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Research Questions vs Hypothesis: Understanding the Difference Between Them

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by  Antony W

August 20, 2021

research questions vs hypothesis

You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?

In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.

We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic . 

Let’s get to it.

What’s a Research Question?

We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.

Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question. 

While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.

When is a Research Question Useful? 

A research question may be useful when and if: 

  • There isn’t enough previous research on the topic
  • You want to report a wider range out of outcome when doing your research project
  • You want to conduct a more open ended inquiries 

Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.

Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.

What’s a Hypothesis? 

A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.

Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.

Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.

A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.

If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.

In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.

The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study. 

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

  • There’s enough previous research on the topic
  • You want to test a specific model or a particular theory
  • You anticipate a likely outcome in advance 

The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.

Research Question vs Hypothesis: Which One Should Come First 

Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.

Such a case requires coming up with a research question first, followed by a scientific method.

Since a hypothesis is part of a research method, it will come after the research question.

Research Question vs Hypothesis: What’s the Difference? 

The following are the differences between a research question and a hypothesis.

We look at the differences in purpose and structure, writing, as well as conclusion. 

Research Questions vs Hypothesis: Some Useful Advice 

As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.

Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.

If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.

You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand. 

Research Questions vs Hypothesis: Conclusion 

If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

  • Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
  • Descriptive: “What factors most influence the academic achievement of senior high school students?”
  • Comparative: “What is the performance difference between teaching methods A and B?”
  • Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
  • Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
  • Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
  • The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
  • It’s advisable to focus on a single primary research question for the study.
  • The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
  • The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
Feasible
Interesting
Novel
Ethical
Relevant
  • The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
Population (patients)
Intervention (for intervention studies only)
Comparison group
Outcome of interest
Time
  • Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
  • The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
  • A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
  • A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
  • An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

relationship between research questions and hypothesis

Research Hypotheses

Present the researcher’s predictions based on specific statements.

  • These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
  • Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
  • The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
  • Hypotheses can express a single outcome or multiple outcomes.
  • After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
  • Hypothesis testing helps determine if observed findings are due to true differences and not chance.
  • Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
  • 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
  • A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

  • In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
  • Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
  • Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
  • In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
  • Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
  • Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
  • Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
  • Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
  • Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
  • Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
  • Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
  • Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
  • Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
  • Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
  • Example : “Teaching method A will improve student performance more than method B.”
  • Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
  • Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
  • Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

  • Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
  • Learn about current trends and technological advances on the topic.
  • Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
  • Use the FINER criteria in the development of the research question.
  • Ensure that the research question follows PICOT format.
  • Develop a research hypothesis from the research question.
  • Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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Research Question Vs Hypothesis

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Research Question Vs Hypothesis

Research questions and hypotheses are both important elements of a research study, but they serve different purposes.

Research Question

A Research Question is a clear, concise, and specific question that a researcher asks to guide their study. Research questions are used to define the scope of the research project and to guide the collection and analysis of data. Research questions are often used in exploratory or descriptive studies, and they are open-ended in nature. Research questions should be answerable through data collection and analysis and should be linked to the research objectives or goals of the study.

A Hypothesis is a statement that predicts the relationship between two or more variables in a research study. Hypotheses are used in studies that aim to test cause-and-effect relationships between variables. A hypothesis is a tentative explanation for an observed phenomenon, and it is often derived from existing theory or previous research. Hypotheses are typically expressed as an “if-then” statement, where the “if” part refers to the independent variable, and the “then” part refers to the dependent variable. Hypotheses can be either directional (predicting the direction of the relationship between variables) or non-directional (predicting the presence of a relationship without specifying its direction).

Difference Between Research Question and Hypothesis

Here are some key differences between research questions and hypotheses:

AspectResearch QuestionHypothesis
PurposeTo guide the research project and define its scopeTo test a cause-and-effect relationship between variables
Type of studyExploratory or descriptiveExperimental or quasi-experimental
FormulationOpen-ended questionStatement that predicts the relationship between variables
Level of specificityGeneral and open-endedSpecific and testable
Type of dataType of StudyQuantitative
Analytical approachInductiveDeductive

Both Research Questions and Hypotheses are essential elements of a research study, but they serve different purposes. Research questions guide the study and help researchers define its scope, while hypotheses are used to test specific cause-and-effect relationships between variables. The choice of which to use depends on the nature of the research question, the study design, and the research objectives.

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  • How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

  • An independent variable is something the researcher changes or controls.
  • A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

  • The relevant variables
  • The specific group being studied
  • The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

  • H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
  • H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.
Research question Hypothesis Null hypothesis
What are the health benefits of eating an apple a day? Increasing apple consumption in over-60s will result in decreasing frequency of doctor’s visits. Increasing apple consumption in over-60s will have no effect on frequency of doctor’s visits.
Which airlines have the most delays? Low-cost airlines are more likely to have delays than premium airlines. Low-cost and premium airlines are equally likely to have delays.
Can flexible work arrangements improve job satisfaction? Employees who have flexible working hours will report greater job satisfaction than employees who work fixed hours. There is no relationship between working hour flexibility and job satisfaction.
How effective is high school sex education at reducing teen pregnancies? Teenagers who received sex education lessons throughout high school will have lower rates of unplanned pregnancy teenagers who did not receive any sex education. High school sex education has no effect on teen pregnancy rates.
What effect does daily use of social media have on the attention span of under-16s? There is a negative between time spent on social media and attention span in under-16s. There is no relationship between social media use and attention span in under-16s.

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

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Research bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Research: Articulating Questions, Generating Hypotheses, and Choosing Study Designs

Introduction.

Articulating a clear and concise research question is fundamental to conducting a robust and useful research study. Although “getting stuck into” the data collection is the exciting part of research, this preparation stage is crucial. Clear and concise research questions are needed for a number of reasons. Initially, they are needed to enable you to search the literature effectively. They will allow you to write clear aims and generate hypotheses. They will also ensure that you can select the most appropriate research design for your study.

This paper begins by describing the process of articulating clear and concise research questions, assuming that you have minimal experience. It then describes how to choose research questions that should be answered and how to generate study aims and hypotheses from your questions. Finally, it describes briefly how your question will help you to decide on the research design and methods best suited to answering it.

TURNING CURIOSITY INTO QUESTIONS

A research question has been described as “the uncertainty that the investigator wants to resolve by performing her study” 1 or “a logical statement that progresses from what is known or believed to be true to that which is unknown and requires validation”. 2 Developing your question usually starts with having some general ideas about the areas within which you want to do your research. These might flow from your clinical work, for example. You might be interested in finding ways to improve the pharmaceutical care of patients on your wards. Alternatively, you might be interested in identifying the best antihypertensive agent for a particular subgroup of patients. Lipowski 2 described in detail how work as a practising pharmacist can be used to great advantage to generate interesting research questions and hence useful research studies. Ideas could come from questioning received wisdom within your clinical area or the rationale behind quick fixes or workarounds, or from wanting to improve the quality, safety, or efficiency of working practice.

Alternatively, your ideas could come from searching the literature to answer a query from a colleague. Perhaps you could not find a published answer to the question you were asked, and so you want to conduct some research yourself. However, just searching the literature to generate questions is not to be recommended for novices—the volume of material can feel totally overwhelming.

Use a research notebook, where you regularly write ideas for research questions as you think of them during your clinical practice or after reading other research papers. It has been said that the best way to have a great idea is to have lots of ideas and then choose the best. The same would apply to research questions!

When you first identify your area of research interest, it is likely to be either too narrow or too broad. Narrow questions (such as “How is drug X prescribed for patients with condition Y in my hospital?”) are usually of limited interest to anyone other than the researcher. Broad questions (such as “How can pharmacists provide better patient care?”) must be broken down into smaller, more manageable questions. If you are interested in how pharmacists can provide better care, for example, you might start to narrow that topic down to how pharmacists can provide better care for one condition (such as affective disorders) for a particular subgroup of patients (such as teenagers). Then you could focus it even further by considering a specific disorder (depression) and a particular type of service that pharmacists could provide (improving patient adherence). At this stage, you could write your research question as, for example, “What role, if any, can pharmacists play in improving adherence to fluoxetine used for depression in teenagers?”

TYPES OF RESEARCH QUESTIONS

Being able to consider the type of research question that you have generated is particularly useful when deciding what research methods to use. There are 3 broad categories of question: descriptive, relational, and causal.

Descriptive

One of the most basic types of question is designed to ask systematically whether a phenomenon exists. For example, we could ask “Do pharmacists ‘care’ when they deliver pharmaceutical care?” This research would initially define the key terms (i.e., describing what “pharmaceutical care” and “care” are), and then the study would set out to look for the existence of care at the same time as pharmaceutical care was being delivered.

When you know that a phenomenon exists, you can then ask description and/or classification questions. The answers to these types of questions involve describing the characteristics of the phenomenon or creating typologies of variable subtypes. In the study above, for example, you could investigate the characteristics of the “care” that pharmacists provide. Classifications usually use mutually exclusive categories, so that various subtypes of the variable will have an unambiguous category to which they can be assigned. For example, a question could be asked as to “what is a pharmacist intervention” and a definition and classification system developed for use in further research.

When seeking further detail about your phenomenon, you might ask questions about its composition. These questions necessitate deconstructing a phenomenon (such as a behaviour) into its component parts. Within hospital pharmacy practice, you might be interested in asking questions about the composition of a new behavioural intervention to improve patient adherence, for example, “What is the detailed process that the pharmacist implicitly follows during delivery of this new intervention?”

After you have described your phenomena, you may then be interested in asking questions about the relationships between several phenomena. If you work on a renal ward, for example, you may be interested in looking at the relationship between hemoglobin levels and renal function, so your question would look something like this: “Are hemoglobin levels related to level of renal function?” Alternatively, you may have a categorical variable such as grade of doctor and be interested in the differences between them with regard to prescribing errors, so your research question would be “Do junior doctors make more prescribing errors than senior doctors?” Relational questions could also be asked within qualitative research, where a detailed understanding of the nature of the relationship between, for example, the gender and career aspirations of clinical pharmacists could be sought.

Once you have described your phenomena and have identified a relationship between them, you could ask about the causes of that relationship. You may be interested to know whether an intervention or some other activity has caused a change in your variable, and your research question would be about causality. For example, you may be interested in asking, “Does captopril treatment reduce blood pressure?” Generally, however, if you ask a causality question about a medication or any other health care intervention, it ought to be rephrased as a causality–comparative question. Without comparing what happens in the presence of an intervention with what happens in the absence of the intervention, it is impossible to attribute causality to the intervention. Although a causality question would usually be answered using a comparative research design, asking a causality–comparative question makes the research design much more explicit. So the above question could be rephrased as, “Is captopril better than placebo at reducing blood pressure?”

The acronym PICO has been used to describe the components of well-crafted causality–comparative research questions. 3 The letters in this acronym stand for Population, Intervention, Comparison, and Outcome. They remind the researcher that the research question should specify the type of participant to be recruited, the type of exposure involved, the type of control group with which participants are to be compared, and the type of outcome to be measured. Using the PICO approach, the above research question could be written as “Does captopril [ intervention ] decrease rates of cardiovascular events [ outcome ] in patients with essential hypertension [ population ] compared with patients receiving no treatment [ comparison ]?”

DECIDING WHETHER TO ANSWER A RESEARCH QUESTION

Just because a question can be asked does not mean that it needs to be answered. Not all research questions deserve to have time spent on them. One useful set of criteria is to ask whether your research question is feasible, interesting, novel, ethical, and relevant. 1 The need for research to be ethical will be covered in a later paper in the series, so is not discussed here. The literature review is crucial to finding out whether the research question fulfils the remaining 4 criteria.

Conducting a comprehensive literature review will allow you to find out what is already known about the subject and any gaps that need further exploration. You may find that your research question has already been answered. However, that does not mean that you should abandon the question altogether. It may be necessary to confirm those findings using an alternative method or to translate them to another setting. If your research question has no novelty, however, and is not interesting or relevant to your peers or potential funders, you are probably better finding an alternative.

The literature will also help you learn about the research designs and methods that have been used previously and hence to decide whether your potential study is feasible. As a novice researcher, it is particularly important to ask if your planned study is feasible for you to conduct. Do you or your collaborators have the necessary technical expertise? Do you have the other resources that will be needed? If you are just starting out with research, it is likely that you will have a limited budget, in terms of both time and money. Therefore, even if the question is novel, interesting, and relevant, it may not be one that is feasible for you to answer.

GENERATING AIMS AND HYPOTHESES

All research studies should have at least one research question, and they should also have at least one aim. As a rule of thumb, a small research study should not have more than 2 aims as an absolute maximum. The aim of the study is a broad statement of intention and aspiration; it is the overall goal that you intend to achieve. The wording of this broad statement of intent is derived from the research question. If it is a descriptive research question, the aim will be, for example, “to investigate” or “to explore”. If it is a relational research question, then the aim should state the phenomena being correlated, such as “to ascertain the impact of gender on career aspirations”. If it is a causal research question, then the aim should include the direction of the relationship being tested, such as “to investigate whether captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”.

The hypothesis is a tentative prediction of the nature and direction of relationships between sets of data, phrased as a declarative statement. Therefore, hypotheses are really only required for studies that address relational or causal research questions. For the study above, the hypothesis being tested would be “Captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”. Studies that seek to answer descriptive research questions do not test hypotheses, but they can be used for hypothesis generation. Those hypotheses would then be tested in subsequent studies.

CHOOSING THE STUDY DESIGN

The research question is paramount in deciding what research design and methods you are going to use. There are no inherently bad research designs. The rightness or wrongness of the decision about the research design is based simply on whether it is suitable for answering the research question that you have posed.

It is possible to select completely the wrong research design to answer a specific question. For example, you may want to answer one of the research questions outlined above: “Do pharmacists ‘care’ when they deliver pharmaceutical care?” Although a randomized controlled study is considered by many as a “gold standard” research design, such a study would just not be capable of generating data to answer the question posed. Similarly, if your question was, “Is captopril better than placebo at reducing blood pressure?”, conducting a series of in-depth qualitative interviews would be equally incapable of generating the necessary data. However, if these designs are swapped around, we have 2 combinations (pharmaceutical care investigated using interviews; captopril investigated using a randomized controlled study) that are more likely to produce robust answers to the questions.

The language of the research question can be helpful in deciding what research design and methods to use. Subsequent papers in this series will cover these topics in detail. For example, if the question starts with “how many” or “how often”, it is probably a descriptive question to assess the prevalence or incidence of a phenomenon. An epidemiological research design would be appropriate, perhaps using a postal survey or structured interviews to collect the data. If the question starts with “why” or “how”, then it is a descriptive question to gain an in-depth understanding of a phenomenon. A qualitative research design, using in-depth interviews or focus groups, would collect the data needed. Finally, the term “what is the impact of” suggests a causal question, which would require comparison of data collected with and without the intervention (i.e., a before–after or randomized controlled study).

