examples of research questions in statistics

Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the  four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Once you feel confident that you can correctly identify the nature of your data, the following examples of statistical research questions will strengthen your understanding. Asking these questions can help you unravel unexpected outcomes or discoveries particularly while doing exploratory data analysis .

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.

Statistical Research Question No. 1

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of  the number of physical fitness tests  that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the  number of passing scores  in both Mathematics and English.

Both variables are ratio variables. 

Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.

In the particular study mentioned, the researchers used  multivariate logistic regression analyses  to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about  Education 4.0 .

The following video sheds light on the frequently used statistical tests and their selection. It is an excellent resource for beginners. Just maintain an open mind to get rid of your dislike for numbers; that is, if you are one of those who have a hard time understanding mathematical concepts. My ebook on  statistical tests and their selection  provides many examples.

Source: Chomitz et al. (2009)

Topic 2: Climate Conditions and Consumption of Bottled Water

This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.

Statistical Research Question No. 2

Is there a significant relationship between average temperature and amount of bottled water consumed?

In this instance, the variables measured include the  average temperature in the areas studied  and the  volume of water consumed . Temperature is an  interval variable,  while volume is a  ratio variable .

In this example, the variables include the  average temperature  and  volume of bottled water . The first variable (average temperature) is an interval variable, and the latter (volume of water) is a ratio variable.

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled  Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Source: Zapata (2021)

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

An investigation sought to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 3

Is there a significant relationship between the number of unique employees working in skilled nursing homes and the following:

• number of weekly confirmed COVID-19 cases among residents and staff, and
• number of weekly COVID-19 deaths among residents.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

We call the variable  number of unique employees  the  independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff  and  number of weekly COVID-19 deaths among residents ) as the  dependent variables .

This correlation study determined if the number of staff members in nursing homes influences the number of COVID-19 cases and deaths. It aims to understand if staffing has got to do with the transmission of the deadly coronavirus. Thus, the study’s outcome could inform policy on staffing in nursing homes during the pandemic.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced  regression models  that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population.  Stepwise multiple regression models  take care of those redundancies. Using this statistical test requires further study and experience.

Source: McGarry et al. (2021)

Topic 4: Surrounding Greenness, Stress, and Memory

Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.

Statistical Research Question No. 4

Is there a significant relationship between exposure to and use of natural environments, stress, and memory performance?

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Referring to the abstract of this study,  exposure to and use of natural environments  as a variable of the study may be measured in terms of the days spent by the respondent in green surroundings. That will be a ratio variable as we can count it and has an absolute zero point. Stress levels can be measured using standardized instruments like the  Perceived Stress Scale . The third variable, i.e., memory performance in terms of short-term, working memory, and overall memory may be measured using a variety of  memory assessment tools as described by Murray (2016) .

As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.

Source: Lega et al. (2021)

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

Reading the abstract, we can readily identify one of the variables used in the study, i.e., money. It’s easy to count that. But for happiness, that is a largely subjective matter. Happiness varies between individuals. So how did the researcher measured happiness? As previously mentioned, we need to see the methodology portion to find out why.

If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

Is there a significant relationship between income and happiness?

Source: Måseide (2021)

Now the statistical test used by the researcher is, honestly, beyond me. I may be able to understand it how to use it but doing so requires further study. Although I have initially did some readings on logit models, ordered logit model and generalized ordered logit model are way beyond my self-study in statistics.

Anyhow, those variables found with asterisk (***, **, and **) on page 24 tell us that there are significant relationships between income and happiness. You just have to look at the probability values and refer to the bottom of the table for the level of significance of those relationships.

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Chomitz, V. R., Slining, M. M., McGowan, R. J., Mitchell, S. E., Dawson, G. F., & Hacker, K. A. (2009). Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States.  Journal of School Health ,  79 (1), 30-37.

Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory.  Urban Forestry & Urban Greening ,  59 , 126974.

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

McGarry, B. E., Gandhi, A. D., Grabowski, D. C., & Barnett, M. L. (2021). Larger Nursing Home Staff Size Linked To Higher Number Of COVID-19 Cases In 2020: Study examines the relationship between staff size and COVID-19 cases in nursing homes and skilled nursing facilities. Health Affairs, 40(8), 1261-1269.

Zapata, O. (2021). The relationship between climate conditions and consumption of bottled water: A potential link between climate change and plastic pollution. Ecological Economics, 187, 107090.

© P. A. Regoniel 12 October 2021 | Updated 08 January 2024

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How to write survey questions, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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Research Questions Tutorial

What is a Quantitative Research Question?

A research question is the driving question(s) behind your research. It should be about an issue that you are genuinely curious and/or passionate about. A good research question is:

Clear :  The purpose of the study should be clear to the reader, without additional explanation.

Focused :  The question is specific. Narrow enough in scope that it can be thoroughly explored within the page limits of the research paper. It brings the common thread that weaves throughout the paper.

Concise :  Clarity should be obtained in the fewest possible words. This is not the place to add unnecessary descriptors and fluff (i.e. “very”).

Complex :  A true research question is not a yes/no question. It brings together a collection of ideas obtained from extensive research, without losing focus or clarity.

Arguable :  It doesn’t provide a definitive answer. Rather, it presents a potential position that future studies could debate.

The format of a research question will depend on a number of factors, including the area of discipline, the proposed research design, and the anticipated analysis.

Unclear:   Does loneliness cause the jitters? Clear:   What is the relationship between feelings of loneliness, as measured by the Lonely Inventory, and uncontrollable shaking?

Unfocused:   What’s the best way to learn? Focused:   In what ways do different teaching styles affect recall and retention in middle schoolers?

Verbose :  Can reading different books of varying genres influence a person’s performance on a test that measures familiarity and knowledge of different words?

Concise:   How does exposure to words through reading novels influence a person’s language development?

Definitive:   What is my favorite color? Arguable:   What is the most popular color amongst teens in America?

Developing a Quantitative Research Question

Developing a research question, was this resource helpful.

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Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

Need a helping hand?

Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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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.

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10.1 - setting the hypotheses: examples.

A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations or odds ratios or any other numerical summary of the population. The alternative hypothesis is typically the research hypothesis of interest. Here are some examples.

Example 10.2: Hypotheses with One Sample of One Categorical Variable Section  

About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.

• Research Question : Are artists more likely to be left-handed than people found in the general population?
• Response Variable : Classification of the student as either right-handed or left-handed

State Null and Alternative Hypotheses

• Null Hypothesis : Students in the College of Arts and Architecture are no more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture = 10% or p = .10).
• Alternative Hypothesis : Students in the College of Arts and Architecture are more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Arts and Architecture > 10% or p > .10). This is a one-sided alternative hypothesis.

Example 10.3: Hypotheses with One Sample of One Measurement Variable Section  

A generic brand of the anti-histamine Diphenhydramine markets a capsule with a 50 milligram dose. The manufacturer is worried that the machine that fills the capsules has come out of calibration and is no longer creating capsules with the appropriate dosage.

• Research Question : Does the data suggest that the population mean dosage of this brand is different than 50 mg?
• Response Variable : dosage of the active ingredient found by a chemical assay.
• Null Hypothesis : On the average, the dosage sold under this brand is 50 mg (population mean dosage = 50 mg).
• Alternative Hypothesis : On the average, the dosage sold under this brand is not 50 mg (population mean dosage ≠ 50 mg). This is a two-sided alternative hypothesis.

Example 10.4: Hypotheses with Two Samples of One Categorical Variable Section  

Many people are starting to prefer vegetarian meals on a regular basis. Specifically, a researcher believes that females are more likely than males to eat vegetarian meals on a regular basis.

