hypothesis nursing research definition

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

16 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

Trackbacks/Pingbacks

• What Is Research Methodology? Simple Definition (With Examples) - Grad Coach - […] Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example,…

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

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.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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.

• 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

Prevent plagiarism. Run a free check.

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, what is your plagiarism score.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Introduction to Statistical Hypothesis Testing in Nursing Research

Affiliation.

• 1 Courtney Keeler is an associate professor and Alexa Colgrove Curtis is assistant dean of graduate nursing and director of the MPH-DNP dual degree program, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Courtney Keeler, [email protected] . Bernadette Capili, PhD, NP-C, is the column coordinator: [email protected] . This manuscript was supported in part by grant No. UL1TR001866 from the National Institutes of Health's National Center for Advancing Translational Sciences Clinical and Translational Science Awards Program. The authors have disclosed no potential conflicts of interest, financial or otherwise.
• PMID: 37345783
• DOI: 10.1097/01.NAJ.0000944936.37768.29

Editor's note: This is the 16th article in a series on clinical research by nurses. The series is designed to be used as a resource for nurses to understand the concepts and principles essential to research. Each column will present the concepts that underpin evidence-based practice-from research design to data interpretation. To see all the articles in the series, go to https://links.lww.com/AJN/A204.

Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

PubMed Disclaimer

Similar articles

• Descriptive and Inferential Statistics in Nursing Research. Keeler C, Curtis AC. Keeler C, et al. Am J Nurs. 2024 Jan 1;124(1):48-52. doi: 10.1097/01.NAJ.0001004944.46230.42. Am J Nurs. 2024. PMID: 38126835
• An Introduction to Implementing and Conducting the Study. Capili B, Anastasi JK. Capili B, et al. Am J Nurs. 2024 May 1;124(5):58-61. doi: 10.1097/01.NAJ.0001016388.26001.50. Epub 2024 Apr 25. Am J Nurs. 2024. PMID: 38661704
• Sample Size Planning in Quantitative Nursing Research. Curtis AC, Keeler C. Curtis AC, et al. Am J Nurs. 2023 Nov 1;123(11):42-46. doi: 10.1097/01.NAJ.0000995360.84994.3b. Am J Nurs. 2023. PMID: 37882402
• How Does Research Start? Capili B. Capili B. Am J Nurs. 2020 Oct;120(10):41-44. doi: 10.1097/01.NAJ.0000718644.96765.b3. Am J Nurs. 2020. PMID: 32976154 Free PMC article. Review.
• Interpretation and use of statistics in nursing research. Giuliano KK, Polanowicz M. Giuliano KK, et al. AACN Adv Crit Care. 2008 Apr-Jun;19(2):211-22. doi: 10.1097/01.AACN.0000318124.33889.6e. AACN Adv Crit Care. 2008. PMID: 18560290 Review.
• Kessler RC, et al. Screening for serious mental illness in the general population. Arch Gen Psychiatry 2003;60(2):184–9.
• Woolridge JM. Fundamentals of mathematical statistics. In: Introductory econometrics: a modern approach . 3rd ed. Boston, MA: Cengage Learning; 2006.
• Gerstman BB. Chapter 9. Basics of hypothesis testing. In: Basic biostatistics: statistics for public health practice . 2nd ed. Burlington, MA: Jones and Bartlett Learning; 2015.
• Search in MeSH

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.
• Wolters Kluwer

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

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."

The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6034 Accesses

A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

Leedy Paul D. Practical research. Planning and design. 2nd ed. New York: Macmillan; 1960.

Bernard C. Introduction to the study of experimental medicine. New York: Dover; 1957.

Erren TC. The quest for questions—on the logical force of science. Med Hypotheses. 2004;62:635–40.

Article   PubMed   Google Scholar  

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 7. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1966.

Aristotle. The complete works of Aristotle: the revised Oxford Translation. In: Barnes J, editor. vol. 2. Princeton/New Jersey: Princeton University Press; 1984.

Polit D, Beck CT. Conceptualizing a study to generate evidence for nursing. In: Polit D, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. 8th ed. Philadelphia: Wolters Kluwer/Lippincott Williams and Wilkins; 2008. Chapter 4.

Jenicek M, Hitchcock DL. Evidence-based practice. Logic and critical thinking in medicine. Chicago: AMA Press; 2005.

Bacon F. The novum organon or a true guide to the interpretation of nature. A new translation by the Rev G.W. Kitchin. Oxford: The University Press; 1855.

Popper KR. Objective knowledge: an evolutionary approach (revised edition). New York: Oxford University Press; 1979.

Morgan AJ, Parker S. Translational mini-review series on vaccines: the Edward Jenner Museum and the history of vaccination. Clin Exp Immunol. 2007;147:389–94.

Article   PubMed   CAS   Google Scholar  

Pead PJ. Benjamin Jesty: new light in the dawn of vaccination. Lancet. 2003;362:2104–9.

Lee JA. The scientific endeavor: a primer on scientific principles and practice. San Francisco: Addison-Wesley Longman; 2000.

Allchin D. Lawson’s shoehorn, or should the philosophy of science be rated, ‘X’? Science and Education. 2003;12:315–29.

Article   Google Scholar  

Lawson AE. What is the role of induction and deduction in reasoning and scientific inquiry? J Res Sci Teach. 2005;42:716–40.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 2. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Bonfantini MA, Proni G. To guess or not to guess? In: Eco U, Sebeok T, editors. The sign of three: Dupin, Holmes, Peirce. Bloomington: Indiana University Press; 1983. Chapter 5.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 5. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Flach PA, Kakas AC. Abductive and inductive reasoning: background issues. In: Flach PA, Kakas AC, ­editors. Abduction and induction. Essays on their relation and integration. The Netherlands: Klewer; 2000. Chapter 1.

Murray JF. Voltaire, Walpole and Pasteur: variations on the theme of discovery. Am J Respir Crit Care Med. 2005;172:423–6.

Danemark B, Ekstrom M, Jakobsen L, Karlsson JC. Methodological implications, generalization, scientific inference, models (Part II) In: explaining society. Critical realism in the social sciences. New York: Routledge; 2002.

Pasteur L. Inaugural lecture as professor and dean of the faculty of sciences. In: Peterson H, editor. A treasury of the world’s greatest speeches. Douai, France: University of Lille 7 Dec 1954.

Swineburne R. Simplicity as evidence for truth. Milwaukee: Marquette University Press; 1997.

Sakar S, editor. Logical empiricism at its peak: Schlick, Carnap and Neurath. New York: Garland; 1996.

Popper K. The logic of scientific discovery. New York: Basic Books; 1959. 1934, trans. 1959.

Caws P. The philosophy of science. Princeton: D. Van Nostrand Company; 1965.