CONCLUSIONS

This paper has briefly outlined how to articulate research questions, formulate your aims, and choose your research methods. It is crucial to realize that articulating a good research question involves considerable iteration through the stages described above. It is very common that the first research question generated bears little resemblance to the final question used in the study. The language is changed several times, for example, because the first question turned out not to be feasible and the second question was a descriptive question when what was really wanted was a causality question. The books listed in the “Further Reading” section provide greater detail on the material described here, as well as a wealth of other information to ensure that your first foray into conducting research is successful.

This article is the second in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous article in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Competing interests: Mary Tully has received personal fees from the UK Renal Pharmacy Group to present a conference workshop on writing research questions and nonfinancial support (in the form of travel and accommodation) from the Dubai International Pharmaceuticals and Technologies Conference and Exhibition (DUPHAT) to present a workshop on conducting pharmacy practice research.

Further Reading

  • Cresswell J. Research design: qualitative, quantitative and mixed methods approaches. London (UK): Sage; 2009. [ Google Scholar ]
  • Haynes RB, Sackett DL, Guyatt GH, Tugwell P. Clinical epidemiology: how to do clinical practice research. 3rd ed. Philadelphia (PA): Lippincott, Williams & Wilkins; 2006. [ Google Scholar ]
  • Kumar R. Research methodology: a step-by-step guide for beginners. 3rd ed. London (UK): Sage; 2010. [ Google Scholar ]
  • Smith FJ. Conducting your pharmacy practice research project. London (UK): Pharmaceutical Press; 2005. [ Google Scholar ]

Educational resources and simple solutions for your research journey

Research hypothesis: What it is, how to write it, types, and examples

What is a Research Hypothesis: How to Write it, Types, and Examples

relationship between research questions and hypothesis

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

relationship between research questions and hypothesis

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

  • Clearly formulated and free of language errors and ambiguity  
  • Concise and not unnecessarily verbose  
  • Has clearly defined variables  
  • Testable and stated in a way that allows for it to be disproven  
  • Can be tested using a research design that is feasible, ethical, and practical   
  • Specific and relevant to the research problem  
  • Rooted in a thorough literature search  
  • Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

  • Identify a research problem or question: Start by identifying a specific research problem.   
  • Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
  • Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
  • State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
  • Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
  • Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

relationship between research questions and hypothesis

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

  • Testable: A research hypothesis should be able to be tested via experimentation or observation.  
  • Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
  • Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
  • Falsifiable: A research hypothesis should be able to be disproven through testing.  
  • Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
  • Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
  • Relevant: A research hypothesis should be relevant to the research question and objectives.  
  • Feasible: A research hypothesis should be feasible to test within the scope of the study.  
  • Reflects the population: A research hypothesis should consider the population or sample being studied.  
  • Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Research hypothesis: What it is, how to write it, types, and examples

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

relationship between research questions and hypothesis

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Research hypothesis: What it is, how to write it, types, and examples

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

  • Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
  • The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
  • You should be able to collect the necessary data within the constraints of your study.  
  • It should be possible for other researchers to replicate your study, using the same methods and variables.   
  • Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
  • The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Exploring Research Question and Hypothesis Examples: A Comprehensive Guide

Exploring Research Question and Hypothesis Examples: A Comprehensive Guide

This comprehensive guide explores the intricacies of formulating research questions and hypotheses across various academic disciplines. By delving into examples and methodological approaches, the article aims to provide scholars and researchers with the tools necessary to develop robust and effective research frameworks. Understanding and crafting well-formed research questions and hypotheses are pivotal in conducting meaningful research that can significantly contribute to knowledge within a field.

Key Takeaways

  • Understand the fundamental differences and connections between research questions and hypotheses.
  • Learn how to craft effective and precise research questions that guide the research process.
  • Explore various types of hypotheses and methods for testing and refining them.
  • Examine practical examples of research questions and hypotheses across multiple disciplines.
  • Gain insights into the impact of well-constructed research questions and hypotheses on research outcomes, academic publishing, and grant applications.

Understanding the Fundamentals of Research Questions and Hypotheses

Defining research questions.

Research questions are the backbone of any scholarly inquiry, guiding you through the exploration of your chosen topic. They help you focus your study and determine the direction of your research. A well-crafted research question should be clear, focused, and answerable within the constraints of your study.

Characteristics of a Strong Hypothesis

A strong hypothesis provides a specific, testable prediction about the expected outcomes of your research. It is not merely a guess but is grounded in existing literature and theory. To develop a robust hypothesis, consider the variables involved and ensure that it is feasible to test them within your study's design.

Interrelation Between Research Questions and Hypotheses

Understanding the interrelation between research questions and hypotheses is crucial for structuring your research effectively. Your hypothesis should directly address the gap in the literature highlighted by your research question, providing a clear pathway for investigation. This alignment ensures that your study can contribute valuable insights to your field.

Crafting Effective Research Questions

Identifying the purpose.

To craft an effective research question , you must first identify the purpose of your study. This involves understanding what you aim to discover or elucidate through your research. Ask yourself what the core of your inquiry is and what outcomes you hope to achieve. This clarity will guide your entire research process, ensuring that your question is not only relevant but also deeply rooted in your specific academic or practical goals.

Scope and Limitations

It's crucial to define the scope and limitations of your research early on. This helps in setting realistic boundaries and expectations for your study. Consider factors such as time, resources, and the breadth of the subject area. Narrowing down your focus to a manageable scope can prevent the common pitfall of an overly broad or vague question, which can dilute the impact of your findings.

Formulating Questions that Drive Inquiry

The final step in crafting your research question is formulating it in a way that drives inquiry. This means your question should be clear, concise, and structured to prompt detailed investigation and critical analysis. It should challenge existing knowledge and push the boundaries of what is already known. Utilizing strategies like the Thesis Dialogue Blueprint or the Research Proposal Compass can be instrumental in refining your question to ensure it is both innovative and feasible.

Developing Hypotheses in Research

From research questions to hypotheses.

When you transition from research questions to hypotheses, you are essentially moving from what you want to know to what you predict will happen. This shift involves formulating a specific, testable prediction that directly stems from your initial question. Ensure your hypothesis is directly linked to and derived from your research question to maintain a coherent research strategy.

Types of Hypotheses

There are several types of hypotheses you might encounter, including simple, complex, directional, nondirectional, associative, causal, null, and alternative. Each type serves a different purpose and is chosen based on the specifics of the research question and the nature of the study. For instance, a null hypothesis might be used to test the effectiveness of a new teaching method compared to the standard.

Testing and Refining Hypotheses

Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes. This iterative process is essential for narrowing down the most accurate explanation or prediction for your research question.

Examples of Research Questions in Various Disciplines

Humanities and social sciences.

In the realm of Humanities and Social Sciences, research questions often explore cultural, social, historical, or philosophical aspects. How does gender representation in 20th-century American literature reflect broader social changes? This question not only seeks to uncover specific literary trends but also ties them to societal shifts, offering a rich field for analysis.

Natural Sciences

Research questions in the Natural Sciences are typically aimed at understanding natural phenomena or solving specific scientific problems. A common question might be, What are the effects of plastic pollutants on marine biodiversity? This inquiry highlights the environmental concerns and seeks empirical data to understand the impact.

Applied Sciences

In Applied Sciences, the focus is often on improving technology or engineering solutions. A pertinent question could be, How can renewable energy sources be integrated into existing power grids? This question addresses the practical challenges and potential innovations in energy systems, crucial for advancing sustainable technologies.

Analyzing Hypothesis Examples Across Fields

Case studies in psychology.

In psychology, hypotheses often explore the causal relationships between cognitive functions and behaviors. Consider how a hypothesis might predict the impact of stress on memory recall . By examining various case studies, you can see how hypotheses are specifically tailored to address intricate psychological phenomena.

Experimental Research in Biology

Biology experiments frequently test hypotheses about physiological processes or genetic information. For instance, a hypothesis might propose that a specific gene influences plant growth rates. Through rigorous testing, these hypotheses contribute significantly to our understanding of biological systems.

Field Studies in Environmental Science

Field studies in environmental science provide a rich ground for testing hypotheses related to ecosystem dynamics and conservation strategies. A common hypothesis might explore the effects of human activity on biodiversity. These studies often involve complex data collection and analysis, highlighting the interrelation between empirical evidence and theoretical predictions.

Methodological Approaches to Formulating Hypotheses

Quantitative vs. qualitative research.

When you embark on hypothesis formulation, understanding the distinction between quantitative and qualitative research methodologies is crucial. Quantitative research focuses on numerical data and statistical analysis, ideal for hypotheses that require measurable evidence. In contrast, qualitative research delves into thematic and descriptive data, providing depth and context to hypotheses that explore behaviors, perceptions, and experiences.

The Role of Theoretical Frameworks

Theoretical frameworks serve as the backbone for developing robust hypotheses. They provide a structured way to align your hypothesis with existing knowledge. By integrating theories and models relevant to your study, you ensure that your hypothesis has a solid foundation and aligns with established academic thought.

Utilizing Existing Literature to Form Hypotheses

A thorough review of existing literature is indispensable for crafting a well-informed hypothesis. This process not only highlights gaps in current research but also allows you to build on the work of others. By synthesizing findings from previous studies, you can formulate hypotheses that are both innovative and grounded in academic precedent.

Evaluating the Impact of Well-Formed Research Questions and Hypotheses

On research outcomes.

Understanding the impact of well-formed research questions and hypotheses on research outcomes is crucial. Well-crafted questions and hypotheses serve as a framework that guides the entire research process , ensuring that the study remains focused and relevant. They help in defining the scope of the study and in identifying the variables that need to be measured, thus directly influencing the validity and reliability of the research findings.

In Academic Publishing

The role of well-defined research questions and hypotheses extends beyond the research process into the realm of academic publishing. A clear hypothesis provides a strong foundation for the research paper, enhancing its chances of acceptance in prestigious journals. The clarity and direction afforded by a solid hypothesis make the research more appealing to a scholarly audience, potentially increasing citation rates and academic recognition.

In Grant Applications

When applying for research grants, the clarity of your research questions and hypotheses can significantly impact the decision-making process of funding bodies. A well-articulated hypothesis demonstrates a clear vision and a structured approach to addressing a specific issue, which can be crucial in securing funding. Grant reviewers often look for proposals that promise substantial contributions to the field, and a strong hypothesis can be a key factor in showcasing the potential impact of your research.

In our latest article, 'Evaluating the Impact of Well-Formed Research Questions and Hypotheses,' we delve into the crucial role that precise questions and hypotheses play in academic research. Understanding this can significantly enhance your thesis writing process. For a deeper exploration and practical tools to apply these concepts, visit our website and discover how our Thesis Action Plan can transform your academic journey. Don't miss out on our special offers tailored just for you!

In this comprehensive guide, we have explored various examples of research questions and hypotheses, shedding light on their significance and application in academic research. Understanding the distinction between a research question and a hypothesis, as well as knowing how to effectively formulate them, is crucial for conducting methodical and impactful studies. By examining different scenarios and examples, this guide aims to equip researchers with the knowledge to craft well-defined research questions and hypotheses that can drive meaningful investigations and contribute to the broader field of knowledge. As we continue to delve into the intricacies of research design, it is our hope that this guide serves as a valuable resource for both novice and experienced researchers in their scholarly endeavors.

Frequently Asked Questions

What is a research question.

A research question is a clearly defined query that guides a scientific or academic study. It sets the scope and focus of the research by asking about a specific phenomenon or issue.

How does a hypothesis differ from a research question?

A hypothesis is a specific, testable prediction about what will happen in a study based on prior knowledge or theory, while a research question is an open query that guides the direction of the investigation.

What are the characteristics of a strong hypothesis?

A strong hypothesis is clear, testable, based on existing knowledge, and it states an expected relationship between variables.

How can research questions and hypotheses interrelate?

Research questions define the scope of inquiry, while hypotheses provide a specific prediction about the expected outcomes that can be tested through research methods.

What should be considered when formulating a research question?

When formulating a research question, consider clarity, focus, relevance, and the feasibility of answering the question through available research methods.

Why is it important to have a well-formed hypothesis?

A well-formed hypothesis directs the research process, allows for clear testing of assumptions, and helps in drawing meaningful conclusions that can contribute to the body of knowledge.

10 Effective Strategies for Research Question Help

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The Craft of Writing a Strong Hypothesis

Deeptanshu D

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types-of-hypotheses

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

  • Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
  • Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

  • A research hypothesis has to be simple yet clear to look justifiable enough.
  • It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
  • It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
  • A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
  • If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
  • A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick-tips-on-how-to-write-a-hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

relationship between research questions and hypothesis

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The Difference Between Research Questions & Hypothesis

Researchers use one or both of these tools to guide their research.

To Calculate Arcsine, What Buttons Do You Press on a Scientific ...

Research questions and hypothesis are tools used in similar ways for different research methods. Both hypothesis and research questions are written before research begins and are used to help guide the research. Hypothesis are used in deductive research, where researchers use logic and scientific findings to either prove or disprove assumptions. Heuristic research is based on experience, where researchers use observations to learn about the research subject.

Definitions

A hypothesis is defined as an educated guess, while a research question is simply the researcher wondering about the world. Hypothesis are part of the scientific research method. They are employed in research in science, sociology, mathematics and more. Research questions are part of heuristic research methods, and are also used in many fields including literature, and sociology.

As its name suggests, research questions are always written as questions. Hypothesis are written as statements preceded with the words "I predict." For example, a research question would ask, "What is the effect of heat on the effectiveness of bleach?" A hypothesis would state, "I predict heat will diminish the effectiveness of bleach."

Before Writing

Before writing a hypothesis, the researcher must determine what others have discovered about this subject. On the other hand, a research question requires less preparation, but focus and structure is critical.

For example, a researcher using a hypothesis would look up studies about bleach, information on the chemical properties of the chemical when heated and data about its effectiveness before writing the hypothesis. When using a research question, the researcher would think about how to phrase the question to ensure its scope is not too broad, too narrow or impossible to answer.

Writing Conclusions

When writing the conclusion for research conducted using a hypothesis, the researcher will write whether the hypothesis was correct or incorrect, followed by an explanation of the results of the research. The researcher using only a research question will write the answer to the question, followed by the findings of the research.

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  • The Research Assistant: The Relationship Between the Research Question, Hypotheses, Specific Aims, and Long-Term Goals of the Project

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Clarifying the Research Questions or Hypotheses

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relationship between research questions and hypothesis

  • Kenan Dikilitaş 3 &
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This chapter deals with the important, but often neglected, issue of establishing research questions or hypotheses, whether this is done before or (in the “real world”) often after the study has been conducted. The point is made that, in fact, research questions tend to be more common than hypotheses in action research, and guidelines are suggested for delineating such questions and deciding on appropriate question types according to the research purpose. Some example questions are provided to stimulate ideas, and an example action research study which will proceed in stages throughout the book is begun here.