• Research Question : Does the data suggest that females are more likely than males to eat vegetarian meals on a regular basis?
• Response Variable : Classification of whether or not a person eats vegetarian meals on a regular basis
• Explanatory (Grouping) Variable: Sex
• Null Hypothesis : There is no sex effect regarding those who eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis = population percent of males who eat vegetarian meals on a regular basis or p females = p males ).
• Alternative Hypothesis : Females are more likely than males to eat vegetarian meals on a regular basis (population percent of females who eat vegetarian meals on a regular basis > population percent of males who eat vegetarian meals on a regular basis or p females > p males ). This is a one-sided alternative hypothesis.

Example 10.5: Hypotheses with Two Samples of One Measurement Variable Section  

Obesity is a major health problem today. Research is starting to show that people may be able to lose more weight on a low carbohydrate diet than on a low fat diet.

• Research Question : Does the data suggest that, on the average, people are able to lose more weight on a low carbohydrate diet than on a low fat diet?
• Response Variable : Weight loss (pounds)
• Explanatory (Grouping) Variable : Type of diet
• Null Hypothesis : There is no difference in the mean amount of weight loss when comparing a low carbohydrate diet with a low fat diet (population mean weight loss on a low carbohydrate diet = population mean weight loss on a low fat diet).
• Alternative Hypothesis : The mean weight loss should be greater for those on a low carbohydrate diet when compared with those on a low fat diet (population mean weight loss on a low carbohydrate diet > population mean weight loss on a low fat diet). This is a one-sided alternative hypothesis.

Example 10.6: Hypotheses about the relationship between Two Categorical Variables Section  

• Research Question : Do the odds of having a stroke increase if you inhale second hand smoke ? A case-control study of non-smoking stroke patients and controls of the same age and occupation are asked if someone in their household smokes.
• Variables : There are two different categorical variables (Stroke patient vs control and whether the subject lives in the same household as a smoker). Living with a smoker (or not) is the natural explanatory variable and having a stroke (or not) is the natural response variable in this situation.
• Null Hypothesis : There is no relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is = 1).
• Alternative Hypothesis : There is a relationship between whether or not a person has a stroke and whether or not a person lives with a smoker (odds ratio between stroke and second-hand smoke situation is > 1). This is a one-tailed alternative.

This research question might also be addressed like example 11.4 by making the hypotheses about comparing the proportion of stroke patients that live with smokers to the proportion of controls that live with smokers.

Example 10.7: Hypotheses about the relationship between Two Measurement Variables Section  

• Research Question : A financial analyst believes there might be a positive association between the change in a stock's price and the amount of the stock purchased by non-management employees the previous day (stock trading by management being under "insider-trading" regulatory restrictions).
• Variables : Daily price change information (the response variable) and previous day stock purchases by non-management employees (explanatory variable). These are two different measurement variables.
• Null Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) = 0.
• Alternative Hypothesis : The correlation between the daily stock price change (\$) and the daily stock purchases by non-management employees (\$) > 0. This is a one-sided alternative hypothesis.

Example 10.8: Hypotheses about comparing the relationship between Two Measurement Variables in Two Samples Section  

• Research Question : Is there a linear relationship between the amount of the bill (\$) at a restaurant and the tip (\$) that was left. Is the strength of this association different for family restaurants than for fine dining restaurants?
• Variables : There are two different measurement variables. The size of the tip would depend on the size of the bill so the amount of the bill would be the explanatory variable and the size of the tip would be the response variable.
• Null Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the same at family restaurants as it is at fine dining restaurants.
• Alternative Hypothesis : The correlation between the amount of the bill (\$) at a restaurant and the tip (\$) that was left is the difference at family restaurants then it is at fine dining restaurants. This is a two-sided alternative hypothesis.

educational research techniques

Research techniques and education.

Research Questions, Variables, and Statistics

Working with students over the years has led me to the conclusion that often students do not understand the connection between variables, quantitative research questions and the statistical tools

used to answer these questions. In other words, students will take statistics and pass the class. Then they will take research methods, collect data, and have no idea how to analyze the data even though they have the necessary skills in statistics to succeed.

This means that the students have a theoretical understanding of statistics but struggle in the application of it. In this post, we will look at some of the connections between research questions and statistics.

Variables are important because how they are measured affects the type of question you can ask and get answers to. Students often have no clue how they will measure a variable and therefore have no idea how they will answer any research questions they may have.

Another aspect that can make this confusing is that many variables can be measured more than one way. Sometimes the variable “salary” can be measured in a continuous manner or in a categorical manner. The superiority of one or the other depends on the goals of the research.

It is critical to support students to have a thorough understanding of variables in order to support their research.

Types of Research Questions

In general, there are two types of research questions. These two types are descriptive and relational questions. Descriptive questions involve the use of descriptive statistic such as the mean, median, mode, skew, kurtosis, etc. The purpose is to describe the sample quantitatively with numbers (ie the average height is 172cm) rather than relying on qualitative descriptions of it (ie the people are tall).

Below are several example research questions that are descriptive in nature.

• What is the average height of the participants in the study?
• What proportion of the sample is passed the exam?
• What are the respondents perceptions towards the cafeteria?

These questions are not intellectually sophisticated but they are all answerable with descriptive statistical tools. Question 1 can be answered by calculating the mean. Question 2 can be answered by determining how many passed the exam and dividing by the total sample size. Question 3 can be answered by calculating the mean of all the survey items that are used to measure respondents perception of the cafeteria.

Understanding the link between research question and statistical tool is critical. However, many people seem to miss the connection between the type of question and the tools to use.

Relational questions look for the connection or link between variables. Within this type there are two sub-types. Comparison question involve comparing groups. The other sub-type is called relational or an association question.

Comparison questions involve comparing groups on a continuous variable. For example, comparing men and women by height. What you want to know is whether there is a difference in the height of men and women. The comparison here is trying to determine if gender is related to height. Therefore, it is looking for a relationship just not in the way that many student understand. Common comparison questions include the following.male

• Is there a difference in height by gender among the participants?
• Is there a difference in reading scores by grade level?
• Is there a difference in job satisfaction in based on major?

Each of these questions can be answered using ANOVA or if we want to get technical and there are only two groups (ie gender) we can use t-test. This is a broad overview and does not include the complexities of one-sample test and or paired t-test.

Relational or association question involve continuous variables primarily. The goal is to see how variables move together. For example, you may look for the relationship between height and weight of students. Common questions include the following.

•  Is there a relationship between height and weight?
• Does height and show size explain weight?

Questions 1 can be answered by calculating the correlation. Question 2 requires the use of linear regression in order to answer the question.

The challenging as a teacher is showing the students the connection between statistics and research questions from the real world. It takes time for students to see how the question inspire the type of statistical tool to use. Understanding this is critical because it helps to frame the possibilities of what to do in research based on the statistical knowledge one has.

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415 Research Question Examples Across 15 Disciplines

A research question is a clearly formulated query that delineates the scope and direction of an investigation. It serves as the guiding light for scholars, helping them to dissect, analyze, and comprehend complex phenomena. Beyond merely seeking answers, a well-crafted research question ensures that the exploration remains focused and goal-oriented.

The significance of framing a clear, concise, and researchable question cannot be overstated. A well-defined question not only clarifies the objective of the research but also determines the methodologies and tools a researcher will employ. A concise question ensures precision, eliminating the potential for ambiguity or misinterpretation. Furthermore, the question must be researchable—posing a question that is too broad, too subjective, or unanswerable can lead to inconclusive results or an endless loop of investigation. In essence, the foundation of any meaningful academic endeavor rests on the articulation of a compelling and achievable research question.

