example of research topic for quantitative research

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100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

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Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

• Consider your area of studies
• Look out for relevant subjects in the area
• Expert advice may come in handy
• Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

• The title should seem interesting to readers
• Ensure that the title represents the content of the research paper
• Reflect on the tone of the writing in the title
• The title should contain important keywords in your chosen subject to help readers find your paper
• The title should not be too lengthy
• It should be grammatically correct and creative
• It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

• What is the correlation between inflation rates and unemployment rates?
• Has climate adaptation influenced the mitigation of funds allocation?
• Job satisfaction and employee turnover: What is the link?
• A look at the relationship between poor households and the development of entrepreneurship skills
• Urbanization and economic growth: What is the link between these elements?
• Does education achievement influence people’s economic status?
• What is the impact of solar electricity on the wholesale energy market?
• Debt accumulation and retirement: What is the relationship between these concepts?
• Can people with psychiatric disorders develop independent living skills?
• Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

• The difference between the colorific intake of men and women in your country
• Top strategies used to measure customer satisfaction and how they work
• Black Friday sales: are they profitable?
• The correlation between estimated target market and practical competitive risk assignment
• Are smartphones making us brighter or dumber?
• Nuclear families Vs. Joint families: Is there a difference?
• What will society look like in the absence of organized religion?
• A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
• How does emotional stability influence your overall well-being?
• The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

• How much exercise is good for lasting physical well-being?
• A comparison of the nutritional therapy uses and contemporary medical approaches
• Does sugar intake have a direct impact on diabetes diagnosis?
• Education attainment: Does it influence crime rates in society?
• Is there an actual link between obesity and cancer rates?
• Do kids with siblings have better social skills than those without?
• Computer games and their impact on the young generation
• Has social media marketing taken over conventional marketing strategies?
• The impact of technology development on human relationships and communication
• What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

• Habitation fragmentation and biodiversity loss: What is the link?
• Radiation has affected biodiversity: Assessing its effects
• An assessment of the impact of the CORONA virus on global population growth
• Is the pandemic truly over, or have human bodies built resistance against the virus?
• The ozone hole and its impact on the environment
• The greenhouse gas effect: What is it and how has it impacted the atmosphere
• GMO crops: are they good or bad for your health?
• Is there a direct link between education quality and job attainment?
• How have education systems changed from traditional to modern times?
• The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

• Online classes: are they helpful or not?
• What changes has the global CORONA pandemic had on the population growth curve?
• Daily habits influenced by the global pandemic
• An analysis of the impact of culture on people’s personalities
• How has feminism influenced the education system’s approach to the girl child’s education?
• Academic competition: what are its benefits and downsides for students?
• Is there a link between education and student integrity?
• An analysis of how the education sector can influence a country’s economy
• An overview of the link between crime rates and concern for crime
• Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

• What are the impacts of online games on students?
• Sex education in schools: how important is it?
• Should schools be teaching about safe sex in their sex education classes?
• The correlation between extreme parent interference on student academic performance
• Is there a real link between academic marks and intelligence?
• Teacher feedback: How necessary is it, and how does it help students?
• An analysis of modern education systems and their impact on student performance
• An overview of the link between academic performance/marks and intelligence
• Are grading systems helpful or harmful to students?
• What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

• A look at the pre-corona and post-corona economy
• How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
• An evaluation of mortality rates of heart attacks
• Effective treatments for cardiovascular issues and their prevention
• A comparison of the effectiveness of home care and nursing home care
• Strategies for managing effective dissemination of information to modern students
• How does educational discrimination influence students’ futures?
• The impacts of unfavorable classroom environment and bullying on students and teachers
• An overview of the implementation of STEM education to K-12 students
• How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

• An elaborate study of the influence of telemedicine in healthcare practices
• How has scientific innovation influenced the defense or military system?
• The link between technology and people’s mental health
• Has social media helped create awareness or worsened people’s mental health?
• How do engineers promote green technology?
• How can engineers raise sustainability in building and structural infrastructures?
• An analysis of how decision-making is dependent on someone’s sub-conscious
• A comprehensive study of ADHD and its impact on students’ capabilities
• The impact of racism on people’s mental health and overall wellbeing
• How has the current surge in social activism helped shape people’s relationships?

Are you looking for an example of a quantitative research title? These ten examples below will get you started.

• The prevalence of nonverbal communication in social control and people’s interactions
• The impacts of stress on people’s behavior in society
• A study of the connection between capital structures and corporate strategies
• How do changes in credit ratings impact equality returns?
• A quantitative analysis of the effect of bond rating changes on stock prices
• The impact of semantics on web technology
• An analysis of persuasion, propaganda, and marketing impact on individuals
• The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
• The role of income inequality in economy growth
• An examination of juvenile delinquents’ treatment in your country

Excellent Topics For Quantitative Research

Here are some titles for quantitative research you should consider:

• Does studying mathematics help implement data safety for businesses
• How are art-related subjects interdependent with mathematics?
• How do eco-friendly practices in the hospitality industry influence tourism rates?
• A deep insight into how people view eco-tourisms
• Religion vs. hospitality: Details on their correlation
• Has your country’s tourist sector revived after the pandemic?
• How effective is non-verbal communication in conveying emotions?
• Are there similarities between the English and French vocabulary?
• How do politicians use persuasive language in political speeches?
• The correlation between popular culture and translation

Here are some quantitative research titles examples for your consideration:

• How do world leaders use language to change the emotional climate in their nations?
• Extensive research on how linguistics cultivate political buzzwords
• The impact of globalization on the global tourism sector
• An analysis of the effects of the pandemic on the worldwide hospitality sector
• The influence of social media platforms on people’s choice of tourism destinations
• Educational tourism: What is it and what you should know about it
• Why do college students experience math anxiety?
• Is math anxiety a phenomenon?
• A guide on effective ways to fight cultural bias in modern society
• Creative ways to solve the overpopulation issue

An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones:

• The link between global warming and climate change
• What is the greenhouse gas impact on biodiversity and the atmosphere
• Has the internet successfully influenced literacy rates in society
• The value and downsides of competition for students
• A comparison of the education system in first-world and third-world countries
• The impact of alcohol addiction on the younger generation
• How has social media influenced human relationships?
• Has education helped boost feminism among men and women?
• Are computers in classrooms beneficial or detrimental to students?
• How has social media improved bullying rates among teenagers?

