mentioning the drawbacks still present in their study will jeopardize their chances of publication. As such, researchers will sometimes skirt around the issue. They will present “boilerplate faults”—generalized concerns about sample size/diversity and time limitations that all researchers face—rather than honestly discussing their own study. Alternatively, they will describe their limitations in a defensive manner, positioning their problems as something that “could not be helped”—as something beyond what science can currently achieve.

However, their audience can see through this, because they are largely peers who understand and have experienced how modern research works. They can tell the difference between global challenges faced by every scientific study and limitations that are specific to a single study. Avoiding these specific limitations can therefore betray a lack of confidence that the study is good enough to withstand problems stemming from legitimate limitations. As such, researchers should actively engage with the greater scientific implications of the limitations that they face. Indeed, doing this is actually a way to demonstrate an author’s proficiency and aptitude.

In an example, neurogeneticist Nancy Bonini and colleagues, in their publication in Nature , discussed a question raised by their data that they have elected not to directly investigate in this study, writing “ Among the intriguing questions raised by these data is how senescent glia promote LDs in other glia. ” To show both the legitimacy of the question and how seriously they have considered it, the authors provided a comprehensive summary of the literature in the following seven sentences, offering two hypotheses backed by a combined eight different sources. 2 Rather than shying away from a limitation, they attacked it as something to be curious about and to discuss. This is not just a very effective way of demonstrating their expertise, but it frames the limitation as something that, when overcome, will build upon the present study rather than something that negatively affects the legitimacy of their current findings.

Striking the Right Balance

Scientists have to navigate the fine line between acknowledging the limitations of their study while also not diminishing the effect and value of their own work. To be aware of legitimate limitations and properly assess and dissect them shows a profound understanding of a field, where the study fits within that field, and what the rest of the scientific community are doing and what challenges they face.

All studies are parts of a greater whole. Pretending otherwise is a disservice to the scientific community.

Looking for more information on scientific writing? Check out  The Scientist’ s  TS SciComm  section. Looking for some help putting together a manuscript, a figure, a poster, or anything else?  The Scientist ’s  Scientific Services  may have the professional help that you need.

• He L, et al. Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors . Cell Rep . 2021;37(5):109955.
• Byrns CN, et al. Senescent glia link mitochondrial dysfunction and lipid accumulation . Nature . 2024.

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Infant mortality rate rose 8% in wake of Texas abortion ban, study shows

FILE - Claire Fritz rallies for abortion rights at the Capitol, in Austin, Texas, May 14, 2022. A new study released by Johns Hopkins University on Monday, June 24, 2024, shows the infant death rate in Texas went up in the wake of the state’s abortion ban. ( Jay Janner/Austin American-Statesman via AP, File)

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In the wake of Texas’ abortion ban, the state’s infant death rate increased and more died of birth defects, a study published Monday shows.

The analysis out of Johns Hopkins University is the latest research to find higher infant mortality rates in states with abortion restrictions.

The researchers looked at how many infants died before their first birthday after Texas adopted its abortion ban in September 2021. They compared infant deaths in Texas to those in 28 states — some also with restrictions. The researchers calculated that there were 216 more deaths in Texas than expected between March and December the next year.

In Texas, the 2022 mortality rate for infants went up 8% to 5.75 per 1,000 births, compared to a 2% increase in the rest of the U.S., according to the study in the journal JAMA Pediatrics.

Among causes of deaths, birth defects showed a 23% increase, compared to a decrease of about 3% in the rest of the U.S. The Texas law blocks abortions after the detection of cardiac activity, usually five or six weeks into pregnancy, well before tests are done to detect fetal abnormalities.

“I think these findings make clear the potentially devastating consequences that abortion bans can have,” said co-author Suzanne Bell, a fertility researcher.

Doctors have argued that the law is too restrictive toward women who face pregnancy complications, though the state’s Supreme Court last month rejected a case that sought to weaken it .

Infant deaths are relatively rare , Bell said, so the team was a bit surprised by the findings. Because of the small numbers, the researchers could not parse out the rates for different populations, for example, to see if rates were rising more for certain races or socioeconomic groups.

But the results did not come as a surprise to Tiffany Green, a University of Wisconsin-Madison economist and population health scientist who studies the consequences of racial inequities on reproductive health. She said the results were in line with earlier research on racial disparities in infant mortality rates due to state differences in Medicaid funding for abortions . Many of the people getting abortions are vulnerable to pregnancy complications, said Green, who was not part of the research.

