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Research Question Vs Hypothesis

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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:

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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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.
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.
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)

how to differentiate between hypothesis and research question

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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The Difference Between Research Questions & Hypothesis

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

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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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Home » Education » Difference Between Hypothesis and Research Question

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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How Does a Hypothesis Differ From a Research Question?

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:

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

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

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.

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.

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.

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.

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.

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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What are research questions and hypotheses? (2 hours)

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Read at least two of the excerpts below. Each of these discusses what a hypothesis is (and how it is different from a research question) in a different way. I am asking you to read multiple explanations because I think the best way to understand a concept is not to just seek one "right" definition, but to learn about it from multiple perspectives and see what different perspectives have in common and what makes them different. This will give you a fuller understanding of the topic, as things in the world rarely have a single fixed "correct" definition.

You do not need to read every word of these. Your goal in reading these is to understand the difference between a hypothesis and a research question; you can read just enough to find that information (from at least two different readings). If there are some parts that you notice are not directly relevant to that goal, you can skip or skim them. (In particular, for this assignment, you can ignore anything about "null hypotheses" in the readings; null hypotheses are a particular statistical concept and are not relevant for this module, although you will learn more about them in the "Introduction to inferential statistics" module.) Reading should always be active (thinking about what you are trying to get from the reading, and constantly thinking about what you have read and how it relates to your goal), not just passively looking at every word of the text.

After you have read at least two texts, continue to the reflection questions below. As before, your answers to these are not graded; this is just an activity to get you thinking about what you've read.

" The research question and hypothesis ", Ryerson University English language support.
This Facebook discussion (make sure to click to see the comments below the post). It's on a public page, so you do not need a Facebook account to see it.
Brown, J. (2008). Chapter 9: Statistical logic (pp. 108-111). Understanding Research in Second Language Learning . Cambridge University Press.
Prasad, S., Rao, A., Rehani, E. (2001). Developing hypotheses and research questions . From Michael Kroelinger's research methods class at Arizona State University .
Rasinger, S. (2014). Section 2.9 Hypotheses, laws and theories (pp. 30-33). Quantitative Research in Linguistics: An Introduction . Bloomsbury.
Salkind, N. (2009). Hypotheses (pp. 25-30). Exploring Research . Pearson.

Describe, in your own words (no copying or paraphrasing from the readings), what you think is the difference between a research topic, a research question, and a hypothesis.

Describe, in your own words, the characteristics of a good hypothesis. What makes a research hypothesis good? What's the difference between a good and a bad hypothesis?

Below I have written several passages. Each passage describes a research topic, research question, hypothesis, and the link between them. Figure out which passages have a good, well-stated research questions and hypotheses with good links between the research questions and hypotheses. (Below I sometimes use vague terms like "Theory A" or "Condition Y", but that's just because these examples are completely made-up. Don't base your judgment on how clear the names of the theories/conditions are; you can try to imagine whether a given passage would have a clear link between the research questions and hypotheses if I had given more descriptive names to things.) My answers are at the bottom of the page.

