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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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• v.53(4); 2010 Aug

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.

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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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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

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.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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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)

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.

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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2.1 Approaches to Sociological Research

Learning objectives.

By the end of this section, you should be able to:

• Define and describe the scientific method.
• Explain how the scientific method is used in sociological research.
• Describe the function and importance of an interpretive framework.
• Describe the differences in accuracy, reliability and validity in a research study.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behavior is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behavior as people move through that world. Using sociological methods and systematic research within the framework of the scientific method and a scholarly interpretive perspective, sociologists have discovered social patterns in the workplace that have transformed industries, in families that have enlightened family members, and in education that have aided structural changes in classrooms.

Sociologists often begin the research process by asking a question about how or why things happen in this world. It might be a unique question about a new trend or an old question about a common aspect of life. Once the question is formed, the sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a scientific approach or an interpretive framework. The following sections describe these approaches to knowledge.

The Scientific Method

Sociologists make use of tried and true methods of research, such as experiments, surveys, and field research. But humans and their social interactions are so diverse that these interactions can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving ideas right or wrong rather than about exploring the nuances of human behavior.

However, this is exactly why scientific models work for studying human behavior. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results.

The scientific method involves developing and testing theories about the social world based on empirical evidence. It is defined by its commitment to systematic observation of the empirical world and strives to be objective, critical, skeptical, and logical. It involves a series of six prescribed steps that have been established over centuries of scientific scholarship.

Sociological research does not reduce knowledge to right or wrong facts. Results of studies tend to provide people with insights they did not have before—explanations of human behaviors and social practices and access to knowledge of other cultures, rituals and beliefs, or trends and attitudes.

In general, sociologists tackle questions about the role of social characteristics in outcomes or results. For example, how do different communities fare in terms of psychological well-being, community cohesiveness, range of vocation, wealth, crime rates, and so on? Are communities functioning smoothly? Sociologists often look between the cracks to discover obstacles to meeting basic human needs. They might also study environmental influences and patterns of behavior that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on negative behaviors or challenging situations, social researchers might study vacation trends, healthy eating habits, neighborhood organizations, higher education patterns, games, parks, and exercise habits.

Sociologists can use the scientific method not only to collect but also to interpret and analyze data. They deliberately apply scientific logic and objectivity. They are interested in—but not attached to—the results. They work outside of their own political or social agendas. This does not mean researchers do not have their own personalities, complete with preferences and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in collecting and analyzing data in research studies.

With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. They provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton 1963). Typically, the scientific method has 6 steps which are described below.

Step 1: Ask a Question or Find a Research Topic

The first step of the scientific method is to ask a question, select a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geographic location and time frame. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. Sociologists strive to frame questions that examine well-defined patterns and relationships.

In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?”

Step 2: Review the Literature/Research Existing Sources

The next step researchers undertake is to conduct background research through a literature review , which is a review of any existing similar or related studies. A visit to the library, a thorough online search, and a survey of academic journals will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted, identify gaps in understanding of the topic, and position their own research to build on prior knowledge. Researchers—including student researchers—are responsible for correctly citing existing sources they use in a study or that inform their work. While it is fine to borrow previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized.

To study crime, a researcher might also sort through existing data from the court system, police database, prison information, interviews with criminals, guards, wardens, etc. It’s important to examine this information in addition to existing research to determine how these resources might be used to fill holes in existing knowledge. Reviewing existing sources educates researchers and helps refine and improve a research study design.

Step 3: Formulate a Hypothesis

A hypothesis is an explanation for a phenomenon based on a conjecture about the relationship between the phenomenon and one or more causal factors. In sociology, the hypothesis will often predict how one form of human behavior influences another. For example, a hypothesis might be in the form of an “if, then statement.” Let’s relate this to our topic of crime: If unemployment increases, then the crime rate will increase.

In scientific research, we formulate hypotheses to include an independent variables (IV) , which are the cause of the change, and a dependent variable (DV) , which is the effect , or thing that is changed. In the example above, unemployment is the independent variable and the crime rate is the dependent variable.

In a sociological study, the researcher would establish one form of human behavior as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)?

Taking an example from Table 12.1, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Note, however, this hypothesis can also work the other way around. A sociologist might predict that increasing a child’s sense of self-esteem (the independent variable) will increase or improve habits of hygiene (now the dependent variable). Identifying the independent and dependent variables is very important. As the hygiene example shows, simply identifying related two topics or variables is not enough. Their prospective relationship must be part of the hypothesis.

Step 4: Design and Conduct a Study

Researchers design studies to maximize reliability , which refers to how likely research results are to be replicated if the study is reproduced. Reliability increases the likelihood that what happens to one person will happen to all people in a group or what will happen in one situation will happen in another. Cooking is a science. When you follow a recipe and measure ingredients with a cooking tool, such as a measuring cup, the same results is obtained as long as the cook follows the same recipe and uses the same type of tool. The measuring cup introduces accuracy into the process. If a person uses a less accurate tool, such as their hand, to measure ingredients rather than a cup, the same result may not be replicated. Accurate tools and methods increase reliability.

Researchers also strive for validity , which refers to how well the study measures what it was designed to measure. To produce reliable and valid results, sociologists develop an operational definition , that is, they define each concept, or variable, in terms of the physical or concrete steps it takes to objectively measure it. The operational definition identifies an observable condition of the concept. By operationalizing the concept, all researchers can collect data in a systematic or replicable manner. Moreover, researchers can determine whether the experiment or method validly represent the phenomenon they intended to study.

A study asking how tutoring improves grades, for instance, might define “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” However, one researcher might define a “good” grade as a C or better, while another uses a B+ as a starting point for “good.” For the results to be replicated and gain acceptance within the broader scientific community, researchers would have to use a standard operational definition. These definitions set limits and establish cut-off points that ensure consistency and replicability in a study.

We will explore research methods in greater detail in the next section of this chapter.

Step 5: Draw Conclusions

After constructing the research design, sociologists collect, tabulate or categorize, and analyze data to formulate conclusions. If the analysis supports the hypothesis, researchers can discuss the implications of the results for the theory or policy solution that they were addressing. If the analysis does not support the hypothesis, researchers may consider repeating the experiment or think of ways to improve their procedure.

However, even when results contradict a sociologist’s prediction of a study’s outcome, these results still contribute to sociological understanding. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns. In a study of education, a researcher might predict that high school dropouts have a hard time finding rewarding careers. While many assume that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results may substantiate or contradict it.