Stephen Chasen, a maternal-fetal medicine specialist with Weill Cornell Medicine, said abortion restrictions have other consequences. Chasen, who had no role in the research, said people who carry out pregnancies with fetal anomalies need extra support, education and specialized medical care for the mother and newborn — all of which require resources.

The Associated Press Health and Science Department receives support from the Robert Wood Johnson Foundation. The AP is solely responsible for all content.

Texas abortion ban linked to 13% increase in infant and newborn deaths

A Texas law that banned abortions in early pregnancy is associated with a stark increase in infant and newborn deaths, a study published Monday in JAMA Pediatrics found. 

Lawmakers passed Texas Senate Bill 8 , or SB8, in September 2021. The state law banned abortions as soon as a fetal heartbeat is detected, which can be as early as five weeks. This effectively banned abortion in the state, which used to allow abortion up to 22 weeks of pregnancy. 

The law did not include exemptions for congenital anomalies, including conditions that will cause a newborn to die soon after birth. 

The new study compared infant death rates in Texas from 2018 to 2022 to those of 28 other states. The data included newborns 28 days or younger and infants up to 12 months old. Infant deaths in Texas rose by nearly 13% the year after SB8 was passed, from 1,985 in 2021 to 2,240 in 2022. During that same period, infant deaths rose by about 2% nationwide.

Babies born with congenital anomalies also increased in Texas, by nearly 23%, but decreased by about 3% nationwide. 

“This is pointing to a causal effect of the policy; we didn’t see this increase in infant deaths in other states,” said Alison Gemmill, assistant professor of population, family and reproductive Health at the Johns Hopkins Bloomberg School of Public Health, who led the research. 

While some congenital anomalies can be corrected after birth, including cleft palate and some heart defects, others are deemed “incompatible with life.” 

“The specific increase in deaths attributable to congenital anomalies really makes an ironclad link between the change in the law and the terrible outcomes that they’re seeing for infants and families,” said Nan Strauss, senior policy analyst of maternal health at the National Partnership for Women & Families, who was not involved with the research. “The women and families have to suffer through an excruciating later part of pregnancy, knowing that their baby is likely to die in the first weeks of life.”

Gemmill said the new insight is important for other states, since Texas passed SB8 about a year before the Dobbs decision overturned federal abortion protections , leading to total bans on abortion in 14 states, according to the latest data from the Guttmacher Institute, an organization that researches and supports sexual and reproductive rights. 

“This might foreshadow what is happening in other states,” Gemmill said. “Texas is basically a year ahead.”

A Centers for Disease Control and Prevention report already found that infant and newborn mortality rates in the U.S. rose in 2022 for the first time since 2001. 

“This shows what probably was expected before the Dobbs decision, that there would be downstream unintended consequences by banning abortions in early pregnancy,” said Dr. Mary Rosser, director of Integrated Women’s Health at Columbia University Irving Medical Center, who was not involved with the study. 

Rosser added that such bans disproportionately affect marginalized populations including low-income families and people of color, and that further research is needed to better understand these effects. 

The researchers of the new study also highlighted the ripple effect that a newborn or infant’s death can have on a family, including trauma and medical bills. 

“Behind these numbers are people,” said Dr. Erika Werner, chair of obstetrics and gynecology at Tufts Medical Center, who was not involved in the research. “For each of these pregnancies, that’s a pregnant person who had to stay pregnant for an additional 20 weeks, carrying a pregnancy that they knew likely wouldn’t result in a live newborn baby.”

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.

Jason Kane is a producer in the NBC News Health & Medical Unit. 

Older women are being significantly shortchanged by medical research

Men are the focus of more health studies, leaving unanswered questions about women and cancer, Alzheimer’s and other serious conditions.

Medical research has shortchanged women for decades. This is particularly true of older women, leaving physicians without critically important information about how to best manage their health.

Late last year, the Biden administration promised to address this problem with a new effort called the White House Initiative on Women’s Health Research . That inspires a compelling question: What priorities should be on the initiative’s list when it comes to older women?

Stephanie Faubion, director of the Mayo Clinic’s Center for Women’s Health, launched into a critique when I asked about the current state of research on older women’s health. “It’s completely inadequate,” she told me.

One example: Many drugs widely prescribed to older adults, including statins for high cholesterol , were studied mostly in men, with results extrapolated to women.

“It’s assumed that women’s biology doesn’t matter and that women who are premenopausal and those who are postmenopausal respond similarly,” Faubion said.

