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Research Questions vs Hypothesis: What’s The Difference?

by  Antony W

August 1, 2024

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

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

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

Let’s get to it.

What’s a Research Question?

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

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

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

When is a Research Question Useful? 

A research question may be useful when and if: 

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

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

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

What’s a Hypothesis? 

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

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

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

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

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

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

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

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

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

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

Research Question vs Hypothesis: Which One Should Come First 

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

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

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

Research Question vs Hypothesis: What’s the Difference? 

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

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

Research Questions vs Hypothesis: Some Useful Advice 

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

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

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

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

Research Questions vs Hypothesis: Conclusion 

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

About the author 

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

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Research Questions & Hypotheses

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

Research Questions

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

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

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

Research Hypotheses

Present the researcher’s predictions based on specific statements.

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

Types of Research Hypothesis

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

Tips for developing research questions and hypotheses for research studies

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

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

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

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

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

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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

Methodology

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

 Statistics

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

Research bias

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

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

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

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

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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McCombes, S. (2023, November 21). Writing Strong Research Questions | Criteria & Examples. Scribbr. Retrieved September 18, 2024, from https://www.scribbr.com/research-process/research-questions/

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, 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 variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise 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.

Step 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

Step 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.

Step 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 .

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.

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).

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

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

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Home » Research Questions – Types, Examples and Writing Guide

Research Questions – Types, Examples and Writing Guide

Table of Contents

Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

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

The Importance of Research Questions and Hypotheses in Guiding Research

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

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

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

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

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

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

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

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

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

7.2 Understanding Research Questions

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

Definition and Characteristics of a Good Research Question

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

Characteristics of a Good Research Question:

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

Distinction Between Exploratory and Descriptive Research Questions

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

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

Developing Clear and Focused Research Questions

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

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

7.3 Types of Research Questions

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

Nondirectional Research Questions

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

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

Crafting Questions:

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

Directional Research Questions

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

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

Formulating Questions:

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

7.4 Strategies for Formulating Research Questions

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

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

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

7.5 Operationalization of Concepts

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

Defining Operationalization and Its Significance in Research

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

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

Steps to Operationalize Variables

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

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

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

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

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

Examples of Operationalizing Common Variables in Mass Communications Research

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

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

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

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

7.6 Developing Hypotheses

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

Definition and Function of Hypotheses in Quantitative Research

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

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

The Relationship Between Research Questions and Hypotheses

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

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

Criteria for a Testable Hypothesis

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

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

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

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

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

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

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

7.7 Types of Hypotheses

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

Null Hypotheses (H0)

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

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

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

Alternative Hypotheses (H1)

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

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

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

7.8 Strategic Formulation of Hypotheses

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

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

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

7.9 Directional and Nondirectional Hypotheses

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

Understanding the Distinction and When to Use Each Type

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

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

Examples of Both Directional and Nondirectional Hypotheses in Mass Communications Research

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

7.10 Deciding Between Directional and Nondirectional Hypotheses

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

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

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

7.11 Criteria for Good Research Questions and Hypotheses

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

Clarity and Precision

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

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

Strategies for Achieving Clarity and Precision:

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

Relevance to the Field of Study

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

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

Strategies for Ensuring Relevance:

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

Researchability and Empirical Testing Potential

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

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

Strategies for Enhancing Researchability:

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

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

7.12 Common Mistakes to Avoid in Formulating Research Questions and Hypotheses

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

Formulating Questions and Hypotheses That Are Too Broad or Vague

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

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

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

Confusing Research Questions with Interview or Survey Questions

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

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

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

Creating Untestable Hypotheses

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

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

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

7.13 Best Practices for Robust Formulation

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

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

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

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

Research Aims, Objectives & Questions

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

T he research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

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

What is the “golden thread”?

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

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

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

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

Research Aims: What are they?

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

Research Aims: Examples

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

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

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

Need a helping hand?

Research Objectives: What are they?

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

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

Research Objectives: Examples

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

For the digital transformation topic:

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

And for the student wellness topic:

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

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

Research Questions: What are they?

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

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

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

Research Questions: Examples 

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

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

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

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

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

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

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

The importance of strong alignment

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

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

Recap: The golden thread

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

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

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41 Comments

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

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

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

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

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

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

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

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

Thanks so much. This was really helpful.

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

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

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

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

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

This is quite helpful. I really appreciate!

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

Very innovative and educational in approach to conducting research.

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

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

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

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

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

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

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

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

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

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

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

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

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

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

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

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

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

Well explained

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

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

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

I found this article helpful. Thanks for sharing this.

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

This is a well researched and superbly written article for learners of research methods at all levels in the research topic from conceptualization to research findings and conclusions. I highly recommend this material to university graduate students. As an instructor of advanced research methods for PhD students, I have confirmed that I was giving the right guidelines for the degree they are undertaking.

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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How to Write a Research Question in 2024: Types, Steps, and Examples

by Imed Bouchrika, Phd

Co-Founder and Chief Data Scientist

A note from the author, Imed Bouchrika, PhD, career planning and academic research expert:

From conducting preliminary literature reviews to collecting data, every part of the research process relies on a research question. As an expert with more than 10 years of experience in academic research and writing, I know well that identifying a research question can be challenging even with primary and secondary research sources as the literature body continues to expand. Given this challenge, I have created this guide on how to create a good research question based on actual practices in the academe. Through this guide, I hope to impart knowledge that can help you in identifying a research question and also in creating a study that can significantly impact your field.

How to Write a Research Question Table of Contents

What is a research question, types of research questions, steps to developing a good research question, examples of good and bad research questions, important points to keep in mind in creating a research question.

A research question is a question that a study or research project, through its thesis statement , aims to answer. This question often addresses an issue or a problem, which, through analysis and interpretation of data, is answered in the study’s conclusion. In most studies, the research question is written so that it outlines various aspects of the study, including the population and variables to be studied and the problem the study addresses.

As their name implies, a research question is often grounded on research. As a result, these questions are dynamic; this means researchers can change or refine the research question as they review related literature and develop a framework for the study. While many research projects will focus on a single research question, larger studies often use more than one research question.

Importance of the research question

The primary importance of developing a research question is that it narrows down a broad topic of interest into a specific area of study (Creswell, 2014). Research questions, along with hypotheses, also serve as a guiding framework for research. These questions also specifically reveal the boundaries of the study, setting its limits, and ensuring cohesion.

Moreover, the research question has a domino effect on the rest of the study. These questions influence factors, such as the research methodology, sample size, data collection, and data analysis (Lipowski, 2008).

Research questions can be classified into different categories, depending on the type of research to be done. Knowing what type of research one wants to do—quantitative, qualitative, or mixed-methods studies—can help in writing effective research questions.

Doody and Bailey (2016) suggest a number of common types of research questions, as outlined below.

Quantitative research questions

Quantitative research questions are precise. These questions typically include the population to be studied, dependent and independent variables, and the research design to be used. They are usually framed and finalized at the start of the study (Berger, 2015).

Quantitative research questions also establish a link between the research question and the research design. Moreover, these questions are not answerable with “yes" or “no" responses. As a result, quantitative research questions don’t use words such as “is," “are," “do," or “does."

Quantitative research questions usually seek to understand particular social, familial, or educational experiences or processes that occur in a particular context and/or location (Marshall & Rossman, 2011). They can be further categorized into three types: descriptive, comparative, and relationship.

• Descriptive research questions aim to measure the responses of a study’s population to one or more variables or describe variables that the research will measure. These questions typically begin with “what". Students aim for a what is research question to uncover particular processes.
• Comparative research questions aim to discover the differences between two or more groups for an outcome variable. These questions can be causal, as well. For instance, the researcher may compare a group where a certain variable is involved and another group where that variable is not present.
• Relationship research questions seek to explore and define trends and interactions between two or more variables. This research question design often includes both dependent and independent variables and use words such as “association" or “trends."

Qualitative research questions

Qualitative research questions may concern broad areas of research or more specific areas of study. Similar to quantitative research questions, qualitative research questions are linked to research design. Unlike their quantitative counterparts, though, qualitative research questions are usually adaptable, non-directional, and more flexible (Creswell, 2013). As a result, studies using these questions generally aim to “discover," “explain," or “explore."

Ritchie et al. (2014) and Marshall and Rossman (2011) have also further categorized qualitative research questions into a number of types, as listed below:

• Contextual research questions seek to describe the nature of what already exists.
• Descriptive research questions attempt to describe a phenomenon.
• Emancipatory research questions aim to produce knowledge that allows for engagement in social action, especially for the benefit of disadvantaged people.
• Evaluative research questions assess the effectiveness of existing methods or paradigms.
• Explanatory research questions seek to expound on a phenomenon or examine reasons for and associations between what exists.
• Exploratory research questions investigate little-known areas of a particular topic.
• Generative research questions aim to provide new ideas for the development of theories and actions.
• Ideological research questions are used in research that aims to advance specific ideologies of a position.