High school students can apply research titles on social issues  or other elements, depending on the subject. Let’s look at some quantitative topics for students:

• What is the right age to introduce sex education for students
• Can extreme punishment help reduce alcohol consumption among teenagers?
• Should the government increase the age of sexual consent?
• The link between globalization and the local economy collapses
• How are global companies influencing local economies?

There are numerous possible quantitative research topics you can write about. Here are some great quantitative research topics examples:

• The correlation between video games and crime rates
• Do college studies impact future job satisfaction?
• What can the education sector do to encourage more college enrollment?
• The impact of education on self-esteem
• The relationship between income and occupation

You can find inspiration for your research topic from trending affairs on social media or in the news. Such topics will make your research enticing. Find a trending topic for quantitative research example from the list below:

• How the country’s economy is fairing after the pandemic
• An analysis of the riots by women in Iran and what the women gain to achieve
• Is the current US government living up to the voter’s expectations?
• How is the war in Ukraine affecting the global economy?
• Can social media riots affect political decisions?

A proposal is a paper you write proposing the subject you would like to cover for your research and the research techniques you will apply. If the proposal is approved, it turns to your research topic. Here are some quantitative titles you should consider for your research proposal:

• Military support and economic development: What is the impact in developing nations?
• How does gun ownership influence crime rates in developed countries?
• How can the US government reduce gun violence without influencing people’s rights?
• What is the link between school prestige and academic standards?
• Is there a scientific link between abortion and the definition of viability?

You can never have too many sample titles. The samples allow you to find a unique title you’re your research or proposal. Find a sample quantitative research title here:

• Does weight loss indicate good or poor health?
• Should schools do away with grading systems?
• The impact of culture on student interactions and personalities
• How can parents successfully protect their kids from the dangers of the internet?
• Is the US education system better or worse than Europe’s?

If you’re a business major, then you must choose a research title quantitative about business. Let’s look at some research title examples quantitative in business:

• Creating shareholder value in business: How important is it?
• The changes in credit ratings and their impact on equity returns
• The importance of data privacy laws in business operations
• How do businesses benefit from e-waste and carbon footprint reduction?
• Organizational culture in business: what is its importance?

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Interesting, creative, unique, and easy quantitative research topics allow you to explain your paper and make research easy. Therefore, you should not take choosing a research paper or proposal topic lightly. With your topic ready, reach out to us today for excellent research paper writing services .

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Best 151+ Quantitative Research Topics for STEM Students

In today’s rapidly evolving world, STEM (Science, Technology, Engineering, and Mathematics) fields have gained immense significance. For STEM students, engaging in quantitative research is a pivotal aspect of their academic journey. Quantitative research involves the systematic collection and interpretation of numerical data to address research questions or test hypotheses. Choosing the right research topic is essential to ensure a successful and meaningful research endeavor. 

In this blog, we will explore 151+ quantitative research topics for STEM students. Whether you are an aspiring scientist, engineer, or mathematician, this comprehensive list will inspire your research journey. But we understand that the journey through STEM education and research can be challenging at times. That’s why we’re here to support you every step of the way with our Engineering Assignment Help service. 

What is Quantitative Research in STEM?

Table of Contents

Quantitative research is a scientific approach that relies on numerical data and statistical analysis to draw conclusions and make predictions. In STEM fields, quantitative research encompasses a wide range of methodologies, including experiments, surveys, and data analysis. The key characteristics of quantitative research in STEM include:

• Data Collection: Systematic gathering of numerical data through experiments, observations, or surveys.
• Statistical Analysis: Application of statistical techniques to analyze data and draw meaningful conclusions.
• Hypothesis Testing: Testing hypotheses and theories using quantitative data.
• Replicability: The ability to replicate experiments and obtain consistent results.
• Generalizability: Drawing conclusions that can be applied to larger populations or phenomena.

Importance of Quantitative Research Topics for STEM Students

Quantitative research plays a pivotal role in STEM education and research for several reasons:

1. Empirical Evidence

It provides empirical evidence to support or refute scientific theories and hypotheses.

2. Data-Driven Decision-Making

STEM professionals use quantitative research to make informed decisions, from designing experiments to developing new technologies.

3. Innovation

It fuels innovation by providing data-driven insights that lead to the creation of new products, processes, and technologies.

4. Problem Solving

STEM students learn critical problem-solving skills through quantitative research, which are invaluable in their future careers.

5. Interdisciplinary Applications 

Quantitative research transcends STEM disciplines, facilitating collaboration and the tackling of complex, real-world problems.

Also Read: Google Scholar Research Topics

Quantitative Research Topics for STEM Students

Now, let’s explore important quantitative research topics for STEM students:

Biology and Life Sciences

Here are some quantitative research topics in biology and life science:

1. The impact of climate change on biodiversity.

2. Analyzing the genetic basis of disease susceptibility.

3. Studying the effectiveness of vaccines in preventing infectious diseases.

4. Investigating the ecological consequences of invasive species.

5. Examining the role of genetics in aging.

6. Analyzing the effects of pollution on aquatic ecosystems.

7. Studying the evolution of antibiotic resistance.

8. Investigating the relationship between diet and lifespan.

9. Analyzing the impact of deforestation on wildlife.

10. Studying the genetics of cancer development.

11. Investigating the effectiveness of various plant fertilizers.

12. Analyzing the impact of microplastics on marine life.

13. Studying the genetics of human behavior.

14. Investigating the effects of pollution on plant growth.

15. Analyzing the microbiome’s role in human health.

16. Studying the impact of climate change on crop yields.

17. Investigating the genetics of rare diseases.

Let’s get started with some quantitative research topics for stem students in chemistry:

1. Studying the properties of superconductors at different temperatures.

2. Analyzing the efficiency of various catalysts in chemical reactions.

3. Investigating the synthesis of novel polymers with unique properties.

4. Studying the kinetics of chemical reactions.

5. Analyzing the environmental impact of chemical waste disposal.

6. Investigating the properties of nanomaterials for drug delivery.

7. Studying the behavior of nanoparticles in different solvents.

8. Analyzing the use of renewable energy sources in chemical processes.

9. Investigating the chemistry of atmospheric pollutants.

10. Studying the properties of graphene for electronic applications.

11. Analyzing the use of enzymes in industrial processes.

12. Investigating the chemistry of alternative fuels.

13. Studying the synthesis of pharmaceutical compounds.

14. Analyzing the properties of materials for battery technology.

15. Investigating the chemistry of natural products for drug discovery.

16. Analyzing the effects of chemical additives on food preservation.

17. Investigating the chemistry of carbon capture and utilization technologies.

Here are some quantitative research topics in physics for stem students:

1. Investigating the behavior of subatomic particles in high-energy collisions.

2. Analyzing the properties of dark matter and dark energy.

3. Studying the quantum properties of entangled particles.

4. Investigating the dynamics of black holes and their gravitational effects.

5. Analyzing the behavior of light in different mediums.

6. Studying the properties of superfluids at low temperatures.

7. Investigating the physics of renewable energy sources like solar cells.

8. Analyzing the properties of materials at extreme temperatures and pressures.

9. Studying the behavior of electromagnetic waves in various applications.

10. Investigating the physics of quantum computing.

11. Analyzing the properties of magnetic materials for data storage.

12. Studying the behavior of particles in plasma for fusion energy research.

13. Investigating the physics of nanoscale materials and devices.

14. Analyzing the properties of materials for use in semiconductors.

15. Studying the principles of thermodynamics in energy efficiency.

16. Investigating the physics of gravitational waves.

17. Analyzing the properties of materials for use in quantum technologies.

Engineering

Let’s explore some quantitative research topics for stem students in engineering: 

1. Investigating the efficiency of renewable energy systems in urban environments.

2. Analyzing the impact of 3D printing on manufacturing processes.

3. Studying the structural integrity of materials in aerospace engineering.

4. Investigating the use of artificial intelligence in autonomous vehicles.

5. Analyzing the efficiency of water treatment processes in civil engineering.

6. Studying the impact of robotics in healthcare.

7. Investigating the optimization of supply chain logistics using quantitative methods.

8. Analyzing the energy efficiency of smart buildings.

9. Studying the effects of vibration on structural engineering.

10. Investigating the use of drones in agricultural practices.

11. Analyzing the impact of machine learning in predictive maintenance.

12. Studying the optimization of transportation networks.

13. Investigating the use of nanomaterials in electronic devices.

14. Analyzing the efficiency of renewable energy storage systems.

15. Studying the impact of AI-driven design in architecture.

16. Investigating the optimization of manufacturing processes using Industry 4.0 technologies.

17. Analyzing the use of robotics in underwater exploration.

Environmental Science

Here are some top quantitative research topics in environmental science for students:

1. Investigating the effects of air pollution on respiratory health.

2. Analyzing the impact of deforestation on climate change.

3. Studying the biodiversity of coral reefs and their conservation.

4. Investigating the use of remote sensing in monitoring deforestation.

5. Analyzing the effects of plastic pollution on marine ecosystems.

6. Studying the impact of climate change on glacier retreat.

7. Investigating the use of wetlands for water quality improvement.

8. Analyzing the effects of urbanization on local microclimates.

9. Studying the impact of oil spills on aquatic ecosystems.

10. Investigating the use of renewable energy in mitigating greenhouse gas emissions.

11. Analyzing the effects of soil erosion on agricultural productivity.

12. Studying the impact of invasive species on native ecosystems.

13. Investigating the use of bioremediation for soil cleanup.

14. Analyzing the effects of climate change on migratory bird patterns.

15. Studying the impact of land use changes on water resources.

16. Investigating the use of green infrastructure for urban stormwater management.

17. Analyzing the effects of noise pollution on wildlife behavior.

Computer Science

Let’s get started with some simple quantitative research topics for stem students:

1. Investigating the efficiency of machine learning algorithms for image recognition.

2. Analyzing the security of blockchain technology in financial transactions.

3. Studying the impact of quantum computing on cryptography.

4. Investigating the use of natural language processing in chatbots and virtual assistants.

5. Analyzing the effectiveness of cybersecurity measures in protecting sensitive data.

6. Studying the impact of algorithmic trading in financial markets.

7. Investigating the use of deep learning in autonomous robotics.

8. Analyzing the efficiency of data compression algorithms for large datasets.

9. Studying the impact of virtual reality in medical simulations.

10. Investigating the use of artificial intelligence in personalized medicine.

11. Analyzing the effectiveness of recommendation systems in e-commerce.

12. Studying the impact of cloud computing on data storage and processing.

13. Investigating the use of neural networks in predicting disease outbreaks.

14. Analyzing the efficiency of data mining techniques in customer behavior analysis.

15. Studying the impact of social media algorithms on user behavior.

16. Investigating the use of machine learning in natural language translation.

17. Analyzing the effectiveness of sentiment analysis in social media monitoring.

Mathematics

Let’s explore the quantitative research topics in mathematics for students:

1. Investigating the properties of prime numbers and their distribution.

2. Analyzing the behavior of chaotic systems using differential equations.

3. Studying the optimization of algorithms for solving complex mathematical problems.

4. Investigating the use of graph theory in network analysis.

5. Analyzing the properties of fractals in natural phenomena.

6. Studying the application of probability theory in risk assessment.

7. Investigating the use of numerical methods in solving partial differential equations.

8. Analyzing the properties of mathematical models for population dynamics.

9. Studying the optimization of algorithms for data compression.

10. Investigating the use of topology in data analysis.

11. Analyzing the behavior of mathematical models in financial markets.

12. Studying the application of game theory in strategic decision-making.

13. Investigating the use of mathematical modeling in epidemiology.

14. Analyzing the properties of algebraic structures in coding theory.

15. Studying the optimization of algorithms for image processing.

16. Investigating the use of number theory in cryptography.

17. Analyzing the behavior of mathematical models in climate prediction.

Earth Sciences

Here are some quantitative research topics for stem students in earth science:

1. Investigating the impact of volcanic eruptions on climate patterns.

2. Analyzing the behavior of earthquakes along tectonic plate boundaries.

3. Studying the geomorphology of river systems and erosion.

4. Investigating the use of remote sensing in monitoring wildfires.

5. Analyzing the effects of glacier melt on sea-level rise.

6. Studying the impact of ocean currents on weather patterns.

7. Investigating the use of geothermal energy in renewable power generation.

8. Analyzing the behavior of tsunamis and their destructive potential.

9. Studying the impact of soil erosion on agricultural productivity.

10. Investigating the use of geological data in mineral resource exploration.

11. Analyzing the effects of climate change on coastal erosion.

12. Studying the geomagnetic field and its role in navigation.

13. Investigating the use of radar technology in weather forecasting.

14. Analyzing the behavior of landslides and their triggers.

15. Studying the impact of groundwater depletion on aquifer systems.

16. Investigating the use of GIS (Geographic Information Systems) in land-use planning.

17. Analyzing the effects of urbanization on heat island formation.

Health Sciences and Medicine

Here are some quantitative research topics for stem students in health science and medicine:

1. Investigating the effectiveness of telemedicine in improving healthcare access.

2. Analyzing the impact of personalized medicine in cancer treatment.

3. Studying the epidemiology of infectious diseases and their spread.

4. Investigating the use of wearable devices in monitoring patient health.

5. Analyzing the effects of nutrition and exercise on metabolic health.

6. Studying the impact of genetics in predicting disease susceptibility.

7. Investigating the use of artificial intelligence in medical diagnosis.

8. Analyzing the behavior of pharmaceutical drugs in clinical trials.

9. Studying the effectiveness of mental health interventions in schools.

10. Investigating the use of gene editing technologies in treating genetic disorders.

11. Analyzing the properties of medical imaging techniques for early disease detection.

12. Studying the impact of vaccination campaigns on public health.

13. Investigating the use of regenerative medicine in tissue repair.

14. Analyzing the behavior of pathogens in antimicrobial resistance.

15. Studying the epidemiology of chronic diseases like diabetes and heart disease.

16. Investigating the use of bioinformatics in genomics research.

17. Analyzing the effects of environmental factors on health outcomes.

Quantitative research is the backbone of STEM fields, providing the tools and methodologies needed to explore, understand, and innovate in the world of science and technology . As STEM students, embracing quantitative research not only enhances your analytical skills but also equips you to address complex real-world challenges. With the extensive list of 155+ quantitative research topics for stem students provided in this blog, you have a starting point for your own STEM research journey. Whether you’re interested in biology, chemistry, physics, engineering, or any other STEM discipline, there’s a wealth of quantitative research topics waiting to be explored. So, roll up your sleeves, grab your lab coat or laptop, and embark on your quest for knowledge and discovery in the exciting world of STEM.

I hope you enjoyed this blog post about quantitative research topics for stem students.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

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

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Educational resources and simple solutions for your research journey

What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

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Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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100 Unique Quantitative Research Paper Topics

Every month, a group of terrified students starts looking for good quantitative research paper topics. Some of them want to be done with this annoying college task as soon as possible while others are genuinely hopeful to investigate something relevant. In both cases, the question is, where to find great topics? First of all, let’s make sure you understand what quantitative research is. It’s an essay where you analyze numerical data to find meaningful patterns, prove some point, and present results to your readers.

Assignments like this teach students how to analyze information and understand what numbers are telling you. It’s a useful skill to have, especially if you plan on continuing your education for years to come. Choosing topics is one of the central problems, but our  top educational blog  experts have a few tips that could help you out.

Ways of Looking for Quantitative Research Ideas

How to make sure you don’t make a mistake when selecting research topics for your paper? As it was mentioned, there are several strategies that usually assist students regardless of what subject they study. Here are four major ones.

• Understand the difference between quantitative & qualitative research.  Before you proceed with your paper, ascertain that you have a clear idea of what your goal is. Students confuse qualitative research with quantitative, so they end up making a fundamental mistake and choosing the wrong topic. For avoiding it, dig up some definitions. Check what these research types entail, look at examples, or even go through some tests. Only when you realize the difference should you focus on the paper itself.
• Choose a subject you like.  No matter how serious your project must be, it is better to conduct it on quantitative research topics that you find interesting. Students rarely succeed if they investigate a boring or uninspiring issue because in this case, they have no motivation. When a paper is a chore, getting a good grade for it is nearly impossible. So, think about stuff that you wouldn’t mind researching. For example, if you are a part of the LGBTQ community, you could explore the rates of hate crimes committed against local LGBTQ members to point out how destructive the problem of homophobia still is. Whether you are interested in health, literature, computers, or anything else, you could turn this into solid quantitative research — all you need is creativity and imagination.
• Assess topics objectively.  It is always better to search for quantitative research topics examples and check how possible it would be to explore them before you make a final choice. Some students might want to investigate rates of specific diseases in Nigeria, but what if the data are unavailable? Not everything could be found online, and in numerous cases, you won’t be able to request information from hospitals or other sources. That’s why you need something that you could research and get numbers on.
• Find enough sources & clarify with a professor . Students should look for sources that will help them support their work. In addition, they should ask their professors questions in case they feel uncertain about their direction. Quantitative projects usually take lots of time, so you should make sure you’re on the right track before committing to any topic.

Your List of Quantitative Research Topics

Students can always benefit from extra help. To let you have a variety of quantitative paper topics, we’ve prepared this list with 100 diverse ideas. Try them out! Use them right the way you see them or edit them until they meet your demands.

Quantitative Research Paper Education Topics

All students have something to say about education. If you have strong feelings about it, check quantitative research questions below.

• How Successful Are Students Who Initially Got High SAT Score?
• Do Schools That Have Extra Anti-Bullying Tactics Actually Succeed in Curbing It? Provide Data
• Do Most Scientists Hold Solid Knowledge in Math?
• Young People Who’re Likely to Apply to Colleges in 2021 Based on Data From 2020.
• What Percentage of Students Is Satisfied With Studying From Home Due to COVID?
• How Frequent Does Education Become a Reason for People’s Suicide?
• What Biases Are Encountered Most Often in a Classroom?
• What Kinds of Application Paper Tend to Appeal to College Committees More Frequent Statistically?
• How Many Students Pick Math as Their Favorite Subject?
• Based on Statistics, How Popular Art Is in Modern Schools?

Technology and Engineering Research Topics

If you love technologies and would like to answer some questions populations have about them, look at the following quantitative research topics ideas.

• How Often Do Flawed Engineering Projects Cause Death?
• What Kinds of Green Technology Exist & Which Are Seen as Most Effective?
• Compare Statistics Related to Facebook Popularity: Is It Rising or Declining?
• Which Computers Are Preferred by Our Population in 2020?
• Compare Several Largest Social Media Platforms: Which Are Most Popular?
• Does Evolution of Technologies Result In Increased Numbers of Mental Health Issues?
• From All Major Engineering Projects, How Many End Up Successful?
• Compare Student Statistics & Number of Them Who Become Engineers.
• Which Technology-Based Learning Method Is Most Effective?
• Individuals Who Actively Use Virtual Reality Options?

Psychology Quantitative Research Paper Topic Ideas

How about psychological quantitative topics? This sector has some outstanding ideas.

• What Triggers Affect People with PTSD Most Often?
• Murders Are Actually Committed by Mentally Ill People.
• Are Police Officers More Likely to Kill Black People Than White? Study Statistics
• In Which Cases Is Pack Mentality Triggered Most Frequently?
• At What Age Are People More Likely to Start Using Drugs?
• Do Males Or Females Suffer from ADHD More Frequently?
• Are Ads Really Effective? Compare Reactions & Responses
• What Ads Are Preferred by Most Companies for Promoting Their Services?
• Students Who Manage to Overcome Bullying They Faced at High School.
• What Factors Are Most Common Motivators for Partners Cheating on Each Other?