“This has got to stop: The FDA has to require that clinical trial data be reported by sex and age for us to tell if drugs work the same, better or not as well in women,” she added.

Consider the Alzheimer’s drug Leqembi , approved by the Food and Drug Administration last year after the manufacturer reported a 27 percent slower rate of cognitive decline in people who took the medication. A supplementary appendix to a Leqembi study published in the New England Journal of Medicine revealed that sex differences were substantial — a 12 percent slowdown for women, compared with a 43 percent slowdown for men — raising questions about the drug’s effectiveness for women.

This is especially important because nearly two-thirds of older adults with Alzheimer’s disease are women. Older women are also more likely than older men to have multiple medical conditions , disabilities, autoimmune illnesses , depression and anxiety, uncontrolled high blood pressure and osteoarthritis, among other issues, according to scores of research studies.

Even so, women are resilient and outlive men by more than five years in the United States. As people move into their 70s and 80s, women outnumber men by significant margins. If we’re concerned about the health of the older population, we need to be concerned about the health of older women.

As for research priorities, here’s some of what physicians and medical researchers suggested:

Heart disease

Why is it that women with heart disease, which becomes far more common after menopause and kills more women than any other condition — are given less recommended care than men?

“We’re notably less aggressive in treating women,” said Martha Gulati, director of preventive cardiology and associate director of the Barbra Streisand Women’s Heart Center at Cedars-Sinai in Los Angeles. “We delay evaluations for chest pain. We don’t give blood thinners at the same rate. We don’t do procedures like aortic valve replacements as often. We’re not adequately addressing hypertension.

“We need to figure out why these biases in care exist and how to remove them.”

Gulati also noted that older women are less likely than their male peers to have obstructive coronary artery disease — blockages in large blood vessels — and more likely to have damage to smaller blood vessels that remains undetected. When they get procedures such as cardiac catheterizations, women have more bleeding and complications.

What are the best treatments for older women given these issues? “We have very limited data. This needs to be a focus,” Gulati said.

Brain health

How can women reduce their risk of cognitive decline and dementia as they age?

“This is an area where we really need to have clear messages for women and effective interventions that are feasible and accessible,” said JoAnn Manson, chief of the Division of Preventive Medicine at Brigham and Women’s Hospital in Boston and a key researcher for the Women’s Health Initiative , the largest study of women’s health in the United States.

Numerous factors affect women’s brain health, including stress — dealing with sexism, caregiving responsibilities and financial strain — which can fuel inflammation. Women experience the loss of estrogen, a hormone important to brain health, with menopause. They also have a higher incidence of conditions with serious impacts on the brain, such as multiple sclerosis and stroke.

“Alzheimer’s disease doesn’t just start at the age of 75 or 80,” said Gillian Einstein, the Wilfred and Joyce Posluns chair in women’s brain health and aging at the University of Toronto. “Let’s take a life course approach and try to understand how what happens earlier in women’s lives predisposes them to Alzheimer’s.”

Mental health

What accounts for older women’s greater vulnerability to anxiety and depression?

Studies suggest a variety of factors, including hormonal changes and the cumulative impact of stress. In the journal Nature Aging, Paula Rochon, a professor of geriatrics at the University of Toronto, also faults “ gendered ageism ,” an unfortunate combination of ageism and sexism that renders older women “largely invisible.”

Helen Lavretsky, a professor of psychiatry at the University of California at Los Angeles and past president of the American Association for Geriatric Psychiatry, suggests several topics that need further investigation. How does the menopausal transition impact mood and stress-related disorders? What nonpharmaceutical interventions can promote psychological resilience in older women and help them recover from stress and trauma? (Think yoga, meditation, music therapy, tai chi, sleep therapy and other possibilities.) What combination of interventions is likely to be most effective?

How can cancer screening recommendations and cancer treatments for older women be improved?

Supriya Gupta Mohile, director of the Geriatric Oncology Research Group at the Wilmot Cancer Institute at the University of Rochester, wants better guidance about breast cancer screening for older women, broken down by health status. Currently, women 75 and older are lumped together even though some are remarkably healthy and others notably frail.

Recently, the U. S. Preventive Services Task Force noted that “ the current evidence is insufficient to assess the balance of benefits and harms of screening mammography in women 75 years or older,” leaving physicians without clear guidance. “Right now, I think we’re underscreening fit older women and overscreening frail older women,” Mohile said.

She also wants more research about effective and safe treatments for lung cancer in older women, many of whom have multiple medical conditions and functional impairments.