Sociologists carefully keep in mind how operational definitions and research designs impact the results as they draw conclusions. Consider the concept of “increase of crime,” which might be defined as the percent increase in crime from last week to this week, as in the study of Swedish crime discussed above. Yet the data used to evaluate “increase of crime” might be limited by many factors: who commits the crime, where the crimes are committed, or what type of crime is committed. If the data is gathered for “crimes committed in Houston, Texas in zip code 77021,” then it may not be generalizable to crimes committed in rural areas outside of major cities like Houston. If data is collected about vandalism, it may not be generalizable to assault.

Step 6: Report Results

Researchers report their results at conferences and in academic journals. These results are then subjected to the scrutiny of other sociologists in the field. Before the conclusions of a study become widely accepted, the studies are often repeated in the same or different environments. In this way, sociological theories and knowledge develops as the relationships between social phenomenon are established in broader contexts and different circumstances.

Interpretive Framework

While many sociologists rely on empirical data and the scientific method as a research approach, others operate from an interpretive framework . While systematic, this approach doesn’t follow the hypothesis-testing model that seeks to find generalizable results. Instead, an interpretive framework, sometimes referred to as an interpretive perspective , seeks to understand social worlds from the point of view of participants, which leads to in-depth knowledge or understanding about the human experience.

Interpretive research is generally more descriptive or narrative in its findings. Rather than formulating a hypothesis and method for testing it, an interpretive researcher will develop approaches to explore the topic at hand that may involve a significant amount of direct observation or interaction with subjects including storytelling. This type of researcher learns through the process and sometimes adjusts the research methods or processes midway to optimize findings as they evolve.

Critical Sociology

Critical sociology focuses on deconstruction of existing sociological research and theory. Informed by the work of Karl Marx, scholars known collectively as the Frankfurt School proposed that social science, as much as any academic pursuit, is embedded in the system of power constituted by the set of class, caste, race, gender, and other relationships that exist in the society. Consequently, it cannot be treated as purely objective. Critical sociologists view theories, methods, and the conclusions as serving one of two purposes: they can either legitimate and rationalize systems of social power and oppression or liberate humans from inequality and restriction on human freedom. Deconstruction can involve data collection, but the analysis of this data is not empirical or positivist.

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Social Research: Definitions, Types, Nature, and Characteristics

• First Online: 27 October 2022

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• Kanamik Kani Khan 4 &
• Md. Mohsin Reza 5  

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Social research is often defined as a study of mankind that helps to identify the relations between social life and social systems. This kind of research usually creates new knowledge and theories or tests and verifies existing theories. However, social research is a broad spectrum that requires a discursive understanding of its varied nature and definitions. This chapter aims to explain the multifarious definitions of social research given by different scholars. The information used in this chapter is solely based on existing literature regarding social research. There are various stages discussed regarding how social research can be effectively conducted. The types and characteristics of social research are further analysed in this chapter. Social research plays a substantial role in investigating knowledge and theories relevant to social problems. Additionally, social research is important for its contribution to national and international policymaking, which explains the importance of social research.

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Home » Research Methodology » Social Research – Definition, Steps and Objectives

Social Research – Definition, Steps and Objectives

Definitions of social research.

The term ‘social research’ has been defined by different scholars differently. The few definitions are as follows:

• Prof. C.A. Moser defined it as “systematized investigation to give new knowledge about social phenomena and surveys, we call social research”.
• Rummel defined it as “it is devoted to a study to mankind in his social environment and is concerned with improving his understanding of social orders, groups, institutes and ethics”.
• M.H. Gopal defined it as “it is scientific analysis of the nature and trends of social phenomena of groups or in general of human behavior so as to formulate broad principles and scientific concepts”.
• Mary Stevenson defined it as “social research is a systematic method of exploring, analyzing and conceptualizing social life in order to extend, correct or verify knowledge, whether that knowledge aid in the construction of a theory or in the practice of an art.

A broad comprehensive definition of social research has been given by P.V. Young which is as follows:

“Social Research may be defined as a scientific undertaking which by means of logical and systematized techniques, aims to discover new factor verify a test old facts, analyze their sequence, interrelationship and causal explanation which were derived within an appropriate theoretical frame of reference, develop new scientific tolls, concepts and theories which would facilities reliable and valid study of human behavior. A researcher’s primary goal distant and immediate is to explore and gain an understanding of human behavior and social life and thereby gain a greater control over time”. (adsbygoogle = window.adsbygoogle || []).push({});

Steps in Social Research

Although different methods are used in social science research, the common goal of a social research is one the same, i.e. furthering our understanding of society and thus all share certain basic stages such as:

• Choosing the research problems and stating the hypothesis .
• Formulating the Research Design.
• Gathering the Data.
• Coding and Analysis the Data .
• Interpreting the results so as to test the hypothesis .

Each of these steps is dependent upon the others. The researcher needs to have adequate knowledge of the later stages before he undertakes research.

The research process is best conceived as circle. The researcher has to select suitable research design among different designs. Thereafter he has to collect the data (primary as well as secondary data). Once the data are collected those have to be coded and analyzed and finally the researcher in social science has to interpret the data so collected.

Objectives of Social Research

Social Research is a scientific approach of adding to the knowledge about society and social phenomena. Knowledge to be meaningful should have a definite purpose and direction. The growth of knowledge is closely linked to the methods and approaches used in research investigation. Hence the social science research must be guided by certain laid down objectives enumerated below:

• Development of Knowledge: As we know ‘science’ is the systematic body of knowledge which is recorded and preserved. The main object of any research is to add to the knowledge. As we have seen earlier, research is a process to obtain knowledge. Similarly social research is an organized and scientific effort to acquire further knowledge about the problem in question. Thus social science helps us to obtain and add to the knowledge of social phenomena. This is one of the most important objectives of social research.
• Scientific Study of Social Life: Social research is an attempt to acquire knowledge about the social phenomena. Man being the part of a society, social research studies human being as an individual, human behavior and collects data about various aspects of the social life of man and formulates law in this regards. Once the law is formulated, then the scientific study tries to establish the interrelationship between these facts. Thus, the scientific study of social life is the base of the sociological development which is considered as the second best objective of social research.
• Welfare of Humanity: The ultimate objective of the social science study is often and always to enhance the welfare of humanity. No scientific research makes only for the sake of study. The welfare of humanity is the most common objective in social science research.
• Classification of facts: According to Prof. P.V.Young, social research aims to clarify facts. The classification of facts plays important role in any scientific research.
• Social control and Prediction: “The ultimate object of many research undertaking is to make it possible, to predict the behavior of particular type of individuals under the specified conditions. In social research we generally study of the social phenomena, events and the factors that govern and guide them.”

In short, under social research we study social relation and their dynamics.