Business and Finance

Business is always important because it is one of the biggest ways in which we earn money. So, why don’t you check examples of quantitative research topics about it? They could help you write a great paper.

• How Many Startups Succeed in Establishing Their Presence in the Market?
• Businesses That Had to Close Down Because of 2020 Quarantine?
• In Which Ways Do Privacy Laws Influence Businesses? Study Numbers
• What Kinds of Investments Help Strengthen Businesses’ Brand Image?
• Determine the Number of Mistakes an Average Finance Specialist Does Per Year
• Based On Their Salaries, Can Finance Experts Be Called Rich?
• What Kinds of Businesses Flourish Most These Days?
• Which of the Start-Ups in Your City Are Likely to Succeed?
• How Frequently Do CEOs Manage to Cheat Their Firms?
• How Did Pepsi Appearance Affect Coca Cola Sales?

Economics Research Paper Topics

What do you think about economics? Quantitative research projects in this sphere are complex, but they are also extremely exciting.

• How Does Economic Stability Affect Income Inequality: Analysis in Numbers
• Measures Taken to Protect From COVID in Relation to Their Impact on US’ GPD
• Is the Car Market Already Saturated in America? Perform an Analysis
• How Do Countries Affect Each Other’s Economics? Provide Statistics & Explanations
• In Which Spheres Are Institutional Economics Methodologies Applied Often?
• What Causes Stock Prices to Fluctuate & How Often Does It Occur?
• Impact of Wars on the Countries Engaged in Them: Economical Perspective
• Fiscal Policies: How Do They Affect the American Economy?
• What Impact Does the Raising of Minimal Wage Have on Income?
• Which Country Demands the Most Unacceptable Amount of Taxes From Its Citizens?

Social Work Quantitative Paper Topics

Social work can be a curse and a blessing, depending on how effective it is. Take a look at these easy quantitative research topics if this area interests you.

• Comparative Analysis: Which Countries Invest in Their Social Workers Most Heavily?
• How Often Are Social Workers Successful in Their Jobs & Pleased with Their Choice?
• What Percentage of Mistakes Do Social Workers Make That Lead to the Death of Their Clients?
• What Punishments Do Teen Criminals Receive? Provide Data via Numbers
• US Children Who Face Abuse at Home. 2020 Statistics.
• How Many Children Are Malnourished in Accordance with Your Country’s Reports?
• How Frequently Do Social Workers Insist On Separation of Children from Their Parents?
• How Many Which Crimes Are Solved Due to Social Work?
• What Types of Power Abuse Happen Most Commonly among Social Workers?
• Are There More Women or Men in the Field of Social Work?

Mathematics

Those who like Math are interested in difficult but logical tasks others might be wary of. If you’re one of them, the ideas for research paper topics below might fit your bill.

• How Is Logic Interrelated with Math? Perform Quantitative Analysis
• How Many IT Specialists Hold Majors in Math?
• Math Anxiety: How Common Is It & Who Is Most Affected by It?
• Are There More Male or Female Math Majors?
• In Which Spheres Is Math Applied on the Most Common Basis?
• How Many Safety Mechanisms Are Built on Math?
• What Do Students Like More, Algebra, or Geometry?
• Based on Numbers, What Frequency Does Math Have in the US Curriculum?
• Why Do Students Hate Math: List of Reasons Based on Their Frequency
• Who Teaches Math at Colleges? Quantitative Gender Analysis

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Tourism Quantitative Paper Ideas

Travelling and journeys are always exciting. Not surprising that there are various good quantitative research paper topics about them.

• How Many People Lost on Foreign Forests Are Found Alive?
• What Country Is the Best Tourist Spot In Accordance with the Number of Visits There?
• Students From What Country Change Countries for Their Studies Most Often?
• Analyze What Hotel Chain Is Preferred by the Biggest Amount of Tourists
• How Did the Rates of Tourism Fall Down After COVID Measures?
• How Many People Succeed in Visiting North Korea?
• Is Educational Tourism Developed in the UK?
• Trace Interrelation between Tourism and Destruction of Nature
• Tourists Who Visit Your Country on a Yearly Basis & What Is the Common Reason?
• Which Region Has the Lowest Number of Tourists Globally?

Linguistics Quantitative Research Paper Prompts

Foreign languages fascinate and make them learn more. Complex or not, researching them with the purpose to create a research paper topic is certainly interesting!

• How Many People Are Bilingual These Days?
• Compare Statistics: Are Bilingual Children More Successful at Their Studies?
• What Can We Say About Migration Based on Similarities in Our Languages? Explore Patterns
• Consider Statistic: How Relevant Is Linguistics in the World of Politics?
• How Many People Decide on Majoring in Linguistics in the US?
• How Many Which Cultures Grow Closer Due to Language Similarities?
• Quantitative Analysis: Present Similarities between Chinese and Japanese Languages
• Consider Available Data: Which Language Is Viewed as Most Complex?
• What Are the Oldest Languages Based on Information We Have?
• To Which Extent Does Correct Word Choice Influence Efficiency of Public Speeches?

Enjoy What You Write and Write What You Enjoy

After all examples of quantitative research questions above, chances are, you’ve already selected a paper topic to your liking. If not, continue looking until you settle on the best possible option. When you have a passion for a subject, writing a paper about it is exciting. But of course, some other problems might be waiting for you, such as lack of time or personal issues that don’t let you concentrate on your work properly. This is where you can count on us!

Our team of expert writers will gladly research, synthesize, and write all paper types you need. Contact us and tell us what you require. We’ll swiftly find the best specialists who’ll study your guidelines and work on crafting an outstanding quantitative paper based on them. You’ll receive it just by your deadline, and we guarantee that one way or another, but we’ll find a way to make you satisfied!

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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.

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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!

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

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How to Write Quantitative Research Questions: Types With Examples

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Want to create a quantitative research survey hassle-free? Explore our library of 1,000,000+ readymade questions.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive 

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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60+ Best Quantitative Research Topics for STEM Students: Dive into Data

Embark on a captivating journey through the cosmos of knowledge with our curated guide on Quantitative Research Topics for STEM Students. Explore innovative ideas in science, technology, engineering, and mathematics, designed to ignite curiosity and shape the future.