The following table illustrates the differences between quantitative and qualitative research questions.

Mixed-methods studies

Mixed-methods studies typically require a set of both quantitative and qualitative research questions. Separate questions are appropriate when the mixed-methods study focuses on the significance and differences in quantitative and qualitative methods and not on the study’s integrative component (Tashakkori & Teddlie, 2010).

Researchers also have the option to develop a single mixed-methods research question. According to Tashakkori and Teddlie (2010), this suggests an integrative process or component between the study’s quantitative and qualitative research methods.

Before learning how to write a research paper , you must first learn how to create a research question. Based on the research question definition provided, formulate your query. If you are looking for criteria for a good research question, Stone (2002) says that a good research question should be relevant, decided, and meaningful. Creating a research question can be a tricky process, but there is a specific method you can follow to ease the process.

The following steps will guide you on how to formulate a research question:

1. Start with a broad topic.

A broad topic provides writers with plenty of avenues to explore in their search for a viable research question. Techniques to help you develop a topic into subtopics and potential research questions include brainstorming and concept mapping. For example, you can raise thought-provoking questions with your friends and flesh out ideas from your discussions. These techniques can organize your thoughts so you can identify connections and relevant themes within a broad topic.

When searching for a topic, it’s wise to choose an area of study that you are genuinely interested in, since your interest in a topic will affect your motivation levels throughout your research. It’s also wise to consider the interests being addressed recently by the research community, as this may affect your paper’s chances of getting published.

2. Do preliminary research to learn about topical issues.

Once you have picked a topic, you can start doing preliminary research. This initial stage of research accomplishes two goals. First, a preliminary review of related literature allows you to discover issues that are currently being discussed by scholars and fellow researchers. This way, you get up-to-date, relevant knowledge on your topic.

Second, a preliminary review of related literature allows you to spot existing gaps or limitations in existing knowledge of your topic. With a certain amount of fine-tuning, you can later use these gaps as the focus of your research question.

Moreover, according to Farrugia et al. (2010), certain institutions that provide grants encourage applicants to conduct a systematic review of available studies and evidence to see if a similar, recent study doesn’t already exist, before applying for a grant.

3. Narrow down your topic and determine potential research questions.

Once you have gathered enough knowledge on the topic you want to pursue, you can start focusing on a more specific area of study and narrowing down a research question. One option is to focus on gaps in existing knowledge or recent literature. Referred to by Sandberg and Alvesson (2011) as “gap-spotting," this method involves constructing research questions out of identified limitations in literature and overlooked areas of study. Similarly, researchers can choose research questions that extend or complement the findings of existing literature.

Another way of identifying and constructing research questions: problematization (Sandberg & Alvesson, 2011). As a research question methodology, problematization aims to challenge and scrutinize assumptions that support others’ and the researcher’s theoretical position. This means constructing research questions that challenge your views or knowledge of the area of study.

Lipowski (2008), on the other hand, emphasizes the importance of taking into consideration the researcher’s personal experiences in the process of developing a research question. Researchers who are also practitioners, for instance, can reflect on problematic areas of their practice. Patterns and trends in practice may also provide new insights and potential research question examples.

4. Evaluate the soundness of your research question.

At this point, you should have a list of potential research questions to choose from. To narrow them down, you have to evaluate each potential option based on their soundness, which can mean a number of things. Aside from being clear or specific, a good research question will also need to be relevant. There are other factors to consider when choosing which research question to investigate. To create a better play-by-play, here are the most crucial characteristics of the research question that you are looking for according to Hulley et al. (2007) known as the “FINER" criteria to find out if you have a good research question. The FINER criteria are outlined below:

F Feasible A good research question is feasible, which means that the question is well within the researcher’s ability to investigate. Researchers should be realistic about the scale of their research as well as their ability to collect data and complete the research with their skills and the resources available to them. It’s also wise to have a contingency plan in place in case problems arise.

I Interesting The ideal research question is interesting not only to the researcher but also to their peers and community. This interest boosts the researcher’s motivation to see the question answered. For instance, you can do research on student housing trends if it is right up your alley, as they do change often.

N Novel Your research question should be developed to bring new insights to the field of study you are investigating. The question may confirm or extend previous findings on the topic you are researching, for instance.

E Ethical This is one of the more important considerations of making a research question. Your research question and your subsequent study must be something that review boards and the appropriate authorities will approve.

R Relevant Aside from being interesting and novel, the research question should be relevant to the scientific community and people involved in your area of study. If possible, your research question should also be relevant to the public’s interest.

5. Construct your research question properly.

Considering research question importance, research questions should be structured properly to ensure clarity. Look for good research questions examples. There are a number of frameworks that you can use for properly constructing a research question. The two most commonly used frameworks are explained below.

PICOT framework

The PICOT research question framework was first introduced in 1995 by Richardson et al. Using the PICOT framework, research questions can be constructed to address important elements of the study, including the population to be studied, the expected outcomes, and the time it takes to achieve the outcome. With these elements, the framework is more commonly used in clinical research and evidence-based studies.

• P population, patients, or problem
• I intervention or indicator being studied
• C comparison group
• O outcome of interest
• T timeframe of the study

The sample research question below illustrates how to write research questions based on the PICOT framework and its elements:

PEO framework

Like the PICOT framework, the PEO framework is commonly used in clinical studies as well. However, this framework is more useful for qualitative research questions. This framework includes these elements:

• P population being studied
• E exposure to preexisting conditions

Below is an example of research question in the PEO framework:

Other commonly used frameworks for research questions include the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) and CLIP (Client group, Location of provided service, Improvement/Information/Innovation, Professionals) frameworks. Aside from helping researchers properly structure research questions, these frameworks also help refine research results and improve the focus of data analysis.

The following research question examples can further guide researchers on properly constructing a research question.

Example no. 1

Bad: How does social media affect people’s behavior? Good: What effect does the daily use of YouTube have on the attention span of children aged under 16?

The first research question is considered bad because of the vagueness of “social media" as a concept and the question’s lack of specificity. A good research question should be specific and focused, and its answer should be discovered through data collection and analysis. You can also hone your ability to construct well-worded and specific research questions by improving reading skills .

Example no. 2

Bad: Has there been an increase in childhood obesity in the US in the past 10 years? Good: How have school intervention programs and parental education levels affected the rate of childhood obesity among 1st to 6th-grade students?

In the second example, the first research question is not ideal because it’s too simple, and it’s easily answerable by a “yes" or “no." The second research question is more complicated; to answer it, the researcher must collect data, perform in-depth data analysis, and form an argument that leads to further discussion.

Developing the right research question is a critical first step in the research process. The examples of research questions provided in this guide have illustrated what good research questions look like. The key points outlined below should help researchers in the pursuit:

• The development of a research question is an iterative process that involves continuously updating one’s knowledge on the topic and refining ideas at all stages (Maxwell, 2013).
• Remain updated on current trends, state-of-the-art research studies, and technological advances in the field of study you are pursuing.
• Make the research question as specific and concise as possible to ensure clarity. Avoid using words or terms that don’t add to the meaning of the research question.
• Aside from doing a literature review, seek the input of experts in the field, mentors, and colleagues. Such inputs can prove beneficial not only for the research question but also for creating the rest of the study.
• Finally, refrain from committing the two most common mistakes in framing research questions: posing a question as an anticipated contribution and framing a question as a method (Mayo et al., 2013).

Key Insights

• Central Role of Research Questions: A research question is foundational to the entire research process, guiding the scope, methodology, and analysis of a study.
• Types of Research Questions: Research questions can be categorized into quantitative, qualitative, and mixed-methods, each requiring different approaches and designs.
• Quantitative Research Questions: These are precise and structured, often exploring relationships, comparisons, or descriptions within a study.
• Qualitative Research Questions: These are flexible and exploratory, aiming to discover, explain, or describe phenomena.
• Mixed-Methods Research Questions: These incorporate both quantitative and qualitative elements, requiring comprehensive and integrative approaches.
• Steps to Developing Research Questions: The process involves starting with a broad topic, conducting preliminary research, narrowing down the topic, evaluating the soundness of potential questions, and properly constructing the final research question.
• Criteria for Good Research Questions: Good research questions should be feasible, interesting, novel, ethical, and relevant (FINER criteria).
• Frameworks for Constructing Research Questions: Common frameworks include PICOT for quantitative research and PEO for qualitative research, helping to ensure clarity and focus.
• Examples of Research Questions: Clear examples illustrate the difference between poorly constructed and well-formulated research questions, highlighting the importance of specificity and focus.

1. What is a research question?

A research question is a query that a study aims to answer, often addressing an issue or problem. It outlines the study's focus, including the population, variables, and problem being investigated.

2. Why is developing a research question important?

Developing a research question is crucial because it narrows down a broad topic into a specific area of study. It also guides the research framework, methodology, and analysis, ensuring the study's cohesion and relevance.

3. What are the different types of research questions?

Research questions can be categorized into quantitative, qualitative, and mixed-methods. Quantitative questions are precise and structured, qualitative questions are flexible and exploratory, and mixed-methods questions combine both approaches.