Popper K. Conjectures and refutations. The growth of scientific knowledge. 4th ed. London: Routledge and Keegan Paul; 1972.

Feyerabend PK. Against method, outline of an anarchistic theory of knowledge. London, UK: Verso; 1978.

Smith PG. Popper: conjectures and refutations (Chapter IV). In: Theory and reality: an introduction to the philosophy of science. Chicago: University of Chicago Press; 2003.

Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5:7–11.

Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiological research. Principles and quantitative methods. New York: Van Nostrand Reinhold; 1982.

Fortune AE, Reid WJ. Research in social work. 3rd ed. New York: Columbia University Press; 1999.

Kerlinger FN. Foundations of behavioral research. 1st ed. New York: Hold, Reinhart and Winston; 1970.

Hoskins CN, Mariano C. Research in nursing and health. Understanding and using quantitative and qualitative methods. New York: Springer; 2004.

Tuckman BW. Conducting educational research. New York: Harcourt, Brace, Jovanovich; 1972.

Wang C, Chiari PC, Weihrauch D, Krolikowski JG, Warltier DC, Kersten JR, Pratt Jr PF, Pagel PS. Gender-specificity of delayed preconditioning by isoflurane in rabbits: potential role of endothelial nitric oxide synthase. Anesth Analg. 2006;103:274–80.

Beyer ME, Slesak G, Nerz S, Kazmaier S, Hoffmeister HM. Effects of endothelin-1 and IRL 1620 on myocardial contractility and myocardial energy metabolism. J Cardiovasc Pharmacol. 1995;26(Suppl 3):S150–2.

PubMed   CAS   Google Scholar  

Stone J, Sharpe M. Amnesia for childhood in patients with unexplained neurological symptoms. J Neurol Neurosurg Psychiatry. 2002;72:416–7.

Naughton BJ, Moran M, Ghaly Y, Michalakes C. Computer tomography scanning and delirium in elder patients. Acad Emerg Med. 1997;4:1107–10.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. 1991;337:867–72.

Stern JM, Simes RJ. Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ. 1997;315:640–5.

Stevens SS. On the theory of scales and measurement. Science. 1946;103:677–80.

Knapp TR. Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nurs Res. 1990;39:121–3.

The Cochrane Collaboration. Open Learning Material. www.cochrane-net.org/openlearning/html/mod14-3.htm . Accessed 12 Oct 2009.

MacCorquodale K, Meehl PE. On a distinction between hypothetical constructs and intervening ­variables. Psychol Rev. 1948;55:95–107.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: ­conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Williamson GM, Schultz R. Activity restriction mediates the association between pain and depressed affect: a study of younger and older adult cancer patients. Psychol Aging. 1995;10:369–78.

Song M, Lee EO. Development of a functional capacity model for the elderly. Res Nurs Health. 1998;21:189–98.

MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.

Download references

Author information

Authors and affiliations.

Department of Medicine, College of Medicine, SUNY Downstate Medical Center, 450 Clarkson Avenue, 1199, Brooklyn, NY, 11203, USA

Phyllis G. Supino EdD

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Phyllis G. Supino EdD .

Editor information

Editors and affiliations.

, Cardiovascular Medicine, SUNY Downstate Medical Center, Clarkson Avenue, box 1199 450, Brooklyn, 11203, USA

Phyllis G. Supino

, Cardiovascualr Medicine, SUNY Downstate Medical Center, Clarkson Avenue 450, Brooklyn, 11203, USA

Jeffrey S. Borer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

Download citation

DOI : https://doi.org/10.1007/978-1-4614-3360-6_3

Published : 18 April 2012

Publisher Name : Springer, New York, NY

Print ISBN : 978-1-4614-3359-0

Online ISBN : 978-1-4614-3360-6

eBook Packages : Medicine Medicine (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Subscribe to journal Subscribe
• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Research in Nursing Practice

Yates, Morgan BScN, RN

Morgan Yates works as an RN in the ED of Surrey Memorial Hospital, Surrey, British Columbia, Canada. Contact author: [email protected] . The author has disclosed no potential conflicts of interest, financial or otherwise.

Bridging the gap between clinicians and the studies they depend on.

Research provides the foundation for high-quality, evidence-based nursing care. However, there isn't a direct flow of knowledge from research into practice. When I ask nurses where the “evidence” to guide the development of “evidence-based care” comes from, I get an interesting array of answers, from “researchers” to blank stares, as if there's no connection between the worlds of researchers and bedside nurses.

If research evidence informs our nursing practice, why doesn't it come from all of us? Nurses are inquisitive, think critically about their patients’ care, and want to know the best treatments for their patients—all of which makes them perfectly suited for research. Though the majority of nurses don't have the training to conduct research projects without assistance, they know how to ask questions and they know which questions need answering.

Yet research is often perceived as something undertaken by others far removed from the front lines of nursing practice. I believe that many nurses’ notions about who does or doesn't do research are rooted in our identity as nurses, which often manifests in a belief that “good” nurses are not researchers but instead have excellent clinical skills and can manage any crisis on a unit. A 2007 study by Woodward and colleagues in the Journal of Research in Nursing found that nurse clinicians engaged in research often perceive a lack of support from nurse managers and resentment from colleagues who see the research as taking them away from clinical practice.

The distinction often drawn between nursing research and clinical practice is mirrored in the inconsistent translation of research evidence into practice. Despite widespread promotion of evidence-based practice in nursing, creation of new translational research roles for nurses in major medical centers, and Medicare reimbursement policies in the United States tied to implementation of specific evidence-supported practices, studies continue to suggest much room for improvement. In a September 2014 article in this journal, Yoder and colleagues noted that researchers have consistently found that “nurses who valued research were more likely to use research findings in practice.” Such observations suggest a need for a much stronger link between nurse clinicians and the development of research into best practices. Though this has been discussed for years, I do not yet see research as having infiltrated fundamental views of what constitutes “nursing work.”

My discussions with frontline nurses and nurses involved in research have led me to ask three key questions that need addressing before we can fully integrate research into our professional identity. These are:

• How can nurses strive for high-quality research without focusing on randomized controlled trials?
• What are the barriers to and challenges of being involved in research and how can we address these?
• How can nurses at varying education levels be involved in research?

Nurses could turn many quality improvement (QI) projects into research. Research may be viewed as a continuum, with formal projects at one end and QI projects somewhere along the continuum. Though nurses may not think that QI projects would be of interest to others, with increased understanding of the research process and greater institutional support, some QI projects could easily become research projects.

More bedside nurses are likely to engage in research if

• nursing education is strengthened.
• time away from direct care is allocated for conducting research activities.
• consultant resources such as methodologists and biostatisticians are available to staff.
• institutional and organizational support of research are strengthened.