Unleash the power of quantitative research and dive into uncharted territories that go beyond academics, fostering innovation and discovery.

Hey, you future scientists, tech wizards, engineering maestros, and math superheroes – gather ’round! We’re about to dive headfirst into the rad world of quantitative research topics, tailor-made for the rockstars of STEM.

In the crazy universe of science, technology, engineering, and math (STEM), quantitative research isn’t just a nerdy term—it’s your VIP pass to an interstellar adventure. Picture this: you’re strapping into a rocket ship, zooming through the cosmos, and decoding the universe’s coolest secrets, all while juggling numbers like a cosmic DJ.

But here’s the real scoop: finding the ultimate research topic is like picking the juiciest star in the galaxy. It’s about stumbling upon something so mind-blowing that you can’t resist plunging into the data. It’s about choosing questions that make your STEM-loving heart do the cha-cha.

In this guide, we’re not just your sidekicks; we’re your partners in crime through the vast jungle of quantitative research topics. Whether you’re a rookie gearing up for your first lab escapade or a seasoned explorer hunting for a new thrill, think of this article as your treasure map, guiding you to the coolest STEM discoveries.

From the teeny wonders of biology to the brain-bending puzzles of physics, the cutting-edge vibes of engineering, and the downright gorgeous dance of mathematics – we’ve got your back.

So, buckle up, fellow STEM enthusiasts! We’re setting sail on a cosmic adventure through the groovy galaxy of quantitative research topics. Get ready to unravel the secrets of science and tech, one sizzling digit at a time.

Stick around for a ride that’s part data, part disco, and all STEM swagger!

Table of Contents

Benefits of Choosing Quantitative Research

Embarking on the quantitative research journey is like stepping into a treasure trove of benefits across a spectrum of fields. Let’s dive into the exciting advantages that make choosing quantitative research a game-changer:

Numbers That Speak Louder

Quantitative research deals in cold, hard numbers. This means your data isn’t just informative; it’s objective, measurable, and has a voice of its own.

Statistical Swagger

Crunching numbers isn’t just for show. With quantitative research, statistical tools add a touch of pizzazz, boosting the validity of your findings and turning your study into a credible performance.

For the Masses

Quantitative research loves a crowd. Larger sample sizes mean your discoveries aren’t just for the lucky few – they’re for everyone. It’s the science of sharing the knowledge wealth.

Data Showdown

Ready for a duel between variables? Quantitative research sets the stage for epic battles, letting you compare, contrast, and uncover cause-and-effect relationships in the data arena.

Structured and Ready to Roll

Think of quantitative research like a well-organized party. It follows a structured plan, making replication a breeze. Because who doesn’t love a party that’s easy to recreate?

Data Efficiency Dance

Efficiency is the name of the game. Surveys, experiments, and structured observations make data collection a dance – choreographed, smooth, and oh-so-efficient.

Data Clarity FTW

No decoding needed here. Quantitative research delivers crystal-clear results. It’s like reading a good book without the need for interpretation – straightforward and to the point.

Spotting Trends Like a Pro

Ever wish you had a crystal ball for trends? Quantitative analysis is the next best thing. It’s like having a trend-spotting superpower, revealing patterns that might have otherwise stayed hidden.

Bias Be Gone

Quantitative research takes bias out of the equation. Systematic data collection and statistical wizardry reduce researcher bias, leaving you with results that are as unbiased as a judge at a talent show.

Key Components of a Quantitative Research Study

Launching into a quantitative research study is like embarking on a thrilling quest, and guess what? You’re the hero of this research adventure! Let’s unravel the exciting components that make your study a blockbuster:

Quest-Starter: Research Question or Hypothesis

It’s your “once upon a time.” Kick off your research journey with a bang by crafting a captivating research question or hypothesis. This is the spark that ignites your curiosity.

Backstory Bonanza: Literature Review

Think of it as your research Netflix binge. Dive into existing literature for the backstory. It’s not just research – it’s drama, plot twists, and the foundation for your epic tale.

Blueprint Brilliance: Research Design

Time to draw up the plans for your study castle. Choose your research design – is it a grand experiment or a cunning observational scheme? Your design is the architectural genius behind your research.

Casting Call: Population and Sample

Who’s in your star-studded lineup? Define your dream cast – your target population – and then handpick a sample that’s ready for the research red carpet.

Gear Up: Data Collection Methods

Choose your research tools wisely – surveys, experiments, or maybe a bit of detective work. Your methods are like the gadgets in a spy movie, helping you collect the data treasures.

The Numbers Game: Variables and Measures

What’s in the spotlight? Identify your main characters – independent and dependent variables. Then, sprinkle in some measures to add flair and precision to your study.

Magic Analysis Wand: Data Analysis Techniques

Enter the wizardry zone! Pick your magic wand – statistical methods, tests, or software – and watch as it unravels the mysteries hidden in your data.

Ethical Superhero Cape: Ethical Considerations

Every hero needs a moral compass. Clearly outline how you’ll be the ethical superhero of your study, protecting the well-being and secrets of your participants.

Grand Finale: Results and Findings

It’s showtime! Showcase your results like the grand finale of a fireworks display. Tables, charts, and statistical dazzle – let your findings steal the spotlight.

Wrap-Up Party: Conclusion and Implications

Bring out the confetti! Summarize your findings, discuss their VIP status in the research world, and hint at the afterparty – how your results shape the future.

Behind-the-Scenes Blooper Reel: Limitations and Future Research

No Hollywood film is perfect. Share the bloopers – the limitations of your study – and hint at the sequel with ideas for future research. It’s all part of the cinematic journey.

Roll Credits: References

Give a shout-out to the supporting cast! Cite your sources – it’s the credits that add credibility to your blockbuster.

Bonus Scene: Appendix

Think of it as the post-credits scene. Tuck in any extra goodies – surveys, questionnaires, or behind-the-scenes material – for those eager to dive deeper into your research universe.

By weaving these storylines together, your quantitative research study becomes a cinematic masterpiece, leaving a lasting impact on the grand stage of academia. Happy researching, hero!