Research questions can be categorized based on their intent and the nature of the information they seek. Recognizing the different types is essential for crafting an effective inquiry and guiding the research process. Let's delve into the various categories:

• Descriptive Research Questions: These types of questions aim to outline and characterize specific phenomena or attributes. They seek to provide a clear picture of a situation or context without necessarily diving into causal relationships. For instance, a question like "What are the main symptoms of the flu?" is descriptive as it seeks to list the symptoms.
• Explanatory (or Causal) Research Questions: Explanatory questions delve deeper, trying to uncover the reasons or causes behind certain phenomena. They are particularly common in experimental research where researchers are attempting to establish cause-and-effect relationships. An example might be, "Does smoking increase the risk of lung cancer?"
• Exploratory Research Questions: As the name suggests, these questions are used when researchers are entering uncharted territories. They are designed to gather preliminary information on topics that haven't been studied extensively. A question like "How do emerging technologies impact remote tribal communities?" can be seen as exploratory if there's limited existing research on the topic.
• Comparative Research Questions: These questions are formulated when the objective is to compare two or more groups, conditions, or variables. Comparative questions might look like "How do test scores differ between students who study regularly and those who cram?"
• Predictive Research Questions: The goal here is to forecast or predict potential outcomes based on certain variables or conditions. Predictive research might pose questions such as "Based on current climate trends, how will average global temperatures change by 2050?"

Here are examples of research questions across various disciplines, shedding light on queries that stimulate intellectual curiosity and advancement. In this post, we will delve into disciplines ranging from the Natural Sciences, such as Physics and Biology, to the Social Sciences, including Sociology and Anthropology, as well as the Humanities, like Literature and Philosophy. We'll also explore questions from fields as varied as Health Sciences, Engineering, Business, Environmental Sciences, Mathematics, Education, Law, Agriculture, Arts, Computer Science, Architecture, and Languages. This comprehensive overview aims to illustrate the breadth and depth of inquiries that shape our world of knowledge.

Agriculture and forestry examples

Architecture and planning examples, arts and design examples, business and finance examples, computer science and informatics examples, education examples, engineering and technology examples, environmental sciences examples, health sciences examples, humanities examples, languages and linguistics examples, law examples, mathematics and statistics examples, natural sciences examples, social sciences examples.

• Descriptive: What are the primary factors that influence crop yield in temperate climates?
• Explanatory: Why do certain soil types yield higher grain production than others?
• Exploratory: How might new organic farming techniques influence soil health over a decade?
• Comparative: How do the growth rates differ between genetically modified and traditional corn crops?
• Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal science

• Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?
• Explanatory: Why do certain breeds of chickens have a higher egg production rate?
• Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?
• Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?
• Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

• Descriptive: What are the most commonly farmed fish species in Southeast Asia?
• Explanatory: Why do shrimp farms have a higher disease outbreak rate compared to fish farms?
• Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?
• Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?
• Predictive: What will be the impact of ocean acidification on mollusk farming over the next three decades?
• Descriptive: What tree species dominate the temperate rainforests of North America?
• Explanatory: Why are certain tree species more resistant to pest infestations?
• Exploratory: What are the potential benefits of integrating drone technology in forest health monitoring?
• Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?
• Predictive: Given increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

• Descriptive: What are the common characteristics of plants suitable for urban vertical farming?
• Explanatory: Why do roses require specific pH levels in the soil for optimal growth?
• Exploratory: What potential methods might promote year-round vegetable farming in colder regions?
• Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?
• Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil science

• Descriptive: What are the main components of loamy soil?
• Explanatory: Why does clay-rich soil retain more water compared to sandy soil?
• Exploratory: How might biochar applications transform nutrient availability in degraded soils?
• Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?
• Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over the next 30 years?

Architectural design

• Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?
• Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?
• Exploratory: How might sustainable materials revolutionize the future of architectural design?
• Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?
• Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

• Descriptive: What are the primary components of a successful urban park design?
• Explanatory: Why do certain types of vegetation promote greater biodiversity in urban settings?
• Exploratory: What innovative techniques can be employed to restore and integrate wetlands into urban landscapes?
• Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?
• Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban planning

• Descriptive: What are the main components of a pedestrian-friendly city center?
• Explanatory: Why do certain urban layouts promote more efficient traffic flow than others?
• Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?
• Comparative: How do the air quality levels differ between cities with green belts and those without?
• Predictive: Based on increasing telecommuting trends, how will urban planning strategies adjust to potentially reduced daily commutes in the future?

Graphic design

• Descriptive: What are the prevailing typography trends in modern branding?
• Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?
• Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?
• Comparative: How do print and digital designs differ in terms of elements and principles when targeting a young adult audience?
• Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial design

• Descriptive: What characterizes the ergonomic features of leading office chairs in the market?
• Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?
• Exploratory: How might bio-inspired design influence the future of transportation vehicles?
• Comparative: How does user satisfaction differ between traditional versus modular product designs?
• Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

• Descriptive: What techniques define the most popular virtual reality (VR) experiences currently available?
• Explanatory: Why do certain sound designs enhance immersion in video games more effectively than others?
• Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?
• Comparative: How do user engagement levels differ between 2D animations and 3D animations in educational platforms?
• Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing arts

• Descriptive: What styles of dance are currently predominant in global theater productions?
• Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?
• Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?
• Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?
• Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual arts

• Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?
• Explanatory: Why have mixed media installations gained prominence in the 21st-century art scene?
• Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?
• Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?
• Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Entrepreneurship

• Descriptive: What are the main challenges faced by startups in the tech industry?
• Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?
• Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?
• Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?
• Predictive: What sectors are predicted to see the most startup growth in the next decade?
• Descriptive: What are the primary sources of external funding for large corporations?
• Explanatory: Why did the stock market experience a significant drop in Q4 2022?
• Exploratory: How might blockchain technology revolutionize the future of banking?
• Comparative: How do the financial markets in developing countries compare to those in developed countries?
• Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human resources

• Descriptive: What are the most sought-after employee benefits in the tech industry?
• Explanatory: Why is there a high turnover rate in the retail sector?
• Exploratory: How might the rise of remote work affect HR practices in the next decade?
• Comparative: How do HR practices in multinational corporations differ from those in local companies?
• Predictive: What skills will be in highest demand in the workforce by 2030?
• Descriptive: What are the core responsibilities of middle management in large manufacturing firms?
• Explanatory: Why do some management strategies fail in diverse cultural environments?
• Exploratory: How are companies adapting their management structures in response to the gig economy?
• Comparative: How does management style in Eastern companies compare with Western businesses?
• Predictive: How might artificial intelligence reshape management practices in the next decade?
• Descriptive: What are the most effective digital marketing channels for e-commerce businesses?
• Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?
• Exploratory: How might virtual reality change the landscape of product advertising?
• Comparative: How do marketing strategies differ between B2B and B2C sectors?
• Predictive: What consumer behaviors are forecasted to dominate online shopping trends in the next five years?

Operations research

• Descriptive: What are the primary optimization techniques used in supply chain management?
• Explanatory: Why do certain optimization algorithms perform better in specific industries?
• Exploratory: How can quantum computing impact the future of operations research?
• Comparative: How does operations strategy differ between service and manufacturing industries?
• Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?