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• Significance and Problems of Social Research
• Significance of Research in Social Sciences
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• Steps in Management by Objectives (MBO) Process
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• Published: 05 September 2024

Navigating poverty in developing nations: unraveling the impact of political dynamics on sustainable well-being

• Yuda Kou 1 &
• Iftikhar Yasin   ORCID: orcid.org/0000-0001-6214-7989 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1143 ( 2024 ) Cite this article

Metrics details

Political instability, dismal governance, and corruption are among the factors that currently distress poverty. The primary objective of this research was to determine how political factors distress poverty in developing nations, which has perhaps not been investigated yet. The main objective was also to observe if the effect of political factors on poverty is a dilemma or a reality. Poverty (dependent variable) has been divided into two segments: income poverty index and Human Health Poverty Index; however, political factors (independent variables) studied were corruption, democracy, governance, and political globalization. Twenty-six years of data were taken from 1997 to 2022 of twenty-four developing nations. The poverty and institutional quality indices were constructed through Principal Component Analysis (PCA). The Fixed Effect, System GMM, and 2SLS approaches were used to determine the dynamic impact on poverty. Furthermore, this study incorporated fixed effects with Driscoll-Kraay standard errors to address cross-sectional dependence. The findings indicated that, although there was a significant connection between governance and income and human poverty. Democracy also showed a negative and significant relationship in the income poverty model but insignificant in the human poverty model. Political globalization showed negative and significant associations with poverty models (income and human poverty). Conversely, corruption showed a significant positive relationship in poverty models, i.e., income and human poverty.

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Introduction.

Poverty refers to a state or condition characterized by the absence of adequate financial resources and critical provisions necessary to meet the minimal requirements for a satisfactory standard of living, both at an individual and community level (Bununu, 2020 ; Wu et al., 2024 ). Poverty is a state characterized by a significant deficiency in meeting the fundamental needs of humans. It is currently an extreme challenge for the world and the foremost developmental objective in attaining equity in income distribution, leading to poverty reduction (Ogbeide and Agu, 2015 ). Moreover, It is the most critical problem hindering the progression of humanity as it happens to be a considerable phenomenon (Abbas et al., 2018 ). Poverty has additional harmful impacts on developing nations compared to developed nations. Notably, global developmental agendas such as the MDGs and SDGs, complete list of acronyms is given in Table S1 , Supplementary Information, have taken poverty, a developmental concern, into general consideration. MDGs focused on decreasing worldwide poverty severity between 1990 and 2015, whereas SDGs concentrate on ending poverty until 2030.

Poverty in developing countries is a significant issue, as the abstract indicates. The poorest regions on the planet are often represented by nations with many people living in extreme poverty (Hotez and Thompson, 2009 ). Figure 1 illustrates that in Honduras, 14.2% of the population resides in extreme poverty, with a poverty gap of 5.1%. In contrast, Georgia and Brazil exhibit lower rates, with 5.6% and 5.3% of their populations living in extreme poverty, respectively. Their poverty gaps also fall within the highest quantile, but they are nonetheless lower than those of Honduras. Here, Fig. 2 indicates that Pakistan faced infant mortality of 57.4 per 1000 live births, with child mortality being in the top quantile among the selected nations. Whereas the Dominican Republic exhibits the second-highest infant mortality rate at 28.6 per 1000 live births, positioning its child mortality under five within the uppermost quantile.

Poverty head count by poverty gap of selected Countries.

Infant mortality rate by mortality rate under 5 of selected Countries.

Moreover, different countries, provincial organizations, and Non-governmental Organizations have designed their agendas to decrease poverty or bring it to an end to defeat the problem according to their capability. However, the aspiration to eradicate poverty persists in numerous emerging countries (Deyshappria, 2018 ). Approximately 719 million people are predicted to live below the poverty level (World Band, 2023 ). Though it is also a familiar reality that the global poverty level has considerably decreased in the previous twenty years, much still needs to be done.

The topic of poverty alleviation is notable for developing nations’ development economists and economic plan builders. Efficiently, poverty begins the hypothesis that families’ welfare is essentially and absolutely tied to their capability to utilize commodities and services. More consumption results in better well-being (Wu et al., 2024 ). A household is poor if its spending capacity is low and it meets a few conditions (Satti et al., 2015 ). There are two ways to depict poverty. The first step is to quickly affect people with low incomes. This will break the poverty cycle. Another way to reduce poverty is to create a policy that boosts economic growth (Chani et al., 2011 ).

This study investigated factors distressing poverty in chosen developing nations, like democracy, corruption, governance, and political globalization. Democracies, as opposed to non-democracies, enhance the welfare of the deprived people. These arguments align with prominent political economy models that posit democracies as generators of numerous public goods and proponents of more significant income redistribution than non-democratic systems (Ross, 2006 ). It is supported by the redistribution theory, which argues that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions (Adserà et al., 2003 ; Besley and Persson, 2011 ).

Corruption disturbs the lives of poor people in several ways, such as distracting government expenses from socially priceless goods like education, distracting communal assets like health clinics through infrastructure investments, and increasing government expenditures on capital-abundant investments, which offer many opportunities for bribes like defense agreements (Ajisafe, 2016 ). Gupta et al. ( 2002 ) also claimed that corruption benefits the elite while depriving the rest. Political globalization has many complex effects on poverty in developing nations. Political globalization can boost trade, investment, jobs, and economic growth. However, political globalization can promote inequality and exploitation, worsening poverty. Globalization increases absolute poverty in the short- and long-term. According to Age’nor and Pierre-Richard ( 2004 ), globalization may immediately increase absolute poverty due to many causes. These factors include transaction costs, insufficient human capital, and inflation. It may also reduce poverty over time. Kawachi and Wamala ( 2007 ) indicated that openness may accelerate and expand transferable diseases like HIV Footnote 1 and H5N1, which can exacerbate poverty by reducing labor productivity and supply. This negative outcome can hurt the poor more than the rich, showing that openness may promote growth without reducing poverty. It may also affect societal norms and habits like eating and smoking (Yach et al., 2007 ), impacting health and efficiency.

The preceding discussion shows that hardly any updated study has probed the political factors of income and human health poverty in developing nations and performed a comparative analysis. Clarifying this relationship is the goal of this study. Our main goal is to quantify how corruption, governance, democracy, and political globalization directly affect income and human health poverty in developing nations. This novel approach examines how often these factors cause poverty, not just correlations. To better understand poverty’s multidimensionality, we create income and human health poverty indices. For robustness, we use advanced econometric methods like two-stage least squares (2SLS), system generalized method of moments (SGMM), and Driscoll-Kraay (DK) standard errors to address endogeneity and cross-sectional dependence (CD). This study examines political factors causing poverty in selected developing nations to fill the gap.