4. How do you start developing a research question?

Start by choosing a broad topic of interest. Conduct preliminary research to learn about current issues and gaps in existing literature. Narrow down the topic to a specific area of study and identify potential research questions.

5. What criteria should a good research question meet?

A good research question should be feasible, interesting, novel, ethical, and relevant. This means it should be realistically investigable, engaging, provide new insights, be ethically sound, and pertinent to the field of study.

6. How can frameworks help in constructing research questions?

Frameworks like PICOT for quantitative research and PEO for qualitative research help ensure that research questions are structured clearly and address essential elements such as population, intervention, and outcome, improving the study's focus and clarity.

7. Can you provide examples of good and bad research questions?

Yes. A bad question might be vague or too simple, such as "How does social media affect people’s behavior?" A good question is specific and focused, like "What effect does the daily use of YouTube have on the attention span of children aged under 16?"

8. What are some common mistakes to avoid when framing research questions?

Avoid posing a question as an anticipated contribution or framing a question as a method. Ensure the question is clear, specific, and avoids terms that don't add meaningful context or clarity to the research focus.

References:

• Berger, R. (2015). Now I see it, now I don’t: Researcher’s position and reflexivity in qualitative research. Qualitative Research, 15 (2), 219-234. https://doi.org/10.1177/1468794112468475
• Creswell, J.W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 3rd ed . Thousand Oaks, CA: Sage.
• Creswell, J.W. (2014). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research,   5th ed . Upper Saddle River, NJ: Pearson Education.
• Doody, O., & Bailey, M. E. (2016). Setting a research question, aim, and objective.  Nurse Researcher ,  23  (4). https://journals.rcni.com/doi/pdfplus/10.7748/nr.23.4.19.s5
• Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Research questions, hypotheses, and objectives. Canadian Journal of Surgery , 53 (4), 278. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912019/
• Lipowski, E. E. (2008). Developing great research questions.  American Journal of Health-System Pharmacy ,  65  (17), 1667-1670.  https://academic.oup.com/ajhp/article-abstract/65/17/1667/5128061
• Marshall, C., & Rossman, G. B. (2014).  Designing qualitative research . Sage publications. Google Books
• Mayo, N., Asano, M., & Barbic, S.P. (2013). When is a research question not a research question? Journal of Rehabilitation Medicine, 45 (6), 513-518. https://doi.org/10.2340/16501977-1150
• Patnaik, S., & Swaroop, S. (2019). Hypothesizing the research question. Indian Journal of Public Health Research & Development , 10  (11).  http://www.indianjournals.com/ijor.aspx?target=ijor:ijphrd&volume=10&issue=11&article=097
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  Acp j club ,  123  (3), A12-3. https://doi.org/10.7326/ACPJC-1995-123-3-A12
• Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (Eds.). (2013).  Qualitative Research Practice: A Guide for Social Science Students and Researchers . Thousand Oaks, CA: Sage.   http://jbposgrado.org/icuali/Qualitative%20Research%20practice.pdf
• Sandberg, J., & Alvesson, M. (2011). Ways of constructing research questions: gap-spotting or problematization?  Organization ,  18  (1), 23-44. https://journals.sagepub.com/doi/abs/10.1177/1350508410372151
• Stone, P. (2002). Deciding upon and refining a research question. Palliative Medicine , 16, 265267.  https://doi.org/10.1191/0269216302pm562xx
• Tashakkori, A., & Teddlie, C. (Eds.). (2010).  Sage Handbook of Mixed Methods in Social & Behavioral Research . Thousand Oaks, CA: Sage.  https://doi.org/10.4135/9781506335193

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

An Archival Study of the Relationship Between Treatment Duration, Functioning, and Out-of-Home Placement for Youth with Serious Emotional Disturbance in a State-Wide Intensive In-Home Family Treatment Program

• Original Paper
• Open access
• Published: 19 September 2024

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• C. Wayne Jones   ORCID: orcid.org/0009-0007-5640-8725 1 ,
• Steve Simms 2 ,
• Jesse Troy 3 ,
• Scott Suhring 4 ,
• Dan Warner 3 &
• Tara Byers 2  

The main aim of this study was to evaluate the effectiveness of Pennsylvania’s state-wide intensive in-home treatment for youth with serious emotional disturbance (SED), EcoSystemic Structural Family Therapy-Family Based Mental Health Services (ESFT-FBMHS). Despite its long history of implementation, the program remains empirically under-evaluated. In this archival study, out-of-home placement and youth functioning outcomes were compared across four tiers of length of stay. Given the high-risk population treated in ESFT-FBMHS, it was hypothesized that the families and youth who completed the full duration of treatment (169–224 days) would have better outcomes than those who stopped treatment after 168 days or less. We utilized an ex post facto quantitative research design analyzing archived medical claims data of 2251 youth treated between 2018 and 2022 to assess out-of-home placement rates and analyzed archived data from six domains of the Child and Adolescent Needs and Strengths (CANS; Problem Presentation, Risk Behaviors, Functioning, Child Safety, Caregiver Needs, and Child Strengths) to assess changes in youth functioning post-discharge (90 and 180 days). An analysis using generalized estimating equations (GEE), controlling for potential confounding variables such as demographics and clinical features, suggest that length of stay in ESFT-FBMHS was significantly associated with out-of-home placement and youth improvement on the CANS at both 90- and 180-days post-discharge. As a group, youth with SED who did not complete the full duration of the program had 2–3 times the odds of out-of-home placement at 90 days post-discharge and 1–3 times the odds at 180 days post-discharge as compared to program completers. CANS scores showed improvement in 40.1% of youth who completed the program as compared to only 11.7%–18.2% for those who did not. The results of this study suggest that ESFT-FBMHS is effective for youth with SED as a group and can improve youth functioning and reduce out-of-home placement.

ESFT-FBMHS, an intensive in-home family-based program, is effective in reducing the odds of out-of-home placement for youth who complete the program.

Treatment duration in ESFT-FBMHS is significantly associated with the odds of out-of-home placement and changes in youth functioning.

Youth who extend their stay in the program show improvement in functioning on the CANS but have similar odds of out-of-home placement as those who do not complete the program.

Youth who complete ESFT-FBMHS are significantly more likely to show improvements in functioning on the CANS than those who leave early.

Improved youth functioning as measured by the CANS is associated with a 56% reduction in out-of-home placement.

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It is estimated that six million children and adolescents—or about one in 10—have emotional, social, and behavioral problems so severe they have trouble functioning in their day-to-day activities at home, at school and in their communities (Williams et al., 2018 ). The term “serious emotional disturbance” (SED) is used by state and federal agencies to describe this diagnostically diverse group of children and adolescents (youth) with multiple and severe emotional and behavioral problems. The safety risks these youth can pose to themselves, their families, and their communities results in frequent mental health crises that demand more intensive levels of care. Youth with SED drive the public mental health system’s most costly out-of-home services such as psychiatric inpatient hospitals and residential treatment facilities (Feng et al., 2017 ; Pires et al., 2013 ; Soni, 2014 ). To address this important and vulnerable population, there has been a proliferation of intensive home-based treatment programs emerge across all 50 US states designed as a first line of intervention to support, stabilize, and treat these youth in the community (Bruns et al., 2021 ). The argument for treating youth with SED in their home and communities as opposed to out-of-home placement is that it helps to maintain the youth’s attachments to the family unit and to natural supports and resources, creates opportunities for addressing the systemic issues that perpetuate or exacerbate children’s psychiatric disorders, and fosters generalization of treatment effects (Adnopoz, 2002 ).

In Pennsylvania, an intensive in-home approach, EcoSystemic Structural Family Therapy-Family Based Mental Health Services (ESFT-FBMHS), has been a part of the public behavioral health offerings for over three decades. Like most time-limited in-home intensive programs serving youth with SED the goal of ESFT-FBMHS is to improve youth functioning and to reduce repeated out-of-home placements in both residential treatment facilities and psychiatric hospitals (Lindblad-Goldberg et al., 1998 ; Pennsylvania Department of Public Welfare, 1993 ). Unlike the strong evidence base established in the literature by home-based family treatment models designed to reduce out-of-home placements of youth involved in either the child welfare system or juvenile justice system, such as Family Centered Treatment (FCT; Bright et al., 2017 ; Pierce et al., 2022 ), Multi-Systemic Therapy (MST; Vidal et al. ( 2017 )), Functional Family Therapy (FFT; Sexton & Turner, 2010 ), and Brief Strategic Family Therapy (BSFT; Szapocnik et al., 2012 ), the empirical evidence for the effectiveness of family treatment models integrated with intensive in-home programs serving youth with severe psychiatric problems remains limited (Moffett et al., 2018 ). One possible reason for the disparity in the volume of research generated by intensive in-home approaches with an embedded family therapy component is that FCT, MST, FFT, and BSFT represent clinical treatment approaches developed for narrowly defined clinical populations while home-based approaches for youth with SED developed primarily as an out-of-home placement diversion program, de-linked from specific diagnoses or specific clinical models.