The role of feedback in language learning is an important topic in pedagogy. There are different kinds of feedback, and some may be more useful than others. This study compares two kinds of feedback (Feedback A and Feedback B) to see which has a more positive impact on learning outcomes. According to Theory X, Feedback A should be more effective for language learning. We designed an experiment in which different groups of students got different types of feedback. We predict that students who get Feedback A will show greater improvement in test scores, compared to students who get Feedback B, over the course of the semester.
The aim of this study is to better understand the neural mechanisms supporting language processing. We use EEG to record Chinese speakers' brain activity while they read different kinds of sentences, including SOV sentences with BA structure (把字句) and canonical SVO sentences. We compare the brain responses in these different types of sentence to determine what are the brain correlates of understanding different sentence structures.
Understanding the extent and role of language transfer in language learners is crucial to understanding the language acquisition process. Language acquisition theory distinguishes between forward transfer (e.g., when a person's first language influences their second language) and backward transfer (e.g., when a person's second language influences their first language). However, different aspects of language may be affected, or not affected, by language transfer in different ways—for example, a language learner may experience forward transfer in terms of their production of syntax, but backward transfer in terms of their pronunciation. These patterns may also differ between individual learners. The present study will evaluate both production and comprehension of syntax, semantics, and phonetics, in a group of L1 Mandarin - L2 Korean learners, to determine what aspects of language show transfer. Individual differences between learners will also be investigated.
One of the fundamental questions of linguistics is how language is represented in the mind. This issue is especially complicated for elements of language which undergo change. For example, some Mandarin syllables change their tone as a function of the context in which they are spoken: e.g., 水 ( shui 3 , "water") is pronounced with a low tone when it is produced alone, but pronounced with a rising tone when it is produced in a compound word like 水果 ( shui 2 guo 3 , "fruit"). In the mental lexicon of a Mandarin speaker, is the 水 in a word like 水果 represented as having a low tone (its true underlying form), or a rising tone (the form with which it is pronounced)? We test this question using an auditory priming experiment. Previous research has shown that listeners can respond to a word faster when they have previously been "primed" by a word with the same tone. In the present experiment, listeners respond to words like 水果 after being primed either by a low-tone syllable, or a rising-tone syllable. If these words are represented with the underlying (low) tone in the mind, participants should respond faster when primed by a low tone than when primed by a rising tone. On the other hand, if these words are represented with the surface (rising) tone in the mind, participants should respond faster when primed by a rising tone.

When you have finished these activities, continue to the next section of the module: " Identifying research questions and hypotheses ".

Answers for the last activity: A is good; it describes a big-picture question, mentions a theory that provides a link from the big-picture question to specific experiment results, and explains what is expected to happen in the experiment under a given big-picture theory. B is not good; there are no specific hypotheses or predictions, no detail about what aspect of brain activity will be "compared". C is not good; it just says that a lot of things will be looked at, but offers no specific hypotheses or predictions. D is good; a big-picture question is raised and linked to specific predictions about what will occur in the experiment (and why) under different theories; in fact this is the design of a real study .

by Stephen Politzer-Ahles . Last modified on 2021-05-02. CC-BY-4.0.

Clarifying the Research Questions or Hypotheses

First Online: 28 March 2017

Cite this chapter

Kenan Dikilitaş 3 &
Carol Griffiths 4

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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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Faculty of Educational Sciences, Bahçeşehir University, Istanbul, Turkey

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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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10 Significant Differences Between Research Question vs Research Hypothesis

Stating, developing and addressing a research question and developing & justifying the research hypothesis has vital significance in the research process. Both help researchers to approach PhD/research/ projects.

“Research” is a word important for PhD which includes complex processes of finding new knowledge. A PhD candidate has to prepare a project & research proposal, identify a research gap , state a question, prepare a hypothesis and then do research.

It includes tedious pre-preparation, lucrative research and frustrating post-preparations phases. So overall the research process though is inquisitive but can be managed by discipline and zero date planning.

So to prepare for PhD, do it with ease and complete it joyfully; one has to understand every element correctly before starting their research. And for that tons and tons of articles and previous research must be read first.

In addition, as we talked about, precisely identified research problem helps in stating an excellent research question or research hypothesis. Notwithstanding, students usually don’t understand what a research question or hypothesis is!

The present blog content will focus on differences between research question and hypothesis and may let you understand what each term is. I hope this article will help you learn the PhD research process more accurately.

Stay tuned,

Research question: Does this article explain some common differences between a research question and a research hypothesis?

Research hypothesis: This article explains the major differences between the research question and the research hypothesis.

Example of research question:

Some other examples of research questions are: , example of research hypothesis: , some of the examples of research hypotheses: , summary: research question vs hypothesis: , wrapping up: , what is a research question- simple explanation.

Put simply, a research question is a clear and concise question of the study that must be answered at the end. The answer usually is Yes/No type but clearly fills the gap.

Let’s take an example,

What are some common problems the LGBT community faces globally?

Suppose, this one is one research question around which the researcher has to prepare its study. What can he or she do with this topic?

Conduct gatherings of the community.
Conduct one on one interviews.
Conduct News sessions
Study previous literature.
Organize some Games and invite LGBT community people to take part.