Artificial intelligence

• Descriptive: What are the primary algorithms used in deep learning?
• Explanatory: Why do certain neural network architectures outperform others in image recognition tasks?
• Exploratory: How might quantum computing influence the development of AI models?
• Comparative: How do reinforcement learning methods compare to supervised learning in game playing scenarios?
• Predictive: Based on current trends, how will AI impact the job market over the next decade?

Cybersecurity

• Descriptive: What are the most common types of cyberattacks reported in 2022?
• Explanatory: Why are certain industries more vulnerable to ransomware attacks?
• Exploratory: How might advances in quantum computing challenge existing encryption methods?
• Comparative: How do open-source software vulnerabilities compare to those in proprietary systems?
• Predictive: Given emerging technologies, what types of cyber threats will likely dominate in the next five years?

Data science

• Descriptive: What are the main tools used by data scientists in large-scale data analysis?
• Explanatory: Why does algorithm X yield more accurate predictions than algorithm Y for certain datasets?
• Exploratory: How can machine learning models improve real-time data processing in IoT devices?
• Comparative: How does the performance of traditional statistical models compare to machine learning models in predicting stock prices?
• Predictive: Based on current data trends, what industries will likely benefit the most from data analytics advancements in the coming decade?

Information systems

• Descriptive: What are the core components of a modern enterprise resource planning (ERP) system?
• Explanatory: Why have cloud-based information systems seen a rapid adoption rate in recent years?
• Exploratory: How might the integration of blockchain technology revolutionize supply chain information systems?
• Comparative: How do information system strategies differ between e-commerce and brick-and-mortar retailers?
• Predictive: Given the rise of remote work, how will information systems evolve to support decentralized teams in the future?

Software engineering

• Descriptive: What are the standard practices in agile software development?
• Explanatory: Why do some software projects face significant delays despite rigorous planning?
• Exploratory: How are emerging programming languages shaping the future of software development?
• Comparative: How does the software development lifecycle in startup environments compare to that in large corporations?
• Predictive: Based on current development trends, which software platforms are forecasted to dominate market share by 2030?

Adult education

• Descriptive: What are the primary motivations behind adults seeking further education later in life?
• Explanatory: Why do some adult education programs have a higher success rate compared to others?
• Exploratory: How might online learning platforms revolutionize adult education in the next decade?
• Comparative: How do adult education methodologies differ from traditional collegiate teaching techniques?
• Predictive: Given current trends, how will the demand for adult education courses change in the upcoming years?

Curriculum studies

• Descriptive: What are the core components of a modern high school curriculum in the United States?
• Explanatory: Why have certain subjects, like financial literacy, become more emphasized in recent curriculum updates?
• Exploratory: How can interdisciplinary studies be better incorporated into traditional curricula?
• Comparative: How does the math curriculum in the US compare to that in other developed countries?
• Predictive: Based on pedagogical research, what subjects are forecasted to gain prominence in curricula over the next decade?

Educational administration

• Descriptive: What are the main responsibilities of a school principal in large urban schools?
• Explanatory: Why do some schools consistently perform better in standardized testing than others, despite similar resources?
• Exploratory: How might emerging technologies shape the administrative tasks of educational institutions in the future?
• Comparative: How does school administration differ between private and public educational institutions?
• Predictive: Given the rise of online education, how will the role of educational administrators evolve in the coming years?

Educational psychology

• Descriptive: What cognitive strategies are commonly used by students to enhance memory retention during studies?
• Explanatory: Why do certain teaching methodologies resonate better with students having specific learning styles?
• Exploratory: How can insights from behavioral psychology improve student engagement in virtual classrooms?
• Comparative: How does the motivation level of students differ between self-paced versus instructor-led courses?
• Predictive: With the increasing integration of technology in education, how will student learning behaviors change in the next decade?

Special education

• Descriptive: What interventions are commonly used to support students with autism spectrum disorders in inclusive classrooms?
• Explanatory: Why do some special education programs yield better academic outcomes for students with specific learning disabilities?
• Exploratory: How can augmented reality technologies be utilized to enhance learning for students with visual impairments?
• Comparative: How does special education support differ between urban and rural school districts?
• Predictive: Based on advancements in assistive technologies, how will the landscape of special education transform in the near future?

Aerospace engineering

• Descriptive: What are the key materials and technologies utilized in modern spacecraft design?
• Explanatory: Why are certain alloys preferred in high-temperature aerospace applications?
• Exploratory: How might advances in propulsion technologies revolutionize space travel in the next decade?
• Comparative: How do commercial aircraft designs differ from military aircraft designs in terms of aerodynamics?
• Predictive: Given current research trends, how will the efficiency of jet engines change in the upcoming years?

Biomedical engineering

• Descriptive: What are the foundational principles behind the design of modern prosthetic limbs?
• Explanatory: Why have bio-compatible materials like titanium become crucial in implantable medical devices?
• Exploratory: How can nanotechnology be leveraged to improve drug delivery systems in the future?
• Comparative: How do MRI machines differ from CT scanners in terms of their underlying technology and application?
• Predictive: Based on emerging trends, how will wearable health monitors evolve in the next decade?

Chemical engineering

• Descriptive: What processes are involved in the large-scale production of ethylene?
• Explanatory: Why is distillation the most common separation method in the petroleum industry?
• Exploratory: How might green chemistry principles transform traditional chemical manufacturing processes?
• Comparative: How does the production of biofuels compare to traditional fossil fuels in terms of yield and environmental impact?
• Predictive: Given global sustainability goals, how will the chemical industry's reliance on fossil resources shift in the future?

Civil engineering

• Descriptive: What are the primary considerations in the structural design of skyscrapers in earthquake-prone regions?
• Explanatory: Why are steel-reinforced concrete beams commonly used in bridge construction?
• Exploratory: How can smart city concepts influence the infrastructure planning of urban centers in the future?
• Comparative: How do tunneling methods differ between soft soil and hard rock terrains?
• Predictive: With the increasing threat of climate change, how will coastal infrastructure design criteria change to account for rising sea levels?

Computer engineering

• Descriptive: What are the main components of a modern central processing unit (CPU) and their functions?
• Explanatory: Why is silicon predominantly used in semiconductor manufacturing?
• Exploratory: How might quantum computing redefine the landscape of traditional computing architectures?
• Comparative: How do solid-state drives (SSDs) compare to traditional hard disk drives (HDDs) in terms of performance and longevity?
• Predictive: Given advancements in chip miniaturization, how will the form factor of consumer electronics evolve in the coming years?

Electrical engineering

• Descriptive: What are the standard stages involved in the transmission and distribution of electrical power?
• Explanatory: Why are transformers essential in the power distribution network?
• Exploratory: How can emerging smart grid technologies improve the efficiency and reliability of electrical distribution systems?
• Comparative: How do AC and DC transmission methods differ in terms of efficiency and infrastructure requirements?
• Predictive: With the rise of renewable energy sources, how will power grid management complexities change in the next decade?

Mechanical engineering

• Descriptive: What are the fundamental principles behind the operation of a four-stroke internal combustion engine?
• Explanatory: Why are certain polymers used as vibration dampeners in machinery?
• Exploratory: How might advancements in materials science impact the design of future automotive systems?
• Comparative: How do hydraulic systems compare to pneumatic systems in terms of energy efficiency and application?
• Predictive: With the push towards sustainability, how will traditional manufacturing methods evolve to reduce their carbon footprint?

Climatology

• Descriptive: What are the primary factors that influence the El Niño and La Niña phenomena?
• Explanatory: Why have certain regions experienced more intense and frequent heatwaves in the past decade?
• Exploratory: How might changing atmospheric CO2 concentrations impact global wind patterns in the future?
• Comparative: How do urban areas differ from rural areas in terms of microclimate conditions?
• Predictive: Given current greenhouse gas emission trends, what will be the average global temperature increase by the end of the century?