Pennsylvania’s ESFT-FBMHS program, first launched in 1988, is a 32-week, trauma-informed intensive, in-home team-delivered service for children and youth ages birth to 21 deemed at-risk for out-of-home placement (Pennsylvania Department of Public Welfare, 1993 ). There are one and often two or more of these programs in Pennsylvania’s 67 counties, serving over 4000 youth annually (A. Herschell, personal communication, January 10, 2024). Families enrolled in ESFT-FBMHS typically receive between three to 10 hours of service/week, which usually includes an individual session with the identified youth, a conjoint session with the caregivers, and a family therapy session (Jones, 2019 ). In addition to youth, parent, and family therapy, service components include parent education, family support services, school-based consultation, case management, service coordination, crisis intervention, and 24 hour on-call emergency service. Family focused interventions are designed to strengthen caregivers’ executive skills, caregivers’ emotional support of the identified youth, and the family’s ability to de-escalate conflict and problem solve when tensions are high. Therapists work through caregivers to address youth-focused goals which include reducing symptoms and distress, helping them to participate more fully and adaptively in the home, school, and community, and building skills for managing emotions and coping with stress (Lindblad-Goldberg et al., 1998 ).

What makes Pennsylvania’s Family Based Mental Health Services unique among intensive in-home programs is that it is fully integrated with a highly specified trauma-informed family therapy clinical model, EcoSystemic Structural Family Therapy (ESFT; Jones, 2019 ; Lindblad-Goldberg et al., 1998 ). Adapted from Structural Family Therapy (Minuchin, 1974 ), ESFT is designed specifically to meet the needs of youth with serious emotional disturbance. Intensive in-home services blended with a clinical model, such as ESFT-FBMHS, theoretically have the potential not only to stabilize youth and families in crisis, but also to address underlying systemic issues creating and maintaining the youth’s vulnerability, thereby reducing the need for out-of-home placement for the long term. However, despite its 35-year history of implementation, and considerable accumulated practice-based evidence of its effectiveness (Lindblad-Goldberg & Northey, 2013 ), there is only one major quasi-experimental study of ESFT-FBMHS effectiveness (Dore, 1996 ; Lindblad-Goldberg et al., 2004 ), a study completed during the first seven years of implementation, before the model had well-established fidelity measures and a manual operationalizing concepts and treatment tasks.

There is promising evidence from studies of other models, however, that a clinically focused intensive home-based approach, such as ESFT-FBMHS, can be effective for youth with SED and their families. For example, Connecticut’s Intensive In-Home Child and Adolescent Psychiatric Services (IICAPS) was studied in a large-scale pre–post quasi-experimental study with a sample of 7169 youth (Barbot et al., 2016 ). It was found that the IICAPS approach to intensive in-home treatment reduced problem severity and improved youth functioning at discharge. Although this study is encouraging and contributes to the evidence that intensive in-home services informed by a clear, specified family focused clinical model can be beneficial to youth with SED, the research did not include a comparison group nor data on out-of-home placements, and was thus limited in explaining the program’s impact, or being able to generalize findings. There was also an experimental study of intensive in-home services in Ontario’s system of care (Wilmshurst, 2002 ), where youth were randomly assigned to either a 5-day/week residential treatment program utilizing a solution focused brief therapy approach or an intensive in-home program utilizing an amalgam of cognitive behavioral treatment and Multi-Systemic Therapy (MST). Both treatments lasted three months and included family involvement. The results suggested that youth receiving intensive in-home treatment reaped greater benefits, such as reductions in anxiety, depression, and externalizing symptoms as compared to those receiving residential treatment, but the absence of clear differentiation between the treatment conditions leave open the possibility that the changes were more a function of treatment modality (such as cognitive behavioral treatment) rather than a function of home-based treatment (Preyde et al., 2011 ).

MST-Psychiatric (Henggeler et al., 1999 ; Pane et al., 2013 ; Rowland et al., 2005 ) and adaptations of MST, such as Youth Villages Intercept (Barth et al., 2007 ), provide an example of an effective and established clinical model of family therapy specifically targeting youth with serious emotional problems that is integrated with intensive in-home services. Both MST-Psychiatric (Henggeler et al., 1999 ) and ESFT-FBMHS (Jones, 2019 ) identify underlying vulnerabilities and negative interactional patterns in the family and community ecosystem that are linked to the youth’s emotional or behavioral functioning and then focus interventions on reducing these vulnerabilities. MST-Psychiatric prioritizes family structure and the caregivers’ executive functioning (Henggeler et al., 2003 ) and ESFT-FBMHS prioritizes the attachment relationship between youth and their caregivers, the co-caregiver alliance, and family emotion regulation (Jones, 2019 ). Further, ESFT-FBMHS has a longer standard course of treatment (32 weeks) compared to MST-Psychiatric (4 months). In 1999, Henggeler demonstrated that compared to hospitalized youth, youth receiving MST-Psychiatric reported improved school attendance, lower levels of externalizing behaviors, higher caregiver satisfaction, and improved family functioning at the end of four months of treatment (Henggeler et al., 1999 ). However, follow-up studies (Henggeler et al., 2003 ; Huey et al., 2004) found short-lived impacts on out-of-home placement. One possible explanation for reduced impact over time is treatment duration. The youth and families receiving MST-Psychiatric treatment was limited to four months (Henggeler et al., 1999 ) which may be sufficient for briefly stabilizing high-risk severely symptomatic youth, but inadequate for addressing underlying chronic and severe problems in family functioning. Thus, while MST-Psychiatric research shows strong promise for in-home clinical models, testing other models which can show longer term impact are warranted.

One factor that may be responsible for the dearth of experimental studies on intensive in-home approaches is the difficulty in identifying clinically relevant comparison groups. In some studies, youth outcomes in intensive in-home treatment are compared to youth in treatments that were far less intensive, such as enhanced case management (Evans et al., 2003 ) and office-based therapy (Crane et al., 2005 ). Other studies compare intensive in-home services to highly restrictive out-of-home treatments, such as inpatient psychiatric hospitalization (e.g., Henggeler et al., 1999 ) or residential treatment (Barth et al., 2007 ; Wilmshurst, 2002 ), treatment approaches which have little in common with intensive in-home treatment. There is a question of whether the population of youth served by more restrictive programs are the same as those in intensive, in-home programs. For example, Preyde et al. ( 2011 ) found major demographic differences between the two groups in their 3-year follow-up study of youth outcomes in Ontario’s system of care. Although the randomized clinical trial can control for selection bias and is the gold standard for establishing treatment efficacy, it is not always ethically possible to randomly assign children and adolescents with severe, and potentially life-threatening mental health issues to nontreatment conditions or treatment conditions with unknown benefits (De Meulemeester et al., 2018 ). This methodological dilemma is particularly true in the Pennsylvania system of care where there is a widely available treatment, such as ESFT-FBMHS, which has a long-standing history of practice-based evidence of effectiveness (Lindblad-Goldberg & Northey, 2013 ). In Pennsylvania, the option of “no treatment” is not an option.

A solution recommended by Moffet et al. ( 2018 ) is that studies consider comparing one intensive in-home treatment approach to itself but at different dosage levels. This study of ESFT-FBMHS uses this methodological approach to demonstrate its effectiveness. While dose-effect has been widely examined in studies of individual therapy in outpatient settings and have found little relationship between treatment duration and outcomes (e.g., Salzer et al., 1999 ; Stulz et al., 2013 ), a search of the literature reveals no published studies of dose-effect or treatment duration focused on intensive in-home treatment for youth with SED. The closest dose-effect study on this population is a retrospective study of SED youth enrolled in a Children’s Psychosocial Rehabilitation (CPSR) program, a less intensive service than intensive in-home treatment. Williams ( 2009 ) found that youth who received more hours of CPSR showed a greater reduction in suicidal thoughts and self-harm behaviors and greater clinical improvement in their day-to-day lives. This is the relationship between treatment duration and outcomes that would be expected for youth with SED in intensive in-home treatment given the severity of youth and family problems in the treatment population. Youth with SED served by intensive in-home treatment programs often live in highly stressed, multi-problem families destabilized by significant adversity and trauma (Byers et al., 2021 ; Jones, 2019 ; Landy & Menna, 2007 ). These are conditions which affect caregivers’ well-being and can compromise their ability to participate in treatment and provide sustained emotional support to the child, critical to the child’s recovery.