That’s it, Nothing else he or she can do.

No statistical analysis is required and performed for this study so the outcome of this study possibly is “problems”. And it can be solved, perhaps. Note that in-depth mathematical models, statistical analysis and other scientific studies aren’t required here.

What are the side effects of social media addiction on youth?
What are the factors that negatively impact the mental health of US people?
How effective carbon emission control strategies are?

Now let’s understand the research hypothesis.

What is a research hypothesis?- Simpler explanation

A research hypothesis is postulated in order to predict the results either negative or positive. Notedly is used so often in scientific, experimental and quantitative research.

The research hypothesis is a predictive model for getting results.

Let’s take an example,

The effect of time and temperature on biological sample transportation.

This study includes exclusive statistical analysis and data-driven studies to investigate the effect of various temperatures and times on biological specimen transportation.

Outcomes of the study will prove that at which temperature a biological sample can be safely transported.

The outcomes are,

Statistics
The temperature which isn’t good
The temperature which has is best
The tolerable zone for transportation

To interpret these kinds of results in-depth mathematical models, statistical analysis, scientific experiments and other biological studies are needed.

The effect of time and temperature on biological sample transportation.
Effect of various doses of antiviral Oseltamivir drug against viral pathogenicity.
Various global warming agents and their impact.

I think you get a brief idea about how each term is different. Some of the technical differences between the research question and hypothesis are explained here.

Differences between research question and research hypothesis:

A research question is developed depending upon the problem or gap identified while the hypothesis is prepared based on the existing knowledge.

More than one research question is present in a single study, while the entire research is developed around a single hypothesis that is either proven or disproven at last.

In-depth knowledge of the subject and huge data or research studies are required to state a research hypothesis; whereas the research question can be stated using a small group of research data or knowledge.

This indicates that the relatedness among different variables is pretty uncertain for the research question while is highly related in the case of hypothesis.

A research question is “brief” yet includes all the important information and is open to debate which typically gives an excellent varied degree of output.

On the other hand, the research hypothesis is a kind of formal statement- (will be proved or disproved) which assumes the relatedness between two or more variables selected for the study.

For example,

The number of patients, population size, sample type or method selected for the study.

Both- qualitative and quantitative studies rely on the research question, however, the hypothesis can be postulated mostly for the quantitative or experimental studies.

Depending upon the nature of the study, the research questions are of three various types which are casual, descriptive and comparative questions while the hypotheses are causal, null, directional or non-directional.

A thesis question must be answered; A hypothesis must be tested.

The research question is more an elaborative research term while the hypothesis is more scientific and predictive in nature.

Henceforth, research questions are usually used in elaborate studies in subjects such as language, arts and literature. And as we said, that’s pretty straightforward.

The impact of the “Macbeth play” on European people.

On the other hand, the research hypothesis is based on possibilities and probabilities whose final results either or neither prove the study and therefore include a purely scientific explanation, mathematics, equations and statistical analysis.

Studies in science, biology and sociology rely on hypotheses (that must be tested first).

For example, the impact of temperature and time duration on sample transportation and storage.

If you are designing scientific research for your PhD, perhaps stating a hypothesis may help you more, although you can raise a question as well to investigate the knowledge.

Research, as I said, is a complex process, needs the experience to design.

Early learning may pretty helpful for students to understand the thing well. And hence this article and series of articles on this blog are meant for PhD students.

Dr. Tushar Chauhan is a Scientist, Blogger and Scientific-writer. He has completed PhD in Genetics. Dr. Chauhan is a PhD coach and tutor.

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What is PhD?- History, Definition, Origin, Requirement, Fees, Duration and Process

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Research Question vs Hypothesis: Difference and Comparison

This kind of research is called a research paper and includes various headings that are related to the given topic.

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

The difference between Research Question and Hypothesis is that the research question is the question whose answer needs to be found through the research paper, whereas a hypothesis is an assertion that either approves or negates the matter in question. The two also differ in their structure, aim, nature, and so on.