Several aspects distinguish this study from existing research. Firstly, we move beyond establishing mere correlations and quantify the frequency with which political factors distress poverty. This novel approach provides valuable insights into the circumstances under which political factors most significantly impact poverty. Secondly, by taking into account both the economic and the health aspects of poverty, our dual poverty indices enable a more complex understanding of the multiple nature of poverty. Finally, using advanced econometric techniques strengthens the reliability and generalizability of our findings.

Furthermore, the period from 1997 to 2022, chosen for this study, aligns with several significant global economic events and trends that have impacted developing nations. This era witnessed the aftermath of the Asian financial crisis (1997–1998), the global financial crisis (2007–2008), and the rise of globalization, characterized by increased international trade and investment. These events have had profound effects on economic stability, governance structures, and poverty levels worldwide. Understanding the political and economic dynamics during this period provides essential context for analyzing how political factors distress poverty in developing nations.

Prior studies have investigated the possible correlation between political issues and poverty, although there remains a dearth of comprehension regarding the frequency and magnitude with which these elements directly contribute to poverty in developing countries. The objective of this study is to fill this gap by examining the following hypothesis:

H1: Political factors (democracy, governance, corruption, and political globalization) have a significant impact on poverty (income and human health) in developing nations.

This study aims to contribute valuable insights to the ongoing fight against poverty by investigating the prevalence of political factors distressing poverty. Our novel approach, detailed methodology, and robust results offer a deeper understanding of these complex dynamics, ultimately informing the development of more effective poverty reduction strategies for developing nations.

The next part illustrates the literature review. Theoretical framework and methodology have been provided in the third section. The results and discussions are briefly given in the fourth part. The last and final section brings about the conclusion and policy implications.

Literature review

Poverty, a complex issue with profound economic and social impacts, weighs heavily on developing nations. Although economic factors undeniably play a significant role, the influence of political forces on poverty outcomes is profound. This review thoroughly examines existing academic literature, analyzing the various ways in which governance, democracy, corruption, and political globalization interact with poverty in developing contexts.

Wu et al.’s ( 2024 ) study focused on the social determinants of poverty in a few developing nations that have received little prior research. According to the findings, there is a substantial and positive correlation between poverty and the age-dependence ratio, whereas there is a significant and negative correlation between poverty and social globalization. The income poverty model is unaffected by health and education, yet these factors have a negative and substantial association with human poverty. Similarly, population expansion significantly and favorably affected human poverty but had little effect on income poverty. While Wu et al. ( 2024 ) investigate the influence of social determinants, it is crucial to explore how political dynamics might interact with these factors to exacerbate or alleviate poverty.

In a study by Fambeu and Yomi ( 2023 ), an examination was carried out on 40 economies in Sub-Saharan Africa from 1999 to 2018. This study’s findings indicated no clear correlation between the presence of democracy and the reduction of poverty in these particular nations. In their study, Zang et al. ( 2023 ) analyzed data from 1992 to 2017, encompassing 117 economies. Their research examined the relationship between political globalization and national poverty, revealing a significant positive correlation. The exacerbation of national poverty resulting from political corruption was mitigated by implementing primary education and utilizing the Gini index as an intervening mechanism. However, the timeframe of these studies might not capture the most recent developments.

According to Salahuddin et al. ( 2020 ), their research indicates that globalization has reduced poverty and increased corruption in South Africa from 1991 to 2016. The research study focused on South Africa, a country classified as an upper-middle-income country, where poverty is prevalent in developing economies. Additionally, its time frame may not encompass the latest trends. Using PCSE and the SGMM model, Dossou et al. ( 2023 ) confirmed that governance superiority leads to poverty decline in 15 Latin American countries from 2003 to 2015. While Dossou et al. ( 2023 ) focus on Latin America, their time frame might not capture the recent trends. By applying data from five waves of China Family Panel Studies, Han et al. ( 2022 ) found that the anti-corruption campaign in China raises income and declines the poverty occurrence of the (possible) poor group. This study focused on China, so the results might not be generalized.

Corruption has a significant impact on poverty in developing countries. Poor people are more likely to be victims of corrupt behavior by government officials, as they heavily rely on government services (Olken and Pande, 2012 ). Corruption in developing nations represents regressive taxation that disproportionately affects low-income people and hampers development (Nwabuzor, 2005 ). Studies have shown that corruption is prevalent in many developing nations and forms a prominent feature of bureaucratic life (Justesen and Bjørnskov, 2014 ).

Ajisafe ( 2016 ) suggested that corruption affected poverty in the short run, but not in the long run, in Nigeria from 1986 to 2014. This study focused on Nigeria only, which might not be generalized to all developing economies. Aguilar ( 2017 ) selected poor democracies, rich democracies, poor non-democratic nations, and prosperous nations. The results suggested that in a democracy, circumstances, and citizens might affect the declining level of poverty. Cepparulo et al. ( 2017 ) examined whether financial and institutional development interrelates in poverty impacts. The results showed that financial development considerably and positively affected poverty alleviation. Although researchers used institutions, they did not categorize which types of institutions. Moreover, the timeframe of these studies might not capture the recent trends.

Yunan and Andini ( 2018 ) found that economic growth affected corruption considerably, and it also happened between poverty and corruption in ASEAN economies from 2002 to 2015. They used a small period; only the Granger causality test and random effect model were insufficient. Khan and Majeed ( 2018 ) depicted that economic and social globalization considerably alleviates overall poverty, whereas political globalization does not considerably alleviate poverty in 113 developing economies from 1980 to 2014. Besides globalization, other political factors affecting poverty were ignored. Aloui ( 2019 ) observed the governance impact on poverty in Sub-Saharan African nations and found that governance indicators positively and negatively influenced poverty alleviation from 1996 to 2016.

In their study, Gupta et al. ( 2002 ) examined the impact of corruption on poverty and income disparity. Their findings revealed that a one-standard-deviation rise in corruption was associated with an eleven-point increase in income inequality. Interestingly, individuals experiencing poverty observed a five-percentage point annual improvement in income growth.

In their study, N’Zue and N’Guessan ( 2005 ) examined the relationship between corruption, poverty, and economic growth. Their research revealed a complex interplay between these variables in 18 African economies from 1996 to 2001. Specifically, the authors identified multiple dimensions of this relationship. Firstly, they observed that the condition of economic growth can lead to both corruption and inequality. Secondly, they found that inequality serves as a causal factor for corruption. Thirdly, the authors noted that corruption and poverty jointly impact economic growth. Additionally, they discovered that poverty and growth simultaneously influence corruption. Lastly, N’Zue and N’Guessan ( 2005 ) found that inequality and growth affect corruption. They have undoubtedly used panel data from 18 African countries, but the time was minimal, only five years.