Conservation science

• Descriptive: What are the primary threats faced by tropical rainforests around the world?
• Explanatory: Why are certain species more vulnerable to habitat fragmentation than others?
• Exploratory: How can community involvement enhance conservation efforts in protected areas?
• Comparative: How does the effectiveness of in-situ conservation compare to ex-situ conservation for endangered species?
• Predictive: If current deforestation rates continue, how many species are predicted to go extinct in the next 50 years?
• Descriptive: What are the dominant flora and fauna in a temperate deciduous forest biome?
• Explanatory: Why do certain ecosystems, like wetlands, have higher biodiversity than others?
• Exploratory: How might the spread of invasive species alter nutrient cycling in freshwater lakes?
• Comparative: How do the trophic dynamics of grassland ecosystems differ from those of desert ecosystems?
• Predictive: How will global ecosystems change if bee populations continue to decline at current rates?

Environmental health

• Descriptive: What are the major pollutants found in urban air?
• Explanatory: Why do certain pollutants cause respiratory diseases in humans?
• Exploratory: How might green building designs reduce the health risks associated with indoor air pollutants?
• Comparative: How do the health impacts of living near coal-fired power plants compare to living near nuclear power plants?
• Predictive: Given increasing urbanization trends, how will air quality in major cities change over the next two decades?

Marine biology

• Descriptive: What are the primary species that comprise a coral reef ecosystem?
• Explanatory: Why are coral reefs particularly sensitive to changes in sea temperature?
• Exploratory: How might deep-sea exploration reveal unknown marine species and their adaptations?
• Comparative: How do the feeding strategies of pelagic fish differ from benthic fish in oceanic ecosystems?
• Predictive: If ocean acidification trends continue, what will be the impact on shell-forming marine organisms in the next 30 years?
• Descriptive: What are the most common oral health issues faced by elderly individuals?
• Explanatory: Why do sugary foods lead to a higher prevalence of cavities?
• Exploratory: How might emerging technologies revolutionize dental procedures in the coming decade?
• Comparative: How do the effects of electric toothbrushes compare to manual ones in reducing plaque?
• Predictive: Given current trends, how might the prevalence of gum diseases change in populations with increased sugar consumption over the next decade?

Kinesiology

• Descriptive: What are the primary physiological changes that occur during aerobic exercise?
• Explanatory: Why do some athletes experience muscle cramps during extensive physical activity?
• Exploratory: How might different stretching routines impact athletic performance?
• Comparative: How do the biomechanics of running on a treadmill differ from running outdoors?
• Predictive: If sedentary lifestyles continue to rise, what could be the potential impact on musculoskeletal health in the next 20 years?
• Descriptive: What are the main symptoms associated with the early stages of Parkinson's disease?
• Explanatory: Why are some viruses, like the flu, more prevalent in colder months?
• Exploratory: How might genetic editing technologies, like CRISPR, be utilized to treat hereditary diseases in the future?
• Comparative: How does the efficacy of traditional chemotherapy compare to targeted therapy in treating certain cancers?
• Predictive: Given advances in telemedicine, how might patient-doctor interactions evolve over the next decade?
• Descriptive: What are the primary responsibilities of nurses in intensive care units?
• Explanatory: Why is there a higher burnout rate among nurses compared to other healthcare professionals?
• Exploratory: How can training programs be improved to better equip nurses for challenges in emergency situations?
• Comparative: How does the patient recovery rate differ when cared for by specialized nurses versus general ward nurses?
• Predictive: How will the role of nurses change with the integration of more AI-based diagnostic tools in hospitals?
• Descriptive: What are the main nutritional components of a Mediterranean diet?
• Explanatory: Why does a diet high in processed sugars lead to increased risks of type 2 diabetes?
• Exploratory: How might gut microbiota be influenced by various diets and what are the potential health implications?
• Comparative: How does the nutritional profile of plant-based proteins compare to animal-based proteins?
• Predictive: If global meat consumption trends continue, what could be the implications for population-wide nutritional health in 30 years?
• Descriptive: What are the primary active ingredients in over-the-counter pain relievers?
• Explanatory: Why do certain medications cause drowsiness as a side effect?
• Exploratory: How might nanoparticle-based drug delivery systems enhance the efficacy of certain treatments?
• Comparative: How do the effects of generic drugs compare to their brand-name counterparts?
• Predictive: Given the rise of antibiotic-resistant bacteria, how might pharmaceutical approaches to bacterial infections change in the future?

Public health

• Descriptive: What are the main factors contributing to public health disparities in urban vs rural areas?
• Explanatory: Why did certain regions have higher transmission rates during the COVID-19 pandemic?
• Exploratory: How can community engagement strategies be optimized for more effective health campaigns?
• Comparative: How do vaccination rates and outcomes differ between countries with public vs private healthcare systems?
• Predictive: Based on current trends, how will global public health challenges evolve over the next 50 years?

Art history

• Descriptive: What are the primary artistic styles observed in the Renaissance era?
• Explanatory: Why did the Baroque art movement emerge after the Renaissance?
• Exploratory: How might newly discovered ancient art pieces reshape our understanding of prehistoric artistic practices?
• Comparative: How does European Romantic art differ from Asian Romantic art of the same period?
• Predictive: Given current trends, how might digital art impact traditional art gallery setups in the next decade?
• Descriptive: What are the primary themes in Homer's "Odyssey"?
• Explanatory: Why did Greek tragedies place a strong emphasis on the concept of fate?
• Exploratory: Are there undiscovered works that might provide more insight into daily life in ancient Rome?
• Comparative: How do Roman epics compare to their Greek counterparts in terms of character development?
• Predictive: How will emerging technologies like virtual reality affect the study of ancient ruins?

Cultural studies

• Descriptive: How is the concept of family portrayed in contemporary American media?
• Explanatory: Why has the influence of Western culture grown in certain Eastern countries over the last century?
• Exploratory: What are the emerging subcultures in the digital age and how do they communicate?
• Comparative: How does the representation of masculinity vary between Eastern and Western films?
• Predictive: In what ways might globalization affect cultural identities in the next two decades?
• Descriptive: What events led to the fall of the Berlin Wall?
• Explanatory: Why did the Industrial Revolution begin in Britain?
• Exploratory: Are there undocumented civilizational interactions in ancient times that new archaeological findings might reveal?
• Comparative: How did the responses to the Black Plague differ between European and Asian nations?
• Predictive: Given historical patterns, how might major global powers react to dwindling natural resources in the future?
• Descriptive: What are the main narrative techniques used in James Joyce's "Ulysses"?
• Explanatory: Why did the Gothic novel become popular in 19th-century England?
• Exploratory: How might translations of ancient texts reveal different interpretations based on the translator's cultural background?
• Comparative: How does the portrayal of war differ between post-WWII American and French literature?
• Predictive: How might the rise of AI-authored literature change the publishing industry?
• Descriptive: What are the core principles of existentialism as described by Jean-Paul Sartre?
• Explanatory: Why did the philosophy of existentialism gain prominence post-WWII?
• Exploratory: How might ancient Eastern philosophies provide insights into modern ethical dilemmas surrounding technology?
• Comparative: How does Nietzsche's concept of the "Ubermensch" compare to Aristotle's "virtuous person"?
• Predictive: As AI becomes more prevalent, how might philosophical discussions around consciousness evolve?