The research question poses a question that is then to be solved or answered through the research paper. Since it puts forth a question, it is inquisitive.

Hypothesis, on the other hand, is an assumption on the possibility or impossibility of the task in question. It is a prediction of what the results of the research might yield.

Comparison Table

What is research question.

The research question is the question that forms the basis of the research. It is to find the answer to this question that one embarks on the journey of analyzing and researching a particular topic.

What is Hypothesis?

Hypothesis is a statement that suggests or predicts the outcome of the research. It predicts how a certain thing or project will pan out or behave.

The hypothesis is mostly used in experimental and quantitative research and studies. It is used in scientific experiments and projects, and it can also be used in other fields such as mathematics, sociology, etc.

A hypothesis can be written or derived when ample research is conducted on a specific topic or the topic in question.

Since a hypothesis is a predictive statement, it does not allow for the possibility of multiple outcomes. It leaves very little space for diversified results.

Main Differences Between Research Question and Hypothesis

A research question allows for the possibility of multiple and varied results. On the other hand, the hypothesis does not provide a multitude of possibilities.
A research question is inquisitive since it puts forth a question, whereas a hypothesis is predictive as it assumes the experiment’s outcome or result based on prior studies.

Difference Between Research Question and Hypothesis

https://www.researchgate.net/profile/Inga-Jenset/publication/313876542_Linking_practice_to_theory_in_teacher_education_Teacher_candidates’_opportunities_to_talk_about_field_experiences/links/58ad822d45851503be91af6f/Linking-practice-to-theory-in-teacher-education-Teacher-candidates-opportunities-to-talk-about-field-experiences.pdf
https://academicjournals.org/journal/IJEAPS/article-abstract/334464E40792

Last Updated : 22 July, 2023

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19 thoughts on “research question vs hypothesis: difference and comparison”.

The precise description of a research question and a hypothesis is highly informative. The differences in nature and structure are clearly outlined, offering a thorough understanding of both aspects.

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The explicit differentiation between a research question and a hypothesis is truly beneficial for anyone engaging in research. The predictive nature of a hypothesis and the inquisitive nature of a research question are well elucidated.

Absolutely. The article provides a clear comparison that enhances the understanding of these components in research papers.

I couldn’t agree more. The comprehensive explanation enables researchers to discern the roles and characteristics of a research question and a hypothesis effectively.

The detailed differentiation between Research Question and Hypothesis is truly enlightening. It’s really crucial to understand the distinction in order to conduct a thorough and successful research.

Indeed, the comprehensive information regarding the nature and structure of a research question and a hypothesis serves as a valuable guide for researchers.

The section explaining what a hypothesis is and its application in various fields is enlightening. It’s good to see relevant references provided to support the information presented.

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research 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 knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives 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.
Develop clear and well-defined primary and secondary (if needed) objectives.
Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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.

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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.

Need a helping hand?

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.

Psst... there’s more!

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38 Comments

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.

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

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.

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.

Thanks so much. This was really helpful.

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

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

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!

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.

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

This is quite helpful. I really appreciate!

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

Very innovative and educational in approach to conducting research.

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.

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

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

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?

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?

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.

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

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

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

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

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?

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

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.

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.

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).

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

Well explained

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.

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

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

I found this article helpful. Thanks for sharing this.

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Difference Between A Hypothesis And A Research Question