“For this population, it’s decisions about who can tolerate treatment based on health status and whether there are sex differences in tolerability for older men and women that need investigation,” Mohile said.

Bone health, functional health and frailty

How can older women maintain mobility and preserve their ability to take care of themselves?

Osteoporosis, which causes bones to weaken and become brittle, is more common in older women than in older men, increasing the risk of dangerous fractures and falls. Once again, the loss of estrogen with menopause is implicated.

“This is hugely important to older women’s quality of life and longevity, but it’s an overlooked area that is understudied,” said Manson of Brigham and Women’s.

Jane Cauley, a distinguished professor at the University of Pittsburgh School of Public Health who studies bone health, would like to see more data about osteoporosis among older Black, Asian and Hispanic women, who are undertreated for the condition. She would also like to see better drugs with fewer side effects.

Marcia Stefanick, a professor of medicine at Stanford University School of Medicine, wants to know which strategies are most likely to motivate older women to be physically active. And she’d like more studies investigating how older women can best preserve muscle mass, strength and the ability to care for themselves.

“ Frailty is one of the biggest problems for older women, and learning what can be done to prevent that is essential,” she said.

KFF Health News is a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF.

Groundbreaking study shows why drinking from plastic bottles may increase your risk of type 2 diabetes

• BPA is an industrial chemical that scientists have linked to hormone disruption and diabetes risk.
• Plastic water bottles and food containers can leach BPA into what you eat and drink. 
• A new study found it can be risky at levels previously considered safe by government agencies. 

Scientists have long suspected that industrial chemicals used in plastic water bottles can disrupt human hormones .

But, to date, evidence has been observational, meaning it shows an association between plastics exposure and certain diseases, but can't prove a causal effect.

Now, a groundbreaking new study shows direct evidence that bisphenol A — or, BPA, a chemical used to package food and drink — can reduce sensitivity to insulin, a hormone that helps regulate blood sugar.

An impaired ability to respond to insulin, known as insulin resistance , can mean chronically high blood sugar levels and a much higher risk of type 2 diabetes.

The researchers, who presented their findings at the 2024 Scientific Sessions of the American Diabetes Association , said this study shows the EPA may need to reconsider the safe limits for exposure to BPA in plastic bottles, food containers, and other containers.

Even so-called safe levels of BPA may cause health issues

Researchers from California Polytechnic State University studied 40 healthy adults who were randomly assigned to receive either a placebo or a small dose of BPA daily.

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After four days, the participants who were given BPA were less responsive to insulin, while the placebo group did not experience any change.

The dose of BPA that participants received, 50 micrograms per kilogram of body weight per day, is an amount currently classified as safe by the EPA .

"These results suggest that maybe the US EPA safe dose should be reconsidered and that healthcare providers could suggest these changes to patients," Todd Hagobian, senior author of the new study and professor at California Polytechnic State University, said in a press release.

The FDA considers BPA to be safe at low levels occurring in food containers, up to 5 mg per kg body weight per day, or 1,000 times the amount the new study found to be risky. Some researchers argue the FDA guidelines are outdated .

Other regulatory agencies around the world have taken a tougher stance on the chemical — the European Commission proposed to ban BPA in products that come into contact with food or beverages by the end of 2024.

Environmental contaminants can be a major threat to human health

The concern about BPA is part of a broader alarm being raised about our everyday exposure to substances that may be harmful to our health.

Other recent research has found microplastics , particles so tiny they can infiltrate human cells, may potentially wreak havoc with our health. They've been found everywhere, from human lungs to reproductive organs .

Understanding how the substances we encounter every day may affect our health long-term could help us make better decisions about how to reduce the risk of chronic illnesses like type 2 diabetes.

"Given that diabetes is a leading cause of death in the US, it is crucial to understand even the smallest factors that contribute to the disease," Hagobian said in the press release. " We were surprised to see that reducing BPA exposure, such as using stainless steel or glass bottles and BPA-free cans, may lower diabetes risk."

Watch: Every difference between US and UK Coca-Cola

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Cannabis use tied to increased risk of severe COVID-19

Similar to smokers, cannabis users nearly twice as likely to need hospitalization, intensive care when infected with the virus

by Tamara Schneider • June 21, 2024

Nurse Megan Roberts cares for a COVID-19 patient in an intensive care unit at Barnes-Jewish Hospital in 2020. A study by researchers at Washington University School of Medicine in St. Louis shows that people with COVID-19 who used cannabis were more likely to be hospitalized and require intensive care than those who did not use the drug.