Governance, political globalization, and corruption significantly impact poverty in developing countries. Good governance, including effective government, control of corruption, and a stable political system, can promote economic growth, minimize income distribution conflicts, and reduce poverty (Hassan et al., 2020 ). On the other hand, poor governance, characterized by corruption, ineffective governments, and political instability, not only hampers income levels through market inefficiencies but also increases poverty incidence through income inequality (Tebaldi and Mohan, 2010 ). Additionally, the study suggests that economic liberalization in countries with high levels of corruption can lead to faster economic growth but does not improve distributive justice, resulting in increased poverty and unchanged inequality levels (Hanlon, 2012 ). Furthermore, the relationship between good governance and poverty is beneficial for middle-income countries but not low-income countries, indicating that governance reforms alone may not be sufficient to reduce poverty in all countries (Choi and Woo, 2011 ).

A review study undertaken by Resnick and Birner ( 2006 ) found that indicators of governance that describe a healthy decision-making environment for investment and policy achievement. political stability and the rule of law, are linked by growth; however, they give diverse consequences concerning poverty alleviation. Ross ( 2006 ) confirmed that democracy had a slight or no impact on poverty variables.

According to Hasan et al. ( 2006 ), the measurement of good governance, which includes factors such as a solid commitment to the rule of law, significantly impacts poverty reduction primarily due to its influence on economic growth. Tebaldi and Mohan ( 2010 ) examined the detrimental effects of corruption, poor governance, and political instability on income levels. The incorporated timeframes in the above studies might not capture the recent trends.

In a study by Nwankwo ( 2014 ), the author examined the impact of corruption on Nigeria’s economic growth. A significant correlation between corruption and economic growth over an extended period has been identified. This study is limited to Nigeria, which might not be generalized to all developing economies. According to Dzhumashev’s ( 2014 ) recommendation, the impact of corruption on public expenditures is influenced by the correlation between corruption and governance, which in turn influences economic growth. According to the findings of Goryakin et al. ( 2015 ), there is a significant association between globalization and the increasing prevalence of overweight among women. Surprisingly, the phenomenon of social and political globalization gives rise to the impact of economic considerations. Although the study used 56 low- and middle-income countries, the time was limited.

While existing literature has shed light on the complex relationship between political factors and poverty in developing nations, crucial gaps remain. Many of the studies’ timeframes are old enough, so they might not capture the recent trends in poverty. Notably, only a few studies have quantified the frequency with which specific political factors directly affect income and human health poverty across various contexts in developing economies. The delicate interplay between these factors and poverty dimensions, like human health, requires further exploration. Hence, this study aims to bridge these gaps by utilizing advanced econometric techniques to systematically investigate the prevalence of political factors affecting various facets of poverty in selected developing nations. By filling these critical knowledge gaps, our research aspires to inform the development of more targeted and effective poverty reduction strategies for those nations most burdened by this persistent challenge.

Theoretical framework and methodology

This research study examines the political factors influencing poverty in twenty-four developing countries. According to the inclusive institutions theory given by Daron Acemoglu and Robinson ( 2013 ), broad access to economic opportunities and property rights are protected by inclusive political and economic institutions, which are associated with increased economic success and decreased poverty. The redistribution theory highlights that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions. A more equitable allocation of resources is thought to reduce poverty (Adserà et al., 2003 ; Besley and Persson, 2011 ). Gupta et al. ( 2002 ) argued that corruption causes income inequalities as the elite benefit from corruption while the rest of the population stays in poverty. At the same time, greater income inequality is linked to greater poverty levels. Hence, following Acemoglu and Robinson ( 2013 ), Gupta et al. ( 2002 ), and Hanmer et al. ( 2003 ), we developed the following model:

In the above equation, Poverty, CORRUP, and DEMOC capture poverty, democracy, and corruption. Panel data covering the twenty-six-year period from 1997 to 2022 was employed. Twenty-four developing nations from lower-, middle-, and upper-middle-income countries were chosen for this study. The nations were selected based on data availability for poverty variables. The list of nations is detailed in Table S1 , Supplementary Information. The empirical dataset utilized in this research comprises key indicators reflecting poverty metrics, including poverty headcount and poverty gap, alongside vital health indices such as infant and child mortality rates, sourced from the World Development Indicators database, a repository maintained by the World Bank.

Additionally, metrics indicative of political dimensions encompassing government effectiveness, control of corruption, voice and accountability, absence of violence, political stability, regulatory quality, and rule of law were acquired from the Worldwide Governance Indicators dataset, also sourced from the World Bank. The data about democracy metrics were obtained from the Freedom House Data, whereas indices such as the Corruption Perception Index and Political Globalization were sourced from the Transparency International and KOF Globalization datasets. These diverse and meticulously acquired datasets collectively underpin the empirical foundation for this study’s comprehensive analysis of the interplay between poverty dynamics and multifaceted political determinants. Data was investigated by applying E-views 13 along with STATA 17.

The proposed model of the impact of political factors on poverty, presented in the above equation, has been extended as follows:

The subscript “i” denotes countries, which are 1–24, and “t” indicates the period. The continuous and some poverty level estimates are shown by \({\alpha }_{0}\) and \({\beta }_{0}\) . IPI = Income Poverty Index generated through the combination of poverty headcount and poverty gap. IPIit-1 = Income Poverty Index lag, HHPI = Human Health Poverty Index generated through the combination of child and infant mortality rates. HHPIit-1 = Human Health Poverty Index lag, LNCORRUP = Log of Corruption, LNDEMOC = Log of Democracy, GOV = Governance as measured by developing an index of government effectiveness, control of corruption, voice, and accountability, absence of violence, political stability, regulatory quality and rule of law, LNPGLOB = Log of Political Globalization.

The present segment includes the description of the variable incorporated in this study. The variables are chosen because of their comparative significance on a theoretical and empirical basis. The definitions of these selected variables are given below in Table 1 :

Limitations of conventional econometric methods, like fixed and random effects, can lead to unreliable results due to heteroskedasticity, endogeneity, and serial correlation. This study uses the 2SLS methodology to address these concerns. Developed by Cumby et al. ( 1983 ), 2SLS offers an advantage over Ordinary econometric methods by relaxing the assumption of no correlation between regressors and the error term. This assumption violation can lead to biased estimates and undermine the homogeneity hypothesis (Pesaran and Yamagata, 2008 ). To address the endogeneity issue, 2SLS replaces potentially endogenous regressors with instrumental variables, mitigating the bias and providing more reliable estimates. Recognizing these advantages, we opt for 2SLS as our analytical tool, offering a robust alternative to conventional methods.