Quantitative Research Topics for STEM Students

Check out the best quantitative research topics for STEM students:-

• Investigating the Effects of Different Soil pH Levels on Plant Growth.
• Analyzing the Impact of Pesticide Exposure on Bee Populations.
• Studying the Genetic Variability in Endangered Species.
• Quantifying the Relationship Between Temperature and Microbial Growth in Water.
• Analyzing the Effects of Ocean Acidification on Coral Reefs.
• Investigating the Correlation Between Pollinator Diversity and Crop Yield.
• Studying the Role of Gut Microbiota in Human Health and Disease.
• Quantifying the Impact of Antibiotics on Soil Microbial Communities.
• Analyzing the Effects of Light Pollution on Nocturnal Animal Behavior.
• Investigating the Relationship Between Altitude and Plant Adaptations in Mountain Ecosystems.
• Measuring the Speed of Light Using Interferometry Techniques.
• Investigating the Quantum Properties of Photons in Quantum Computing.
• Analyzing the Factors Affecting Magnetic Field Strength in Electromagnets.
• Studying the Behavior of Superfluids at Ultra-Low Temperatures.
• Quantifying the Efficiency of Energy Transfer in Photovoltaic Cells.
• Analyzing the Properties of Quantum Dots for Future Display Technologies.
• Investigating the Behavior of Particles in High-Energy Particle Accelerators.
• Studying the Effects of Gravitational Waves on Space-Time.
• Quantifying the Frictional Forces on Objects at Different Surfaces.
• Analyzing the Characteristics of Dark Matter and Dark Energy in the Universe.

Engineering

• Optimizing the Design of Wind Turbine Blades for Maximum Efficiency.
• Investigating the Use of Smart Materials in Structural Engineering.
• Analyzing the Impact of 3D Printing on Prototyping in Product Design.
• Studying the Behavior of Composite Materials Under Extreme Temperatures.
• Evaluating the Efficiency of Water Treatment Plants in Removing Contaminants.
• Investigating the Aerodynamics of Drones for Improved Flight Control.
• Quantifying the Effects of Traffic Flow on Roadway Maintenance.
• Analyzing the Impact of Vibration Damping in Building Structures.
• Studying the Mechanical Properties of Biodegradable Polymers in Medical Devices.
• Investigating the Use of Artificial Intelligence in Autonomous Robotic Systems.

Mathematics

• Exploring Chaos Theory and Its Applications in Nonlinear Systems.
• Modeling the Spread of Infectious Diseases in Population Dynamics.
• Analyzing Data Mining Techniques for Predictive Analytics in Business.
• Studying the Mathematics of Cryptography Algorithms for Data Security.
• Quantifying the Patterns in Stock Market Price Movements Using Time Series Analysis.
• Investigating the Applications of Fractal Geometry in Computer Graphics.
• Analyzing the Behavior of Differential Equations in Climate Modeling.
• Studying the Optimization of Supply Chain Networks Using Linear Programming.
• Investigating the Mathematical Concepts Behind Machine Learning Algorithms.
• Quantifying the Patterns of Prime Numbers in Number Theory.
• Investigating the Chemical Mechanisms Behind Enzyme Catalysis.
• Analyzing the Thermodynamic Properties of Chemical Reactions.
• Studying the Kinetics of Chemical Reactions in Different Solvents.
• Quantifying the Concentration of Pollutants in Urban Air Quality.
• Evaluating the Effectiveness of Antioxidants in Food Preservation.
• Investigating the Electrochemical Properties of Batteries for Energy Storage.
• Studying the Behavior of Nanomaterials in Drug Delivery Systems.
• Analyzing the Chemical Composition of Exoplanet Atmospheres Using Spectroscopy.
• Quantifying Heavy Metal Contamination in Soil and Water Sources.
• Investigating the Correlation Between Chemical Exposure and Human Health.

Computer Science

• Analyzing Machine Learning Algorithms for Natural Language Processing.
• Investigating Quantum Computing Algorithms for Cryptography Applications.
• Studying the Efficiency of Data Compression Methods for Big Data Storage.
• Quantifying Cybersecurity Threats and Vulnerabilities in IoT Devices.
• Evaluating the Impact of Cloud Computing on Distributed Systems.
• Investigating the Use of Artificial Intelligence in Autonomous Vehicles.
• Analyzing the Behavior of Neural Networks in Deep Learning Applications.
• Studying the Performance of Blockchain Technology in Supply Chain Management.
• Quantifying User Behavior in Social Media Analytics.
• Investigating Quantum Machine Learning for Enhanced Data Processing.

These additional project ideas provide a diverse range of opportunities for STEM students to engage in quantitative research and explore various aspects of their respective fields. Each project offers a unique avenue for discovery and contribution to the world of science and technology.

What is an example of a quantitative research?

Quantitative research is a powerful investigative approach, wielding numbers to shed light on intricate relationships and phenomena. Let’s dive into an example of quantitative research to understand its workings:

Research Question

What is the correlation between the time students devote to studying and their academic grades?

Students who invest more time in studying are likely to achieve higher grades.

Research Design

Imagine a researcher embarking on a journey within a high school. They distribute surveys to students, inquiring about their weekly study hours and their corresponding grades in core subjects.

Data Analysis

Equipped with statistical tools, our researcher scrutinizes the collected data. Lo and behold, a significant positive correlation emerges—students who dedicate more time to studying generally earn higher grades.

With data as their guide, the researcher concludes that indeed, a relationship exists between study time and academic grades. The more time students commit to their studies, the brighter their academic stars tend to shine.

This example merely scratches the surface of quantitative research’s potential. It can delve into an extensive array of subjects and investigate complex hypotheses. Here are a few more examples:

• Assessing a New Drug’s Effectiveness: Quantifying the impact of a  novel medication  in treating a specific illness.
• Socioeconomic Status and Crime Rates: Investigating the connection between economic conditions and criminal activity.
• Analyzing the Influence of an Advertising Campaign on Sales: Measuring the effectiveness of a marketing blitz on product purchases.
• Factors Shaping Customer Satisfaction: Using data to pinpoint the elements contributing to customer contentment.
• Government Policies and Employment Rates: Evaluating the repercussions of new governmental regulations on job opportunities.

Quantitative research serves as a potent beacon, illuminating the complexities of our world through data-driven inquiry. Researchers harness its might to collect, analyze, and draw valuable conclusions about a vast spectrum of phenomena. It’s a vital tool for unraveling the intricacies of our universe. 

As we bid adieu to our whirlwind tour of quantitative research topics tailor-made for the STEM dreamers, it’s time to soak in the vast horizons that science, technology, engineering, and mathematics paint for us.

We’ve danced through the intricate tango of poverty and crime, peeked into the transformative realm of cutting-edge technologies, and unraveled the captivating puzzles of quantitative research. But these aren’t just topics; they’re open invitations to dive headfirst into the uncharted seas of knowledge.

To you, the STEM trailblazers, these research ideas aren’t mere academic pursuits. They’re portals to curiosity, engines of innovation, and blueprints for shaping the future of our world. They’re the sparks that illuminate the trail leading to discovery.