Many nurses are intimidated by research, but change is possible if we stop seeing research as someone else's job and start making it a part of who we are and what we do. This will pave the way to evidence-based practice truly becoming the norm.

• + Favorites
• View in Gallery

Readers Of this Article Also Read

Evidence-based practice: step by step: the seven steps of evidence-based..., ce: addressing implicit bias in nursing: a review, nursing theory in hospital models of care, ce: a review of the revised sepsis care bundles, early, nurse-directed sepsis care.

• Physician Physician Board Reviews Physician Associate Board Reviews CME Lifetime CME Free CME MATE and DEA Compliance
• Student USMLE Step 1 USMLE Step 2 USMLE Step 3 COMLEX Level 1 COMLEX Level 2 COMLEX Level 3 96 Medical School Exams Student Resource Center NCLEX - RN NCLEX - LPN/LVN/PN 24 Nursing Exams
• Nurse Practitioner APRN/NP Board Reviews CNS Certification Reviews CE - Nurse Practitioner FREE CE
• Nurse RN Certification Reviews CE - Nurse FREE CE
• Pharmacist Pharmacy Board Exam Prep CE - Pharmacist
• Allied Allied Health Exam Prep Dentist Exams CE - Social Worker CE - Dentist
• Point of Care
• Free CME/CE

Hypothesis Testing, P Values, Confidence Intervals, and Significance

Definition/introduction.

Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.

Issues of Concern

Register for free and read the full article, learn more about a subscription to statpearls point-of-care.

Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.

Hypothesis Testing

Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:

Research Question: Is Drug 23 an effective treatment for Disease A?

Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.

Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.

The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.

Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.

Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).

To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1]  When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]

Significance

Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3]  Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4]  When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5]  One criterion often used to determine statistical significance is the utilization of p values.

P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6]  Hypothesis testing allows us to determine the size of the effect.

An example of findings reported with p values are below:

Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.

Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.

For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7]  The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.  

While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3]  In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]

When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]

Confidence Intervals

A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12]  Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13]  A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14]  Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15]  confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]

In consideration of the similar research example provided above, one could make the following statement with 95% CI:

Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14]  A larger width indicates a smaller sample size or a larger variability. [16]  A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]

Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15]  Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.

Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14]  In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13]  An example is below:

Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

Clinical Significance

Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14]  Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.

Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4]  Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]

The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.

Nursing, Allied Health, and Interprofessional Team Interventions

All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care. 

Jones M, Gebski V, Onslow M, Packman A. Statistical power in stuttering research: a tutorial. Journal of speech, language, and hearing research : JSLHR. 2002 Apr:45(2):243-55     [PubMed PMID: 12003508]

Sedgwick P. Pitfalls of statistical hypothesis testing: type I and type II errors. BMJ (Clinical research ed.). 2014 Jul 3:349():g4287. doi: 10.1136/bmj.g4287. Epub 2014 Jul 3     [PubMed PMID: 24994622]

Fethney J. Statistical and clinical significance, and how to use confidence intervals to help interpret both. Australian critical care : official journal of the Confederation of Australian Critical Care Nurses. 2010 May:23(2):93-7. doi: 10.1016/j.aucc.2010.03.001. Epub 2010 Mar 29     [PubMed PMID: 20347326]

Hayat MJ. Understanding statistical significance. Nursing research. 2010 May-Jun:59(3):219-23. doi: 10.1097/NNR.0b013e3181dbb2cc. Epub     [PubMed PMID: 20445438]

Ferrill MJ, Brown DA, Kyle JA. Clinical versus statistical significance: interpreting P values and confidence intervals related to measures of association to guide decision making. Journal of pharmacy practice. 2010 Aug:23(4):344-51. doi: 10.1177/0897190009358774. Epub 2010 Apr 13     [PubMed PMID: 21507834]

Infanger D, Schmidt-Trucksäss A. P value functions: An underused method to present research results and to promote quantitative reasoning. Statistics in medicine. 2019 Sep 20:38(21):4189-4197. doi: 10.1002/sim.8293. Epub 2019 Jul 3     [PubMed PMID: 31270842]

Dorey F. Statistics in brief: Interpretation and use of p values: all p values are not equal. Clinical orthopaedics and related research. 2011 Nov:469(11):3259-61. doi: 10.1007/s11999-011-2053-1. Epub     [PubMed PMID: 21918804]

Liu XS. Implications of statistical power for confidence intervals. The British journal of mathematical and statistical psychology. 2012 Nov:65(3):427-37. doi: 10.1111/j.2044-8317.2011.02035.x. Epub 2011 Oct 25     [PubMed PMID: 22026811]

Tijssen JG, Kolm P. Demystifying the New Statistical Recommendations: The Use and Reporting of p Values. Journal of the American College of Cardiology. 2016 Jul 12:68(2):231-3. doi: 10.1016/j.jacc.2016.05.026. Epub     [PubMed PMID: 27386779]

Spanos A. Recurring controversies about P values and confidence intervals revisited. Ecology. 2014 Mar:95(3):645-51     [PubMed PMID: 24804448]

Freire APCF, Elkins MR, Ramos EMC, Moseley AM. Use of 95% confidence intervals in the reporting of between-group differences in randomized controlled trials: analysis of a representative sample of 200 physical therapy trials. Brazilian journal of physical therapy. 2019 Jul-Aug:23(4):302-310. doi: 10.1016/j.bjpt.2018.10.004. Epub 2018 Oct 16     [PubMed PMID: 30366845]

Dorey FJ. In brief: statistics in brief: Confidence intervals: what is the real result in the target population? Clinical orthopaedics and related research. 2010 Nov:468(11):3137-8. doi: 10.1007/s11999-010-1407-4. Epub     [PubMed PMID: 20532716]

Porcher R. Reporting results of orthopaedic research: confidence intervals and p values. Clinical orthopaedics and related research. 2009 Oct:467(10):2736-7. doi: 10.1007/s11999-009-0952-1. Epub 2009 Jun 30     [PubMed PMID: 19565303]

Gardner MJ, Altman DG. Confidence intervals rather than P values: estimation rather than hypothesis testing. British medical journal (Clinical research ed.). 1986 Mar 15:292(6522):746-50     [PubMed PMID: 3082422]

Cooper RJ, Wears RL, Schriger DL. Reporting research results: recommendations for improving communication. Annals of emergency medicine. 2003 Apr:41(4):561-4     [PubMed PMID: 12658257]

Doll H, Carney S. Statistical approaches to uncertainty: P values and confidence intervals unpacked. Equine veterinary journal. 2007 May:39(3):275-6     [PubMed PMID: 17520981]

Colquhoun D. The reproducibility of research and the misinterpretation of p-values. Royal Society open science. 2017 Dec:4(12):171085. doi: 10.1098/rsos.171085. Epub 2017 Dec 6     [PubMed PMID: 29308247]