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Dikilitaş, K., Griffiths, C. (2017). Clarifying the Research Questions or Hypotheses. In: Developing Language Teacher Autonomy through Action Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-50739-2_2

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Quantitative Research in Mass Communications : R and RStudio

7 formulating research questions and hypotheses, 7.1 introduction to research questions and hypotheses.

In the realm of academic research, particularly within the field of mass communications, the formulation of research questions and hypotheses is a foundational step that sets the direction and scope of a study. These elements are crucial not only for guiding the research process but also for defining the study’s objectives and expectations. This section highlights the significance of research questions and hypotheses and elucidates the role they play in framing a study.

The Importance of Research Questions and Hypotheses in Guiding Research

Defining the Research Focus: Research questions serve as the cornerstone of any study, clearly outlining the specific issue or phenomenon that the research aims to explore. They help narrow down the broad area of interest into a focused inquiry that can be systematically investigated.

Guiding Methodology: The nature of the research question—whether it seeks to describe, compare, or determine cause and effect—directly influences the choice of research design, methods, and analysis techniques. Well-formulated questions ensure that the research methodology is appropriately aligned with the study’s objectives.

Facilitating Hypothesis Formulation: In quantitative research, hypotheses often stem from the research questions, proposing specific predictions or expectations based on theoretical foundations or previous studies. Hypotheses provide a testable statement that guides the empirical investigation and analysis.

7.1.1 Overview of the Role These Elements Play in Framing a Study

Structuring the Research Framework: Together, research questions and hypotheses establish the conceptual framework for a study, defining its boundaries and specifying the variables of interest. This framework serves as a blueprint, guiding all subsequent steps of the research process.

Informing Literature Review: Research questions and hypotheses inform the scope and focus of the literature review, directing attention to relevant theories, concepts, and empirical findings. This ensures that the review is tightly integrated with the study’s aims and contributes to building a solid theoretical foundation.

Determining Data Collection and Analysis: The formulation of research questions and hypotheses has direct implications for data collection methods, sampling strategies, and analytical techniques. They dictate what data are needed, how they should be collected, and the statistical tests or analytical approaches required to address the research questions and test the hypotheses.

Communicating the Study’s Purpose: Research questions and hypotheses effectively communicate the purpose and direction of the study to the academic community, stakeholders, and the broader public. They articulate the study’s contribution to knowledge, its relevance to theoretical debates or practical issues, and the potential implications of the findings.

In summary, research questions and hypotheses are indispensable components of the research process, serving as the guiding light for the entire study. They provide clarity, direction, and purpose, ensuring that the research is coherent, focused, and methodologically sound. By meticulously crafting these elements, researchers in mass communications lay the groundwork for meaningful and impactful studies that advance our understanding of complex media landscapes and communication dynamics.

7.2 Understanding Research Questions

Research questions are the foundation of any scholarly inquiry, guiding the direction and focus of the study. In mass communications research, where topics can range from analyzing media effects to understanding audience behaviors, formulating effective research questions is crucial for defining the scope and objectives of a study. This section delves into the definition and characteristics of a good research question, distinguishes between exploratory and descriptive research questions, and discusses strategies for developing clear and focused questions.

Definition and Characteristics of a Good Research Question

Definition: A research question is a clearly formulated question that outlines the issue or problem your study aims to address. It sets the stage for the research design, data collection, and analysis, directing the inquiry toward a specific goal.

Characteristics of a Good Research Question:

  • Clarity: It should be clearly stated, avoiding ambiguity and ensuring that the research focus is understandable to others.
  • Relevance: The question should be significant to the field of study, addressing gaps in the literature or emerging issues in mass communications.
  • Researchability: It must be possible to answer the question through empirical investigation, using available research methods and tools.
  • Specificity: A good question is specific, targeting a particular aspect of the broader topic to make the research manageable and focused.

Distinction Between Exploratory and Descriptive Research Questions

Exploratory Research Questions: These questions are used when little is known about the topic or phenomenon. Exploratory questions aim to investigate and gain insights into a subject, seeking to understand how or why something happens. In mass communications, an exploratory question might ask, “How do emerging social media platforms influence political engagement among young adults?”

Descriptive Research Questions: Descriptive questions aim to describe the characteristics or features of a subject. They are used when the goal is to provide an accurate representation or count of a phenomenon. A descriptive research question in mass communications might be, “What are the predominant themes in news coverage of environmental issues?”

Developing Clear and Focused Research Questions

  • Specificity: Your research question should be narrowly tailored to address a specific issue within the broader field of mass communications. This specificity helps in defining the study’s scope and focusing the research efforts.
  • Feasibility: Consider the practical aspects of answering your research question, including the availability of data, time constraints, and resource limitations. A feasible question is one that can be realistically investigated within the parameters of your study.
  • Literature Review: Conduct a thorough review of existing research to identify gaps or unresolved questions in the field. This can inspire focused and relevant research questions.
  • Consultation: Discuss your ideas with peers, mentors, or experts in mass communications. Feedback can help refine your questions and ensure they are both specific and feasible.
  • Pilot Studies: Small-scale pilot studies or preliminary investigations can provide insights that help in formulating or refining your research questions.

Crafting clear and focused research questions is a critical step in the research process, setting the stage for meaningful and impactful inquiry. By ensuring that your questions are specific, feasible, and relevant to the field of mass communications, you lay the groundwork for a study that can contribute valuable insights to our understanding of media and communication phenomena.

7.3 Types of Research Questions

In the pursuit of scientific inquiry within mass communications, research questions serve as the navigational compass guiding the research process. These questions can be broadly categorized into two types: nondirectional and directional. Each type serves a distinct purpose and is formulated based on the nature of the study and the specific objectives the researcher aims to achieve. This section explores the definitions, uses, and strategies for crafting both nondirectional and directional research questions.

Nondirectional Research Questions

Definition: Nondirectional research questions are open-ended queries that explore the existence of a relationship between variables without specifying the anticipated direction of this relationship. They are used when the literature does not strongly suggest which outcome is expected or when exploring new or under-researched areas.

When to Use Them: Employ nondirectional questions when previous research is inconclusive, conflicting, or absent. They are particularly useful in exploratory studies where the aim is to uncover patterns, relationships, or phenomena without presupposing outcomes.

Crafting Questions:

  • Focus on Exploration: Phrase your question to emphasize exploration, such as “Is there a relationship between social media usage and political participation among young adults?”
  • Avoid Implied Direction: Ensure the wording does not inadvertently suggest a presumed direction of the relationship. The question should remain open to any outcome, whether positive, negative, or neutral.

Directional Research Questions

Definition: Directional research questions specify the expected direction of the relationship between variables. These questions are based on predictions that are often derived from theoretical frameworks or existing literature.

Purposes: Directional questions are used when there is sufficient theoretical or empirical basis to hypothesize a particular outcome. They guide the research towards testing specific hypotheses, making them suitable for studies aiming to confirm or refute theoretical predictions.

Formulating Questions:

  • Specify Expected Outcomes: Clearly articulate the anticipated direction of the relationship in the question. For example, “Does increased exposure to environmental news lead to higher levels of environmental activism among viewers?”
  • Ground in Literature: Ensure that the directionality implied by your question is supported by theoretical rationales or empirical evidence from previous research. This alignment strengthens the justification for expecting a particular outcome.

7.4 Strategies for Formulating Research Questions

Regardless of the type, crafting effective research questions requires a deep understanding of the topic at hand, a thorough review of the existing literature, and a clear articulation of the research’s goals. Here are some strategies to consider:

  • Engage with Current Research: Immerse yourself in the latest studies and debates within the field of mass communications to identify trends, gaps, and areas ripe for investigation.
  • Consult Theoretical Frameworks: Draw on established theories to guide the formulation of your questions, whether seeking to explore uncharted territory (nondirectional) or test specific propositions (directional).
  • Iterative Refinement: Research questions often evolve during the initial stages of a study. Be prepared to refine your questions as you delve deeper into the literature and sharpen your study’s focus.

By thoughtfully selecting the type of research question that best suits the aims and scope of your study, you lay a solid foundation for a coherent, rigorous, and insightful exploration of mass communications phenomena.

7.5 Operationalization of Concepts

Operationalization is a critical process in the research design phase, particularly in quantitative studies within the realm of mass communications. It involves defining the abstract concepts or variables in measurable terms, determining how they will be observed, measured, or manipulated within the study. This section outlines the essence of operationalization, its pivotal role in research, the steps involved in operationalizing variables, and provides examples pertinent to mass communications research.

Defining Operationalization and Its Significance in Research

Definition: Operationalization is the process by which researchers define how to measure or manipulate the variables of interest in a study. It transforms theoretical constructs into measurable indicators, allowing for empirical observation and quantitative analysis.

Significance: The operationalization of concepts is fundamental to ensuring the reliability and validity of a study. By clearly specifying how variables are measured, researchers enable the replication of the study, enhance the clarity and coherence of their research design, and facilitate the objective analysis of findings.

Steps to Operationalize Variables

Identify the Key Concepts: Begin by clearly identifying the key concepts or variables you intend to study. In mass communications, this might include phenomena like media influence, audience engagement, or digital literacy.

Define the Variables Conceptually: Provide clear, conceptual definitions for each variable, drawing on existing literature or theoretical frameworks to delineate the boundaries of the concept.

Specify the Variables Operationally: Decide on the specific operations, techniques, or instruments you will use to measure or manipulate each variable. This includes determining the type of data to be collected, the scale of measurement, and the method of data collection.

Develop or Select Measurement Instruments: Choose or develop instruments that accurately measure your operationalized variables. This could involve creating surveys, designing experiments, or developing coding schemes for content analysis.

Pilot Test: Conduct a pilot test of your measurement instruments to ensure they effectively capture the operationalized variables. Adjustments based on feedback from the pilot test can improve the reliability and validity of the measures.

Examples of Operationalizing Common Variables in Mass Communications Research

Audience Engagement: Conceptually defined as the level of interaction and involvement an individual has with media content. Operationally, it could be measured through the number of social media shares, comments, or time spent viewing content.

Media Influence on Public Opinion: Conceptually, this refers to the impact media content has on shaping individuals’ attitudes and beliefs. Operationally, it could be measured by changes in attitudes before and after exposure to specific media messages, using pretest-posttest surveys.

Digital Literacy: Conceptually defined as the ability to find, evaluate, create, and communicate information using digital technologies. Operationally, digital literacy could be measured through a questionnaire assessing skills in these areas, with items rated on a Likert scale.

Operationalization is a cornerstone of rigorous research methodology, bridging the gap between theoretical concepts and empirical evidence. By meticulously defining and measuring variables, researchers in mass communications can ground their studies in observable reality, enhancing the validity of their findings and contributing meaningful insights into the complex dynamics of media and communication.

7.6 Developing Hypotheses

In the framework of quantitative research, particularly within the expansive field of mass communications, hypotheses serve as pivotal elements that further refine and operationalize the research questions. This section elucidates the definition and function of hypotheses in quantitative research, explores the relationship between research questions and hypotheses, and outlines the criteria that make a hypothesis testable.

Definition and Function of Hypotheses in Quantitative Research

Definition: A hypothesis is a predictive statement that proposes a possible outcome or relationship between two or more variables. It is grounded in theory or prior empirical findings and serves as a basis for scientific inquiry.

Function: The primary function of a hypothesis is to provide a specific, testable proposition derived from the broader research question. Hypotheses guide the research design, data collection, and analysis process, offering a clear focus for empirical investigation. They enable researchers to apply statistical methods to test the proposed relationships or effects, thereby contributing to the accumulation of scientific knowledge.

The Relationship Between Research Questions and Hypotheses

From Questions to Hypotheses: Research questions set the stage for the research by identifying the key phenomena or relationships of interest. Hypotheses take this a step further by specifying the expected direction or nature of these relationships based on theoretical or empirical groundwork. Essentially, while research questions identify “what” the study aims to explore, hypotheses propose “how” these explorations will unfold.

Complementarity: Research questions and hypotheses are complementary, with the former providing a broad inquiry framework and the latter offering a focused, conjectural answer that can be empirically tested. This synergy ensures that the research is both guided by curiosity and anchored in a framework that facilitates systematic investigation.

Criteria for a Testable Hypothesis

For a hypothesis to effectively contribute to the research process, it must be testable. The following criteria are essential for constructing a hypothesis that can be empirically evaluated:

Specificity: A testable hypothesis must clearly and specifically define the variables involved and the expected relationship between them. This clarity ensures that the hypothesis can be directly linked to observable and measurable outcomes.

Empirical Referents: The variables within the hypothesis must have empirical referents – that is, they must be capable of being measured or manipulated in the real world. This allows the hypothesis to be subjected to empirical testing.

Predictive Nature: A testable hypothesis should make a predictive statement about the expected outcome of the study, enabling the research to confirm or refute the proposed relationship or effect based on empirical evidence.

Grounding in Theory or Prior Research: The hypothesis should be grounded in existing theoretical frameworks or empirical findings, providing a rationale for the expected relationship or outcome. This grounding not only lends credibility to the hypothesis but also ensures that it contributes to the ongoing academic discourse.

Falsifiability: Finally, a testable hypothesis must be falsifiable. This means it should be possible to conceive of an outcome that would contradict the hypothesis, allowing for the possibility of it being disproven through empirical evidence.

Developing well-crafted hypotheses is a critical step in the quantitative research process, particularly in mass communications, where the rapid evolution of media technologies and platforms continually opens new avenues for inquiry. By adhering to these criteria, researchers can ensure that their hypotheses are not only testable but also meaningful, contributing valuable insights to our understanding of complex media landscapes and their impacts on society.

7.7 Types of Hypotheses

In the empirical research landscape, especially within the domain of mass communications, hypotheses are indispensable tools that guide the investigative process. They are typically categorized into null hypotheses and alternative hypotheses, each serving a distinct role in framing the research inquiry. This section provides definitions for these two types of hypotheses, discusses their roles in research, and offers guidance on formulating them effectively.

Null Hypotheses (H0)

Definition: The null hypothesis (H0) posits that there is no difference, effect, or relationship between the variables under investigation. It represents a statement of skepticism or neutrality, suggesting that any observed differences or relationships in the data are due to chance rather than a systematic effect.

Role in Research: The null hypothesis serves as a benchmark for testing the existence of an effect or relationship. By attempting to disprove or reject the null hypothesis through statistical analysis, researchers can provide evidence supporting the presence of a meaningful effect or relationship. The null hypothesis is foundational in hypothesis testing, enabling researchers to apply statistical methods to determine the likelihood that observed data could have occurred under the null condition.