Difference between a Hypothesis and a Research Question Difference between a Hypothesis and a Research Question Introduction The underlying objective of any research is to acquire knowledge. Or, in other words, it can be said that the purpose of research is to get information on the topic of research. Each research is different from the other researches in that it has different objectives and different motivation factors. Research questions and hypotheses are ways to conduct qualitative and quantitative research. In order to understand the difference between hypothesis and research questions, it is imperative to understand the difference between the various methods of research. The level of motivation and the objectives behind the research determine the level of input that the researcher will put in the research (Stangor, 2007). The paper discusses the hypothesis and research questions in detail and describes the appropriate choices of research methodology that each of these would entail. Definitions of hypothesis and research questions Hypothesis may be defined as a guess or a hunch or an assumption. In other words, “A hypothesis is a conjectural statement of the relation between two or more variables”. It is a formal statement that expects to create a relationship between two variables. One of the variables is dependent whereas the others are independent. It is this relationship, direct indirect or no relationship, that the hypothesis aims to test and prove. A research question, on the other hand, is a point of departure and is used in many fields of academic and professional research. It is used in humanities as well as science. Types of hypothesis There are three major types of hypothesis discussed hereunder. A null hypothesis is one that states that there is no relationship between the independent and the dependent variable. A directional hypothesis is a hypothesis that studies the state of relationship between the two variables. For instance, it determines that the relationship between two variables X and Y is direct. Or, it can be that X and Y are negatively correlated. Hence, there is a predetermined direction which needs to be tested (Grinnell & Richard, 2005). A non-directional hypothesis is one in which the researcher at the time of the formulation of research hypothesis does not know whether there is any relationship between the dependent and independent variables or not. The purpose of the hypothesis is to assess the existence of the relationship. Types of research questions The nature of research questions differs with the type of research undertaken. Hence, before the formulation of research questions it is imperative that the researcher is clear on the usage of these questions. There are two broad categories of research questions. These are central question and sub questions associated with the central question. The central question is to question that the research is trying to explore. It is pretty broad in nature and asks for the main theme of the topic. Here, the researcher should ask himself of the broadest question that will the scope of the research. Most researches contain a single or, at most, two central ...

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The Difference between a Hypothesis and a Research Question Report (Assessment)

A hypothesis can be said to be a form of research where information in terms of data is gathered by taking a preliminary or hypothetical investigation to come up with a tentative explanation of how the final result will reflect (White, 2002).

A hypothesis can also be said to be the ability of a researcher to attempt to pose questions on a particular topic and also give forth plausible explanations as what constitutes as being the final result in a yet to a proven fact through a rigorously testing method.

Hypothesis explains how a value of one variable is affected or changes in comparison with another variable either positively or negatively. Some researchers say that the prediction of the outcome may be based on a kind of educated guess and at times through a formal theory (Kasi, 2009). These postulations by a researcher try to predict what he or she may consider to be the outcome of an investigation.

A hypothesis indicates the outcome of a research based on certain variables. On the other hand, research questions deal only with a specific ideal. Some researchers say that these variables in business and management may include factors that may directly or indirectly affect the outcome of either production or service.

The major difference between a research question and a hypothesis may be the fact that in a hypothesis, the results are postulated and the questions are usually framed towards getting a similar outcome or something near the postulated outcome and therefore, biased (Hiraoka, 2008).

However, in research questions, there is basically no predictable or estimated outcome that may be said to guide the researcher (Buddenbaum & Novak, 2001). In a research question, the problem or the variable is generally formulated in question form.

One of the best demonstrations given to show the difference between a hypothesis and a research question was advanced by Hiraoka, (2008), who held the view that stating research question in form of hypothesis, was advantageous as it leads to a better understanding of what each variable stands for and what each question seek to address.

In addition to this, it enables a researcher to ascertain whether or not there is a relationship between the variables.

A research hypothesis may take various forms, but generally, it takes a declarative form meaning that it states an expected relationship or the difference between the stated variables.

Hypotheses are normally used to state the relationship that exists between two or more variable, where else the research question usually focuses on a single idea. Hypothesis tries to come out with a comparison of various ideals related to the study in order to give a comprehensive conclusion about the study.

It is evident that there exist fundamental differences between a research hypothesis and a research question. It is clear that the researcher should have adequate knowledge on the research topic. The main aim of a research process is to formulate a very good thesis statement for the problem at hand with the hypothesis stipulating the possible outcome of the research, but on the other hand the research questions leave possible outcome open.

From the above analysis, it is clear that there is a major difference between a research question and a hypothesis especially due to the knowledge that a research question tries to establish the exactness of a hypothesis.

Buddenbaum, J., M, & Novak, K., B. 2001. Applied communication research. New York. Wiley-Blackwell, print.