Use the mouse wheel to zoom in and out, click and drag to pan the image

Fastest Nurse Insight Engine

• MEDICAL ASSISSTANT
• Abdominal Key
• Anesthesia Key
• Basicmedical Key
• Otolaryngology & Ophthalmology
• Musculoskeletal Key
• Obstetric, Gynecology and Pediatric
• Oncology & Hematology
• Plastic Surgery & Dermatology
• Clinical Dentistry
• Radiology Key
• Thoracic Key
• Veterinary Medicine
• Gold Membership

Research Problems, Purposes, and Hypotheses

Chapter 5 Research Problems, Purposes, and Hypotheses Chapter Overview What Are Research Problems and Purposes? Identifying the Problem and Purpose in Quantitative, Qualitative, and Outcomes Studies Problems and Purposes in Types of Quantitative Studies Problems and Purposes in Types of Qualitative Studies Problems and Purposes in Outcomes Research Determining the Significance of a Study Problem and Purpose Influences Nursing Practice Builds on Previous Research Promotes Theory Testing or Development Addresses Nursing Research Priorities Examining the Feasibility of a Problem and Purpose Researcher Expertise Money Commitment Availability of Subjects, Facilities, and Equipment Ethical Considerations Examining Research Objectives, Questions, and Hypotheses in Research Reports Research Objectives or Aims Research Questions Hypotheses Understanding Study Variables and Research Concepts Types of Variables in Quantitative Research Conceptual and Operational Definitions of Variables in Quantitative Research Research Concepts Investigated in Qualitative Research Demographic Variables Key Concepts References Learning Outcomes After completing this chapter, you should be able to: 1.  Identify research topics, problems, and purposes in published quantitative, qualitative, and outcomes studies. 2.  Critically appraise the research problems and purposes in studies. 3.  Critically appraise the feasibility of a study problem and purpose by examining the researcher’s expertise, money commitment, availability of subjects, facilities, and equipment, and the study’s ethical considerations. 4.  Differentiate among the types of hypotheses (simple versus complex, nondirectional versus directional, associative versus causal, and statistical versus research) in published studies. 5.  Critically appraise the quality of objectives, questions, and hypotheses presented in studies. 6.  Differentiate the types of variables in studies. 7.  Critically appraise the conceptual and operational definitions of variables in published studies. 8.  Critically appraise the demographic variables measured and the sample characteristics described in studies. Key Terms Associative hypothesis, p. 149 Background for a problem, p. 131 Causal hypothesis, p. 149 Complex hypothesis, p. 150 Conceptual definition, p. 155 Confounding variables, p. 155 Demographic variables, p. 157 Dependent (outcome) variable, p. 153 Directional hypothesis, p. 150 Environmental variables, p. 154 Extraneous variables, p. 154 Feasibility of a study, p. 143 Hypothesis, p. 149 Independent (treatment or intervention) variable, p. 153 Nondirectional hypothesis, p. 150 Null hypothesis (H 0 ), p. 151 Operational definition, p. 155 Problem statement, p. 131 Research concepts, p. 156 Research hypothesis, p. 151 Research objective or aim, p. 145 Research problem, p. 131 Research purpose, p. 131 Research question, p. 147 Research topic, p. 130 Research variables, p. 154 Sample characteristics, p. 157 Significance of a research problem, p. 131 Simple hypothesis, p. 150 Statistical hypothesis, p. 151 Testable hypothesis, p. 152 Variables, p. 153 We are constantly asking questions to gain a better understanding of ourselves and the world around us. This human ability to wonder and ask creative questions is the first step in the research process. By asking questions, clinical nurses and nurse researchers are able to identify significant research topics and problems to direct the generation of research evidence for use in practice. A research topic is a concept or broad issue that is important to nursing, such as acute pain, chronic pain management, coping with illness, or health promotion. Each topic contains numerous research problems that might be investigated through quantitative, qualitative, and outcomes studies. For example, chronic pain management is a research topic that includes research problems such as “What is it like to live with chronic pain?” and “What strategies are useful in coping with chronic pain?” Qualitative studies have been conducted to investigate these problems or areas of concern in nursing ( Munhall, 2012 ). Quantitative studies have been conducted to address problems such as “What is the most accurate way to assess chronic pain?” and “What interventions are effective in managing chronic pain?” Outcomes research methodologies have been used to examine patient outcomes and the cost-effectiveness of care provided in chronic pain management centers ( Doran, 2011 ). The problem provides the basis for developing the research purpose. The purpose is the goal or focus of a study that guides the development of the objectives, questions, or hypotheses in quantitative and outcomes studies. The objectives, questions, or hypotheses bridge the gap between the more abstractly stated problem and purpose and the detailed design for conducting the study. Objectives, questions, and hypotheses include the variables, relationships among the variables, and often the population to be studied. In qualitative research, the purpose and broadly stated research questions guide the study of selected research concepts. This chapter includes content that will assist you in identifying problems and purposes in a variety of quantitative, qualitative, and outcomes studies. Objectives, questions, and hypotheses are discussed, and the different types of study variables are introduced. Also presented are guidelines that will assist you in critically appraising the problems, purposes, objectives, questions, hypotheses, and variables or concepts in published quantitative, qualitative, and outcomes studies. What Are Research Problems and Purposes? A research problem is an area of concern in which there is a gap in the knowledge needed for nursing practice. Research is required to generate essential knowledge to address the practice concern, with the ultimate goal of providing evidence-based nursing care ( Brown, 2014 ; Craig & Smyth, 2012 ). In a study, the research problem (1) indicates the significance of the problem, (2) provides a background for the problem, and (3) includes a problem statement. The significance of a research problem indicates the importance of the problem to nursing and health care and to the health of individuals, families, and communities. The background for a problem briefly identifies what we know about the problem area, and the problem statement identifies the specific gap in the knowledge needed for practice. Not all published studies include a clearly expressed problem, but the problem usually can be identified in the first page of the report. The research purpose is a clear, concise statement of the specific goal or focus of a study. In quantitative and outcomes studies, the goal of a study might be to identify, describe, or examine relationships in a situation, examine the effectiveness of an intervention, or determine outcomes of health care. In qualitative studies, the purpose might be to explore perceptions of a phenomenon, describe elements of a culture, develop a theory of a health situation or issue, or describe historical trends and patterns. The purpose includes the variables or concepts, the population, and often the setting for the study. A clearly stated research purpose can capture the essence of a study in a single sentence and is essential for directing the remaining steps of the research process. The research problem and purpose from the study of Piamjariyakul, Smith, Russell, Werkowitch, and Elyachar (2013) of the effectiveness of a telephone coaching program on heart failure home management by family caregivers are presented as an example. This example is critically appraised using the following guidelines. Critical Appraisal Guidelines Problems and Purposes in Studies 1.  Is the problem clearly and concisely expressed early in the study? 2.  