Formulating Null Hypotheses: Null hypotheses are formulated as statements of no difference or no relationship. For example, in a study examining the impact of social media usage on political engagement, a null hypothesis might state, “There is no difference in political engagement levels between users and non-users of social media.”

Alternative Hypotheses (H1)

Definition: The alternative hypothesis (H1) is the counter proposition to the null hypothesis. It posits that there is a significant difference, effect, or relationship between the variables being studied. The alternative hypothesis reflects the researcher’s theoretical expectation or prediction about the outcome of the study.

Complementing Null Hypotheses: The alternative hypothesis directly complements the null hypothesis by specifying the expected effect or relationship that the research aims to demonstrate. While the null hypothesis posits the absence of an effect, the alternative hypothesis asserts its presence, guiding the direction of the study’s empirical investigation.

Crafting Alternative Hypotheses: Alternative hypotheses are crafted to predict specific outcomes based on the research question and theoretical framework. They should clearly articulate the anticipated direction or nature of the relationship or difference between variables. Continuing the earlier example, an alternative hypothesis might state, “Users of social media exhibit higher levels of political engagement than non-users.”

7.8 Strategic Formulation of Hypotheses

The formulation of null and alternative hypotheses is a strategic exercise that sets the stage for empirical testing. Effective hypotheses are:

  • Specific and Concise: Clearly define the variables and the expected relationship or difference, avoiding ambiguity.
  • Empirically Testable: Ensure that the hypotheses can be tested using available research methods and data.
  • Theoretically Grounded: Base your hypotheses on existing literature, theories, or preliminary evidence, providing a rationale for the expected outcomes.

In mass communications research, where the interplay of media, technology, and society offers a rich tapestry of phenomena to explore, the thoughtful formulation of null and alternative hypotheses is crucial. It not only delineates the scope of the investigation but also ensures that the research contributes meaningful insights into the dynamics of communication processes and their impacts.

7.9 Directional and Nondirectional Hypotheses

In the nuanced world of quantitative research, particularly within the field of mass communications, hypotheses serve as a bridge between theoretical inquiry and empirical investigation. They are typically formulated as either directional or nondirectional, each with specific implications for the study’s design and analysis. This section clarifies the distinction between these two types of hypotheses and provides guidance on when to use each, complemented by examples from mass communications research.

Understanding the Distinction and When to Use Each Type

Directional Hypotheses: Directional hypotheses specify the expected direction of the relationship or difference between variables. They are based on theoretical predictions or empirical evidence suggesting a particular outcome. Directional hypotheses are used when prior research or theory provides a strong basis for anticipating the direction of the effect.

Nondirectional Hypotheses: Nondirectional hypotheses indicate that a relationship or difference exists between variables but do not specify the direction. They are appropriate when there is uncertainty about the expected outcome or when previous studies have yielded mixed or inconclusive results.

Examples of Both Directional and Nondirectional Hypotheses in Mass Communications Research

  • “Individuals who frequently engage with news content on social media platforms will exhibit higher levels of political awareness than those who do not engage with news content on these platforms.” This hypothesis predicts a specific direction of the relationship between social media news engagement and political awareness.
  • “Exposure to environmental documentaries will increase viewers’ concern for environmental issues more than exposure to traditional news coverage of the same issues.” This hypothesis specifies an expected difference in the effect of two types of media content on environmental concern.
  • “There is a relationship between the frequency of smartphone use for social media and the level of social isolation experienced by young adults.” This hypothesis suggests a relationship exists but does not predict whether more frequent use increases or decreases social isolation.
  • “The introduction of interactive digital learning tools in communication courses affects students’ academic performance.” This hypothesis indicates that an effect is expected but does not specify whether the effect is positive or negative on academic performance.

7.10 Deciding Between Directional and Nondirectional Hypotheses

The choice between directional and nondirectional hypotheses hinges on several factors:

  • Theoretical Basis: Strong theoretical foundations or extensive empirical evidence supporting a specific outcome favor the use of directional hypotheses.
  • Research Objectives: Exploratory studies aiming to identify patterns or relationships might initially employ nondirectional hypotheses, especially in emerging areas of mass communications where less is known.
  • Statistical Considerations: Directional hypotheses allow for more focused statistical tests (e.g., one-tailed tests), which can be more powerful in detecting specified effects. However, they require a strong justification for predicting the direction of the effect.

By carefully considering these factors, researchers in mass communications can effectively choose the type of hypothesis that best suits their study’s objectives and theoretical framework. Whether directional or nondirectional, the formulation of hypotheses is a critical step in the research process, guiding empirical inquiry and contributing to the advancement of knowledge in the dynamic field of mass communications.

7.11 Criteria for Good Research Questions and Hypotheses

In the rigorous academic landscape of mass communications research, the construction of research questions and hypotheses serves as the bedrock upon which studies are built and conducted. These foundational elements not only guide the direction of the research but also determine its scope, focus, and potential contribution to the field. To ensure the effectiveness and integrity of research, certain criteria must be met. This section outlines the essential qualities of good research questions and hypotheses: clarity and precision, relevance to the field of study, and researchability with empirical testing potential.

Clarity and Precision

Definition: Clarity in research questions and hypotheses means that they are stated in a straightforward and unambiguous manner, easily understood by those within and outside the field. Precision involves the specific delineation of the variables and constructs involved, leaving no room for misinterpretation.

Importance: Clear and precise formulations allow for a focused investigation, guiding the research design, data collection, and analysis process. They ensure that the study addresses the intended concepts and relationships directly and effectively.

Strategies for Achieving Clarity and Precision:

  • Use specific, defined terms and avoid jargon that may not be universally understood.
  • Clearly specify the variables or phenomena being studied and their expected relationships.
  • Ensure that hypotheses are directly testable, with defined criteria for confirmation or refutation.

Relevance to the Field of Study

Definition: Relevance implies that the research questions and hypotheses address significant issues, gaps, or debates within the field of mass communications. They should contribute to advancing understanding, theory, or practice in meaningful ways.

Importance: Research that is relevant to the field is more likely to receive attention from scholars, policymakers, and practitioners, and to secure funding and publication opportunities. It ensures that the study contributes to the ongoing discourse and development of mass communications as a discipline.

Strategies for Ensuring Relevance:

  • Conduct a thorough review of current literature to identify gaps, emerging trends, or unresolved questions.
  • Align research questions and hypotheses with theoretical frameworks or pressing societal issues.
  • Consider the practical implications and potential impact of the research on the field.

Researchability and Empirical Testing Potential

Definition: Researchability refers to the feasibility of addressing the research questions and testing the hypotheses through empirical methods. This includes the availability of data, appropriateness of methodology, and the potential for gathering evidence to support or refute the hypotheses.

Importance: For research to contribute to the body of knowledge, it must be capable of being rigorously investigated using empirical methods. Research questions and hypotheses with high empirical testing potential allow for the derivation of meaningful, verifiable insights.

Strategies for Enhancing Researchability:

  • Ensure that the variables involved can be accurately measured or observed using existing tools or methods.
  • Design hypotheses that are testable within the constraints of time, resources, and ethical considerations.
  • Consider the practical aspects of data collection, including access to participants, media content, or archival resources.

Crafting research questions and hypotheses that are clear and precise, relevant to the field, and amenable to empirical investigation is crucial for conducting impactful research in mass communications. These criteria not only guide the research process but also enhance the study’s validity, reliability, and contribution to the field, fostering a deeper understanding of the complex dynamics that shape media and communication in society.

7.12 Common Mistakes to Avoid in Formulating Research Questions and Hypotheses

When embarking on a research project, especially in a field as dynamic as mass communications, the formulation of research questions and hypotheses is a critical step that sets the stage for the entire study. However, researchers, particularly those new to the field, may encounter pitfalls that can compromise the clarity, relevance, and feasibility of their research. This section highlights common mistakes to avoid in the formulation process, ensuring that research questions and hypotheses are both robust and actionable.

Formulating Questions and Hypotheses That Are Too Broad or Vague

Issue: Broad or vague questions and hypotheses lack specificity and focus, making it difficult to define the scope of the study or determine the appropriate methodology for investigation.

Impact: They can lead to an unwieldy research project with diffuse objectives, posing challenges in data collection, analysis, and interpretation of findings.

Avoidance Strategy: Narrow down the research topic by focusing on specific aspects, populations, or contexts. Use the literature review to identify gaps and refine the research focus to a manageable scope.

Confusing Research Questions with Interview or Survey Questions

Issue: There is a distinction between overarching research questions that guide a study and the specific questions posed in interviews or surveys. Confusing the two can lead to a misalignment between the study’s objectives and the data collection process.

Impact: This confusion can result in collecting data that do not effectively address the research questions, undermining the study’s ability to generate meaningful insights.

Avoidance Strategy: Clearly delineate between the broad research questions that frame your study and the specific items or prompts used in data collection instruments. Ensure that each interview or survey question is directly linked to and serves the purpose of answering the overarching research questions.

Creating Untestable Hypotheses

Issue: Hypotheses that are not empirically testable, either due to the abstract nature of the constructs involved or the lack of available methods for measurement, pose significant challenges to the research process.

Impact: Untestable hypotheses cannot be substantiated or refuted through empirical evidence, limiting the study’s contribution to the field and its scientific merit.

Avoidance Strategy: Ensure that all variables in the hypothesis can be measured or manipulated with existing research methods. Operationalize abstract concepts clearly and consider the feasibility of empirical testing during the hypothesis formulation stage.

7.13 Best Practices for Robust Formulation

Alignment with Theoretical Frameworks: Ground your research questions and hypotheses within established theories or models in mass communications, ensuring they contribute to the broader academic dialogue.

Consultation with Peers and Mentors: Engage in discussions with peers, mentors, or experts in the field to refine your research questions and hypotheses, leveraging their insights to avoid common pitfalls.

Pilot Testing: Consider conducting a pilot study or preliminary analysis to test the feasibility of your research questions and hypotheses, allowing for adjustments before the full-scale study.

By avoiding these common mistakes and adhering to best practices, researchers can formulate research questions and hypotheses that are clear, focused, and empirically testable. This careful preparation enhances the quality and impact of research in mass communications, contributing valuable insights into the complex interplay between media, technology, and society.

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Difference Between Hypothesis and Research Question

Main difference – hypothesis vs research question.

Research question and hypothesis are the foundations of a research study. Formulating the research question or developing the hypothesis can help you to decide on the approach of the research. A research question is the question the research study sets out to answer. Hypothesis is the statement the research study sets out to prove or disprove. The main difference between hypothesis and research question is that hypothesis is predictive in nature whereas research question is inquisitive in nature.

In this article, we’ll discuss,

1. What is a Hypothesis? – Meaning, Features, Characteristics, and Usage

2. What is a Research Question? – Meaning, Features, Characteristics, and Usage

Difference Between Hypothesis and Research Question - Comparison Summary

What is a Hypothesis

A hypothesis is a prediction about the relationship between two or more variables. It can be described as an educated guess about what happens in an experiment. Researchers usually tend to use hypotheses when significant knowledge is already available on the subject. The hypothesis is based on this existing knowledge. After the hypothesis is developed, the researcher can develop data, analyze and use them to support or negate the hypothesis.

Not all studies have hypotheses. They are usually used in experimental quantitative research studies. They are useful in testing a specific theory or model.  A complete hypothesis always includes the variables, population and the predicted relationship between the variables. The main disadvantage of hypotheses is that their tendency to blind a researcher to unexpected results. 

Difference Between Hypothesis and Research Question

What is a Research Question

A research question is the question a research study sets to answer. However, a research study can have more than one research question. The research methodologies , tools used to collect data, etc. all depend on the research question.

Research questions are often used in qualitative research, which seek to answer open-ended questions . But they can also be used in quantitative studies. Research questions can be used instead of hypotheses when there is little previous research on the subject. Research questions allow the researcher to conduct more open-ended queries, and a wide range of results can be reported.

A properly constructed research question should always be clear and concise. It should include the variables, population and the topic being studied.

Hypothesis is a tentative prediction about the relationship between two or more variables.

Research Question is the question a research study sets to answer.

Hypothesis is predictive in nature.

Research Question is inquisitive in nature.

Existing Research

Hypothesis can be used if there is significant knowledge or previous research on this subject.

Research Question can be used if there is little previous research on the subject.

Quantitative vs Qualitative

Hypothesis is mainly used in experimental quantitative studies.

Research Question can be used in both quantitative and qualitative studies.

Hypothesis doesn’t allow a wide range of outcomes.

Research Question allows a wide range of outcomes.

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relationship between research questions and hypothesis

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

  • What is the golden thread
  • What are research aims ( examples )
  • What are research objectives ( examples )
  • What are research questions ( examples )
  • The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Free Webinar: How To Find A Dissertation Research Topic

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

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relationship between research questions and hypothesis

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

The research objectives detail the specific steps that you, as the researcher, will take to achieve the research aims you laid out.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

relationship between research questions and hypothesis

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39 Comments

Isaac Levi

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Hatimu Bah

Well appreciated. This has helped me greatly in doing my dissertation.

Dr. Abdallah Kheri

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

Ekwunife, Chukwunonso Onyeka Steve

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Tosin

Thanks so much. This was really helpful.

Ishmael

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

sylas

i found this document so useful towards my study in research methods. thanks so much.

Michael L. Andrion

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Scarlett

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

Enoch Tindiwegi

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

Sora Dido Boru

This is quite helpful. I really appreciate!

Chulyork

The article made it simple for researcher students to differentiate between three concepts.

Afowosire Wasiu Adekunle

Very innovative and educational in approach to conducting research.

Sàlihu Abubakar Dayyabu

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

Mohammed Shamsudeen

A very helpful piece. thanks, I really appreciate it .

Sonam Jyrwa

Very well explained, and it might be helpful to many people like me.

JB

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

UN

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

Derek Jansen

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Saen Fanai

Exactly what I need in this research journey, I look forward to more of your coaching videos.

Abubakar Rofiat Opeyemi

This helped a lot. Thanks so much for the effort put into explaining it.

Lamin Tarawally

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

Latifat Muhammed

This is quite useful thanks

Yetunde

I’m excited and thankful. I got so much value which will help me progress in my thesis.

Amer Al-Rashid

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Webby

Very helpful and important tips on Aims, Objectives and Questions.

Refiloe Raselane

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Annabelle Roda-Dafielmoto

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

Joe

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Abdella

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Sheikh

Well explained

New Growth Care Group

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

yaikobe

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

UMAR SALEH

I really found these tips helpful. Thank you very much Grad Coach.

Rahma D.

I found this article helpful. Thanks for sharing this.

Juhaida

thank you so much, the explanation and examples are really helpful

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How Does a Hypothesis Differ From a Research Question?