Religious studies

• Descriptive: What are the Five Pillars of Islam?
• Explanatory: Why did Protestantism emerge within Christianity during the 16th century?
• Exploratory: Are there common motifs in creation myths across various religions?
• Comparative: How do concepts of the afterlife compare between Christianity, Buddhism, and Ancient Egyptian beliefs?
• Predictive: How might interfaith dialogue shape religious practices in multi-faith societies over the next decade?

Classic languages

• Descriptive: What are the primary grammatical structures in Ancient Greek?
• Explanatory: Why did Latin play a foundational role in the development of many modern European languages?
• Exploratory: Are there yet-to-be-deciphered scripts from ancient civilizations that might provide insight into lost languages?
• Comparative: How do the verb conjugation patterns in Latin compare to those in Sanskrit?
• Predictive: Given the ongoing research in classical studies, how might our understanding of certain ancient texts change in the next decade?

Comparative literature

• Descriptive: What are the main themes in Japanese Haiku and English Sonnets?
• Explanatory: Why do certain folklore tales appear with variations across different cultures?
• Exploratory: How might newly translated works from lesser-known languages reshape the world literature canon?
• Comparative: How does the role of the tragic hero in French literature differ from its portrayal in Russian literature?
• Predictive: As global communication becomes more interconnected, how might the study of world literature evolve in universities?

Modern languages

• Descriptive: What are the primary tonal patterns observed in Mandarin Chinese?
• Explanatory: Why has English become a dominant lingua franca in international business and diplomacy?
• Exploratory: Which lesser-studied languages might become more prominent due to socio-political changes in their regions?
• Comparative: How do the grammatical complexities of Russian compare to those of German?
• Predictive: Given current global trends, which languages are predicted to become more widely spoken in the next two decades?
• Descriptive: What are the primary articulatory features of plosive sounds?
• Explanatory: Why do certain accents develop specific pitch fluctuations and intonations?
• Exploratory: How do various environmental factors affect vocal cord vibrations and sound production?
• Comparative: How does the pronunciation of fricatives differ between Spanish and Portuguese speakers?
• Predictive: How might advancements in voice recognition technology influence phonetics research in the next decade?
• Descriptive: What are the primary signs and symbols used in American road signage?
• Explanatory: Why do red roses universally symbolize love or passion in many cultures?
• Exploratory: Are there emerging symbols in digital communication that could become universally recognized signs in the future?
• Comparative: How do the semiotic structures in print advertisements differ between Western and Eastern cultures?
• Predictive: As emoji usage becomes more widespread, how might they impact written language semantics in the coming years?
• Descriptive: What are the key statutes governing tenant rights in residential leases?
• Explanatory: Why do personal injury claims vary significantly in settlement amounts even under similar circumstances?
• Exploratory: How might alternative dispute resolution mechanisms evolve in civil law contexts over the next decade?
• Comparative: How do defamation laws differ between jurisdictions that adopt the British common law system versus the Napoleonic code?
• Predictive: How might the rise of online transactions affect the volume and nature of civil law cases related to contract disputes?

Constitutional law

• Descriptive: What are the main principles enshrined in the First Amendment of the U.S. Constitution?
• Explanatory: Why have some constitutional rights been subject to varying interpretations over time?
• Exploratory: Are there emerging debates around digital rights and freedoms that might reshape constitutional interpretations in the future?
• Comparative: How does the protection of freedom of speech differ between the U.S. Constitution and the German Basic Law?
• Predictive: Given global socio-political trends, how might constitutional democracies adjust their foundational texts in the next two decades?

Corporate law

• Descriptive: What are the primary duties and liabilities of a board of directors in a publicly traded company?
• Explanatory: Why do mergers and acquisitions often involve extensive due diligence processes?
• Exploratory: How might the rise of digital currencies impact the regulatory landscape for corporations in the finance sector?
• Comparative: How does the legal framework for shareholder rights in the U.S. compare to that of Japan?
• Predictive: How might changing global trade dynamics influence corporate structuring and international partnerships?

Criminal law

• Descriptive: What constitutes first-degree murder in the majority of jurisdictions?
• Explanatory: Why are certain offenses classified as misdemeanors while others are felonies?
• Exploratory: Are there emerging patterns in cybercrime that suggest new areas of legal vulnerability?
• Comparative: How does the treatment of juvenile offenders differ between Scandinavian countries and the U.S.?
• Predictive: Given advancements in technology, how might criminal law evolve to address potential misuses of artificial intelligence?

International law

• Descriptive: What are the foundational principles of the Geneva Conventions?
• Explanatory: Why have some nations refused to recognize or be bound by certain international treaties?
• Exploratory: How might global climate change reshape international agreements and treaties in the coming years?
• Comparative: How do regional trade agreements in Africa compare to those in Southeast Asia in terms of provisions and enforcement mechanisms?
• Predictive: How might geopolitical shifts influence the role and effectiveness of international courts in resolving state disputes?

Applied mathematics

• Descriptive: What are the primary mathematical models used to predict the spread of infectious diseases?
• Explanatory: Why does the Navier–Stokes equation play a pivotal role in fluid dynamics?
• Exploratory: How might new computational methods enhance the efficiency of existing algorithms in applied mathematics?
• Comparative: How do optimization techniques in operations research differ from those in machine learning applications?
• Predictive: Given the rapid growth of quantum computing, how might it reshape the landscape of applied mathematical problems in the next decade?

Applied statistics

• Descriptive: What are the standard procedures for handling missing data in a large-scale survey?
• Explanatory: Why do statisticians use bootstrapping techniques in hypothesis testing?
• Exploratory: How might emerging data sources, like wearables and IoT devices, introduce new challenges and opportunities in applied statistics?
• Comparative: How does the performance of Bayesian methods compare to frequentist methods in complex hierarchical models?
• Predictive: With the increasing availability of big data, how might the role of applied statisticians evolve in the next five years?

Pure mathematics

• Descriptive: What are the axioms underpinning Euclidean geometry?
• Explanatory: Why is Gödel's incompleteness theorem considered a foundational result in the philosophy of mathematics?
• Exploratory: Are there newly emerging areas of study within number theory due to advancements in computational mathematics?
• Comparative: How do algebraic structures differ between rings and fields?
• Predictive: Considering current research trends, what areas of pure mathematics are poised for significant breakthroughs in the next decade?

Theoretical statistics

• Descriptive: What foundational principles underlie the Central Limit Theorem?
• Explanatory: Why is the concept of sufficiency crucial in the design of statistical tests?
• Exploratory: How might advances in artificial intelligence influence theoretical developments in statistical inference?
• Comparative: How do likelihood-based inference methods compare to Bayesian methods in terms of theoretical underpinnings?
• Predictive: As data generation mechanisms evolve, how might the theoretical foundations of statistics need to adapt in the future?
• Descriptive: What are the key features and behaviors of black holes?
• Explanatory: Why does the expansion of the universe appear to be accelerating?
• Exploratory: What potential insights might the study of exoplanets provide about the conditions necessary for life?
• Comparative: How do the properties of spiral galaxies differ from those of elliptical galaxies?
• Predictive: Based on current data, what are the projected future behaviors of our sun as it ages?
• Descriptive: What are the primary functions and structures of ribosomes in a cell?
• Explanatory: Why does DNA replication occur semi-conservatively?
• Exploratory: How might emerging technologies like CRISPR redefine our understanding of genetic engineering?
• Comparative: How do the metabolic processes of prokaryotic cells differ from those of eukaryotic cells?
• Predictive: Given the current trajectory of climate change, how might the biodiversity in tropical rainforests be affected over the next century?
• Descriptive: What are the key properties and uses of the noble gases?
• Explanatory: Why do exothermic reactions release heat?
• Exploratory: How might advances in nanochemistry influence drug delivery systems?
• Comparative: How do ionic bonds differ in strength and characteristics from covalent bonds?
• Predictive: Considering the rise in antibiotic-resistant bacteria, how might the field of medicinal chemistry adapt to produce effective treatments in the future?