Moffett et al. ( 2018 ) noted that among the intensive in-home treatment empirical studies they reviewed, interventions of longer durations (4–6 months) reported consistently greater youth improvement than those enrolled in programs of shorter duration. Four to six months is the typical length for Medicaid-funded intensive in-home programs in most states (Stroul et al., 2021 ). In ESFT-FBMHS, youth with SED and their families typically receive services up to 32 weeks. The longer range of treatment duration and the real-world likelihood that not all youth and families who enroll in ESFT-FBMHS will complete the program, leaving at different times during treatment, creates naturally occurring comparison groups for a test of the link between treatment duration and outcomes, and a test of the model’s effectiveness. If youth who complete the full duration of ESFT-FBMHS treatment show significantly reduced risk of out-of-home placement and improved youth functioning when compared to youth who received less of the treatment, this suggests that the treatment is effective, and it provides valuable information about which duration of treatment is optimal.

An early large-scale program evaluation study of ESFT-FBMHS suggests that this intensive in-home approach has the potential to reduce out-of-home placements for both youth and other family members (e.g., Dore, 1991 , 1996 ). An analysis of pre- and post-treatment measures of 1968 families at 39 different program sites showed that prior to enrollment in the program, 80% of youth experienced psychiatric hospitalization. After 32 weeks of ESFT-FBMHS, the rate fell to 28% for any family member, 20% for the identified youth with SED, and only 13% of families utilized emergency room care for psychiatric crises (Lindblad-Goldberg et al., 2004 ). The generalizability of the findings is limited, however, by the absence of a control or comparison group, the absence of information about the youth who did not complete the program, and the absence of treatment fidelity measures to ensure all youth were receiving the intervention as intended. At the time of this first study, ESFT-FBMHS suffered from many of the same inadequate quality assurance mechanisms as other state homegrown intensive in-home treatment programs described by Hammond and Czyszczon ( 2014 ).

Today, ESFT-FBMHS has considerable and robust oversight consistent with other evidence-based treatments (e.g., a treatment manual, therapist and supervisory measures of adherence, and 3 hours of required weekly team supervision). All therapists and supervisors in ESFT-FBMHS participate in a 3-year (85 hours annually) training program focused on cultivating systemic thinking, assuring adherence, and developing competence (Lindblad-Goldberg et al., 1998 ; Simms et al., 2021 ). There are multi-layered structures built into ESFT-FBMHS implementation that ensure clear practice parameters, organizational requirements, training and coaching expectations, and fidelity and outcomes monitoring. Likewise, since ESFT-FBMHS is regulated by and monitored by the state, all intensive in-home treatment service characteristics are controlled and consistent across all agencies including length of stay, level of service intensity, team sizes, and staff training (Pennsylvania Department of Public Welfare, 1993 ). Regardless of which agency houses the ESFT-FBMHS program, or in which county or region, the approach can be expected to be similar. This level of model and program uniformity presents a unique research opportunity for evaluating the effectiveness of a clinical family treatment approach to intensive in-home services. Using this study’s methodological design, which compares groups who receive different dosages of a more operationalized ESFT-FBMHS, revisits the findings of this initial program evaluation study.

Purpose of Study

The main purpose of this study is to empirically examine the effectiveness of ESFT-FBMHS. This study also seeks to (1) add to the current evidence base establishing intensive in-home services blended with clinical family treatment as an effective approach for youth with SED, (2) determine the extent to which treatment duration for youth with SED and their families is linked to outcomes, and (3) explore the role family therapy may play in outcomes maintained post-discharge. These findings may provide data that could guide decision-making in Medicaid-funded intensive in-home programs with respect to standards related to length of stay and required service components, such as family therapy.

Research Hypotheses and Questions

This study asks the question, “Does this intensive in-home treatment, ESFT-FBMHS, do what all intensive in-home treatments are intended to do, reduce out-of-home placement of youth with SED and improve their psychosocial functioning? This question is addressed by analyzing youth and family service utilization via archived claims data, comparing treatment outcomes across four different groups defined by the youth’s length of stay in the program: up to 84 days (i.e., 12 weeks), 85–168 days (i.e., 13–23 weeks), and 169–224 days (i.e., 24–32 weeks). The latter group are considered program completers because the length of stay is within the range called for by the clinical model. A fourth group who remained in the program beyond 224 days, the youth given extensions, were also examined. These four length-of-stay tiers were created by the managed care organization (MCO) as part of a transition to a value-based purchasing (VBP) model of reimbursing agencies for providing services. An examination of service utilization data by the managed care agency showed these four distinct time periods for length of stay in ESFT-FBMHS and suggested that length of stay may be linked to out-of-home placements. The MCO developed a financial incentive system that increased case rates, when compared to the traditional fee for service payment, for retention of families in ESFT-FBMHS. The current study sought to answer the following research questions.

Research Question 1 . Do youth whose families complete ESFT-FBMHS, remaining for 169–224 days, (tier 3) show significantly lower rates of out-of-home placement at 90 days post-discharge, and 180 days post-discharge compared to those who stayed 225+ days (tier 4) or those who left in 168 days or less (tiers 1 and 2)? It is hypothesized that youth who complete ESFT-FBMHS (tier 3) will have fewer out-of-home placements because they received the intended dose of the treatment model. Youth who are granted extensions in the program (tier 4) would have met the MCO’s medical necessity criteria suggesting a continued high level of need and therefore are expected to have similar risks for out-of-home placement as the youth who drop out (tiers 1 and 2).

Research Question 2. Do youth who complete ESFT-FBMHS, remaining for 169–224 days (tier 3), show improvement as measured on the six domains of the Child and Adolescent Needs and Strengths (CANS; Problem Presentation, Risk Behaviors, Functioning, Child Safety, Caregiver Needs, and Child Strengths) assessment? It is hypothesized that youth who complete the program (tier 3) will have fewer needs and more strengths because they received the intended dose of the treatment model.

Research Question 3. Do youth who show improvement on the CANS at discharge show a reduced out-of-home placement risk at 90 days and 180 days? It is hypothesized that youth who have fewer identified needs and more identified strengths at discharge from ESFT-FBMHS will have reduced risk for out-of-home placement.

We utilized an ex post facto quantitative research design with two archived data sets to address each of our research questions. For the first research question on out-of-home placement, we analyzed archived medical claims data from five central Pennsylvania counties comprising the Capital Area Behavioral Health Collaborative (CABHC). CABHC is a not-for-profit, quasi-governmental agency that manages the risk-based contract for the Medicaid managed behavioral health contract with the state of Pennsylvania’s Department of Human Services, Office of Mental Health, and Substance Abuse on behalf of the counties of Cumberland, Dauphin, Lancaster, Lebanon, and Perry. To address the second and third research questions, we focused on changes in child strengths and needs by analyzing archived CANS data collected by the Community Data Roundtable (CDR), a nonprofit organization in Pennsylvania contracted with the MCO serving the central region to implement a Transformational Collaborative Outcomes Management system based on the CANS (Lyons, 2022 ).

One of the advantages of using aggregated claims data is that it provides complete anonymity to patients and their families, leaving all personal health care information fully protected. As such, masked claims data do not require patient authorization for use, and therefore did not warrant Institutional Review Board (IRB) approval. Access to these two sets of masked archival data was authorized by CABHC upon signing Business Associate Agreements with researchers outside the organization. No financial incentives were offered to children or caregivers for participating in ESFT-FBMHS treatment, research, or CANS outcomes assessment.

Participant Characteristics

Both data sets were limited to youth who began and ended ESFT-FBMHS services during the period between December 2017 and September 2022 and who received services at one of the 16 ESFT-FBMHS providers located within the five county CABHC catchment area where value-based purchasing (VBP) was being implemented within the ESFT-FBMHS level of care. The youth and families represented in this archival dataset were authorized by CABHC’s subcontracted managed care organization (MCO), PerformCare, for this voluntary level of care. To be eligible, the youth had to be between the ages of 3 and 21 and meet State regulations outlining Medical Necessity for FBMHS (Commonwealth of Pennsylvania, 2021 ). The criteria for FBMHS included (1) an active DSM-5 (ICD 10) diagnosis; (2) current clinically significant symptoms that create risk for removal from the home through an acute care psychiatric hospitalization or residential treatment placement; and/or (3) risk for the youth’s removal from the home due to neglect, abuse, and/or family instability (e.g., Child Protective Services involvement). All youth who applied for ESFT-FBMHS within this time-period who met program eligibility criteria, and who were then subsequently enrolled and discharged in the defined time-period were included in this study.

Prior to analysis we excluded 305 ESFT-FBMHS episodes for which the CANS ratings were outside of a 60-day window around either the start or end of treatment. The details of the study sample are shown in Supplementary Fig. 1 . Briefly, we identified a total of 2469 ESFT-FBMHS treatment episodes among 2251 clients meeting the criteria for inclusion in our study. From this set, we excluded 305 episodes where the only available CANS evaluations were more than 60 days from either the ESFT-FBMHS start or end dates. This left 2164 episodes from 1998 clients for descriptive analysis. This dataset covered ESFT-FBMHS treatment episodes occurring between 5/29/2018 and 9/14/2022. A small number ( N  = 89) of these episodes were excluded from our statistical models due to missing data for one or more of the variables we identified as potential confounders (age, sex, race, ethnicity, and ICD diagnosis code). Demographic and clinical features for each of the included ESFT-FBMHS treatment episodes are shown in Table 1 . The mean age of clients was 12 years at the start of treatment, with approximately equal representation of males and females. Most treatment episodes were for Caucasian or African American and non-Hispanic clients. The most common primary diagnoses were mood disorders and behavior and emotional disorders. Nearly 20% of episodes were for clients who had multiple primary diagnoses. As expected, nearly 2/3 of the sample ( N  = 1352 episodes, 62.5%) had a tier 3 length of stay. The remaining 812 episodes were at tier 1 ( N  = 205), tier 2 ( N  = 335), or tier 4 ( N  = 272).