David Costello

To understand the difference between a hypothesis and a research question , we must first define the exact nature of scientific inquiry . Essentially, scientific inquiry represents a structured and systematic approach to exploration and discovery, grounded in empirical evidence and guided by the principles of logical reasoning and critical analysis. At the heart of scientific inquiry lies a fundamental commitment to unbiased observation and the rigorous assessment of information, a process that seeks to generate verifiable knowledge based on well-founded theories and methodological robustness.

A pivotal facet of successful scientific investigation is the appropriate framing of research, which serves to delineate the scope and direction of the scholarly endeavor. The meticulous articulation of research parameters not only guides investigators in the methodical exploration of a particular phenomenon but also ensures the reliability and validity of the findings derived from it. Correctly framing a research endeavor equips scholars with a clear framework, thereby preventing research ambiguities and facilitating a coherent and purposeful investigative journey.

Central to the framing of research are two interrelated yet distinct elements: the research question and the hypothesis. While the research question generally articulates the primary inquiry or set of inquiries to be addressed in a study, offering a focal point for the exploration, a hypothesis presents a tentative, testable prediction regarding the expected outcomes of the research. It is grounded in the existing literature and theoretical frameworks, serving as a provisional answer to the research question that is subject to empirical verification.

In essence, a research question seeks to identify and explore potential relationships, patterns, or trends, fostering a deep understanding of the underlying phenomena. In contrast, a hypothesis endeavors to affirm or refute predetermined assumptions through methodical testing and validation, aiming to substantiate or discredit specific theoretical postulates.

To correctly formulate and differentiate between research questions and hypotheses, let us investigate each one in further detail.

Understanding hypotheses

Crafting a well-defined hypothesis is a pivotal step in scholarly research. This task necessitates a profound grasp of the subject matter alongside a comprehensive awareness of existing scholarly dialogues and theories relevant to the topic. The hypothesis acts as a foundational pillar that directs the analytical pathways of the investigation, anchoring the exploration with grounded expectations based on existing knowledge.

In the formulation of a hypothesis, researchers must adhere to vital principles to ensure the creation of a substantial and verifiable statement. A robust hypothesis is delineated by several attributes, including precision, testability, and a congruent alignment with established research and theories. Moreover, it is formulated to facilitate empirical substantiation, aiming to either confirm or refute the established propositions through systematic investigation.

To deepen our comprehension of a hypothesis, let us examine some examples in different research contexts, illustrating how a hypothesis can shape and steer a study:

  • Individuals between the ages of 40 and 60 who engage in regular physical activity are less likely to develop heart diseases than those who do not.
  • Adolescents who experience traumatic events during the COVID-19 pandemic have a higher prevalence of mental health issues than those who do not.
  • Remote learning hampers the development of social skills in elementary school students more than traditional classroom learning does.
  • Implementing multicultural education strategies diminishes the achievement gap in multicultural classrooms.
  • Marine ecosystems that experience high levels of plastic pollution exhibit a substantial reduction in biodiversity.
  • Urbanization leads to a significant decrease in biodiversity in metropolitan areas due to habitat loss.
  • Voting behavior in urban communities is significantly influenced by the socioeconomic status of the individuals.
  • The prevalent use of social media significantly influences the formation of societal norms and behaviors in contemporary society.
  • The integration of artificial intelligence in manufacturing elevates efficiency and productivity.
  • An increased dependence on digital platforms compromises personal privacy and heightens the risk of data security breaches.

Each of these hypothesis examples is constructed to offer focused and testable propositions, rooted in contemporary concerns, creating a pathway for empirical verification and the generation of data-driven insights.

Understanding research questions

A critical first step in any research endeavor is the formulation of a research question, a task that requires a deep understanding of both the topic at hand and the existing scholarly landscape surrounding it. The research question serves as the beacon that guides the trajectory of the investigation, providing a focal point that centers the research activities and objectives.

In constructing a research question, scholars must be guided by certain key principles to ensure that their inquiry is both meaningful and fruitful. A well-framed research question is characterized by clarity, specificity, and a sensible alignment with existing research, which aids in building upon established foundations to foster novel insights within its scholarly domain.

To further understand the concept of research questions, let us consider some concrete examples from various fields that illustrate how a well-articulated research question can guide a research project:

  • How does lifestyle affect the risk of heart disease in adults aged 40-60?
  • What impact has the COVID-19 pandemic had on mental health outcomes in adolescents?
  • How does remote learning impact the academic performance and social skills of elementary school students?
  • What strategies can be employed to reduce the achievement gap in multicultural classrooms?
  • What are the effects of plastic waste on marine ecosystems?
  • How does urbanization impact biodiversity in metropolitan regions?
  • How do socioeconomic factors influence voting behavior in urban communities?
  • What role does social media play in shaping contemporary societal norms and behaviors?
  • How does the implementation of artificial intelligence in manufacturing enhance efficiency and productivity?
  • What are the implications of increasing reliance on digital platforms for personal privacy and data security?

Each of these research question examples not only maintains a clear focus on a specific topic but also stands grounded in current concerns, thereby paving the way for empirical exploration and data-driven conclusions.

Key differences between a hypothesis and a research question

In scholarly research, it is imperative to differentiate clearly between a hypothesis and a research question. The following table delineates the comparative aspects of both concepts:

AspectHypothesisResearch Question
DefinitionA testable statement based on existing knowledge and theories.A question that guides the research, aiming to explore a specific aspect of the study topic.
PurposeTo propose a possible explanation for a phenomenon that can be tested.To identify a topic or issue to be explored and analyzed.
FormationFormed based on literature review and theoretical understanding.Formed through a process of inquiry into the existing literature and identifying gaps or unanswered questions.
TestabilityIt should be testable through experimentation or analysis.It may not be directly testable but guides the research towards data collection and analysis.
ScopeGenerally narrower, focusing on a specific prediction or explanation.Can be broader, seeking to explore a topic deeply and from various angles.
Use in ResearchOften used in experimental, .Frequently utilized in to explore and understand phenomena in depth.
Outcome ExpectationSeeks to prove or disprove a specific statement.Aims to answer open-ended questions and does not seek to prove or disprove a statement.
FlexibilityGenerally fixed; alterations can significantly affect the research outcomes.Can be more flexible, allowing for refinements throughout the research process.
Structural ComplexityCan vary; generally seeks to maintain a level of simplicity to facilitate testing.May involve complex, multi-faceted questions to encourage broad exploration.
FoundationOften grounded in established theories and preliminary research.Can be grounded in a perceived gap in knowledge or arising from exploratory research.
Role in Deductive and Inductive ResearchCentral in deductive research where it guides testing and validation.More frequently used in inductive research where the goal is to develop a theory.

When to use which

The decision to use a hypothesis or a research question largely hinges on the nature and objectives of the study. Essentially, researchers delineate between exploratory and confirmatory research . The former seeks to explore new phenomena and generate new insights, while the latter aims to verify existing theories and hypotheses. Understanding the correct circumstance for employing either a research question or a hypothesis can significantly streamline the research process, directing it towards more targeted conclusions. Let's delve into the specific situations where one may be more appropriate over the other.

Situations where a hypothesis is more appropriate

  • Confirmatory Research: When the research is grounded in existing theories and seeks to validate or invalidate a specific claim or relationship.
  • Quantitative Studies: In research designs that predominantly involve statistical analysis of numerical data to address the research problem.
  • Experimental Research: Where controlled experiments are conducted to explore the causal relationships between different variables.
  • Deductive Approaches: When the research follows a deductive approach , deriving a specific prediction from a general theory.

Situations where a research question is more appropriate

  • Exploratory Research: In studies aiming to explore a new field or topic without much existing literature or established theories.
  • Qualitative Research: When the study involves analyzing non-numerical data such as texts, interviews, or observational data to garner insights.
  • Pilot Studies: Preliminary studies that aim to identify potential issues and refine research tools before a large-scale study.
  • Inductive Approaches: Research approaches that work from specific observations to broader generalizations, aiming to develop new theories.

The interrelation between hypotheses and research questions

Understanding how a research question can give rise to hypotheses.

In scholarly inquiries, the formation of a hypothesis often finds its genesis in a well-articulated research question. This dynamic represents a pivotal juncture in research methodology, facilitating a transition from questioning to hypothesizing and setting the stage for focused analytical scrutiny. Leveraging the exploratory nature of research questions can foster the formulation of grounded hypotheses, guiding the investigative trajectory towards evidence-based conclusions.

Indeed, a well-structured research question can give rise to a series of hypotheses, each presenting a plausible answer to the research question and serving as a focal point for systematic investigation. This correlation facilitates a scaffolded approach to exploration, where researchers can build a layered understanding through a structured inquiry process.

Can a hypothesis transform into a research question?

This iterative process we have described can be envisioned as a cyclic pathway rather than a linear trajectory, wherein hypotheses, once tested and analyzed, can refine or even reformulate the initial research questions. This reflexive relationship fosters a deepened understanding and a more nuanced exploration of the research topic at hand.

To illustrate, consider a research question in the field of healthcare: "What are the primary factors influencing sleep quality in adults?" From this question, a researcher might derive several hypotheses, such as "Adults who engage in regular physical activity experience better sleep quality than those who do not." Once this hypothesis is tested, the findings could lead to further questions, fine-tuning the initial research query to delve into specific age groups, lifestyle factors, or physiological aspects, thereby perpetuating a cycle of inquiry that propels the research into deeper and more focused directions.

Research questions serve as the launchpad for scientific exploration, fostering a direction and scope that steer investigations towards relevant and focused pathways. Conversely, hypotheses act as tentative answers to these research questions, laying a grounded foundation for systematic investigations and guiding the trajectory towards evidence-based conclusions.

Selecting the right approach—whether formulating a hypothesis or crafting a research question—is not merely a procedural choice; it is a strategic decision that significantly influences the outcome of the investigation. Recognizing the interdependent and reflexive relationship between the two can foster a more robust and nuanced approach to scientific inquiry.

By embracing the cyclic pathway that intertwines questioning with hypothesizing, researchers can unlock deeper levels of understanding, paving the way for profound discoveries enriched with insight. Remember, the quality of the answers we obtain is invariably linked to the quality of the questions we ask and the hypotheses we formulate.

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Research Hypothesis vs. Research Question: What's the Difference?

relationship between research questions and hypothesis

Key Differences

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relationship between research questions and hypothesis

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relationship between research questions and hypothesis

Research Question vs Hypothesis: Difference and Comparison

Key Takeaways A research question is a broad inquiry into a topic, while a hypothesis is a statement that explains a phenomenon. Research questions are open-ended and exploratory, while hypotheses are specific and testable. Research questions are used in qualitative research, while hypotheses are used in quantitative research.

 Research Question vs Hypothesis

Comparison table.

DefinitionResearch Questions is the question that the research tends to answer.Hypothesis is the statement that tends to predict the outcome of the research.
NatureIt has an inquisitive nature.It is an assumption.
StructureIt is written as a question. For example, “What will be the effect on the water when cooled up to its freezing point?”It is written in the form of a statement. For example, “Water turns into ice when cooled up to its freezing point.”
FieldsA research question is posed in the theory papers of subjects like sociology, literature, etc.Hypothesis is written in the research papers related to the fields of science, mathematics, etc.
OutcomesSince it is a question, it provides for the possibility of a great number of outcomes.Being a predictive statement, the number of outcomes is reduced to a minimum.

What is Research Question?

Similar reads, what is hypothesis, main differences between research question and hypothesis, share this post, 19 thoughts on “research question vs hypothesis: difference and comparison”.

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Quantitative vs. Qualitative Research Design: Understanding the Differences

relationship between research questions and hypothesis

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

  • often used in fields related to education, sociology and anthropology; 
  • designed to arrive at conclusions regarding social phenomena; 
  • focused on data-gathering techniques like interviews, focus groups or case studies; 
  • dedicated to perpetuating a flexible, adaptive approach to data gathering;
  • known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

  • you work in a field concerned with enhancing humankind through the lens of social change;
  • your research focuses on understanding complex social trends and individual perceptions of those trends;
  • you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

  • often used in fields related to education, data analysis and healthcare; 
  • designed to arrive at numerical, statistical conclusions based on objective facts;
  • focused on data-gathering techniques like experiments, surveys or observations;
  • dedicated to using mathematical principles to arrive at conclusions;
  • known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

  • you work in a field concerned with analyzing data to inform decisions;
  • your research focuses on studying relationships between variables to form data-driven conclusions;
  • you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

At Peabody College, you will experience

  • an inclusive, welcoming community of like-minded professionals;
  • the guidance of expert faculty with real-world industry experience;
  • opportunities for valuable, hands-on learning experiences,
  • the option of specializing depending on your specific area of interest.

Explore our monthly publication — Ideas in Action — for an inside look at how Peabody College translates discoveries into action.

Please click below to explore a few of the graduate degrees offered at Peabody College:

  • Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
  • Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
  • Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
  • Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

Connect with the Community of Professionals Seeking to Enhance Humankind at Peabody College

At Peabody College, we equip you with the marketable, transferable skills needed to secure a valuable career in education and beyond. You will emerge from the graduate program of your choice ready to enhance humankind in more meaningful ways than you could have imagined.

If you want to develop the sought-after skills needed to be a force for change in the social and educational spaces, you are in the right place .

We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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relationship between research questions and hypothesis

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Mary wants to determine if there is a relationship between room color and sleep. Based on her research, Mary makes an educated guess that people fall asleep more quickly in a room painted blue than in a room painted yellow. She asks several people which color they like better—yellow or blue—and uses their responses to determine if her educated guess was correct. What can you infer is missing from Mary's scientific investigation? A testable hypothesis that is based on research An experiment that directly tests the hypothesis Variables to be tested by an experiment Research on the topic to see what others have learned

An experiment that directly test the hypothesis.

Explanation:

Although there is quite a bit missing from Mary's scientific investigation, the thing that sticks out the most is the fact that Mary is missing an experiment. Mary's hypothesis states that people would fall asleep faster if their room color were to be painted blue. To prove that her hypothesis is true, Mary would need to conduct an experiment to see whether or not she was correct.

The missing part from Mary's scientific investigation is an experiment that directly tests the hypothesis . The correct option is B.

A scientific investigation is a revealing the secrets of truth about a scientific thing. They include scientific variables, methods, and instruments. This is research that leads to discoveries of new things.

Mary is researching sleep and whether the color of the room is dependent or not. The variable is the color of the room. She is researching a relationship between room color and sleep.

She just asked people and guess what color the people will like to sleep. But this research lacks proper scientific investigation.

Thus, the correct option is B. An experiment that directly tests the hypothesis.

To learn more about the scientific investigation, refer to the link:

https://brainly.com/question/8386821

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What are the strengths and limitations to this model? (30 Points, answer fast please.) Also, if you know any, any anime reccomendations?