Does the problem include the significance, background, and problem statement? 3.  Does the purpose clearly express the goal or focus of the study? 4.  Is the purpose focused on the study problem statement? 5.  Are the study variables and population identified in the purpose? Research Example Problem and Purpose of a Quantitative Study Research Study Excerpt Problem Significance “Results of meta-analyses and American Heart Association (AHA) guidelines emphasize the critical importance of family caregivers’ involvement in home management of heart failure (HF). Family caregivers perform daily HF home management and provide essential support for patients in recognizing worsening symptoms (i.e., edema, shortness of breath; Riegel et al., 2009 ).” Piamjariyakul et al., 2013 , p. 32 Problem Background “Results of several studies have shown that HF rehospitalization is frequently precipitated by excess dietary sodium, inappropriate changes or reductions in taking prescribed medications, and respiratory infections, most of which family caregivers could help prevent if they were educated to be alert for these problems. . . . One intervention program found that a family partnership program on HF home care was helpful in adherence to diet with significant reductions in patients’ urine sodium ( Dunbar et al., 2005 ).” Piamjariyakul et al., 2013 , p. 32 Problem Statement “Yet, the few available studies on providing instruction for family caregivers are limited in content and lack guidance for implementing HF self-management strategies at home. . . . Also, in developing interventions that involve family caregivers, researchers need to measure caregiver outcomes (i.e., burden) to ensure that interventions do improve patient outcomes but do not have untoward negative impacts on the caregivers.” Piamjariyakul et al., 2013 , pp. 32-33 Research Purpose “The purpose of this study was to determine the feasibility and evaluate the helpfulness and costs of a coaching program for family caregiver HF home care management.” Piamjariyakul et al., 2013 , p. 33 Critical Appraisal Research Problem Piamjariyakul and colleagues (2013) presented a clear, concise research problem that had the relevant areas of (1) significance, (2) background, and (3) problem statement. HF is a significant, costly chronic illness to manage, and family caregivers are essential to the management process. A concise background of the problem was provided by discussing studies of the effects of caregivers on the outcomes of patients with HF. The discussion of the problem concluded with a concise problem statement that indicated the gap in the knowledge needed for practice and provided a basis for the study conducted by these researchers. Each problem provides the basis for generating a variety of research purposes and, in this study, the knowledge gap regarding the effectiveness of interventions on HF home management by family caregivers provides clear direction for the formulation of the research purpose. Research Purpose In a published study, the purpose frequently is reflected in the title of the study, stated in the study abstract, and restated after the literature review. Piamjariyakul and associates (2013) included the purpose of their study in all three places. The focus of this study was to examine the effectiveness of a telephone coaching program on HF home management (independent variable) on caregiving burden, confidence in providing HF care, preparedness, satisfaction, and program cost (dependent variables) for family caregivers (population). The purpose indicated the type of study conducted (quasi-experimental) and clearly identified the independent variable (telephone coaching program), population (patients with HF and their families), and setting (home). However, the dependent variables are not clearly identified in the study purpose but were discussed in the methods section of the study. The study purpose would have been strengthened by the inclusion of the dependent variables measured in this study. Implications for Practice The findings from the study by Piamjariyakul and co-workers (2013, p. 38) indicated that “The telephone coaching program was shown to reduce the caregiving burden and improve caregiver confidence and preparedness in HF home care management. . . . The cost for the program is considerably less than the cost for home healthcare providers ($120-160 per each visit), a single emergency department visit, or one inpatient hospitalization for HF due to poor HF home management.” This study has potential for use in practice to improve the quality of care provided to patients and families; however, the researchers did recognize the need for additional testing of the coaching program with a larger sample to determine its effectiveness. This type of study supports the Quality and Safety Education for Nurses ( QSEN, 2013 ; Sherwood & Barnsteiner, 2012 ) prelicensure competency to ensure safe, quality, and cost- effective health care that actively involves patients and families in this care process. Identifying the Problem and Purpose in Quantitative, Qualitative, and Outcomes Studies Quantitative, qualitative, and outcomes research approaches enable nurses to investigate a variety of research problems and purposes. Examples of research topics, problems, and purposes for different types of quantitative, qualitative, and outcomes studies are presented in this section. Problems and Purposes in Types of Quantitative Studies Example research topics, problems, and purposes for the different types of quantitative research (descriptive, correlational, quasi-experimental, and experimental) are presented in Table 5-1 . If little is known about a topic, researchers usually start with descriptive and correlational studies and progress to quasi-experimental and experimental studies as knowledge expands in an area. An examination of the problems and purposes in Table 5-1 will reveal the differences and similarities among the types of quantitative research. The research purpose usually reflects the type of study that was conducted ( Grove, Burns, & Gray, 2013 ). The purpose of descriptive research is to identify and describe concepts or variables, identify possible relationships among variables, and delineate differences between or among existing groups, such as males and females or different ethnic groups. Table 5-1 Quantitative Research Topics, Problems, and Purposes Type of Research Research Topic Research Problem and Purpose Descriptive research Hand hygiene (HH), HH opportunities, HH adherence, infection control, pediatric extended care facilities (ECFs), clinical and nonclinical caregivers Title of study: “Hand hygiene opportunities in pediatric extended care facilities” ( Buet et al., 2013, p. 72 ). Problem: “The population in pediatric ECFs [extended care facilities] is increasingly complex, and such children are at high risk of healthcare-associated infections (HAIs), which are associated with increased morbidity, mortality, resources use, and cost ( Burns et al., 2010 ) [problem significance]. . . . The Centers for Disease Control and Prevention (CDC) … and the World Health Organization ( WHO, 2009 ) have published evidence-based guidelines confirming the causal relationship between poor infection control practices, particularly hand hygiene (HH), and increased risk of HAIs [problem background]. However, most of the HH research has been focused in adult long term care facilities and acute care settings and findings from such studies are unlikely to be applicable to HH in pediatric ECFs given the different care patterns, including the relative distribution of different devices” [problem statement] ( Buet et al., 2013 , pp. 72-73). Purpose: “The purpose of this observational study was to assess the frequency and type of HH opportunities initiated by clinical (e.g., physicians and nurses) and non-clinical (e.g., parents and teachers) care givers, as well as evaluate HH adherence using the WHO’s ‘5 Moments for HH’ observation tool” ( Buet et al., 2013, p. 73 ). Correlational research Insulin resistance; anthropometric measurements (height, weight, body mass index [BMI], and waist circumference); systolic and diastolic blood pressure; laboratory values of lipids and triglycerides; and inflammatory