While the 2SLS method represents a significant advancement in econometrics, it has limitations compared to the Generalized Method of Moments (GMM) for panel analysis (Maydeu-Olivares, Shi, & Rosseel, 2019 ). Specifically, Arellano and Bond’s ( 1991 ) GMM estimation addresses issues like endogeneity, serial correlation, and heteroskedasticity more effectively than 2SLS. This is because GMM uses lagged instrumental variables, mitigating potential endogeneity concerns, and allows for flexible assumptions regarding error structures, accommodating serial correlation and heteroskedasticity. Given these advantages, this study utilizes the GMM estimator for its robustness in handling the challenges mentioned above in panel data analysis. Hence, the model recommended in this study is the GMM Footnote 2 proposed by Arellano and Bond ( 1991 ). The selection of GMM over alternative models was based on several factors. The reasons mentioned above encompass the following. According to Roodman ( 2006 ), the use of the GMM is advantageous in cases when the number of years (T) is smaller than the number of countries (N). In the present study, the number of years (T) is 22, which is indeed less than the number of countries (N), which is 24. (ii) The technique of constructing instrumental variables addresses potential endogeneity concerns in the regressors (Omri and Chaibi, 2014 ). (iii) This technique does not eliminate the presence of cross-country idiosyncrasies. (iv) SGMM captures the cross-country heterogeneity (Gregoriou and Ghosh, 2009 ). Standard estimate approaches, such as most minor square regressions, may be susceptible to dynamic panel bias, facilitating the elimination of country-specific heterogeneities. Finally, the inclusion of a lagged independent variable (namely, one lag of income and the HHPI) as a regressor variable in the model enhances the proficiency of the GMM estimator, enabling it to provide unbiased and trustworthy estimation.

Cross-sectional dependency might provide estimations that are not reliable. We thus used the fixed effect (FE) models with DK standard errors for our regression analysis to allay this concern. Driscoll and Kraay ( 1998 ) developed the DK technique, which was used to address problems with serial correlation, cross-sectional variability, and panel data reliance. This technique works well with missing values and can also be used for balanced and imbalanced datasets. Additionally, it provides robust standard errors and has proven accurate and consistent in handling CD difficulties (Baloch et al., 2019 ).

Before analyzing the panel data, it is crucial to assess the presence of CD. CD arises when there is a reciprocal influence between two or more cross-sectional units (Liu et al., 2021 ; Yasin et al., 2023 ; Yasin et al., 2024 ). This phenomenon emerges due to factors like deep financial and economic integration and exposure to global trade and commerce, all of which render these units susceptible to the effects of global economic shocks. Consequently, these interdependencies between nations can affect panel data from cross-sectional countries. Following the assessment of CD, the homogeneity of slope coefficients is evaluated using the Pesaran and Yamagata ( 2008 ) slope heterogeneity test. This evaluation is necessary because differences in the economic, social, and demographic contexts of 24 developing nations can potentially impact the reliability of panel estimators. To account for such dependencies and potential heterogeneity across variables, second-generation unit root tests (CIPS) are employed.

This study investigates the relationship between income poverty, human health poverty, corruption, globalization, democracy, and governance in 24 developing countries. Before estimating long-term parameters, establishing the co-integration of the underlying variables is crucial. Therefore, this study employs the Pedroni ( 2004 ) co-integration analysis to examine the presence of co-integration among the variables.

Results and discussion

The descriptive statistics of the sample data utilized in the research for political factors have been presented in Table 2 .

Income poverty index

This index is created through two proxy variables, such as poverty gap and headcount, as the highest correlation has been found in both variables. The Principal Component Analysis (PCA) results for constructing the comprehensive index for chosen developing nations are presented in Table 3 . As shown in Fig. 3 , which follows the scree diagram criterion and Kaiser ( 1974 ), only one component is kept that is allocated to hold specifically those factors having eigenvalues above 1. Table 3 brings out just one component having an eigenvalue of 1.90281 above 1. Overall, Kaiser–Meyer–Olkin (KMO) statistics are 0.620, and Kaiser ( 1974 ) argues that 0.5 or above 0.5 is good enough to describe the sample satisfactorily enough to take forward the investigation.

Scree plot of Eigenvalues after PCA for Income Poverty Index.

Human Health Poverty Index

Infant and child mortality rates are vital in determining human health deprivation (Hanmer et al., 2003 ). Hence, this study incorporated the Infant Mortality Rate and Child Mortality Rate to capture the health poverty of humans. This index is created through two proxy variables, including child and infant mortality rates, as the highest correlation has been found in both variables. Table 4 contains the PCA results from which the comprehensive index for chosen developing nations will be derived. According to the scree plot criterion presented in Fig. 4 , one component has been retained. Furthermore, only 1 component has eigenvalues above 1, which is 1.99696. Overall, the statistics for KMO are 0.670, and Kaiser ( 1974 ) pursues 0.5 or greater than 0.5, which is sufficient to confirm sample adequacy for the investigation.

Scree plot of eigenvalues after PCA for Human Poverty Index.

For governance (independent variable of political factors), the institutional quality index is constructed by combining the data of six variables, i.e., government effectiveness, control of corruption, voice and accountability, absence of violence, political stability, regulatory quality, and the rule of law (Apergis and Ozturk, 2015 ; Yasin et al., 2019 ).

Table 5 shows the findings of the PCA analysis used to create the comprehensive index for a subset of developing nations. The table below shows that only one element comprising eigenvalue 3.73263 is above 1. The scree plot, exhibited in Fig. 5 , also indicates the retention of only one component. Overall, the statistics for KMO are 0.7620, which shows that the data is large enough to estimate.

Scree plot of eigenvalues for governance (institutional quality index) after PCA.

Results and discussion in the income poverty model

This research investigates the impact of corruption, governance (specifically Political Institutional Quality), and democracy on income and human health poverty in 24 developing countries from 1997 to 2022. To assess the presence of cross-sectional dependency within the series, we initially employed the CD test proposed by Pesaran ( 2021 ). This is necessary as the first generation’s conventional panel unit root methods may yield unreliable results when confronted with CD, particularly when its magnitude is low. This study employed Pesaran CD tests. Table 6 below demonstrates cross-dependence in this panel. Khan ( 2019 ) also investigated similar results. We use the technique developed by Pesaran and Yamagata ( 2008 ) to verify the slope homogeneity. As observed by Table 7 , the results validate the presence of a heterogeneous slope in model 1 and reject the null hypothesis of a homogeneous slope. The covariances of Model 1 (IPI) are presented in Table S3 , Supplementary Information. The findings suggest a positive association between corruption and governance with the IPI, while a negative association is observed between democracy and political globalization with the index mentioned above.