Earth science

• Descriptive: What are the primary layers of Earth's atmosphere and their respective characteristics?
• Explanatory: Why do certain regions experience more seismic activity than others?
• Exploratory: How might the study of ancient ice cores provide insights into past climate conditions?
• Comparative: How do the processes of weathering differ between arid and humid climates?
• Predictive: Given current data on deforestation, what could be its impact on global soil quality and erosion patterns over the next 50 years?
• Descriptive: What are the fundamental principles underlying quantum mechanics?
• Explanatory: Why does the speed of light in a vacuum remain constant regardless of the observer's frame of reference?
• Exploratory: How might studies in string theory reshape our understanding of the universe at the smallest scales?
• Comparative: How do the effects of general relativity contrast with predictions from Newtonian physics under extreme gravitational conditions?
• Predictive: With advancements in particle physics, what potential new particles or phenomena might be discovered in the next decade?

Anthropology

• Descriptive: What are the primary rituals and customs of the indigenous tribes of the Amazon?
• Explanatory: Why did the ancient Mayan civilization collapse?
• Exploratory: How might modern urbanization impact the preservation of ancient burial sites?
• Comparative: How do hunter-gatherer societies differ from agricultural societies in terms of social structures?
• Predictive: Given global trends, how might indigenous cultures evolve over the next century?

Communication

• Descriptive: What are the main modes of communication used by millennials compared to baby boomers?
• Explanatory: Why has the usage of social media platforms surged in the last two decades?
• Exploratory: How might advancements in virtual reality reshape interpersonal communication in the future?
• Comparative: How do written communication skills differ between those educated in traditional schools versus online schools?
• Predictive: How might the nature of journalism change with the rise of automated content generation?
• Descriptive: What are the primary components of a nation's gross domestic product (GDP)?
• Explanatory: Why did the economic recession of 2008 occur?
• Exploratory: How might the concept of universal basic income impact labor market dynamics?
• Comparative: How do free market economies differ from command economies in terms of resource allocation?
• Predictive: Based on current global economic trends, which industries are predicted to boom in the next decade?
• Descriptive: What are the geographical features of the Himalayan mountain range?
• Explanatory: Why do desert regions exist on the western coasts of continents, such as the Atacama in South America?
• Exploratory: How might rising sea levels reshape the world's coastlines over the next century?
• Comparative: How does urban planning in European cities differ from that in American cities?
• Predictive: Given current urbanization rates, which cities are poised to become megacities by 2050?

Political science

• Descriptive: What are the foundational principles of a parliamentary democracy?
• Explanatory: Why do certain nations adopt federal systems while others prefer unitary systems?
• Exploratory: How might the rise of populism influence global diplomatic relations in the 21st century?
• Comparative: How do the rights of citizens in liberal democracies differ from those in authoritarian regimes?
• Predictive: Based on current political trends, which nations might see significant shifts in governance models over the next two decades?
• Descriptive: What are the primary stages of cognitive development in children according to Piaget?
• Explanatory: Why do certain individuals develop phobias?
• Exploratory: How might emerging neuroscientific tools, like fMRI, alter our understanding of human emotions?
• Comparative: How do coping mechanisms differ between individuals with high resilience versus those with low resilience?
• Predictive: Given the rise in digital communication, how might human attention spans evolve in future generations?

Social work

• Descriptive: What are the core principles and practices in child protective services?
• Explanatory: Why do certain communities have higher rates of child neglect and abuse?
• Exploratory: How might the integration of artificial intelligence in social work affect decision-making in child welfare cases?
• Comparative: How do intervention strategies for substance abuse differ between urban and rural settings?
• Predictive: Based on current societal trends, what challenges might social workers face in the next decade?
• Descriptive: What are the defining characteristics of Generation Z as a social cohort?
• Explanatory: Why have nuclear families become less prevalent in Western societies?
• Exploratory: How might the widespread adoption of virtual realities impact social interactions and community structures in the future?
• Comparative: How do the roles and perceptions of elderly individuals differ between Eastern and Western societies?
• Predictive: Given the rise in remote work, how might urban and suburban living patterns change over the next three decades?

In synthesizing the vast range of research questions posed across diverse disciplines, it becomes clear that every academic field, from the humanities to the social sciences, offers unique perspectives and methodologies to uncover and understand various facets of our world. These questions, whether descriptive, explanatory, exploratory, comparative, or predictive, serve as guiding lights, driving scholarship and innovation. As academia continues to evolve and adapt, these inquiries not only define the boundaries of current knowledge but also pave the way for future discoveries and insights, emphasizing the invaluable role of continuous inquiry in the ever-evolving tapestry of human understanding.

Header image by Zetong Li .

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• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

• Literacy rate in a city.
• Abortion and pregnancy rate in the USA.
• Eating disorders in the citizens.
• Parent role in self-esteem and confidence of the student.
• Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

• Statistical analysis for legs and head injuries in Football.
• Statistical analysis for shoulder and knee injuries in MotoGP.
• Deep statistical evaluation for the doping test in sports from the past decade.
• Statistical observation on the performance of athletes in the last Olympics.
• Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

• Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
• Statistical evolution to find out the suicide reason among students and adults.
• Statistics analysis to find out the effect of divorce on children in a country.
• Psychology affects women because of the gender gap in specific country areas.
• Statistics analysis to find out the cause of online bullying in students’ lives. 
• In Psychology, PTSD and descriptive tendencies are discussed.
• The function of researchers in statistical testing and probability.
• Acceptable significance and probability thresholds in clinical Psychology.
• The utilization of hypothesis and the role of P 0.05 for improved comprehension.
• What types of statistical data are typically rejected in psychology?
• The application of basic statistical principles and reasoning in psychological analysis.
• The role of correlation is when several psychological concepts are at risk.
• Actual case study learning and modeling are used to generate statistical reports.
• In psychology, naturalistic observation is used as a research sample.
• How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

• Does education have a deep impact on the financial success of an individual?
• The investment in digital technology is having a meaningful return for corporations?
• The gap of financial wealth between rich and poor in the USA.
• A statistical approach to identify the effects of high-frequency trading in financial markets.
• Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

• Statistical analysis on the effect of methamphetamine on substance abusers.
• Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
• Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
• Statistics analysis to identify the role of genes in the child’s overall immunity.
• What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

• Generic vs private education is one of the best for the students and has better financial return.
• Psychology vs physiology: which leads the person not to quit their addictions?
• Effect of breastmilk vs packed milk on the infant child overall development
• Which causes more accidents: male alcoholics vs female alcoholics.
• What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

• Application of statistics in the world of data science
• Statistics for finance: how statistics is helping the company to grow their finance
• Advantages and disadvantages of Radar chart
• Minor marriages in south-east Asia and African countries.
• Discussion of ANOVA and correlation.
• What statistical methods are most effective for active sports?
• When measuring the correctness of college tests, a ranking statistical approach is used.
• Statistics play an important role in Data Mining operations.
• The practical application of heat estimation in engineering fields.
• In the field of speech recognition, statistical analysis is used.
• Estimating probiotics: how much time is necessary for an accurate statistical sample?
• How will the United States population grow in the next twenty years?
• The legislation and statistical reports deal with contentious issues.
• The application of empirical entropy approaches with online grammar checking.
• Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