Youth received services from 1 of 16 different ESFT-FBMHS programs in five central Pennsylvania counties. These programs represented nine behavioral health agencies, all of whom were following state-wide ESFT-FBMHS service implementation standards. All ESFT-FBMHS treatment was delivered in the home by two-person teams comprised at minimum of one master’s level therapist. The second therapist was usually a bachelor’s level therapist. The treatment was intensive, with therapists in the home multiple times per week meeting with various family subsystems (individual child, caregivers, and the whole family). All therapists who delivered treatment received three hours of ESFT supervision each week and were either currently in the mandatory 3-year ESFT-FBMHS training (273 h) or were graduates of it.

The ESFT-FBMHS programs who participated in this study were enrolled in the CABHC PerformCare provider network. The 2019 Census data suggest that, together, these five Pennsylvania counties represent a diverse geography and population. They range from sparsely populated rural Perry County with 83.4 people per square mile to the more densely populated Dauphin and Lancaster Counties with 510.6 and 550.4 persons per square mile. The US Census data shows the counties also vary with respect to racial diversity. Perry County, with a population of 46,272, is 95% white, while Dauphin County, with a population of 268,299, shows it is 9.9% Hispanic or Latino and 19.2% Black or African American. Lancaster County, the most populous of the five counties with a population of 545,724, shows 11% Hispanic or Latino and 5.2% Black or African American. Poverty rates range from a low of 7.2% in Cumberland County to 11.3% in Dauphin County.

Sample Size, Power, & Precision

The sample size for this study was constrained by the period of performance of the value-based purchasing program and the number of clients served by ESFT-FBMHS at participating clinics during the study period. However, an initial investigation suggested that we could expect a sample of ~2000 treatment episodes. Under the assumption that each treatment episode reflects the experience of a unique client, we expected the study to have 80–90% power to detect a 50% increase in the odds of out-of-home placement (odds ratio = 1.5) comparing the reduced-incentive lengths of stay (tiers 1, 2, and 4) to the preferred tier 3 length of stay across a range of possible rates for out-of-home placement in tier 3 from 10% to 50%. These power calculations assumed the sample would be split 2/3 among the preferred tier 3 ( N  = 1320 episodes) and 1/3 among the reduced-incentive tiers 1, 2, and 4 ( N  = 680) and a two-sided Type I error rate of 5%. We also treated these calculations as an indication of the utility of the study for identifying factors associated with moderate-to-large sized differences in CANS improvement. It was decided prior to conducting the study that we would not apply statistical adjustments to constrain the false positive rate given our intent to maximize the potential for insight into the functioning of a complex system.

To measure clinical improvement, we used the Community Data Roundtable Child and Adolescent Needs and Strengths assessment (CDR CANS), a tool with demonstrated efficacy as an outcomes measure in community-based behavioral health (Troy et al., 2021 ). The CDR CANS includes 53 top-level items organized into five domains representing needs (Problem Presentation, Risk Behaviors, Functioning, Child Safety, and Caregiver Needs) and a single domain for Child Strengths (Lyons et al., 2022 ). The rater uses available information including observation, caregiver interview, and documentation to generate the best estimate of the child’s and family’s needs and strengths.

Lyons et al. ( 2014 ) and Anderson et al. ( 2003 ) show CANS’ interrater reliability is acceptable with individual item coefficients ranging from 0.6 to 0.9. Lyons compared the CANS to the Child and Adolescent Functional Assessment Survey (CAFS; Hodges et al., 1982 ) and yielded statistically significant correlations between the CANS and CAFS dimension scales supporting the conclusion that the CANS possesses concurrent and predictive validity. Finally, Lyons et al. ( 2014 ), with a large sample of clinical cases, used a discriminant analysis to demonstrate that the CANS correctly classified 63% of all cases into their actual level of care supporting the conclusion of good predictive validity.

All raters in this study were ESFT-FBMHS clinicians who were trained and then certified as reliable on the CDR CANS at a 0.7 level using the Praed Foundation Transformational Collaborative Outcomes Management Training CANS certification web page. The CANS was completed by ESFT-FBMHS clinicians treating the family at the 45-day mark of care, which is when the initial treatment plan is due to the managed care company. The CANS was administered again at the discharge meeting. As is true in all CANS rating events, the initial CANS was used to inform treatment planning, and the discharge CANS was used to inform disposition care planning.

CANS outcomes were used to evaluate program performance for the purposes of receiving a financial incentive through a value behavior purchasing model. A specific set of CANS items were identified by the MCO as requiring improvement to qualify for the incentive. The items were identified through an analytic process conducted by Community Data Roundtable as a part of its implementation of the CDR CANS into the ESFT-FBMHS Value Based Purchasing plan. To qualify for the incentive, a case had to have sufficient needs initially and then undergo meaningful change by the end of treatment so that the patient could transition to a lower level of care. The final analytic used by the managed care company to determine if a program qualified for an incentive was client improvement on at least six items from the Problem Presentation, Risk and/or Functioning Domain, and improvement on at least two items on the Caregiver and Strengths Domains. These scores on the CANS counted as a clear and unambiguous successful outcome for both the Value Based Purchasing model and the analysis of ESFT-FBMHS.

The Episodes Measure Group was used to measure treatment episodes. This is a standardized method developed and used by the Capital Area Behavioral Health Collaborative (CABHC) to determine a treatment episode. It is based on information derived by combining paid claims rendered to the same consumer, within the same Person Level Encounter (PLE) claims service category, within a specific period. Only the last claim in a chain of claims that also contained voided or adjusted claims was included; voids and adjustments earlier in the chain were excluded. An episode for a consumer was counted as ended when there were no subsequent claims for that consumer within the same PLE service category that defines the episode. The number of days that can elapse between services for it to be considered as part of the same episode was configured in the database by PLE service category. For example, the allowable gap for inpatient psychiatric services was one day, while for outpatient psychiatric services ESFT-FBMHS, it was 30 days.

Analytic Strategy

The unit of analysis was the ESFT-FBMHS treatment episode. For each episode, we identified three sets of variables: dependent, independent, and confounding. Set one is comprised of three dependent or outcome variables. Outcome variable 1 is out-of-home placement (OHP) at 90 days post-discharge (or not). Outcome variable 2 is defined as out-of-home placement at 180 days post-discharge (or not). Finally, outcome variable 3 is improvement in CANS scores from intake to discharge (or not). The primary independent variable of interest is broadly labeled as the family’s length of stay in ESFT-FBMHS and was defined as (1) up to 84 days (tier 1), (2) 85–168 days (tier 2), (3) 169–224 days (tier 3), and (4) >224 days (tier 4). The set of confounding variables included age, sex, race, ethnicity, and primary diagnosis.

We collected and summarized relevant demographic and treatment-related data. Next, we fit generalized estimating equations (GEE; Ghisletta & Spini, 2004 ) with a logit link and exchangeable working correlation structure to estimate odds ratios (OR) and 95% confidence intervals (CI) comparing the odds of each outcome in tiers 1, 2, and 4 to tier 3 with adjustment for the confounding variables. In all our models, an odds ratio above 1 suggests the outcome is more common in tiers 1, 2, and 4 as compared to tier 3 whereas a value of 1 indicates no difference, and a value below 1 indicates the outcome is less common in tiers 1, 2, and 4 relative to tier 3. All potentially confounding variables were included in our models regardless of whether they were statistically significant. GEE was chosen because of our interest in estimating population-level impacts on the Pennsylvania ESFT-FBMHS system, e.g., by asking how much of a difference the program might expect in out-of-home placement if all treatment episodes were at the tier 3 length of stay as compared to the tier 2 length of stay. It is important to note that our analyses do not address how an individual youth’s length of stay in ESFT-FBMHS treatment impacts the probability of their out-of-home placement. Stated simply, the results describe the relationship between group membership and outcome, not the individual and their unique clinical outcome (Ghisletta & Spini, 2004 ).

Research Question 1. Do Youth Who Completed ESFT-FBMHS, Occupying Tier 3, Show Significantly Lower Rates of Out-of-Home Placement at 90 Days and 180 Days Post-Discharge Compared to Those Occupying Tiers 1, 2, and 4?