marker high-sensitivity C-reactive protein (hsCRP) Title of study: “Biological correlates and predictors of insulin resistance among early adolescents” ( Bindler et al., 2013, p. 20 ). Problem: “Prevalence of obesity is at historic high levels among youth; for example, worldwide, obesity has doubled, and in developed countries, the numbers of youth who are overweight or obese have tripled in the last three decades ( WHO, 2011 ) [problem significance]. . . . Youth with obesity and insulin resistance (IR) are at increased risk of associated chronic conditions in adulthood, such as elevated blood pressure (BP), cardiovascular disease (CVD), type 2 diabetes, and several types of cancer ( Li et al., 2009 ) [problem background]. Despite the known relationships between IR and cardiometabolic factors, no study has yet examined the independent effects of these factors on a predictive model of IR among early adolescents” [problem statement] ( Bindler et al., 2013, pp. 20-21 ). Purpose: “Therefore, the purposes of this study among a group of early adolescents participating in the Teen Eating and Activity Mentoring in Schools (TEAMS) study were to describe the anthropometric and laboratory markers of the participants and to test the ability of these markers to predict risk of exhibiting IR” ( Bindler et al., 2013, p. 21 ). Quasi-experimental research Nurse-case-managed intervention, hepatitis A and B vaccine completion, sociodemographic factors, risk behaviors, and homeless adults Title of study: “Effects of a nurse-managed program on hepatitis A and B vaccine completion among homeless adults” ( Nyamathi et al., 2009, p. 13 ). Problem: “Hepatitis B virus (HBV) infection poses a serious threat to public health in the United States. Recent estimates place the true prevalence of chronic HBV in the United States at approximately 1.6 cases per 100,000 persons ( CDC, 2008 ). It is estimated that there were 51,000 new cases of HBV infection in 2005 ( Wasley et al., 2007 ), a financial burden reaching $1 billion annually. . . . Homeless populations are at particularly high risk of HBV infection due to high rates of unprotected sexual behavior and sharing of needles and other IDU [injection drug user] paraphernalia. Previous studies have reported that HBV infection rates among homeless populations range from 17% to 31% (i.e., from 17,000 to 31,000 per 100,000) compared with 2.1 per 100,000 in the general United States population [problem significance]. . . . Vaccination is the most effective way to prevent HBV infection ( CDC, 2006 ). . . . Improving vaccination adherence rates among homeless persons is an important step toward reducing the high prevalence of HBV infection in this population [problem background]. … Thus, little is known about adherence to HBV vaccination among community samples of urban homeless person[s] or about the effect of stronger interventions to incorporate additional strategies, such as nurse case management and targeted HBV education along with client tracking [problem statement]” ( Nyamathi et al., 2009, pp. 13-14 ). Purpose: The purpose of this study was to determine the “effectiveness of a nurse-case-managed intervention compared with that of two standard programs on completion of the combined hepatitis A virus (HAV) and HBV vaccine series among homeless adults and to assess sociodemographic factors and risk behaviors related to the vaccine completion” ( Nyamathi et al., 2009, p. 13 ). Experimental research Chronic widespread pain, aerobic exercise, analgesia, neurotrophin-3 synthesis, pain management, animal model Title of study: “Aerobic exercise alters analgesia and neurotrophin-3 [NT-3] synthesis in an animal model of chronic widespread pain” ( Sharma et al., 2010, p. 714 ). Problem: “Chronic widespread pain is complex and poorly understood and affects about 12% of the adult population in developed countries ( Rohrbeck et al., 2007 ) [problem significance]. . . . Management of chronic pain syndromes poses challenges for healthcare practitioners, and pharmacological interventions offer limited efficacy. . . . Exercise training has been long suggested to reduce pain and improve functional outcomes ( Whiteside et al., 2004 ) [problem background]. . . . Surprisingly, the current literature is mainly limited to human studies where the molecular basis for exercise training cannot be easily determined. Relatively few animal studies have addressed the effects and mechanisms of exercise on sensory modulation of chronic pain” [problem statement] ( Sharma et al., 2010, p. 715 ). Purpose: “The purpose of the present study was to examine the effects of moderate-intensity aerobic exercise on pain-like behavior and NT-3 in an animal model of widespread pain” ( Sharma et al., 2010, p. 714 ). Buet and co-workers (2013) conducted a descriptive study to identify the hand hygiene (HH) opportunities and adherence among clinical and nonclinical caregivers in extended pediatric care facilities. These researchers followed the World Health Organization “5 Moments for Hand Hygiene” ( WHO, 2009 ): before touching a patient, before clean or aseptic procedures, after body fluid exposure or risk, after touching a patient, and after touching patient surroundings. Researchers found that HH opportunities were numerous for clinical and nonclinical caregivers, but adherence to HH was low, especially for nonclinical individuals. This study supports the importance of HH in the delivery of quality, safe care based on current evidence-based guidelines ( Melnyk & Fineout-Overholt, 2011 ; QSEN, 2013 ). The purpose of correlational research is to examine the type (positive or negative) and strength of relationships among variables. In their correlational study, Bindler, Bindler, and Daratha (2013) examined the prediction of insulin resistance (IR) in adolescents using anthropometric measurements (height, weight, body mass index [BMI], and waist circumference), systolic and diastolic blood pressure, laboratory values [lipid and triglyceride levels], and the inflammatory marker of high-sensitivity, C-reactive protein (see Table 5-1 ). The researchers found that waist circumference and triglycerides were the strongest predictors of IR in adolescents. The findings from this study stressed the importance of nurses measuring waist circumference, height, and weight; calculating BMI; and examining lipid levels to identify youths at risk for IR. Quasi-experimental studies are conducted to determine the effect of a treatment or independent variable on designated dependent or outcome variables ( Shadish, Cook, & Campbell, 2002 ). Nyamathi and colleagues (2009) conducted a quasi-experimental study to examine the effectiveness of a nurse case-managed intervention on hepatitis A and B vaccine completion among homeless adults. The research topics, problem, and purpose for this study are presented in Table 5-1 . The findings from this study “revealed that a culturally sensitive comprehensive program, which included nurse case management plus targeted hepatitis education, incentives, and client tracking, performed significantly better than did a usual care program” ( Nyamathi et al., 2009 , p. 21). Thus the researchers recommended that public health program planners and funders use this type of program to promote increased completion of hepatitis A and B vaccinations for high-risk groups. Experimental studies are conducted in highly controlled settings, using a highly structured design to determine the effect of one or more independent variables on one or more dependent variables ( Grove et al., 2013 ). Sharma, Ryals, Gajewski, and Wright (2010) conducted an experimental study to determine the effects of an aerobic exercise program on pain like behaviors and neurotrophin-3 synthesis in mice with chronic widespread pain (see Table 5-1 ). These researchers found that moderate-intensity aerobic exercise had the effect of deep tissue mechanical hyperalgesia on chronic pain in mice. This finding provides a possible molecular basis for aerobic exercise training in reducing muscular pain in fibromyalgia patients. Problems and Purposes in Types of Qualitative Studies The problems formulated for qualitative research identify areas of concern that require investigation to gain new insights, expand