Which of the following is a true statement about the map? (1 point) The border between Washington and Oregon is a thin, black line; this indicates a state boundary. The border between Washington and Canada is a thin, black line; this indicates a national boundary. The border between Washington and Oregon is a blue line; this indicates a national boundary. The border between Washington and Canada is a thick, black line; this indicates a state boundary.

The border between Washington and Oregon is a thin, black line; this indicates a state boundary.

Explanation: if you look at the map you can see the black line.

the second one is true

Asexual reproduction + cell ________ itself by itself, exactly the same?

Alana wants to measure the speed at which drops of precipitation fall to the ground in Los Angeles. Which statement identifies a way Alana uses technology to complete her experiment? She observes daily rainfall data from multiple weather stations throughout the city. She graphs the relationship between rainfall speed and time using graphing paper. She analyzes a map to determine different places to collect rainfall for the study. She estimates how fast rain falls by studying the sky at different locations in the city.

I think that it is (b) she graphs the relationship between rainfall speed and time using graphing paper

xfhfhhxhfxh

Choose all that apply. What tools do scientists use to make decisions when employing reasoning skills? skepticism logic creativity knowledge !MULTI ANSWER!

Domain Kingdom Kingdom Kingdom Prokaryote: ________ ________ Eukaryote: ________ __________ _________ ________

Which of the following was derived from an ancestral free-living bacteria? Lysosome Vacuole Mitochondrion Golgi Appartus

Option C Mitochondrion

The mitochondrion was derived from an ancestral free-living bacteria

Do you think monogamy and polygamy are reproductive strategies? In terms of species survival, what are the advantages and disadvantages of each method?

Indeed, both monogamy and polygamy are reproductive strategies adopted by the different species of the animal world.

Polygamy implies the adoption of a unique and stable partner by an individual, with which in principle it will reproduce. Different species, including humans or penguins, in addition to maintaining the same pair for reproductive purposes, generate bonds of coexistence.

Other species (in fact, most animal species) are not guided by this type of relationship, but fulfill their reproductive functions in a random way, that is, with several individuals of the same species in a random way, which is called polygamy. In this case, there is a constant change in the reproductive couple, which responds to multiple factors such as for example a gender disproportion within the species (for example, many females and few males would make those few males have to comply reproductive functions with several females during the mating period), or due to the non-sedentary nature of life of these species.

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prokaryotic and eukaryotic cells

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what is 4.702 x 10–4 ( -4 is the exponent ) in standard notation

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i would think to glucose

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Relationship between visual attention patterns and subjective evaluations in housing sales information: a study using eye-tracking technology.

relationship between research questions and hypothesis

1. Introduction

2. methodology, 2.1. subjects, 2.2. stimuli, 2.3. measures, 2.4. development of study, 2.5. data processing, 3.1. hypothesis i: there is a relationship between subjects’ viewing pattern and their evaluation of the observed houses, 3.2. hypothesis ii: there are significant differences in the viewing pattern based on participants’ gender, 3.3. hypothesis iii: there are significant differences in the viewing pattern based on the level of expertise in the sector, 4. discussion, 5. conclusions, author contributions, data availability statement, conflicts of interest.

  • Seiler, M.; Madhavan, P.; Liechty, M. Toward an understanding of real estate homebuyer internet search behavior: An application of ocular tracking technology. J. Real Estate Res. 2012 , 34 , 211–242. [ Google Scholar ] [ CrossRef ]
  • Baker, R.; Yu, U.J.; Gam, H.J.; Banning, J. Identifying tween fashion consumers’ profile concerning fashion innovativeness, opinion leadership, internet use for apparel shopping, interest in online co-design involvement, and brand commitment. Fash. Text. 2019 , 6 , 8. [ Google Scholar ] [ CrossRef ]
  • Noris, A.; Nobile, T.H.; Kalbaska, N.; Cantoni, L. Digital fashion: A systematic literature review. A perspective on marketing and communication. J. Glob. Fash. Mark. 2021 , 12 , 32–46. [ Google Scholar ] [ CrossRef ]
  • Xiang, Z.; Wang, D.; O’Leary, J.T.; Fesenmaier, D.R. Adapting to the internet: Trends in travelers’ use of the web for trip planning. J. Travel Res. 2015 , 54 , 511–527. [ Google Scholar ] [ CrossRef ]
  • Chung, N.; Koo, C. The use of social media in travel information search. Telemat. Inform. 2015 , 32 , 215–229. [ Google Scholar ] [ CrossRef ]
  • Jeong, Y.; Lee, Y. A study on the customer satisfaction and customer loyalty of furniture purchaser in on-line shop. Asian J. Qual. 2010 , 11 , 146–156. [ Google Scholar ] [ CrossRef ]
  • Montañana, A.; Nolé, M.L.; Llinares, C. Strategic Design Approaches for Eliciting the Perception of ‘Prestige’ in Housing Consumers. Buildings 2024 , 14 , 853. [ Google Scholar ] [ CrossRef ]
  • Montañana, A.; Llinares, C.; Page, Á.F. Modelling design requirements of a floor plan. Open House Int. 2015 , 40 , 88–93. [ Google Scholar ] [ CrossRef ]
  • Kupke, V. Factors important in the decision to buy a first home. Pac. Rim Prop. Res. J. 2008 , 14 , 458–476. [ Google Scholar ] [ CrossRef ]
  • Hofman, E.; Halman, J.I.; Ion, R.A. Variation in housing design: Identifying customer preferences. Hous. Stud. 2006 , 21 , 929–943. [ Google Scholar ] [ CrossRef ]
  • Larson, R.B. Controlling social desirability bias. Int. J. Mark. Res. 2019 , 61 , 534–547. [ Google Scholar ] [ CrossRef ]
  • Tatman, A.W.; Kreamer, S. Psychometric properties of the Social Desirability Scale-17 with individuals on probation and parole in the United States. IJCJS 2014 , 9 , 122. [ Google Scholar ]
  • Zaltman, G. How Customers Think: Essential Insights into the Mind of the Market ; Harvard Business Press: Brighton, MA, USA, 2003. [ Google Scholar ]
  • Bagozzi, R.P.; Verbeke, W.J.; Dietvorst, R.C.; Belschak, F.D.; van den Berg, W.E.; Rietdijk, W.J. Theory of mind and empathic explanations of Machiavellianism: A neuroscience perspective. J. Manag. 2013 , 39 , 1760–1798. [ Google Scholar ] [ CrossRef ]
  • Oatley, K.; Jenkins, J.M. Human emotions: Function and dysfunction. Annu. Rev. Psychol. 1992 , 43 , 55–85. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Lazarus, R.S. Progress on a cognitive-motivational-relational theory of emotion. Am. Psychol. 1991 , 46 , 819–834. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Janowski, A.; Renigier-Biłozor, M.; Walacik, M.; Chmielewska, A. EMOTIF—A system for modeling 3D environment evaluation based on 7D emotional vectors. Inf. Sci. 2024 , 662 , 120256. [ Google Scholar ] [ CrossRef ]
  • Stewart, D.W.; Furse, D.H. Applying psychophysiological measures to marketing and advertising research problems. JCIRA 1982 , 5 , 1–38. [ Google Scholar ]
  • Vartanian, O.; Navarrete, G.; Chatterjee, A.; Fich, L.B.; Gonzalez-Mora, J.L.; Leder, H.; Skov, M. Architectural design and the brain: Effects of ceiling height and perceived enclosure on beauty judgments and approach-avoidance decisions. J. Environ. Psychol. 2015 , 41 , 10–18. [ Google Scholar ] [ CrossRef ]
  • Iñarra, S.; Vidal, F.J.; Llinares, C.; Guixeres, J. Visual attention in the evaluation of architectural spaces. EGA 2015 , 20 , 228–237. [ Google Scholar ]
  • Higuera-Trujillo, J.L.; Llinares, C.; Macagno, E. The cognitive-emotional design and study of architectural space: A scoping review of neuroarchitecture and its precursor approaches. Sensors 2021 , 21 , 2193. [ Google Scholar ] [ CrossRef ]
  • Schachter, S.; Singer, J. Cognitive, social, and physiological determinants of emotional state. Psychol. Rev. 1962 , 69 , 379. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Ma, X.; Monfared, R.; Grant, R.; Goh, Y.M. Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability. Sensors 2024 , 24 , 2010. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Brown, C.L.; Van Doren, N.; Ford, B.Q.; Mauss, I.B.; Sze, J.W.; Levenson, R.W. Coherence between subjective experience and physiology in emotion: Individual differences and implications for well-being. Emotion 2020 , 20 , 818. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Tsai, C.M.; Guan, S.S. Identifying regions of interest in reading an image. Displays 2015 , 39 , 33–41. [ Google Scholar ] [ CrossRef ]
  • Carlson, N.R.; Platón, M.J.R.; Carson, N.R.; Urbano, B.C. Fundamentos de Fisiología de la Conducta ; Pearson Educación: London, UK, 2010. [ Google Scholar ]
  • Coubard, O.A. Saccade and vergence eye movements: A review of motor and premotor commands. Eur. J. Neurosci. 2013 , 38 , 3384–3397. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Carter, B.T.; Luke, S.G. Best practices in eye tracking research. Int. J. Psychophysiol. 2020 , 155 , 49–62. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Hoffman, J.E. Visual attention and eye movements. In Attention ; Psychology Press: London, UK, 2016; pp. 119–153. [ Google Scholar ]
  • Pieters, R.; Wedel, M. Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. J. Mark. 2004 , 68 , 36–50. [ Google Scholar ] [ CrossRef ]
  • Wedel, M.; Pieters, R. Eye tracking for visual marketing. Found. Trends Mark. 2008 , 1 , 231–320. [ Google Scholar ] [ CrossRef ]
  • Duchowski, A.; Duchowski, A. Eye tracking techniques. In Eye Tracking Methodology: Theory and Practice ; Springer: Berlin/Heidelberg, Germany, 2007; pp. 51–59. [ Google Scholar ]
  • Hwang, Y.M.; Lee, K.C. Using an eye-tracking approach to explore gender differences in visual attention and shopping attitudes in an online shopping environment. Int. J. Hum.-Comput. Interact. 2018 , 34 , 15–24. [ Google Scholar ] [ CrossRef ]
  • Sargezeh, B.A.; Tavakoli, N.; Daliri, M.R. Gender-based eye movement differences in passive indoor picture viewing: An eye-tracking study. Physiol. Behav. 2019 , 206 , 43–50. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Shen, W.; Shen, Q.; Sun, Q. Building Information Modeling-based user activity simulation and evaluation method for improving designer–user communications. Autom. Constr. 2012 , 21 , 148–160. [ Google Scholar ] [ CrossRef ]
  • Ishikawa, T.; Nakata, S.; Asami, Y. Perception and conceptualization of house floor plans: An experimental analysis. Environ. Behav. 2011 , 43 , 233–251. [ Google Scholar ] [ CrossRef ]
  • Boumová, I.; Zdráhalová, J. The apartment with the best floor plan layout: Architects versus non-architects. Crit. Hous. Anal. 2016 , 3 , 30–41. [ Google Scholar ] [ CrossRef ]
  • Vogt, S.; Magnussen, S. Expertise in pictorial perception: Eye-movement patterns and visual memory in artists and laymen. Perception 2007 , 36 , 91–100. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Tallon, M.; Greenlee, M.W.; Wagner, E.; Rakoczy, K.; Frick, U. How do art skills influence visual search? Eye movements analyzed with hidden markov models. Front. Psychol. 2021 , 12 , 594248. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Dogusoy-Taylan, B.; Cagiltay, K. Cognitive analysis of experts’ and novices’ concept mapping processes: An eye tracking study. Comput. Human. Behav. 2014 , 36 , 82–93. [ Google Scholar ] [ CrossRef ]
  • Gegenfurtner, A.; Lehtinen, E.; Säljö, R. Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educ. Psychol. Rev. 2011 , 23 , 523–552. [ Google Scholar ] [ CrossRef ]
  • Stofer, K.; Che, X. Comparing experts and novices on scaffolded data visualizations using eye-tracking. JEMR 2014 , 7 , 1–15. [ Google Scholar ] [ CrossRef ]
  • National Association of Realtors (NAR). Highlights From the Profile of Home Buyers and Sellers. 2023. Available online: https://store.realtor/2023-nar-profile-of-home-buyers-and-sellers-download/ (accessed on 9 January 2024).
  • Judd, T.; Durand, F.; Torralba, A. Fixations on low-resolution images. J. Vis. 2011 , 11 , 14. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Torralba, A.; Oliva, A.; Castelhano, M.S.; Henderson, J.M. Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search. Psychol. Rev. 2006 , 113 , 766. [ Google Scholar ] [ CrossRef ]
  • Llinares, C.; Page, A. Application of product differential semantics to quantify purchaser perceptions in housing assessment. Build. Environ. 2007 , 42 , 2488–2497. [ Google Scholar ] [ CrossRef ]
  • Terninko, J. Step-by-Step QFD: Customer-Driven Product Design ; Routledge: London, UK, 2018. [ Google Scholar ]
  • Holmqvist, K.; Nyström, M.; Andersson, R.; Dewhurst, R.; Jarodzka, H.; Van de Weijer, J. Eye Tracking: A Comprehensive Guide to Methods and Measures ; OUP Oxford: Oxford, UK, 2011. [ Google Scholar ]
  • Hessels, R.S.; Benjamins, J.S.; Cornelissen, T.H.; Hooge, I.T. A validation of automatically-generated areas-of-interest in videos of a face for eye-tracking research. Front. Psychol. 2018 , 9 , 1367. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Sugano, Y.; Matsushita, Y.; Sato, Y. Graph-based joint clustering of fixations and visual entities. ACM Trans. Appl. Percept. (TAP) 2013 , 10 , 1–16. [ Google Scholar ] [ CrossRef ]
  • Fisher, R.A. On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1921 , 1 , 3–32. [ Google Scholar ]
  • Zou, G. Toward using confidence intervals to compare correlations. Psychol. Methods 2007 , 12 , 399–413. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Cleophas, T.J.; Zwinderman, A.H.; Cleophas, T.J.; Zwinderman, A.H. Monte Carlo Tests and Bootstraps for Analysis of Complex Data (10, 20, 139, and 55 Patients). In SPSS for Starters, Part 2 ; Springer: Dordrecht, The Netherlands, 2012; pp. 87–90. [ Google Scholar ]
  • Ben-Shachar, M.S.; Lüdecke, D.; Makowski, D. Effectsize: Estimation of effect size indices and standardized parameters. J. Open Source Softw. 2020 , 5 , 2815. [ Google Scholar ] [ CrossRef ]
  • Tomczak, M.; Tomczak, E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 2014 , 1 , 19–25. [ Google Scholar ]
  • Clement, J.; Kristensen, T.; Grønhaug, K. Understanding consumers’ in-store visual perception: The influence of package design features on visual attention. J. Retail. Consum. Serv. 2013 , 20 , 234–239. [ Google Scholar ] [ CrossRef ]
  • Leahy, W.; Sweller, J. Cognitive load theory, modality of presentation and the transient information effect. Appl. Cogn. Psychol. 2011 , 25 , 943–951. [ Google Scholar ] [ CrossRef ]
  • Spriggs, M.J.; Kirk, I.J.; Skelton, R.W. Hex Maze: A new virtual maze able to track acquisition and usage of three navigation strategies. Behav. Brain Res. 2018 , 339 , 195–206. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Andersen, N.E.; Dahmani, L.; Konishi, K.; Bohbot, V.D. Eye tracking, strategies, and sex differences in virtual navigation. Neurobiol. Learn. Mem. 2012 , 97 , 81–89. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Franěk, M.; Šefara, D.; Petružálek, J.; Cabal, J.; Myška, K. Differences in eye movements while viewing images with various levels of restorativeness. J. Environ. Psychol. 2018 , 57 , 10–16. [ Google Scholar ] [ CrossRef ]
  • Zhang, Y.; Yang, J. Exploring gender differences in the instructor presence effect in video lectures: An eye-tracking study. Brain Sci. 2022 , 12 , 946. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Rodriguez, S.; Regueiro, B.; Piñeiro, I.; Estévez, I.; Valle, A. Gender differences in mathematics motivation: Differential effects on performance in primary education. Front. Psychol. 2020 , 10 , 505859. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Cook, M.P. Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Sci. Educ. 2006 , 90 , 1073–1091. [ Google Scholar ] [ CrossRef ]
  • Beukema, S.; Jennings, B.J.; Olson, J.A.; Kingdom, F.A. The pupillary response to the unknown: Novelty versus familiarity. i-Perception 2019 , 10 , 2041669519874817. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Richmond, J.L.; Zhao, J.L.; Burns, M.A. What goes where? Eye tracking reveals spatial relational memory during infancy. J. Exp. Child Psychol. 2015 , 130 , 79–91. [ Google Scholar ] [ CrossRef ] [ PubMed ]
  • Yoo, C.Y. Unconscious processing of web advertising: Effects on implicit memory, attitude toward the brand, and consideration set. J. Interact. Mark. 2009 , 22 , 2–18. [ Google Scholar ] [ CrossRef ]
  • Godfroid, A.; Hui, B. Five common pitfalls in eye-tracking research. Second Lang. Res. 2020 , 36 , 277–305. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