Hiraoka, C., H, 2008. Influence of pre and post testing on return on investment calculations in training and development. University of North Texas. Pro quest

Kasi, P., M. 2009. Research: What, Why and How? A Treatise from Researchers to Researchers. New York. Author House, Print

White, B. 2002, Dissertation Skills for Business and Management Students . New York: Thomson.

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Why the Cleo from 5 to 7 Film Is Feminist?
T-Test in Its Application to Aggressive Behavior to a Small Group of Students
Memory: Its Functions, Types, and Stages of Storage
Data Collection and Analysis With a Real Teacher-Researcher
Dissertation and Management Knowledge
Content Outline and Assessment
Case Study Approach and Design
Quantitative Data for Research

Frequently asked questions

What’s the difference between a research hypothesis and a statistical hypothesis.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

Budget constraints or any specifics of grant funding
Your proposed timeline and duration
Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
Any inclusion and exclusion criteria
Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

Construct validity : Does the test measure the construct it was designed to measure?
Face validity : Does the test appear to be suitable for its objectives ?
Content validity : Does the test cover all relevant parts of the construct it aims to measure.
Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
A successful replication shows that the reliability of the results is high.
Reproducing research entails reanalysing the existing data in the same manner.
Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

If there is no sampling frame available (e.g., people with a rare disease)
If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

First, the author submits the manuscript to the editor.
Reject the manuscript and send it back to author, or
Send it onward to the selected peer reviewer(s)
Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

In a single-blind study , only the participants are blinded.
In a double-blind study , both participants and experimenters are blinded.
In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

Use masking (blinding) to hide the purpose of your study from all observers.
Triangulate your data with different data collection methods or sources.
Use multiple observers and ensure inter-rater reliability.
Train your observers to make sure data is consistently recorded between them.
Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment
Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

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).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

A positive correlation means that both variables change in the same direction.
A negative correlation means that the variables change in opposite directions.
A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

Open-ended and flexible
Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
Unambiguous, getting straight to the point while still stimulating discussion
Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

Structured interviews : The questions are predetermined in both topic and order.
Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
Unstructured interviews : None of the questions are predetermined.
Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

It’s caused by the independent variable
It influences the dependent variable
When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

The independent variable is the amount of nutrients added to the crop field.
The dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

Discrete variables represent counts (e.g., the number of objects in a collection).
Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

The type of cola – diet or regular – is the independent variable .
The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
Experimenter effects : Unintentional actions by researchers that influence study outcomes
Situational variables : Eenvironmental variables that alter participants’ behaviours
Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

An explanatory variable is the expected cause, and it explains the results.
A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

If you have quantitative variables , use a scatterplot or a line graph.
If your response variable is categorical, use a scatterplot or a line graph.
If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
Statistical generalisation: You use specific numbers about samples to make statements about populations.
Causal reasoning: You make cause-and-effect links between different things.
Sign reasoning: You make a conclusion about a correlational relationship between different things.
Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

Convergent validity : The extent to which your measure corresponds to measures of related constructs
Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

In single-stage sampling , you collect data from every unit within the selected clusters.
In double-stage sampling , you select a random sample of units from within the clusters.
In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

Prevents carryover effects of learning and fatigue.
Shorter study duration.

Disadvantages:

Needs larger samples for high power.
Uses more resources to recruit participants, administer sessions, cover costs, etc.
Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Only requires small samples
Statistically powerful
Removes the effects of individual differences on the outcomes
Internal validity threats reduce the likelihood of establishing a direct relationship between variables
Time-related effects, such as growth, can influence the outcomes
Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

Reduce bias that comes from using a single method, theory, or investigator
Enhance validity by approaching the same topic with different tools
Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

Data triangulation : Using data from different times, spaces, and people
Investigator triangulation : Involving multiple researchers in collecting or analysing data
Theory triangulation : Using varying theoretical perspectives in your research
Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

A testable hypothesis
One or more independent variables that you will manipulate
One or more dependent variables that you will measure

When designing the experiment, first decide:

How your variable(s) will be manipulated
How you will control for any potential confounding or lurking variables
How many subjects you will include
How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions.
Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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.