The utilization of the second-generation CIPS panel unit root test, as reported by Pesaran ( 2007 ), has been motivated by the existence of CD inside the panel dataset. Table 8 presents the findings, indicating that all variables exhibit stationarity at their levels and after being differenced once.

Table 9 contains Pedroni’s panel co-integration test results. The outcome rejects the null hypothesis of no co-integration and asserts that the series has a long-run association. Long-run associations may be recommended to subsist among the variables, meaning they travel collectively to a steady stability phase. The results are the same as those of Dursun and Ogunleye ( 2016 ).

This study employs multiple estimation techniques to explore the identified associations between income poverty and its potential determinants. These techniques include FE, supported by the Hausman and Heterogeneity tests, SGMM, DK, and 2SLS estimators. The results in Table 10 reveal a positive and statistically significant association between past and present poverty levels in our sample of developing countries. The lagged poverty coefficient estimated using the SGMM method is 0.5521, while the 2SLS estimate is 1.1248. These findings indicate that higher poverty levels in the preceding year contributed to increased poverty in the current year. This means that countries with higher poverty rates in the past are more likely to have higher poverty rates in the present, creating a persistent cycle of poverty, which might be due to the poverty trap. Individuals and communities trapped in poverty face various disadvantages, like limited access to education, healthcare, and productive resources. These disadvantages perpetuate poverty by hindering people’s ability to rise above their circumstances and find better opportunities (Banerjee and Duflo, 2011 ). Our analysis further reveals a statistically significant and positive association between corruption and income poverty across several estimation methods, including FE, DK, SGMM, and 2SLS. These findings align with the previous work of Ajisafe ( 2016 ), who also identified a positive relationship between these variables.

Furthermore, the assertion made by Gupta et al. ( 2002 ) is reinforced by this finding, suggesting that corruption contributes to the perpetuation of income inequalities. This is because the privileged few reap the benefits of corrupt practices while most of the population remains impoverished. Corruption diverts public resources for poverty alleviation programs and social services towards private gain. This misallocation deprives the poor of vital resources like healthcare, education, and infrastructure, hindering their ability to escape poverty (Rose-Ackerman, 1997 ). Furthermore, widespread corruption can erode trust in government institutions and weaken social capital. This lack of trust and cooperation hinders collective action and community development, making it difficult for low-income people to advocate for their rights and improve their circumstances collectively (Putnam et al., 1992 ).

The findings further reveal a robust and statistically significant negative association between income poverty and democracy across various estimation methods, including FE, DK, SGMM, and 2SLS. These results support the notion put forth by Ross ( 2006 ) that democracies are more effective in alleviating poverty compared to non-democracies within developing countries. Democratic governments are more likely to prioritize investments in public goods and services that benefit everyone, including the poor, such as healthcare, education, and infrastructure. This can improve human capital and productivity and ultimately reduce poverty Acemoglu and Robinson ( 2013 ). This finding is also supported by the redistribution theory, which argues that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions (Adserà et al., 2003 ; Besley and Persson, 2011 ).

The estimated coefficient for the institutional quality index, reflecting governance quality in developing countries, exhibits mixed statistical significance across various estimation methods. Despite remaining positive across all methods (FE, DK, and SGMM), its significance varies from less significant in SGMM and FE to highly significant in DK and 2SLS. This finding suggests that while governance may positively impact poverty reduction, the current level of institutional quality in developing countries may not be robust enough to exert a statistically significant effect consistently. The same findings are suggested by Karim et al. ( 2013 ). Furthermore, Jindra and Vaz ( 2019 ) argued that the beneficial effect of good governance on poverty reduction is more pronounced in middle-income countries than in low-income countries.

Interestingly, the study by Ochi et al. ( 2023 ) explores a similar question but focuses on South Asian and Sub-Saharan African countries. Their findings suggest a non-linear relationship, where governance quality starts to decrease poverty only above a certain threshold. Furthermore, these results align with Perera and Lee ( 2013 ), who found that institutional quality, such as bureaucratic quality, increases poverty in developing countries. These differing results show how complex governance and poverty associations are. While our findings suggest a potential negative association in some contexts, the referenced study and other research emphasize the potential benefits of good governance for poverty reduction. Further research considering regional variations, non-linear effects, and different poverty definitions is necessary to fully understand this intricate dynamic.

Political globalization is substantial (1%) and has negative coefficients for both FE, DK, and SGMM. Due to political globalization, poverty is significantly declining in these developing nations. Bergh and Nilsson ( 2014 ) and Salahuddin et al. ( 2020 ) also provide similar suggestions on these results. The GMM estimators are subject to efficiency and validity tests, namely the autoregressive coefficient (AR) (2) test for second-order autocorrelation and the Sargan test for over-identifying restrictions. The AR value in Table 10 displayed above is 0.196, exceeding the threshold of 0.05 and lacking statistical significance. The observed result invalidates the presence of second-order autocorrelation in the model. The Sargen test yielded a value of 0.823 in the case of SGMM and 0.6851 in the case of 2SLS, which was deemed statistically insignificant since it was above the threshold of 0.10. This suggests that the instruments employed in the SGMM and 2SLS estimations are valid and that the overidentifying limitations are not violated.

Results and poverty in the model of human health poverty model

Table 6 indicates the cross-dependence occurrence in this panel. The findings stated are identical to Yasin et al. ( 2019 ). Table 11 illustrates how the data negate the null hypothesis of a homogeneous slope and supports the existence of a heterogeneous slope in model 2. The covariances related to Model 2 (HHPI) are displayed in Table S4 , Supplementary Information. The results indicate a positive correlation between corruption and governance with the HHPI, while an inverse correlation is noted between democracy and political globalization with the mentioned index.

The unit root tests are presented in Table 8 , employing the level and the first difference approaches. Based on the results obtained from the CIPS test, it can be observed that all variables exhibit stationarity when differenced once, but only a limited number of variables demonstrate stationarity at the original level.

Pedroni’s panel co-integration test in Table 12 describes the results. The test statistics indicate the incident of long-run association amid the series. It may be recommended that associations subsist among the variables in the long run. This means they travel collectively toward a steady equilibrium phase, and Dursun and Ogunleye ( 2016 ) have the same findings.