As the deadly disease that came to be known as COVID-19 started spreading in late 2019, scientists rushed to answer a critical question: Who is most at risk?

They quickly recognized that a handful of characteristics — including age, smoking history, high body mass index (BMI) and the presence of other diseases such as diabetes — made people infected with the virus much more likely to become seriously ill and even die. But one suggested risk factor remains unconfirmed more than four years later: cannabis use. Evidence has emerged over time indicating both protective and harmful effects.

Now, a new study by researchers at Washington University School of Medicine in St. Louis points decisively to the latter: Cannabis is linked to an increased risk of serious illness for those with COVID-19.

The study, published June 21 in JAMA Network Open, analyzed the health records of 72,501 people seen for COVID-19 at health centers in a major Midwestern health-care system during the first two years of the pandemic. The researchers found that people who reported using any form of cannabis at least once in the year before developing COVID-19 were significantly more likely to need hospitalization and intensive care than were people with no such history. This elevated risk of severe illness was on par with that from smoking.

“There’s this sense among the public that cannabis is safe to use, that it’s not as bad for your health as smoking or drinking, that it may even be good for you,” said senior author Li-Shiun Chen, MD, DSc , a professor of psychiatry. “I think that’s because there hasn’t been as much research on the health effects of cannabis as compared to tobacco or alcohol. What we found is that cannabis use is not harmless in the context of COVID-19. People who reported yes to current cannabis use, at any frequency, were more likely to require hospitalization and intensive care than those who did not use cannabis.”

Cannabis use was different than tobacco smoking in one key outcome measure: survival. While smokers were significantly more likely to die of COVID-19 than nonsmokers — a finding that fits with numerous other studies — the same was not true of cannabis users, the study showed.

“The independent effect of cannabis is similar to the independent effect of tobacco regarding the risk of hospitalization and intensive care,” Chen said. “For the risk of death, tobacco risk is clear but more evidence is needed for cannabis.”

The study analyzed deidentified electronic health records of people who were seen for COVID-19 at BJC HealthCare hospitals and clinics in Missouri and Illinois between Feb. 1, 2020, and Jan. 31, 2022. The records contained data on demographic characteristics such as sex, age and race; other medical conditions such as diabetes and heart disease; use of substances including tobacco, alcohol, cannabis and vaping; and outcomes of the illness — specifically, hospitalization, intensive-care unit (ICU) admittance and survival.

COVID-19 patients who reported that they had used cannabis in the previous year were 80% more likely to be hospitalized and 27% more likely to be admitted to the ICU than patients who had not used cannabis, after taking into account tobacco smoking, vaccination, other health conditions, date of diagnosis, and demographic factors. For comparison, tobacco smokers with COVID-19 were 72% more likely to be hospitalized and 22% more likely to require intensive care than were nonsmokers, after adjusting for other factors.

These results contradict some other research suggesting that cannabis may help the body fight off viral diseases such as COVID-19.

“Most of the evidence suggesting that cannabis is good for you comes from studies in cells or animals,” Chen said. “The advantage of our study is that it is in people and uses real-world health-care data collected across multiple sites over an extended time period. All the outcomes were verified: hospitalization, ICU stay, death. Using this data set, we were able to confirm the well-established effects of smoking, which suggests that the data are reliable.”

The study was not designed to answer the question of why cannabis use might make COVID-19 worse. One possibility is that inhaling marijuana smoke injures delicate lung tissue and makes it more vulnerable to infection, in much the same way that tobacco smoke causes lung damage that puts people at risk of pneumonia, the researchers said. That isn’t to say that taking edibles would be safer than smoking joints. It is also possible that cannabis, which is known to suppress the immune system, undermines the body’s ability to fight off viral infections no matter how it is consumed, the researchers noted.

“We just don’t know whether edibles are safer,” said first author Nicholas Griffith, MD, a medical resident at Washington University. Griffith was a medical student at Washington University when he led the study. “People were asked a yes-or-no question: ‘Have you used cannabis in the past year?’ That gave us enough information to establish that if you use cannabis, your health-care journey will be different, but we can’t know how much cannabis you have to use, or whether it makes a difference whether you smoke it or eat edibles. Those are questions we’d really like the answers to. I hope this study opens the door to more research on the health effects of cannabis.”

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Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Six distinct types of depression identified in Stanford Medicine-led study

Brain imaging, known as functional MRI, combined with machine learning can predict a treatment response based on one’s depression “biotype.”