understanding, and improve comprehension of the whole ( Munhall, 2012 ). The purpose of a qualitative study indicates the focus of the study, which may be a concept such as pain, an event such as loss of a child, or a facet of a culture such as the healing practices of a specific Native American tribe. In addition, the purpose often indicates the qualitative approach used to conduct the study. The basic assumptions for this approach are discussed in the research report ( Creswell, 2014 ). Examples of research topics, problems, and purposes for the types of qualitative research—phenomenological, grounded theory, ethnographic, exploratory-descriptive, and historical—commonly found in nursing are presented in Table 5-2 . Table 5-2 Qualitative Research Topics, Problems, and Purposes Type of Research Research Topic Research Problem and Purpose Phenomenological research Lived experience of children, asthma, health promotion, child health, chronic illness, fears of exacerbations, fears of being ostracized Title of study: “Children’s experiences of living with asthma: Fear of exacerbations and being ostracized” ( Trollvik et al., 2011, p. 295 ). Problem: “Asthma is the most common childhood disease and long-term medical condition affecting children ( Masoli et al., 2004 ). The prevalence of asthma is increasing, and atopic diseases are considered to be a worldwide health problem and an agent of morbidity in children significance]. . . . Studies show that children with asthma have more emotional/behavioral problems than healthy children… It has also been found that asthma control in children is poor and that healthcare professionals (HCPs) and children focus on different aspects of having asthma ( Price et al., 2002 ) [problem background]. . . . Few studies have considered very young children’s, 7-10 years old, perspectives; this study might contribute to new insights into their lifeworld experiences” [problem statement] ( Trollvik et al., 2011, pp. 295-296 ). Purpose: “The aim of this study was to explore and describe children’s everyday experiences of living with asthma to tailor an Asthma Education Program based on their perspectives. . . . In this study, a phenomenological and hermaneutical approach was used to gain an understanding of the children’s lifeworld” ( Trollvik et al., 2011 , p. 296). Grounded theory research Foster care, pregnancy prevention, prevention of sexually transmitted infections, patient-provider relationship Title of study: “Where do youth in foster care receive information about preventing unplanned pregnancy and sexually transmitted infections [STIs]” ( Hudson, 2012, p. 443 ). Problem: “Within the United States, approximately 460,000 children live in foster care, and adolescents comprise half of this population. . . . Children enter the foster care system as a result of sexual abuse, physical abuse, or physical neglect and abandonment ( Child Welfare League of America, 2007 ) [problem significance]. . . . With limited access to health promotion information and education about high-risk sexual behavior, it is not surprising that these young people have a high incidence of unplanned pregnancy and STIs compared with youth not in foster care [problem background]. Little research exists on the extent to which foster youth receive information about sexual activity from healthcare providers” [problem statement] ( Hudson, 2012, p. 443-444 ). Purpose: A grounded theory study was conducted to “describe how and where foster youth receive reproductive health and risk reduction information to prevent pregnancy and sexually transmitted infections. Participants also were asked to describe their relationship with their primary healthcare provider while they were in foster care” ( Hudson, 2012, p. 443 ). Ethnographic research Critical illness, mechanical ventilation, weaning, family presence, surveillance Title of study: “Family presence and surveillance during weaning from prolonged mechanical ventilation” ( Happ et al., 2007, p. 47 ). Problem: “During critical illness, mechanical ventilation imposes physical and communication barriers between family members and their critically ill loved ones [problem signicance]. . . . Most studies of family members in the intensive care unit (ICU) have focused on families’ needs for information, access to the patient, and participation in decisions to withdraw or withhold life-sustaining treatment. … Although numerous studies have been conducted of patient experiences with short- and long-term mechanical ventilation (LTMV), research has not focused on family interactions with patients during weaning from mechanical ventilation [problem background]. Moreover, the importance of family members’ bedside presence and clinicians’ interpretation of family behaviors at the bedside have not been critically examined” [problem statement] ( Happ et al., 2007, pp. 47-48 ). Purpose: “With the use of data from an ethnographic study of the care and communication processes during weaning from LTMV, we sought to describe how family members interact with the patients and respond to the ventilator and associated ICU bedside equipment during LTMV weaning” ( Happ et al., 2007, p. 48 ). Exploratory-descriptive qualitative research Intimate partner violence, abuse of spouse, supporting mothering, parent-child relationships, family health, providers’ perspective, social support Title of study: “Supporting mothering: Service providers’ perspectives of mothers and young children affected by intimate partner violence” ( Letourneau et al., 2011, p. 192 ). Problem: “Estimates of the percent of women with exposure to intimate partner violence (IPV) over their lifetimes by husbands, partners, or boyfriends range between 8% and 66%. . . . The high concentration of preschool-age children in households where women experience IPV… is a major concern [problem significance]. . . . Indeed, preschool-age children exposed to IPV may share many of the adjustment difficulties experienced by victims of direct physical and psychological abuse ( Litrownik et al., 2003 ) [problem background]. The degree to which children from birth to 36 months of age are affected by IPV, however, is not well understood. Even less is known of effective services and supports that target mothers and their young children exposed to IPV” [problem statement] ( Letourneau et al., 2011, p. 193 ). Purpose: “We conducted a qualitative descriptive study of service providers’ understandings of the impact of IPV on mothers, young children (birth to 36 months), and mother-infant/child relationships, and of the support needs of these mothers and young children” ( Letourneau et al., 2011, p. 192 ). Historical research Health disparities, childhood obesity, historical exemplar, prevention of infant mortality, public health nurses’ role Title of study: “Nurses’ role in the prevention of infant mortality in 1884-1925: Health disparities then and now ( Thompson & Keeling, 2012, p. 471 ). Problem: “Over the past several years, health policy makers have directed increased attention to issues of health disparities, an issue that has concerned the nursing profession for over a century[problem significance]. . . . Reutter and Kushner (2010) advocate that addressing health inequities are well within the nursing mandate and yet is an underutilized role [problem background]. . . . Nursing historical research lends insight into the complex health issues that nurses face today and may guide policy and nursing practice [problem statement]” ( Thompson & Keeling, 2012, p. 471 ). Purpose: The purpose of this historical study “was to evaluate the public health nurses’ (PHNs’) role with infant mortality during 1884-1925, specifically how nursing care impacted on conditions of poverty, poor nutrition, poor living conditions, lack of education, and lack of governmental policies that contributed to the poor health of infants a century ago” ( Thompson, & Keeling, 2012, p. 471 ). Phenomenological research is conducted to promote a deeper understanding of complex human experiences as they have been lived by the study participants ( Munhall, 2012 ). Trollvik, Nordbach, Silen, and Ringsberg (2011) conducted a phenomenological study to describe children’s experiences of living with asthma. The research topics, problem, and