The results of our descriptive analysis are shown in Table 2 . A total of 12.2% of tier 1 episodes, 14.3% of tier 2 episodes, and 15.1% of tier 4 episodes resulted in out-of-home placement at 90-days post-discharge compared to 4.7% of tier 3 episodes. Similarly, 14.1% of tier 1, 17.9% of tier 2, and 19.1% of tier 4 episodes resulted in out-of-home placement at 180-days post-discharge compared to 7.2% of tier 3 episodes. This pattern persisted after adjustment for potentially confounding variables. As shown in Table 3 , the tiered length-of-stay in ESFT-FBMHS was significantly associated with out-of-home placement at both 90- and 180-days post-discharge after adjustments for youth age, sex, race, ethnicity, and psychiatric diagnosis ( P  < 0.001 in both models). Length-of-stay at tiers 1, 2, and 4 experienced 2–3 times the odds of out-of-home placement at 90 days post-discharge compared to tier 3. For example, tier 2 was associated with 3.18-times the odds of out-of-home placement (OR) at 90 days compared with tier 1 (OR = 3.18, 95%, confidence interval: 2.07, 4.89) after adjustment for demographic and clinical features. Results were similar for the model of out-of-home placement at 180 days with tiers 1, 2, and 4 experiencing 1–3 times the odds of out-of-home placement compared to tier 3 after adjusting for demographic and clinical features.

Research Question 2. Do Youth Who Completed ESFT-FBMHS, Occupying Tier 3, Show Improvement as Measured on the Child and Adolescent Needs and Strengths (CANS) Assessment?

As shown in Table 3 , CANS scores improved in 40.1% of tier 3 episodes as compared to only 11.7%, 18.2%, and 32.9% of tier 1, 2, and 4 episodes, respectively. This pattern persisted after adjustment for demographic and clinical features ( P  < 0.001) as shown in Table 3 . For example, tier 1 (OR = 0.18, 95% CI: 0.11, 0.29) and tier 2 (OR = 0.33, 95% CI: 0.24, 0.45) had lower odds of CANS improvement compared to tier 3. However, there was no evidence of a difference comparing the odds of CANS improvement in tier 4 vs. tier 3 (OR = 0.99, 95% CI: 0.97, 1.02).

Research Question 3. Do Youth Who Show Improvement on the Child and Adolescent Needs and Strengths (CANS) Assessment at Discharge Demonstrate a Reduced Out-of-Home Placement Risk at 90 Days and 180 Days?

This research question was addressed by inclusion of CANS improvement as a covariate in our GEE models for out-of-home placement at days 90 and 180 post-discharge (Table 4 ). These models show that, after adjustment for the length of stay as well as demographic and clinical features, that CANS improvement was associated with a 56% reduction in the odds of out-of-home placement at Day 90 (OR = 0.44, 95% CI: 0.28, 0.68) and a 45% reduction in odds of out-of-home placement at Day 180 (OR = 0.55, 95% CI: 0.38, 0.80). Importantly, this illustrates that CANS improvement is independently associated with reduced odds of out-of-home placement regardless of tier.

This study evaluated the effectiveness of ESFT-FBMHS by comparing youth outcomes across four different tiers of treatment duration on two indicators of change: reduced out-of-home placement and youth clinical improvement as defined by the Child and Adolescent Needs and Strengths (CANS) scores. The demographic data and the generalized estimating equation (GEE) models generated in this study show that treatment duration in ESFT-FBMHS (up to 84 days, 85–168 days, 169–224 days, and more than 224 days) is associated with both out-of-home placement at 90- and 180-days post-discharge and CANS scores at discharge. Specifically, youth remaining in ESFT-FBMHS for 169–224 days (i.e., 24–32 weeks) are more likely to reside in the home at 90- and 180-days post-discharge. This group is referred to as program completers since it is within the range of the intended duration of time youth receive ESFT-FBMHS services. In contrast, the GEE models predict that youth receiving 84 days or less (i.e., 12 weeks) or between 85 and 167 days (i.e., 13–23 weeks) of ESFT-FBMHS are two to three times more likely to be placed out-of-the-home at 90- and 180-days post-discharge. This was also true for youth remaining in the program longer than 224 days (i.e., 33 weeks or more), who have received service extensions. As for the second indicator of change, youth receiving 84 days or less (i.e., 12 weeks) or between 85 and 168 days (i.e., 13–23 weeks) of ESFT-FBMHS are several times less likely to show meaningful change in CANS scores at discharge. A third of the youth remaining in ESFT-FBMHS more than 224 days showed gains on the CANS (32.9%), which suggests similar odds of benefiting from the program as youth who remained in the program for 169–224 days (40.1%).

Similarity of Results

This study offers a major step forward in establishing the effectiveness of ESFT-FBMHS with youth who have severe emotional disturbance (SED) with a diverse range of psychiatric diagnoses and who are at risk for out-of-home placement. The finding of improved youth outcomes validates and builds on smaller scale studies of ESFT applied to a rapid response home-based program (Clossey et al., 2018 ) and confirms years of practice-based data collected by Pennsylvania MCOs and FBMHS agencies providing ESFT. The significant reduction in out-of-home placement found in this study, the main goal of most intensive in-home treatment programs, replicates the first large-scale assessment of the effectiveness of ESFT-FBMHS (Dore, 1996 ; Lindblad-Goldberg et al., 2004 ), completed almost 30 years ago. Beyond ESFT-FBMHS, the findings are consistent with other studies broadly supporting intensive in-home treatment as an effective mode of service delivery to improve youth functioning and keep high risk youth in their homes and communities (e.g., Bruns et al., 2021 ; Moffet et al., 2018 ).

Most importantly, this study advances the existing empirical evidence supporting the effectiveness of intensive in-home approaches integrated with a highly specified family treatment clinical model, such as MST-Psychiatric (Henggeler et al., 1999 ; Pane et al., 2013 ; Rowland et al., 2005 ) and adaptations of MST, such as Youth Villages Intercept (Barth et al., 2007 ). However, in contrast to findings from MST-Psychiatric (Henggeler et al., 2003 ), which found short-lived impacts on out-of-home placement, this study of ESFT-FBMHS shows sustained reductions in out-of-home placement for youth who remained in the program for 169–224 days (i.e., 24–32 weeks). One explanation for these different findings on the stability of post-treatment changes is treatment duration. The youth and families receiving MST-Psychiatric treatment was limited to 4 months (Henggeler et al., 1999 , 2003 ), which may be sufficient for briefly stabilizing high-risk severely symptomatic youth, but inadequate for addressing underlying chronic and severe problems in family functioning. This study of ESFT-FBMHS lends support to Williams’ ( 2009 ) finding that intensive in-home treatment programs of longer duration result in more sustained improvement for youth with SED, an observation also made by Moffet et al. ( 2018 ) in their comprehensive review of empirical studies on intensive in-home programs.

Interpretation

Although it is beyond the scope of this study to identify the exact reasons 24–32 weeks (169–224 days) of ESFT-FBMHS treatment was more successful for youth, it is possible to speculate based on what is known about the treatment population and the treatment model. The youth and caregivers served by intensive in-home programs, and ESFT-FBMHS specifically (Byers et al., 2021 ), show a high prevalence of complex trauma and chronic adversity, which often undermines their capacity for self-regulation. Collins et al. ( 2010 ) describe caregivers with a history of complex trauma as often struggling to collaborate with the treatment team on behalf of their child because of poor self-regulation, negative attributions toward the child, lack of confidence in their ability to effect change, and difficulty sustaining a recovery-oriented narrative. These caregivers may require additional emotional support before they can engage fully with treatment programs, something that is available to caregivers in the initial stages of ESFT-FBMHS (Jones et al., 2021 ). Perhaps those caregivers who received additional support during the initial stages of ESFT-FBMHS and who were better able to self-regulate were more likely to remain in the program for 24–32 weeks (169–224 days), thereby receiving a full dose of the treatment and more opportunities for growth. This hypothesis is, in part, grounded in the large body of research on parent self-regulation and its link to successful outcomes in parenting intervention programs (e.g., Sanders, 2008 ; Sanders & Mazzucchelli, 2013 ). The family therapy component in ESFT-FBMHS, like that of MST-Psychiatric (Henggeler et al., 1999 ) is designed to strengthen the caregivers’ ability to independently support the youth’s emotional stability and lower the risk of needing more restrictive out-of-home placement. The difference is that families receive the family therapy component for a longer duration in ESFT-FBMHS.

Drop-out rates as high as 30% have been reported by some intensive in-home programs, such as Connecticut’s Intensive In-Home Child and Adolescent Psychiatric Services (Galloway, 2017 ). In this study, 25% of youth and families left ESFT-FBMHS before completing the program (see tiers 1 and 2, Table 1 ). Although the reasons that caregivers and youth left ESFT-FBMHS prematurely were not measured in this study, there is literature on factors associated with treatment retention among youth and adults with severe mental illness (e.g., Lemieux et al., 2018 ) which may help explain early discharges. One major factor found in studies of treatment retention is the strength of the therapeutic alliance (Smith et al., 2013 ). Although the therapists delivering ESFT-FBMHS are well-trained, gaining buy-in to a family systems approach from both the youth and their caregivers is complex. Caregivers may be overwhelmed and angry with their child and the child may have lost trust in their caregivers. The therapeutic relationship can be derailed when therapists have an uneven alliance with the youth and caregivers, a factor associated with premature withdrawal from family treatment (Robbins et al., 2008 ). Although no differences in treatment duration based on race or ethnicity were evident in this study, some studies have found the ethnic match between the family and the treatment team to be a factor in the therapeutic alliance and in clinical outcomes (Gamst et al., 2000 , 2004 ) a factor that deserves further study.