June 17, 2024 - By Rachel Tompa

Researchers have identified six subtypes of depression, paving the way toward personalized treatment. Damerfie -   stock.adobe.com

In the not-too-distant future, a screening assessment for depression could include a quick brain scan to identify the best treatment.

Brain imaging combined with machine learning can reveal subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study , published June 17 in the journal Nature Medicine , sorts depression into six biological subtypes, or “biotypes,” and identifies treatments that are more likely or less likely to work for three of these subtypes.

Better methods for matching patients with treatments are desperately needed, said the study’s senior author,  Leanne Williams , PhD, the Vincent V.C. Woo Professor, a professor of psychiatry and behavioral sciences, and the director of Stanford Medicine’s Center for Precision Mental Health and Wellness . Williams, who lost her partner to depression in 2015, has focused her work on pioneering the field of precision psychiatry .

Around 30% of people with depression have what’s known as treatment-resistant depression , meaning multiple kinds of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully reverse their symptoms to healthy levels.  

That’s in part because there’s no good way to know which antidepressant or type of therapy could help a given patient. Medications are prescribed through a trial-and-error method, so it can take months or years to land on a drug that works — if it ever happens. And spending so long trying treatment after treatment, only to experience no relief, can worsen depression symptoms.

“The goal of our work is figuring out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach.”

Biotypes predict treatment response

To better understand the biology underlying depression and anxiety, Williams and her colleagues assessed 801 study participants who were previously diagnosed with depression or anxiety using the imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the volunteers’ brains at rest and when they were engaged in different tasks designed to test their cognitive and emotional functioning. The scientists narrowed in on regions of the brain, and the connections between them, that were already known to play a role in depression.

Using a machine learning approach known as cluster analysis to group the patients’ brain images, they identified six distinct patterns of activity in the brain regions they studied.

Leanne Williams

The scientists also randomly assigned 250 of the study participants to receive one of three commonly used antidepressants or behavioral talk therapy. Patients with one subtype, which is characterized by overactivity in cognitive regions of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those who have other biotypes. Those with another subtype, whose brains at rest had higher levels of activity among three regions associated with depression and problem-solving, had better alleviation of symptoms with behavioral talk therapy. And those with a third subtype, who had lower levels of activity at rest in the brain circuit that controls attention, were less likely to see improvement of their symptoms with talk therapy than those with other biotypes.

The biotypes and their response to behavioral therapy make sense based on what they know about these regions of the brain, said Jun Ma, MD, PhD, the Beth and George Vitoux Professor of Medicine at the University of Illinois Chicago and one of the authors of the study. The type of therapy used in their trial teaches patients skills to better address daily problems, so the high levels of activity in these brain regions may allow patients with that biotype to more readily adopt new skills. As for those with lower activity in the region associated with attention and engagement, Ma said it’s possible that pharmaceutical treatment to first address that lower activity could help those patients gain more from talk therapy.

“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain,” Williams said. “In essence, it’s a demonstration of a personalized medicine approach for mental health based on objective measures of brain function.”

In another recently published study , Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment. In that study, the scientists focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants. By identifying those with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36% accuracy without using brain imaging. That improved accuracy means that providers may be more likely to get the treatment right the first time. The scientists are now studying novel treatments for this biotype with the hope of finding more options for those who don’t respond to standard antidepressants.

Further explorations of depression

The different biotypes also correlate with differences in symptoms and task performance among the trial participants. Those with overactive cognitive regions of the brain, for example, had higher levels of anhedonia (inability to feel pleasure) than those with other biotypes; they also performed worse on executive function tasks. Those with the subtype that responded best to talk therapy also made errors on executive function tasks but performed well on cognitive tasks.

One of the six biotypes uncovered in the study showed no noticeable brain activity differences in the imaged regions from the activity of people without depression. Williams believes they likely haven’t explored the full range of brain biology underlying this disorder — their study focused on regions known to be involved in depression and anxiety, but there could be other types of dysfunction in this biotype that their imaging didn’t capture.

Williams and her team are expanding the imaging study to include more participants. She also wants to test more kinds of treatments in all six biotypes, including medicines that haven’t traditionally been used for depression.

Her colleague  Laura Hack , MD, PhD, an assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol . The team also wants to establish easy-to-follow standards for the method so that other practicing psychiatrists can begin implementing it.

“To really move the field toward precision psychiatry, we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible,” Ma said. “Having information on their brain function, in particular the validated signatures we evaluated in this study, would help inform more precise treatment and prescriptions for individuals.”