GenderProfessionAgeInterest on the Purchase of a HomeVision
Male
11 (52.38%)
Expert
11 (52.38%)
<30
7 (33.33%)
None
-
Incorrect
-
Female
10 (47.62%)
Non-expert
10 (47.62%)
30–40
4 (19.05%)
Medium
8 (38.1%)
Correct without support
5 (23.81%)
40–50
6 (28.57%)
High
13 (61.9%)
Correct with support
16 (76.19%)
>50
4 (19.05%)
HypothesesStudy VariablesStatistical Test
PhysiologicalAssessmentIntrasubject
IFFT AOI (x7)
TFD AOI (x7)
design atributtes assessments (x9)-Spearman Correlation Test
IIFFT AOI (x7)
TFD AOI (x7)
design atributtes assessments (x9)Gender (Male/Female)Mann Whitney U comparison test
IIIFFT AOI (x7)
TFD AOI (x7)
design atributtes assessments (x9)Profession
(expert/non-expert)
Mann Whitney U comparison test
Mean RankSum of RankUp
FFT-Photorealistic RenderMale29.511803600.059
Female38.6965
FFT-Technical specificationsMale43.3818226770.244
Female37.321418
FFT-OrientationMale36.5914276470.329
Female41.471576
FFT-Floor PlanMale35.861398.5618.50.623
Female38.311302.5
FFT-FloorMale33.5913105300.141
Female40.911391
FFT-AreaMale21.6453.592.50.037 *
Female14.17212.5
FFT-LocationMale18.623911550.936
Female18.33275
TFD-Photorealistic RenderMale39.381614.5752.50.960
Female39.641466.5
TFD-Technical specificationsMale24.29631.5213.5.441
Female21.24403.5
TFD-orientationMale33.281264.5523.50.057
Female42.851585.5
TFD-Floor PlanMale22.965972460.982
Female23.05438
TFD-FloorMale20.1522.5140.50.626
Female18.21218.5
TFD-AreaMale32.681274.5480.50.923
Female32.22805.5
TFD-LocationMale18.44601350.626
Female20.25243
Mean RankSum of RankUp
FFT-Photorealistic RenderNon-expert32.7812135100.916
Expert33.29932
FFT-Technical specificationsNon-expert41.7320037090.562
Expert38.661237
FFT-OrientationNon-expert37.891705572.50.598
Expert40.561298
FFT-Floor PlanNon-expert38.051750.5572.50.579
Expert35.2950.5
FFT-FloorNon-expert38.7217815420.367
Expert34.07920
FFT-AreaNon-expert20.194241220.254
Expert16.13242
FFT-LocationNon-expert19.14011450.688
Expert17.67265
TFD-Photorealistic RenderNon-expert34.7615995180.027 *
Expert46.311482
TFD-Technical specificationsNon-expert22.29579.5228.50.671
Expert23.97455.5
TFD-orientationNon-expert36.715786320.549
Expert39.751272
TFD-Floor PlanNon-expert25.696681770.108
Expert19.32367
TFD-FloorNon-expert19.594311740.953
Expert19.38310
TFD-AreaNon-expert30.510984320.330
Expert35.07982
TFD-LocationNon-expert18.293841530.646
Expert19.94319
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Share and Cite

de-Juan-Ripoll, C.; Nolé, M.L.; Montañana, A.; Llinares, C. Relationship between Visual Attention Patterns and Subjective Evaluations in Housing Sales Information: A Study Using Eye-Tracking Technology. Buildings 2024 , 14 , 2106. https://doi.org/10.3390/buildings14072106

de-Juan-Ripoll C, Nolé ML, Montañana A, Llinares C. Relationship between Visual Attention Patterns and Subjective Evaluations in Housing Sales Information: A Study Using Eye-Tracking Technology. Buildings . 2024; 14(7):2106. https://doi.org/10.3390/buildings14072106

de-Juan-Ripoll, Carla, María Luisa Nolé, Antoni Montañana, and Carmen Llinares. 2024. "Relationship between Visual Attention Patterns and Subjective Evaluations in Housing Sales Information: A Study Using Eye-Tracking Technology" Buildings 14, no. 7: 2106. https://doi.org/10.3390/buildings14072106

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  • Confronting Healthcare Disinformation on Social Media

Benedetta Pagni

July 09, 2024

More than 90% of internet users are active on social media, which had 4.76 billion users worldwide in January 2023. The digital revolution has reshaped the news landscape and changed how users interact with information. Social media has fostered an active relationship with the media, including the ability to interact directly with the content presented. It also has augmented media's ability to reach a large audience with tight deadlines.

These developments suggest that social media can be a useful tool in everyday medical practice for professionals and patients. But social media also can spread misinformation, as happened during the COVID-19 pandemic.

This characteristic is the focus of the latest research by Fabiana Zollo, a computer science professor at Ca' Foscari University of Venice, Italy, and coordinator of the Data Science for Society laboratory. The research was published in The BMJ. Zollo's research group aims to assess the effect of social media on misinformation and consequent behaviors related to health. "The study results focus primarily on two topics, the COVID-19 pandemic and vaccinations, but can also be applied to other health-related behaviors such as smoking and diet," Zollo told Univadis Italy .

Social media has become an important tool for public health organizations to inform and educate citizens. Institutions can use it to monitor choices and understand which topics are being discussed most at a given time, thus comprehending how the topics evolve and take shape in public discourse. "This could lead to the emergence of people's perceptions, allowing us to understand, among other things, what the population's needs might be, including informational needs," said Zollo.

Tenuous Causal Link

While social media offers public health organizations the opportunity to inform and engage the public, it also raises concerns about misinformation and the difficulty of measuring its effect on health behavior. Although some studies have observed correlations between exposure to misinformation on social media and levels of adherence to vaccination campaigns, establishing a causal link is complex. As the authors emphasize, "despite the importance of the effect of social media and misinformation on people's behavior and the broad hypotheses within public and political debates, the current state of the art cannot provide definitive conclusions on a clear causal association between social media and health behaviors." Establishing a clear causal link between information obtained from social media and offline behavior is challenging due to methodologic limitations and the complexity of connections between online and offline behaviors. Studies often rely on self-reported data, which may not accurately reflect real behaviors, and struggle to isolate the effect of social media from other external influences. Moreover, many studies primarily focus on Western countries, limiting the generalizability of the results to other cultural and geographical conditions. 

Another issue highlighted by Zollo and colleagues is the lack of complete and representative data. Studies often lack detailed information about participants, such as demographic or geolocation data, and rely on limited samples. This lack makes it difficult to assess the effect of misinformation on different segments of the population and in different geographic areas.

"The main methodologic difficulty concerns behavior, which is difficult to measure because it would require tracking a person's actions over time and having a shared methodology to do so. We need to understand whether online stated intentions do or do not translate into actual behaviors," said Zollo. Therefore, despite the recognized importance of the effect of social media and misinformation on people's general behavior and the broad hypotheses expressed within public and political debates, the current state of the art cannot provide definitive conclusions on a causal association between social media and health behaviors.

Institutions' Role

Social media are a fertile ground for the formation of echo chambers (where users find themselves dialoguing with like-minded people, forming a distorted impression of the real prevalence of that opinion) and for reinforcing polarized positions around certain topics. "We know that on certain topics, especially those related to health, there is a lot of misinformation circulating precisely because it is easy to leverage factors such as fear and beliefs, even the difficulties in understanding the technical aspects of a message," said Zollo. Moreover, institutions have not always provided timely information during the pandemic. "Often, when there is a gap in response to a specific informational need, people turn elsewhere, where those questions find answers. And even if the response is not of high quality, it sometimes confirms the idea that the user had already created in their mind."

The article published in The BMJ aims primarily to provide information and evaluation insights to institutions rather than professionals or healthcare workers. "We would like to spark the interest of institutions and ministries that can analyze this type of data and integrate it into their monitoring system. Social monitoring (the observation of what happens on social media) is a practice that the World Health Organization is also evaluating and trying to integrate with more traditional tools, such as questionnaires. The aim is to understand as well as possible what a population thinks about a particular health measure, such as a vaccine: Through data obtained from social monitoring, a more realistic and comprehensive view of the problem could be achieved," said Zollo.

A Doctor's Role

And this is where the doctor comes in: All the information thus obtained allows for identifying the needs that the population expresses and that "could push a patient to turn elsewhere, toward sources that provide answers even if of dubious quality or extremely oversimplified." The doctor can enter this landscape by trying to understand, even with the data provided by institutions, what needs the patients are trying to fill and what drives them to seek elsewhere and to look for a reference community that offers the relevant confirmations.

From the doctor's perspective, therefore, it can be useful to understand how these dynamics arise and evolve because they could help improve interactions with patients. At the institutional level, social monitoring would be an excellent tool for providing services to doctors who, in turn, offer a service to patients. If it were possible to identify areas where a disinformation narrative is developing from the outset, both the doctor and the institutions would benefit.

Misinformation Vs Disinformation

The rapid spread of false or misleading information on social media can undermine trust in healthcare institutions and negatively influence health-related behaviors. Zollo and colleagues, in fact, speak of misinformation in their discussion, not disinformation. "In English, a distinction is made between misinformation and disinformation, a distinction that we are also adopting in Italian. When we talk about misinformation, we mean information that is generally false, inaccurate, or misleading but has not been created with the intention to harm, an intention that is present in disinformation," said Zollo.

The distinction is often not easy to define even at the operational level, but in her studies, Zollo is mainly interested in understanding how the end user interacts with content, not the purposes for which that content was created. "This allows us to focus on users and the relationships that are created on various social platforms, thus bypassing the author of that information and focusing on how misinformation arises and evolves so that it can be effectively combated before it translates into action (ie, into incorrect health choices)," said Zollo.

This story was translated from Univadis Italy , which is part of the Medscape Professional Network, using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

Send comments and news tips to [email protected] .

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Microsoft Research Blog

Empowering ngos with generative ai in the fight against human trafficking.

Published July 10, 2024

By Darren Edge , Senior Director Ha Trinh , Senior Data Scientist Dayenne Souza , Software Engineer II

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Tech Against Trafficking, Issara Institute, and Polaris icons on a blue to green gradient background.

Human trafficking and labor exploitation are ancient problems that have evolved with each major leap in technology, from the agricultural revolution to the information age. But what if the right combination of people, data, and technology could help to tackle these problems on an unprecedented scale? With the emergence of generative AI models, which can create rich text and media from natural language prompts and real-world understanding, we are seeing new opportunities to advance the work of organizations that are leading this fight on the front lines.

Presentation of generative AI tools and opportunities at Issara Global Forum, Bangkok, November 2023. Photograph shows presenter, fellow panel members, and audience.

One effort to combat trafficking is the Tech Against Trafficking (opens in new tab) accelerator program, in which tech companies collaborate with anti-trafficking organizations and global experts to help eradicate trafficking with technology. In the latest accelerator, Microsoft worked with Issara Institute (opens in new tab) and Polaris (opens in new tab) to explore how generative AI could help NGOs drive the ethical transformation of global supply chains. By aiming to reduce all forms and levels of worker exploitation, including but not limited to the most serious cases of human trafficking, these organizations aim to make systematic labor exploitation impossible to hide. 

The main issue to contend with, however, is that it is all too easy for such practices to remain hidden, even across datasets that contain evidence of their existence. Many NGOs lack the resources to “connect the dots” at the necessary scale, and time spent on data work is often at the expense of direct assistance activities. Through the accelerator, we developed several first-of-their-kind workflows for real-world data tasks – automating the creation of rich intelligence reports and helping to motivate collective, evidence-based action. We are pleased to announce that we have now combined these workflows into a single system – Intelligence Toolkit (opens in new tab) – and published the code to GitHub for use by the broader community.

Building on multi-stakeholder engagements 

Microsoft Research Podcast

relationship between research questions and hypothesis

What’s Your Story: Jacki O’Neill

Jacki O'Neill saw an opportunity to expand Microsoft research efforts to Africa. She now leads Microsoft Research Africa, Nairobi (formerly MARI). O'Neill talks about the choices that got her there, the lab’s impact, and how living abroad is good for innovation.