There are also caregiver, family, and larger ecosystem factors that can affect the length of stay in intensive in-home treatment. For example, caregivers may not feel they have the emotional resources to participate as actively as the treatment program requires due to undertreated mental health problems or overwhelming environmental stressors and demands in their lives. Byers et al. ( 2021 ) found that caregivers with a high level of current adversity in their life had a reduced length of stay in ESFT-FBMHS. Similar findings have been reported in programs serving families with youth in the juvenile justice system. For example, Sheerin et al. ( 2024 ) found that caregivers with significant levels of psychiatric distress predicted lower levels of engagement in youth treatment. Another potential factor influencing early withdrawal from ESFT-FBMHS is limitations in the current implementation of the treatment model. Perhaps therapists applying the model do not adequately prioritize and address the needs of those caregivers who, at the time of the youth’s admission to the program, are experiencing a high level of stress and adversity in their family and larger ecosystem. It is critical that future studies of intensive in-home treatment in general, and ESFT-FBMHS specifically, include measures that assess the reasons caregivers remain or leave treatment programs.

Likewise, it is important to explore possible reasons that youth who extended their stay in ESFT-FBMHS beyond 224 days (i.e., 33 weeks or more) remain at high risk for out-of-home placement. After all, this group received a higher dose of the clinical model. Demographic data from this study shows these youth had the highest number of multiple, co-morbid psychiatric diagnoses (48.5%). As a group, perhaps they remained unstable and continued to be at risk at the end of the authorization period for ESFT-FBMHS, despite showing gains in functioning (according to CANS scores) commensurate with those who complete the program. This suggests another factor, such as family functioning, may be at play. Except for ratings on the Caregiver Needs domain on the CANS, comprehensive family functioning was not measured in this study. It is reasonable, however, to speculate that the families in this group of extenders could not make enough changes to support the continued recovery of the youth post-discharge without the treatment team. Preyde et al.’s ( 2011 ) study of Ontario’s intensive in-home programs suggests the severity of compromised family functioning can play a big role in who may best benefit from less restrictive in-home treatment.

Limitations and Generalizability

The methodological approach used in this study for establishing efficacy was to compare treatment outcomes of groups with varying levels of exposure to the clinical model, ESFT-FBMHS. This is an approach for testing the efficacy of intensive in-home treatment described by Moffet et al. ( 2018 ) as one alternative to randomized controlled trails. The results of this study suggest comparing groups with different levels of exposure to the intervention may be an effective method for evaluating a treatment model’s efficacy when randomized trials are not feasible. Given the severe, potentially life-threatening mental health issues of the children and adolescents served by intensive in-home treatment programs, random assignment to nontreatment conditions or treatment conditions with unknown benefits cannot be justified, particularly when there is a treatment like ESFT-FBMHS available with a thirty-five-year history of practice-based evidence of effectiveness. To fully evaluate the efficacy of ESFT-FBMHS and the operative clinical model, it would be important to have a meaningful comparison group and randomly assign youth to the different groups. The strength of this quasi-experimental design comparing different dosage levels of the model is that it demonstrates a clear relationship between ESFT-FBMHS intervention and outcomes without the ethical problems of randomization. The main limitation of this approach, however, is that it is impossible to rule out the impact of other variables on the outcomes. An effort was made in this study to reduce this limitation by using adjusted GEE models for both out-of-home placement and CANS scores, factoring in probable confounding variables such as gender, age, race/ethnicity, and diagnosis. Finally, there are the limitations of using archival data based on the referred youth’s individual medical and service utilization record, which limits the detailed information available about caregivers and the family, critical variables in determining the level of family functioning and caregiver distress.

Conclusions and Implications

This study answers the question as to whether ESFT-FBMHS, a family therapy approach, effectively does what intensive in-home services are designed to do for youth with serious emotional disturbance, reduce the risk of out-of-home placement and improve youth functioning. The youth who remained in the program for 168–224 days (i.e., 24–32 weeks), the treatment duration built into the clinical model, and who showed improved CANS scores, showed statistically significant reduced odds of out-of-home placement at 180 days discharge when compared to youth who remained in the program less than 168 days. This study offers another example of a rigorous approach of an in-home intensive family therapy model for youth with serious emotional disturbance (SED), and one that shows promise in accomplishing long-term outcomes. Longer follow-up studies of these youth at one- or two-years post-discharge are needed to evaluate the degree to which the positive outcomes of ESFT-FBMHS are sustained. Prospective studies are recommended to explore the specific factors that predict which youth and families complete ESFT-FBMHS treatment and have positive outcomes vs. those who leave the program early and make few changes. Toward this end, not only should future studies include comprehensive measures of family functioning, but they should also include a measure of parent self-regulation (PSR) and the extent to which treatment teams focus on it. It is predicted that youth in ESFT-FBMHS would have more positive outcomes when treatment teams specifically focus on building parent self-regulation capacity. There is strong evidence that parent self-regulation or dysregulation directly affects children’s mental health functioning and treatment outcomes in parenting programs (Lunkenheimer et al., 2023 ; Deater-Deckard et al., 2012 ). It is important that future studies exam whether more targeted and intense support of caregivers in the early stages of treatment increases caregivers’ self-regulatory processes, and results in the youth and family receiving higher doses of intervention.

Future studies should also clarify the unique needs of the 12.6% youth and families who remained in ESFT-FBMHS beyond 224 days (i.e., 33 weeks or more) and remained at high risk for out-of-home placement. Since family functioning may play a big role in this group of extenders, it is important that intensive in-home treatment programs include measures of clinically meaningful domains of family functioning, such as level of support for caregivers, the strength of caregiver-child attachment, the caregiver alliance, and the caregivers’ executive functioning, are necessary to both plan treatment and to evaluate the role of different domains of family functioning on treatment completion and treatment outcome. One such measure used in some Pennsylvania intensive in-home programs is the Modified Family Assessment Form (Simms et al., 2024 ). A more extensive study of the families of youth who do not show marked improvement from their time in ESFT-FBMHS is critical to tailoring the approach to better meet the needs of this group.

This study’s findings also have important implications for practice. Given the prevalence of complex trauma among the caregivers in the multi-stressed families served by these programs, intensive in-home programs may be more effective at engaging caregivers and sustaining their involvement if given more support and help with self-regulation early in treatment. While the ESFT-FBMHS treatment model promotes this focus theoretically, it is unclear the extent to which treatment teams directly meet these caregivers’ additional needs. The findings of this study indirectly support Byers et al. ( 2021 ) recommendation that caregivers who need extra support should be identified early, during admission, and then an early treatment phase added that focuses only on building natural supports for these caregivers and strengthening self-regulation capacity. The idea would be to reduce the caregiver burden of taking on the usual expectations for partnering with the team in treating the child until they are emotionally ready, and instead more directly and intentionally addressing the caregivers’ basic needs.

There are also policy implications of this study’s findings that broadly apply to all Medicaid-funded intensive in-home programs. One, in states where homegrown intensive in-home programs limit the length of stay to 5 months or less for youth with severe emotional disturbance at risk for inpatient hospitalization or residential treatment (see Stroul et al., 2021 ), consideration should be given to extending standard authorizations for a longer duration, giving treatment programs the opportunity to address systemic issues maintaining problems, going beyond youth and family stabilization. Two, the caregivers and families of youth treated in intensive in-home programs have complex needs and their functioning can either undermine or promote caregiver responsiveness to treatment and youth responsiveness to treatment. It is critical that comprehensive family assessment be as routine in the practice of intensive in-home programs as is the clinical assessment of youth, and that the results inform clinical decision-making. Three, it is critical that intensive in-home treatment, a comprehensive program comprised of multiple services, not only includes family therapy as one of its many components but prioritize it for optimizing long-term youth outcomes.

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All authors contributed to the study conception and design. The archival data and its collection were provided by Dan Warner and Scott Suhring. Data preparation and analysis was performed by Jesse Troy. The first draft of the final manuscript was written by C. Wayne Jones and Steve Simms. All authors read and commented on all previous versions of the manuscript. All authors read and approved the final manuscript.

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Jones, C.W., Simms, S., Troy, J. et al. An Archival Study of the Relationship Between Treatment Duration, Functioning, and Out-of-Home Placement for Youth with Serious Emotional Disturbance in a State-Wide Intensive In-Home Family Treatment Program. J Child Fam Stud (2024). https://doi.org/10.1007/s10826-024-02906-y

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