Researchers from Columbia University; Yale University School of Medicine; the University of California, Los Angeles; UC San Francisco; the University of Sydney; the University of Texas MD Anderson; and the University of Illinois Chicago also contributed to the study.

Datasets in the study were funded by the National Institutes of Health (grant numbers R01MH101496, UH2HL132368, U01MH109985 and U01MH136062) and by Brain Resource Ltd.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Hope amid crisis

Psychiatry’s new frontiers

4-legged lifesavers: Service dogs are working wonders for veterans with PTSD, study shows

TOMS RIVER, N.J. − Before Anthony Certa began talking about his three deployments in Iraq as a U.S. Marine and military police officer , he gave a gentle command to his service dog.

"Mando, on my lap," the veteran said. Mando, a black 2½-year-old England Labrador, hoisted his massive paws onto Certa's legs, then Certa lifted the dog all the way into his lap and began petting the dog, who remained still and quiet.

It was obvious the effect the dog had on Certa, who recalled his experiences guarding convoys and protecting explosives ordnance disposal (EOD) personnel as they worked. Emotional as he spoke of losing comrades, Certa remained calm and spoke softly, in measured tones.

While for years there has been anecdotal evidence of the benefits of emotional support dogs for veterans such as Certa, a new national study offers more definitive proof.

Maggie O'Haire , one of the study's co-authors and a researcher with the University of Arizona College of Veterinary Medicine, and her colleagues followed 156 veterans over three months. The study, funded in part by the National Institutes of Health and released June 4, found veterans with dogs reported decreased severity of PTSD symptoms, anxiety and depression and higher psychosocial functioning. The dogs were provided by a nonprofit, K9s for Warriors .

"We know veterans are struggling," O'Haire said. "They have much higher rates of depression, anxiety and suicidal thoughts (than the general population)."

'Really rough coming home' after combat in Iraq

Certa, who enlisted shortly after the terrorist attacks on Sept. 11, 2001, was just 19 when he was first deployed to Iraq in 2003. He was in Fallujah during the most intense fighting of the war; his final deployment ended in 2005.

After seeing the human cost of war, from service members killed by improvised explosive devices (IEDs) to urban warfare and house-to-house "cordon and knock" operations, Certa found it difficult to adjust to civilian life.

"It was really tough coming home," he said. "You had certain expectations. When you’re in the Marines, you don’t really talk about things."

He struggled, engaging in what he called "reckless behavior" and leaning on alcohol. "You mask a lot of the problems," Certa said, petting and squeezing Mando. "You get reckless; you feel invincible. You feel like, well, you didn't die (in combat), but you also feel guilty that you didn't die, and other guys did."

The 40-year-old, who was worried about becoming a statistic, is not alone. According to a 2023 report by the U.S. Department of Veterans Affairs , suicide is the second-leading cause of death among veterans under the age of 45. In 2021, 6,392 veterans died by suicide – an average of more than 17 lives lost per day.

'Something in me wasn't right'

In 2007, Certa followed the advice of concerned family members and stopped drinking. It helped. He went back to school, earned a graduate degree in education and began teaching.

But a few years ago, he found himself struggling again. The then-superintendent of the Matawan School District, Joseph "Jay" Majka, was himself a Marine Corps veteran and understood the struggles vets sometimes face.

"I didn't realize how far I was spinning out of control," Certa said. "But my colleagues saw something in me wasn't right, and (Majka) came to me and said, 'Let's get you some help.'"

Mighty Oaks, a faith-based program for military veterans , helped Certa get his life back as he reconnected with his Christian faith. He learned to forgive himself and let go of past mistakes. He still gives back through his church and charities such as Semper Fi & America's Fund and Dylan's Wings of Change .

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Saving lives 'at both ends of the leash'

K9s for Warriors, which paired Certa with Mando, is one of several nonprofits that helps veterans obtain service dogs. It was founded in 2011 by a mom who saw her son struggle with PTSD when he returned from Iraq – but she also noticed he seemed more relaxed when he was with his dog.

Most of the animals K9s for Warriors pairs with veterans are rescue dogs, spokesperson Dani Bozzini said.

"We say we are saving lives at both ends of the leash," she said. "(Rescue dogs) have so much love to give; they're smart and cuddly and we believe in second chances, for the veterans we serve and the dogs."

Dogs are screened for temperament and their ability to obey commands and trained for six to eight months.