There are five common approaches to qualitative research :

Grounded theory involves collecting data in order to develop new theories.
Ethnography involves immersing yourself in a group or organisation to understand its culture.
Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
Phenomenological research involves investigating phenomena through people’s lived experiences.
Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

Prepare and organise your data.
Review and explore your data.
Develop a data coding system.
Assign codes to the data.
Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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IMAGES

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Research Question Vs Hypothesis
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COMMENTS

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.
Research Questions & Hypotheses
The primary research question should originate from the hypothesis, not the data, and be established before starting the study. 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 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 ...
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.
How Does a Hypothesis Differ From a Research Question?
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.
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.
PDF Research Questions and Hypotheses
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.".
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 ...
PDF DEVELOPING HYPOTHESIS AND RESEARCH QUESTIONS
RESEARCH QUESTIONS EXAMPLE In a clinical trial of a new drug, the null hypothesis might be that the new drug is no better, on average, than the current drug. We would write H0: there is no difference between the two drugs on average. The alternative hypothesismight be that: the new drug has a different effect, on average, compared to that of the
What are research questions and hypotheses?
When you have finished these activities, continue to the next section of the module: "Identifying research questions and hypotheses".Answers for the last activity: A is good; it describes a big-picture question, mentions a theory that provides a link from the big-picture question to specific experiment results, and explains what is expected to happen in the experiment under a given big-picture ...
Clarifying the Research Questions or Hypotheses
What is the difference between a research question and a hypothesis? A research question is exactly what it says: it asks a question and is punctuated with a question mark. A research project requires at least one question, but there may be several (Nunan 1992). A hypothesis contains the researcher's prediction/s (Dörnyei 2007).
10 Significant Differences Between Research Question vs Research Hypothesis
A thesis question must be answered; A hypothesis must be tested. The research question is more an elaborative research term while the hypothesis is more scientific and predictive in nature. Henceforth, research questions are usually used in elaborate studies in subjects such as language, arts and literature.
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 ...
Research Question vs Hypothesis: Difference and Comparison
The difference between Research Question and Hypothesis is that the research question is the question whose answer needs to be found through the research paper, whereas a hypothesis is an assertion that either approves or negates the matter in question. The two also differ in their structure, aim, nature, and so on.
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 ...
Research Questions, Objectives & Aims (+ Examples)
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 ...
Hypothesis vs Research Question: Pros and Cons
A hypothesis is a statement that expresses a possible relationship between variables or phenomena, based on existing knowledge, theory, or observation. A research question, on the other hand, is a ...
PDF Hypothesis and Research Questions
hypothesis is the predicted answer to the research question, based on the literature reviewed. 2. Learning Outcomes By the end of this module, a student will be able to: Understand the types of research questions, the key elements of good research questions and the possible sources and the process of identifying research questions.
Null & Alternative Hypotheses
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation ("x affects y because …"). A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses.
Difference Between A Hypothesis And A Research Question
In order to understand the difference between hypothesis and research questions, it is imperative to understand the difference between the various methods of research. The level of motivation and the objectives behind the research determine the level of input that the researcher will put in the research (Stangor, 2007).
The Difference between a Hypothesis and a Research Question ...
The major difference between a research question and a hypothesis may be the fact that in a hypothesis, the results are postulated and the questions are usually framed towards getting a similar outcome or something near the postulated outcome and therefore, biased (Hiraoka, 2008). However, in research questions, there is basically no ...
What's the difference between a research hypothesis and a ...
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (' x affects y because …'). A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses.
Chapter 2 & 3 Flashcards
Chapter 2 & 3. The difference between a research question and a hypothesis is: Click the card to flip 👆. a hypothesis attempts to answer the question posed by the research problem. Click the card to flip 👆. 1 / 15.

Research Question Vs Hypothesis

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Difference Between Research Question and Hypothesis

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Research Questions & Hypotheses

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

Research Hypotheses

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What is a hypothesis ?

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1. Null hypothesis:

2. Alternative hypothesis:

3. Directional hypothesis :

4. Non-directional hypothesis:

5. Simple hypothesis :

6 . Complex hypothesis :

7. Associative hypothesis:

8 . Causal hypothesis:

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