purpose for this study are presented in Table 5-2 . Findings from this study described two themes with five subthemes (identified in parentheses): fear of exacerbation (body sensations, frightening experiences, and loss of control) and fear of being ostracized (experiences of being excluded and dilemma of keeping the asthma secret or being open about it). The findings from this study emphasize that asthma management is not only a major issue for the children involved but also for their parents, teachers, and healthcare providers. Asthma educational programs need to be tailored to the individual child based on her or his perspectives and needs. This type of knowledge provides direction for accomplishing the QSEN (2013) competencies of providing patient-centered care. In grounded theory research, the problem identifies the area of concern and the purpose indicates the focus of the theory to be developed to account for a pattern of behavior of those involved in the study ( Wuest, 2012 ). For example, Hudson (2012, p. 443) conducted a grounded theory study to “describe how and where foster youth receive reproductive health and risk reduction information to prevent pregnancy and sexually transmitted infections (STIs)” (see Table 5-2 ). The following three thematic categories emerged from this study: “(a) discomfort visiting and disclosing, (b) receiving and not receiving the bare essentials, and (c) learning from community others” ( Hudson, 2012 , p. 445). The implications for practice were that primary care providers needed to provide time and confidential space for foster youths to disclose their sexual activities, and they (foster youths) need to receive more reproductive and risk prevention information from their school settings. In ethnographic research, the problem and purpose identify the culture and specific attributes of the culture that are to be examined, described, analyzed, and interpreted to reveal the social actions, beliefs, values, and norms of the culture ( Wolf, 2012 ). Happ, Swigart, Tate, Arnold, Sereika, and Hoffman (2007) conducted an ethnographic study of family presence and surveillance during weaning of their family member from a ventilator. Table 5-2 includes the research topics, problem, and purpose of this study. They concluded that “this study provided a potentially useful conceptual framework of family behaviors with long-term critically ill patients that could enhance the dialogue about family-centered care and guide future research on family presence in the intensive care unit” ( Happ et al., 2007 , p. 47). Exploratory-descriptive qualitative research is being conducted by several qualitative researchers to describe unique issues, health problems, or situations that lack clear description or definition. This type of research often provides the basis for future qualitative and quantitative research ( Creswell, 2014 ; Grove et al., 2013 ). Letourneau, Young, Secco, Stewart, Hughes, and Critchley (2011) conducted an exploratory-descriptive qualitative study of service providers’ understandings of the impact of intimate partner violence (IPV) on mothers and their young children to determine their needs for support (see Table 5-2 ). They found that these mothers and their children require more support than is currently available. In addition, the service providers had difficulty identifying interventions to promote and protect them and their children. The problem and purpose in historical research focus on a specific individual, characteristic of society, event, or situation in the past and usually identify the time period in the past that was examined by the study ( Lundy, 2012 ). For example, Thompson and Keeling (2012) examined the role of the public health nurse (PHN) in the prevention of infant mortality from 1884 to 1925 (see Table 5-2 ). They emphasized that studying the past role of PHNs and the health disparities then and now would increase our understanding of current nursing practice with regard to childhood health issues. They provided the following suggestions for nursing practice: “focus on health disparities in childhood obesity, in areas of environmental and policy change, and the development of social programs and education for families to support healthier living” ( Thompson & Keeling, 2012 , p. 471). Problems and Purposes in Outcomes Research Outcomes research is conducted to examine the end results of care ( Doran, 2011 ). This is a growing area of research in nursing to examine the relationships between the nursing process of care and patient outcomes. Table 5-3 includes the topics, problem, and purpose from an outcomes study by Ausserhofer and associates (2013) , who explored the relationship between patient safety climate (PSC) and selected patient outcomes in Swiss acute care hospitals. The adverse events or outcomes examined were medication errors, patient falls, pressure ulcers, and healthcare-associated infections that are common problems in U.S. hospitals ( Institute of Medicine, 2004 ). This study was guided by a common outcomes framework that focused on structure or work system, process of care, and patient outcomes (see Chapter 14 ). These researchers did not find a significant relationship of PSC to the selected patient outcomes. However, they stressed the need for additional research in this area and for selecting more reliable outcome measures. Table 5-3 Outcomes Research Topics, Problem, and Purpose Type of Research Research Topic Research Problem and Purpose Outcomes research Work system of patient safety climate (PSC) Process of care of nurses, workload work, and patient needs Outcomes of adverse events and patient satisfaction Title of study: “The association of patient safety climate and nurse-related organizational factors with selected patient outcomes: A cross-sectional survey” Ausserhofer et al., 2013, p. 240 ). Problem: “Today’s patient care in healthcare organizations is anything but safe, as between 2.9% and 16.6% of hospitalized patients are affected by adverse events such as medication errors, healthcare-associated infection, or patient falls. More than one-third of adverse events lead to temporary (34%) or permanent disability (6-9%) and between 3% and 20.8% of the patients experiencing an adverse event die [problem significance]. . . . As 37-70% of all adverse events are considered preventable, . . . harmful impacts on patients, such as psychological trauma, impaired functionality or loss of trust in the healthcare system as well as socio-economic costs, could be avoided ( Institute of Medicine, 2004 ). . . . Patient safety climate (PSC) is an important work environment factor determining patient safety and quality of care in healthcare organizations [problem background]. Few studies have investigated the relationship between PSC and patient outcomes, considering possible confounding effects of other nurse-related organizational factors [problem statement]” ( Ausserhofer et al., 2013, pp. 240-241 ). Purpose: “The purpose of this study was to explore the relationship between PSC and selected patients outcomes in Swiss acute care hospitals” ( Ausserhofer et al., 2013, p. 242 ).

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)

Related posts:

• Outcomes Research
• Understanding Statistics in Research
• Clarifying Quantitative Research Designs
• Examining Populations and Samples in Research

Stay updated, free articles. Join our Telegram channel

Comments are closed for this page.

Full access? Get Clinical Tree

International Journal of Nursing Science Practice and Research

• Other Journals
• For Readers
• For Authors
• For Librarians
• Announcements
• JournalsPub Home Page
• Editorial Board
• Publication Ethics & Malpractice Statement

Hypothesis Types and Research

Dennis F. Polit. Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th edition. New Delhi: Lippincott Williams and Wilkins; 2012, 58–93p.

Nursing Research society of India, Nursing research and statistics, 1st edition. India: Pearson Publication; 2013, 48–51p.

Polit DF, Hungler BP. Nursing Research Principles and Methods. Philadelphia: Lippincott; 1999.

• There are currently no refbacks.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Prev Med

Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.