• Uses of statistics in chemometrics
• Statistics in business analytics and business intelligence
• Importance of statistics in physics.
• Deep discussion about multivariate statistics
• Uses of Statistics in machine learning

Survey Topics for Statistics

• Gather the data of the most qualified professionals in a specific area.
• Survey the time wasted by the students in watching Tvs or Netflix.
• Have a survey the fully vaccinated people in the USA 
• Gather information on the effect of a government survey on the life of citizens
• Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

• Have a deep decision of Bayes theorems
• Discuss the Bayesian hierarchical models
• Analysis of the process of Japanese restaurants. 
• Deep analysis of Lévy’s continuity theorem
• Analysis of the principle of maximum entropy

AP Statistics Topics

• Discuss about the importance of econometrics
• Analyze the pros and cons of Probit Model
• Types of probability models and their uses
• Deep discussion of ortho stochastic matrix
• Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

• National income and the regulation of cryptocurrency.
• The benefits and drawbacks of regression analysis.
• How can estimate methods be used to correct statistical differences?
• Mathematical prediction models vs observation tactics.
• In sociology research, there is bias in quantitative data analysis.
• Inferential analytical approaches vs. descriptive statistics.
• How reliable are AI-based methods in statistical analysis?
• The internet news reporting and the fluctuations: statistics reports.
• The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

• Role of statistics in business in 2023
• Importance of business statistics and analytics
• What is the role of central tendency and dispersion in statistics
• Best process of sampling business data.
• Importance of statistics in big data.
• The characteristics of business data sampling: benefits and cons of software solutions.
• How may two different business tasks be tackled concurrently using linear regression analysis?
• In economic data relations, index numbers, random probability, and correctness are all important.
• The advantages of a dataset approach to statistics in programming statistics.
• Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

• Evaluate the role of John Tukey’s contribution to statistics.
• The role of statistics to improve ADHD treatment.
• The uses and timeline of probability in statistics.
• Deep analysis of Gertrude Cox’s experimental design in statistics.
• Discuss about Florence Nightingale in statistics.
• What sorts of music do college students prefer?
• The Main Effect of Different Subjects on Student Performance.
• The Importance of Analytics in Statistics Research.
• The Influence of a Better Student in Class.
• Do extracurricular activities help in the transformation of personalities?
• Backbenchers’ Impact on Class Performance.
• Medication’s Importance in Class Performance.
• Are e-books better than traditional books?
• Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

• Introduction of a problem
• Methodology explanation and choice. 
• Statistical research itself is in the main part (Body Part). 
• Samples deviations and variables. 
• Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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• Knowledge Base
• Choosing the Right Statistical Test | Types & Examples

Choosing the Right Statistical Test | Types & Examples

Published on January 28, 2020 by Rebecca Bevans . Revised on June 22, 2023.

Statistical tests are used in hypothesis testing . They can be used to:

• determine whether a predictor variable has a statistically significant relationship with an outcome variable.
• estimate the difference between two or more groups.

Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they determine whether the observed data fall outside of the range of values predicted by the null hypothesis.

If you already know what types of variables you’re dealing with, you can use the flowchart to choose the right statistical test for your data.

Statistical tests flowchart

Table of contents

What does a statistical test do, when to perform a statistical test, choosing a parametric test: regression, comparison, or correlation, choosing a nonparametric test, flowchart: choosing a statistical test, other interesting articles, frequently asked questions about statistical tests.

Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship.

It then calculates a p value (probability value). The p -value estimates how likely it is that you would see the difference described by the test statistic if the null hypothesis of no relationship were true.

If the value of the test statistic is more extreme than the statistic calculated from the null hypothesis, then you can infer a statistically significant relationship between the predictor and outcome variables.

If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.

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You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment , or through observations made using probability sampling methods .

For a statistical test to be valid , your sample size needs to be large enough to approximate the true distribution of the population being studied.

To determine which statistical test to use, you need to know:

• whether your data meets certain assumptions.
• the types of variables that you’re dealing with.

Statistical assumptions

Statistical tests make some common assumptions about the data they are testing:

• Independence of observations (a.k.a. no autocorrelation): The observations/variables you include in your test are not related (for example, multiple measurements of a single test subject are not independent, while measurements of multiple different test subjects are independent).
• Homogeneity of variance : the variance within each group being compared is similar among all groups. If one group has much more variation than others, it will limit the test’s effectiveness.
• Normality of data : the data follows a normal distribution (a.k.a. a bell curve). This assumption applies only to quantitative data .

If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test , which allows you to make comparisons without any assumptions about the data distribution.

If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data (repeated-measures tests or tests that include blocking variables).

Types of variables

The types of variables you have usually determine what type of statistical test you can use.

Quantitative variables represent amounts of things (e.g. the number of trees in a forest). Types of quantitative variables include:

• Continuous (aka ratio variables): represent measures and can usually be divided into units smaller than one (e.g. 0.75 grams).
• Discrete (aka integer variables): represent counts and usually can’t be divided into units smaller than one (e.g. 1 tree).

Categorical variables represent groupings of things (e.g. the different tree species in a forest). Types of categorical variables include:

• Ordinal : represent data with an order (e.g. rankings).
• Nominal : represent group names (e.g. brands or species names).
• Binary : represent data with a yes/no or 1/0 outcome (e.g. win or lose).

Choose the test that fits the types of predictor and outcome variables you have collected (if you are doing an experiment , these are the independent and dependent variables ). Consult the tables below to see which test best matches your variables.

Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests.

The most common types of parametric test include regression tests, comparison tests, and correlation tests.

Regression tests

Regression tests look for cause-and-effect relationships . They can be used to estimate the effect of one or more continuous variables on another variable.

Comparison tests

Comparison tests look for differences among group means . They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

Correlation tests

Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

These can be used to test whether two variables you want to use in (for example) a multiple regression test are autocorrelated.

Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient
• Null hypothesis

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Statistical tests commonly assume that:

• the data are normally distributed
• the groups that are being compared have similar variance
• the data are independent

If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.

A test statistic is a number calculated by a  statistical test . It describes how far your observed data is from the  null hypothesis  of no relationship between  variables or no difference among sample groups.

The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis . Different test statistics are used in different statistical tests.

Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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SW 470: Social Research & Evaluation (Salgado)

• Introduction to Research
• Basic Social Work Research Vocabulary
• How to Read and Interpret a Research Article
• Example Datasets and Statistics
• Example Literature Reviews and Methods
• Sample Articles and Program Evaluations
• APA Citation Guide
• Book-A-Librarian Consultation Service (One-on-One Research Help)

Children's Bureau of the U.S. Department of Health & Human Services

• Adoption and Foster Care Analysis and Reporting Systems (AFCARS)
• Funded Research: (1) Child Abuse Prevention; (2) Adoption and Foster Care; (3) Child Welfare; and (4) Child Abuse and Neglect
• National Child Abuse and Neglect Data System (NCANDS)
• National Youth in Transition Database (NYTD)
• Program Report: Annual Report of State CHild Welfare Expenditures

HealthData - U.S. Department of Health & Human Services

Missouri department of health & senior services.

• Data, Surveillance Systems & Statistical Reports

U.S. General Services Administration

• Data.Gov (Over 291,000 datasets available)

University Libraries of Washington University in St. Louis

• Comprehensive List of Free Datasets

eBook: Statistics in Social Work

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