Since Microsoft co-founded Tech Against Trafficking (TAT) in 2018, we have worked with a range of UN agencies and NGOs to understand the challenges facing the anti-trafficking community, as well as opportunities for new research technologies (opens in new tab) to drive evidence-based action at scale. For example, our collaboration with IOM (UN Migration) (opens in new tab) in the 2019 TAT accelerator program (opens in new tab) resulted in new tools (opens in new tab) for private data release, as well as new open datasets (opens in new tab) for the community. However, while growing the shared evidence base enables better decision making and policy development, it is not sufficient. NGOs and other anti-trafficking organizations need time and resources to analyze such datasets, discover relevant insights, and write the intelligence reports that drive real-world action. 

For the 2023-2024 TAT accelerator program, we worked with Issara and Polaris to understand the potential for generative AI to support such analysis within their own organizations and geographies of concern (South and Southeast Asia for Issara; Mexico and the U.S. for the Polaris Nonechka (opens in new tab) project). Using a combination of open and internal datasets, we developed and refined a series of proof-of-concept interfaces before sharing them for stakeholder feedback at the annual TAT Summit (opens in new tab) , Issara Global Forum (opens in new tab) , and NetHope Global Summit (opens in new tab) events. We learned many lessons through this process, helping to shape what community-oriented tool we should build, how to build it, and when it should be used: 

  • What: Use to automate analysis and reporting under expert supervision . For NGO staff members that need to divide their time between frontline assistance and data work, any tool that increases the efficiency and quality of data work can create more time for more effective assistance. 
  • How: Use an appropriate combination of statistical and generative methods . Generative AI excels at translating data summaries into narrative reports, but statistical methods are also important for identifying all the potential insights (e.g., patterns, clusters, networks) worth reporting.
  • When: Use for individual-level case data and entity data . Worker voice data (e.g., employer grievances) creates the need to both protect the privacy of workers and connect data across employers in ways that reveal aggregate risk. Neither is well supported by existing data tools.

Developing Intelligence Toolkit as a gateway to generative AI 

For the various intelligence-generating activities shared with us by Issara and Polaris, as well as prior accelerator participants, we developed interactive workflows supported by different combinations of statistical methods and generative AI. Each was developed as a lightweight, no-code user interface that supports the end-to-end process of data upload, preparation, analysis, and export. Our Intelligence Toolkit (opens in new tab) application combines six of these workflows with the most relevance to the broader community. Following the recent TAT showcase event (opens in new tab) that shared how this application was being used internally at both Issara and Polaris, we are pleased to announce the general availability of this software on GitHub (opens in new tab) . 

The six workflows currently supported are:

Data Synthesis generates differentially private datasets and summaries from case records

Our approach to private data release using synthetic data was first developed in the 2019 TAT accelerator program with IOM (UN Migration) (opens in new tab) , and IOM recently used our existing open source tools to release the largest individual-level dataset (opens in new tab) on victims of trafficking that is both publicly available and protected by differential privacy. The synthetic datasets we generate retain the structure and statistics of the original sensitive datasets, but individual records do not represent actual people and the presence of any individual in the sensitive dataset is obscured by calibrated noise injected into the synthesis process. 

Because other workflows require access to individual-level case data, we chose to integrate a streamlined approach to synthetic data generation in Intelligence Toolkit. This workflow was used by both Issara and Polaris to translate worker voice datasets into a form that could be shared with the community, as well as used in other workflows to guarantee that the resulting reports preserve privacy by design. 

Attribute Patterns generates reports on attribute patterns detected in streams of case records 

Our approach to detecting patterns of attributes in timestamped case records was first developed in the 2021 TAT accelerator program with Unseen UK , becoming one of our key tools for discovering insights in real-world data . This approach takes the common activity of “drilling down” into data dashboards by progressively selecting data values of interest and inverts it, generating all combinations of record attributes in each time period that appear interesting from a statistical perspective. It is vastly more efficient for domain experts to review lists of such patterns than to manually search for them one at a time. 

Over the last year, we have collaborated with researchers at Johns Hopkins University and the University of Delaware to redesign this approach using Graph Fusion Encoder Embedding (opens in new tab) . Unlike previous iterations, the Intelligence Toolkit workflow does not end with a list of attribute patterns. Instead, the analyst is invited to use generative AI to create reports that describe the pattern in narrative form, including what it represents, how it has varied over time, what other attributes co-occur with the pattern, what competing hypotheses could potentially explain the pattern, and what possible actions could be taken in response. In this and all subsequent workflows, users can edit the AI system prompts in ways that tailor reports to their specific needs. In the latest TAT accelerator, Issara used this workflow to discover and describe patterns of worker-reported grievances over time. 

Alt text: Screenshot of Attribute Patterns workflow at the “Generate AI pattern reports” stage. The target dataset is Issara worker voice data. The selected attribute pattern shows a peak in the first half of 2020 for Burmese males experiencing working conditions issues in Thailand. The AI-generated pattern report explains this pattern in narrative form, and the editable prompt text allows the user to customize the nature of such pattern reports. 

Group Narratives generates reports by defining and comparing groups of case records

This workflow aims to mimic the kinds of group-level comparisons that often lend structure to data narratives. For example, Polaris was interested in the different routes taken by H-2A visa (opens in new tab) workers from their place of origin to their place of work, the different kinds of grievances they reported, and how this varied by worker age. H-2A workers are highly reported as potential victims of labor trafficking to the National Human Trafficking Hotline (opens in new tab) . This analysis was achieved by specifying a prefilter (H-2A visa), group definition (source-destination), comparison attributes (workload issues, conditions issues, etc.), and comparison window (age band). Given the resulting table of counts, ranks, and deltas, the user is then able to generate AI reports for specific groups, reports comparing the top N groups, and so on. 

Alt text: Screenshot of Group Narratives workflow at the “Generate AI group reports” stage. The target dataset is Polaris worker voice data collected in the Nonechka project. The selected top three routes from worker origin to work site all connect regions of Mexico to sites in North Carolina and reveal a range of reported issues linked to conditions, workload, treatment, payment, and control. The AI-generated group report explains these routes in narrative form, and the editable prompt text allows the user to customize the nature of such group reports. 

Record Matching generates reports on record matches detected across entity datasets 

While previous workflows are independent of the identities of data subjects, in some cases such identities are the very focus of analysis. This often occurs not for case data linked to people, but for entity data linked to organizations. In the TAT accelerator, for example, Issara presented the problem of having two product databases describing many of the same employers, but without any links between common entities or any notion of a canonical identity. Connecting these two databases was critical for providing a comprehensive picture of each employer. The problem is also a general one; it arises whenever organizations seek to combine internal and external data sources on the same real-world entities (e.g., supplier companies). 

Our solution was to create a record matching workflow based on the text embedding capabilities of large language models (LLMs). In addition to generating text, LLMs can also map arbitrary chunks of text into points in vector space, where similar vector positions represent similar semantics for the associated text chunks. Given the text embeddings of entity records taken from different databases, the tool is therefore able to identify groups of sufficiently similar entities so as to suggest a real-world match. Generative AI is then used to evaluate and prioritize these matches for human review and potential record linking. 

Risk Networks generates reports on risk exposure for networks of related entities 

Our risk networks workflow builds on our earlier work tackling corruption in the public procurement process , providing a streamlined interface for inferring entity relationships from shared attributes and then propagating red flag risks throughout the resulting networks. As in the record matching workflow, text embeddings are used to identify fuzzy matches between similar entity names and contact details that have different spellings or formats. Since LLMs tend to struggle with graph reasoning problems, the workflow computes and converts to text all shortest paths from flagged entities to the target entity of the network. These path descriptions then provide context for the LLM to reason about the potential for relationship-mediated risk exposure among entities with different degrees of relatedness and similarity. In the TAT accelerator, Polaris used this workflow together with open-source intelligence to analyze risk patterns within networks of employers recruiting temporary agricultural workers via the H-2A visa program. 

Question Answering generates reports from an entity-rich document collection

Question answering is one of the leading use cases for generative AI, given the ability of LLMs to perform in-context learning over a set of input texts. For situations where the size of data to be queried exceeds the context window of the LLM, retrieval-augmented generation (RAG) can enable embedding-based matching of query text against input texts, before using the retrieved texts to help the LLM generate a grounded response. A major limitation of standard RAG, however, is that there is no guarantee that the retrieved texts provide a sufficiently comprehensive grounding to answer user questions, especially if the questions ask for summaries rather than facts. Our recent work (opens in new tab) using LLM-derived knowledge graphs as a RAG index aims to provide such grounding, but requires an extensive indexing process before any questions can be answered. 

For Intelligence Toolkit, we therefore developed a new RAG approach for lightweight yet comprehensive question answering over collections of existing reports, targeted at NGOs wanting to leverage both their own report collections and those of other organizations (e.g., see collections of public reports from Issara (opens in new tab) , Polaris (opens in new tab) , Unseen (opens in new tab) , and IOM (opens in new tab) ). In this approach, text chunks that match the user’s question are first mined for question-answer pairs, before the question is augmented with any partial answers and embedded again alongside both unmined text chunks and the mined questions and answers. This process repeats until sufficient question-answer pairs have been extracted and matched against the augmented question, providing both an independent FAQ and grounding for the LLM answer to the original user question. 

Alt text: Screenshot of Question Answering workflow at the “Generate AI answer reports” stage. The target dataset is a compilation of PDF reports published independently by Issara and Polaris. The user query of “In what ways do Issara and Polaris take a similar approach?” was answering by an AI-generated report titled “Comparative Analysis of Issara and Polaris Approaches to Combatting Modern Slavery”. The editable prompt text allows the user to customize the nature of such answer reports. 

Continuing the fight against all kinds of societal threats

Intelligence Toolkit is our latest example of a human rights technology developed with global experts in the anti-trafficking community, yet applicable to a broad class of problems impacting societal resilience as a whole. As we work with TAT to help NGOs and other organizations use Intelligence Toolkit for their own data challenges, we hope to identify opportunities to refine and expand our initial workflows.

Across multiple stakeholder events, we have helped to raise awareness of generative AI and the real risks that misuse could pose to vulnerable populations. At the same time, generative AI has unprecedented potential to drive insight discovery, communication, and collective action across entire communities, in ways that are essential for tackling societal problems at scale. With Intelligence Toolkit, we have taken our first steps towards understanding how generative AI can be shaped into the tools that society most urgently needs. 

Meet the authors

Portrait of Darren Edge

Darren Edge

Senior Director

Portrait of Ha Trinh

Senior Data Scientist

Portrait of Dayenne Souza

Dayenne Souza

Software Engineer II

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  1. Research Questions vs Hypothesis: Understanding the Difference

    A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement. Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption. Researchers use the null approach for ...

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    Explanation: This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way. Tips for developing research questions and hypotheses for research studies

  3. A Practical Guide to Writing Quantitative and Qualitative Research

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

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    study) Describe the experiences (e.g., phenomenology) Report the stories (e.g., narrative research) Use these more exploratory verbs that are nondirectional rather than directional words that suggest quantitative research, such as "affect," "influence," "impact," "determine," "cause," and "relate.".

  5. Research Question Vs Hypothesis

    A Hypothesis is a statement that predicts the relationship between two or more variables in a research study. Hypotheses are used in studies that aim to test cause-and-effect relationships between variables. A hypothesis is a tentative explanation for an observed phenomenon, and it is often derived from existing theory or previous research.

  6. Research questions, hypotheses and objectives

    Research question. Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know "where the boundary between current ...

  7. How to Write a Strong Hypothesis

    Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.

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    Articulating a clear and concise research question is fundamental to conducting a robust and useful research study. Although "getting stuck into" the data collection is the exciting part of research, this preparation stage is crucial. Clear and concise research questions are needed for a number of reasons. Initially, they are needed to ...

  9. Research Questions and Hypotheses

    A hypothesis is a predictive statement about the relationship between 2 or more variables. Research questions are similar to hypotheses, but they are in question format. We expand on that general definition by splitting research questions into 3 basic types: difference questions, associational questions, and descriptive questions. For difference and associational questions, basic means that ...

  10. What is a Research Hypothesis: How to Write it, Types, and Examples

    It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.

  11. Exploring Research Question and Hypothesis Examples: A Comprehensive G

    Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes.

  12. PDF DEVELOPING HYPOTHESIS AND RESEARCH QUESTIONS

    RESEARCH QUESTIONS. Quantitative Approach. In survey projects the use of research questions and objectives is more frequent In experiments the use of hypotheses are more frequent Represent comparison between variables relationship between variables Characteristics The testable proposition to be deduced from theory.

  13. Research Hypothesis: Definition, Types, Examples and Quick Tips

    3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.

  14. The Difference Between Research Questions & Hypothesis

    A hypothesis is defined as an educated guess, while a research question is simply the researcher wondering about the world. Hypothesis are part of the scientific research method. They are employed in research in science, sociology, mathematics and more. Research questions are part of heuristic research methods, and are also used in many fields ...

  15. Clarifying the Research Questions or Hypotheses

    Research is a systematic process of understanding questions growing in the minds of researchers. It is the research question that triggers one to do research. Selecting or identifying research questions is the initial stage of developing a research plan. It is a critical process because the chosen topic plays a role in completing research.

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    The Relationship Between Research Questions and Hypotheses. ... Role in Research: The null hypothesis serves as a benchmark for testing the existence of an effect or relationship. By attempting to disprove or reject the null hypothesis through statistical analysis, researchers can provide evidence supporting the presence of a meaningful effect ...

  17. PDF Chapter 4 Developing Research Questions: Hypotheses and Variables

    Experiments using sounds suggest that we are less responsive during stages 3 and 4 sleep (deep sleep) than during stages 1, 2, or REM sleep (lighter sleep). Thus, the researcher predicts that research participants will be less responsive to odors during stages 3 and 4 sleep than during the other stages of sleep.

  18. Difference Between Hypothesis and Research Question

    A research question is the question the research study sets out to answer. Hypothesis is the statement the research study sets out to prove or disprove. The main difference between hypothesis and research question is that hypothesis is predictive in nature whereas research question is inquisitive in nature. In this article, we'll discuss, 1.

  19. Research Questions, Objectives & Aims (+ Examples)

    In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study.

  20. How Does a Hypothesis Differ From a Research Question?

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    Therefore, research questions are based on the research problem and the research objectives. Now let us see what research hypothesis means. A research hypothesis is a predictive statement about the possible outcomes of a study. To predict outcomes, you must have a clear idea of the problem that you are studying (research problem) and what you ...

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    Key Takeaways. A research question is a broad inquiry into a topic, while a hypothesis is a statement that explains a phenomenon. Research questions are open-ended and exploratory, while hypotheses are specific and testable. Research questions are used in qualitative research, while hypotheses are used in quantitative research.

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    We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study.. Understanding Social Phenomena: Qualitative Research Design. Qualitative research focuses on understanding a phenomenon ...

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    Marketing studies establish a relationship between fixation patterns and increased interest or processing effort during observation [30,31,32]. This forms the basis for the first hypothesis of this study: (I) There is a relationship between subjects' visual patterns and their evaluation of real estate properties.

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