The dogs' training includes three main cues: "Look," which tells the dog, in military parlance, to "watch my six," helpful for people wary of enclosed spaces or being unable to see all around themselves; "on my lap," in which the dog acts as a comforting weight and calming presence; and "front," which tells the dog to form a buffer between the veteran and others, mitigating hyper-vigilance they might feel in crowds.

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Veterans, too, go through a screening process, Bozzini said. Once they're matched, veterans and dogs spend three weeks at one of two K9s for Warriors sites, in Florida and Texas, bonding and learning to work together. There's no cost to vets; the expense of training the dogs (around $70,000 for each dog, Bozzini said) and hosting veterans is supported by donors and philanthropic organizations.

Mando and Certa have been together for a year, and Certa said they're nearly inseparable. Mando accompanies him to work each day – the dog has his own school ID card – and he's a hit with students at the middle school where Certa teaches and members of the church youth group Certa leads. About the only time they're apart is when Certa, an ultramarathon runner, is on a long run.

"He helps me so much and it’s awesome that he brings such a positive element to wherever he’s at," Certa said. "There’s no crummy attitude around a dog, you know? He's the best."

Positive outcomes for veterans with dogs

O'Haire said using dogs to help people with physical challenges is nothing new, but having dogs ease mental health conditions such as PTSD and anxiety is a relatively recent innovation. That's part of the reason it hasn't really been studied in depth, she said.

But research was needed, O'Haire said, because funding sources, policy makers and insurance companies all rely on evidence and data. The dogs might not work for everyone, she noted, and they're not the only intervention – talk therapy, medications and continued support also help people struggling with mental health – but dogs can be part of the solution, the study shows.

"As I reflect on almost a decade that I've been studying veterans and service dogs, it's not uncommon for me to hear veterans tell me they wouldn't be alive if not for their dog," O'Haire said.

Certa, who married and became a stepdad to two boys in 2022, said Mando is more than a pet. The dog, along with faith and family, helps sustain him.

"The way he looks at me, the way he nudges me," he said, his voice trailing off a bit. "He needs me as much as I need him."

If you or someone you know needs help, the national suicide and crisis lifeline in the U.S. is available by calling or texting 988. There is also an online chat at  988lifeline.org . Veterans can also visit www.veteranscrisisline.net/ or text 838255 . You do not need to be enrolled in VA benefits or services to receive help.

Contact Phaedra Trethan by email at [email protected], on X (formerly Twitter) @wordsbyphaedra, or on Threads @by_phaedra

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Is Fish Oil Helpful or Harmful for the Heart?

Despite decades of research, the evidence for omega-3 supplements is murky.

By Alice Callahan

In 1970, two Danish researchers traveled to Greenland to investigate a nutritional paradox: The Inuit people living in the region consumed foods very high in fat, yet reportedly had very low rates of heart attacks.

That observation flew in the face of nutrition dogma at the time, which held that eating fatty foods — like whale and seal meat and oily fish — would clog your arteries and cause heart disease.

The Inuit on Greenland, a Danish territory, had lower levels of blood cholesterol and triglycerides than people back in Denmark, the researchers reported . The reason, they hypothesized, was that the Inuit diet was rich in omega-3 fatty acids — particularly EPA and DHA, which are concentrated in fish and the animals that eat them.

These findings sparked decades of scientific and commercial interest in the role omega-3 fatty acids play in heart health, even after later studies suggested that, in fact, the Inuit had rates of heart disease similar to those found in Europe, the United States and Canada. Today, omega-3 supplements are among the most popular in the United States, surpassed only by multivitamins and vitamin D. Among U.S. adults 60 and older, about 22 percent reported taking omega-3s in a 2017-2018 survey.

Unlike most other supplements , fish oil has been rigorously studied, said Dr. JoAnn Manson, a professor of medicine at Harvard Medical School. But the results of those studies have been mixed, leaving researchers and doctors still debating whether fish oil is beneficial for heart health. They have also revealed that taking fish oil is linked to a slightly greater risk of developing atrial fibrillation , a type of irregular heartbeat.

Here’s where the evidence for both the benefits and risks of fish oil stands today.

A boatload of studies, but unclear benefits

After reading the dispatches from Greenland, researchers began looking at people elsewhere in the world and finding, in study after study , that those who consumed fish at least once per week were less likely to die from coronary heart disease than those who rarely ate fish. In animal experiments , they found that fish oil helped keep electrical signaling in heart cells functioning properly, said Dr. Dariush Mozaffarian, a cardiologist and director of the Food is Medicine Institute at Tufts University.

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