Table 13 displays the estimated coefficients for the human health poverty variable. Across our panel of developing countries, the lagged human health poverty coefficient exhibits positive and highly significant values (at the 1% level) in both SGMM and 2SLS estimations. This finding indicates a substantial and statistically significant direct impact of the previous year’s poverty levels on current poverty, highlighting the persistence of human health poverty. Human health poverty and corruption also have a positive and substantial association, confirming the findings of Ajisafe ( 2016 ). This outcome is also supported by Gupta et al. ( 2002 ) supposition that corruption causes income inequalities as the elite benefit from corruption while the rest of the population stays in poverty, deteriorating human health. The analysis reveals a negative association between human health poverty and democracy. This finding suggests that democratic institutions in developing nations might contribute to mitigating poverty levels.

The coefficient of governance (Political Institutional Quality Index), an index of six indicators of institutional quality, is highly significant and positive in the case of DK and 2SLS but comparatively less significant in the case of FE and SGMM, indicating that governance, instead of decreasing, significantly increases poverty. The main reason for this outcome is poor and adverse governance in developing countries. Another reason may be that the institutions distinguished through political instability create hindrances for growth instruments, and the capabilities of a nation are restricted. The same findings are suggested by Karim et al. ( 2013 ). Political globalization has a very significant and negative coefficient. This indicates that poverty is declining considerably in these developing nations due to political globalization. Bergh and Nilsson ( 2014 ) and Salahuddin et al. ( 2020 ) also provide recommendations on these results. This outcome might be because stronger international relations facilitate better policy coordination and the exchange of information and resources, which may support initiatives to reduce poverty (Dreher, 2006 ; Dollar and Kraay, 2004 ).

For the GMM estimators, the efficiency and validity tests are AR (2), a test for second-order autocorrelation, and a Sargan test for over-identifying limitation. The AR (2) value in Table 13 above is 0.386, more than 0.05 and not statistically significant. This outcome rejects the incidence of second-order autocorrelation in the model. The Sargan test statistic yields p-values of 0.419 and 0.6109 for the SGMM and 2SLS estimations, respectively. As both values exceed the conventional 0.10 significance level, these results offer no evidence of instrument invalidity or violation of overidentifying restrictions in the SGMM and 2SLS estimations.

Policy recommendations

Our research question focused on understanding how political factors contribute to poverty in developing nations. This study employed a panel data analysis for 24 developing countries from 1997 to 2022, investigating the impact of corruption, governance (political institutional quality), democracy, and political globalization on income and human health poverty.

Our study provides compelling evidence that political factors influence poverty levels in developing countries. The findings provide robust evidence for the significant influence of political factors on poverty levels. Corruption exhibits a positive and statistically significant association with income and human health poverty, highlighting its detrimental effects. Conversely, democracy presents a negative and significant association with poverty, suggesting its potential to alleviate poverty. The relationship between governance and poverty seems more complex, with varying degrees of relevance across various estimating techniques. Political globalization, on the other hand, demonstrates a strong negative association with both income and human health poverty, indicating its potential for fostering poverty reduction.

These findings offer valuable insights for policymakers addressing poverty in developing nations. The detrimental effects of corruption on poverty necessitate robust anti-corruption measures. Empowering anti-corruption agencies, increasing transparency in government spending, and strengthening legal frameworks to deter corruption are crucial steps.

The positive association between democracy and poverty reduction underscores the importance of fostering solid democratic institutions. This might involve supporting initiatives that promote free and fair elections, protect freedom of speech and assembly, and enhance citizen participation in governance.

The positive impact of political globalization suggests that international cooperation can play a vital role in tackling poverty. This includes fostering collaboration among developing countries to share best practices, promoting fair trade agreements, and encouraging international investments contributing to sustainable development.

Real-world examples further illustrate the effectiveness of these policy recommendations. Initiatives like Rwanda’s successful anti-corruption efforts and India’s focus on empowering local governments through democratic processes offer valuable insights. China’s focus on strengthening anti-corruption measures through targeted campaigns and institutional reforms offers a compelling example. This highlights the importance of a comprehensive approach to tackling corruption. Additionally, regional trade agreements like the African Continental Free Trade Area demonstrate the potential of international cooperation in promoting economic growth and poverty reduction.

Conclusions

The primary aim of this study was to assess the influence of political factors on poverty levels within a specific set of developing countries. The dependent variables utilized in this study were the income and HHPI, whereas explanatory variables were selected based on political issues. PCA was employed to create poverty indicators and an institutional quality index. Subsequently, CD tests were utilized to verify the presence of cross-dependency in the panel data. Following the confirmation of cross-sectional dependency, the analysis employed CIPS methodology to investigate stationary variables. The study employed the Pedroni co-integration test to analyze the enduring association between the variables. The SGMM methodology was employed to ascertain the dynamic impact on poverty since it is deemed more appropriate to employ GMM in cases where there are contemporaneous correlations among cross-sections.

Corruption has increased income and caused human poverty in these developing countries. It is a significant economic disaster that harms society’s growth and development and the economy in general. However, democracy showed that it had decreased income and human poverty but with statistically illustrated insignificant results. In democratic societies, openness and freedom should also be able to give weightage to the capacity and capability of each person, group or firm participating in economic activities.

Governance in these selected developing countries had verified constructive, considerable, and insignificant links between income and human poverty. Overall governance, if appropriate, casts a positive effect on all sectors of human life. The economy is one essential part of an individual’s life. The selection of the governance team is the most essential thing in running a country. The governance team ought to include a few members who have at least basic knowledge of the economy and what governance elements economic policies need in their implementation.

Political globalization showed a negative and highly significant relationship between poverty and both kinds of poverty i.e., poverty in terms of income and human health. Countries should work more on political globalization because they have a highly significant relationship with reducing income and human poverty. The focus should be on sharing information, results bearing effective government policies, and further establishing, if already existing, links for flows of goods, capital, and services, international trade, and investment, where both sides of countries can benefit mutually.

Furthermore, fighting corruption and strengthening democracy are crucial to reducing poverty in developing countries. This means empowering anti-corruption agencies, clearing government spending, and protecting free speech. Additionally, developing countries should collaborate with others to share ideas and create fair trade deals. These steps and investments in education and healthcare can help build a brighter future.

Data availability

The data analyzed in this study are available at https://databank.worldbank.org and https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html .

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Kou, Y., Yasin, I. Navigating poverty in developing nations: unraveling the impact of political dynamics on sustainable well-being. Humanit Soc Sci Commun 11 , 1143 (2024). https://doi.org/10.1057/s41599-024-03670-6

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