As you set sail on your research odyssey, remember that quantitative research isn’t just about unlocking answers—it’s about nurturing that profound sense of wonder, igniting innovation, and weaving your unique thread into the fabric of human understanding.

Whether you’re stargazing, decoding the intricate language of genes, engineering marvels, or tackling global challenges head-on, realize that your STEM and quantitative research journey is a perpetual adventure.

May your questions be audacious, your data razor-sharp, and your discoveries earth-shattering. Keep that innate curiosity alive, keep exploring, and let the spirit of STEM be your North Star, guiding you towards a future that’s not just brighter but brilliantly enlightened.

And with that, fellow adventurers, go forth, embrace the unknown, and let your journey in STEM be the epic tale that reshapes the narrative of tomorrow!

Frequently Asked Questions

How can i ensure the ethical conduct of my quantitative research project.

To ensure ethical conduct, obtain informed consent from participants, maintain data confidentiality, and adhere to ethical guidelines established by your institution and professional associations.

Are there any software tools recommended for data analysis in STEM research?

Yes, there are several widely used software tools for data analysis in STEM research, including R, Python, MATLAB, and SPSS. The choice of software depends on your specific research needs and familiarity with the tools.

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List of Topics for Quantitative and Qualitative Research

Dr. stanley gajda, 25 jul 2018.

Choosing the right topic can make all the difference between a long, arduous research experience and a rewarding one. Use a qualitative approach to explore topics that you wish to subjectively describe in rich detail. A quantitative approach is better when investigating topics that can be objectively measured. In some situations, either approach can work depending on your research interests and purpose. For instance, the topic of gun control may studied qualitatively through in-depth interviews with people on both sides of the debate, or quantitatively by administering a closed-ended survey measuring how people voted on gun control legislation.

Explore this article

• Descriptive Research Sample Titles
• List of Qualitative Research Topics
• List of Quantitative Research Topics
• Applied Research in Education

1 Descriptive Research Sample Titles

Examples of descriptive research sample titles are abundant in in the humanities. For instance, you might talk about “What are human rights?” or “What is visual iconography?” Ideas for topics may also be found from endowments, foundations and charitable trusts, such as the National Endowment for the Arts, and from most organizations that fund research. Questions like, “How do local workers interpret and make sense of career paths and obstacles in the cultural economy?” require a qualitative review of documentation, participant observation and interviewing. Questions like, “What is the relationship between the arts, entrepreneurship and innovation in the workplace?” A quantitative concentration on a small, finite number of variables is needed to describe current conditions and investigate relationships such as, “What is the relationship between the arts, entrepreneurship and innovation in the workplace?”

2 List of Qualitative Research Topics

Topics in the social sciences include, for example, abortion, immigration and marijuana legalization. Federal agencies may list topics as well as suggest approaches to research. The Army, for instance, may announce qualitative research grants to better understand the challenges service members face when returning home and reintegrating into the community. Exploratory verbs and phrases including “discover,” “seek to understand” and “report the stories” indicate that qualitative approaches are needed. Conversely, data driven topics such as divorce rates following deployment suggest quantitative research, often indicated by verbs and phrases like “effect,” “cause” and “correlate.”

3 List of Quantitative Research Topics

Science topics are common on the list of quantitative research titles that teacher often assign. Examples include such subjects as bioterrorism, environmental concerns, and nuclear energy. Mother Nature may guide topics, approaches to research and data collection. For instance, the National Science Foundation may sponsor a qualitative case study of reactions to a natural disaster that asks, “What happened? What was involved in response to the disaster? What themes emerged during the months that followed?” Exploratory data collection entails qualitative approaches that are inductive, holistic and process orientated. By comparison, explanatory data collection involves quantitative approaches that are deductive, focused and outcome orientated. For example, to determine the effectiveness of a disaster-relief program, a quantitative survey may compare approval ratings across various demographics.

4 Applied Research in Education

Examples of quantitative research in education can be found in topics related to standardized test scores, placement testing, and achievement gaps between inner city and suburban schools, for instance. Occupational, professional and trade associations may inform the research topics and objectives. The American Educational Research Association, for example, may fund research on effective teachers. To develop specific measures for future study, a qualitative researcher may observe student interactions, review lesson plans and interview teachers, principals and students throughout the year. By contrast, to identify one or more generalizable variables that characterize effective teachers, a quantitative researcher may analyze finite, standardized, numerical data.

• 1 Atlas Ti: Qualitative Research
• 2 American Educational Research Foundation: Research Grants
• 3 National Endowment for the Arts: Research: Art Works Grants Final Papers
• 4 National Science Foundation: A Special Report: Disasters

About the Author

Stanley Jacob Gajda earned a PhD in higher education from the University of North Carolina at Greensboro. He also studied at Bowling Green and Ohio State University. Dr. Gajda writes on socially responsible leadership in and out of the classroom. His work has resulted in documented increases in student engagement, workforce efficiency and program funding.

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What is quantitative data? How to collect, understand, and analyze it

A comprehensive guide to quantitative data, how it differs from qualitative data, and why it's a valuable tool for solving problems.

• Key takeaways
• What is quantitative data?
• Examples of quantitative data
• Difference between quantitative and qualitative data
• Characteristics of quantitative data
• Types of quantitative data
• When should I use quantitative or qualitative research?
• Pros and cons of quantitative data
• Collection methods

Quantitative data analysis tools

Data is all around us, and every day it becomes increasingly important. Different types of data define more and more of our interactions with the world around us—from using the internet to buying a car, to the algorithms behind news feeds we see, and much more. 

One of the most common and well-known categories of data is quantitative data or data that can be expressed in numbers or numerical values. 

This guide takes a deep look at what quantitative data is , what it can be used for, how it’s collected, its advantages and disadvantages, and more. 

Key takeaways: 

Quantitative data is data that can be counted or measured in numerical values.

The two main types of quantitative data are discrete data and continuous data.

Height in feet, age in years, and weight in pounds are examples of quantitative data. 

Qualitative data is descriptive data that is not expressed numerically. 

Both quantitative research and qualitative research are often conducted through surveys and questionnaires. 

What is quantitative data? 

Quantitative data is information that can be counted or measured—or, in other words, quantified—and given a numerical value.

Quantitative data is used when a researcher needs to quantify a problem, and answers questions like “what,” “how many,” and “how often.” This type of data is frequently used in math calculations, algorithms, or statistical analysis. 

In product management, UX design , or software engineering, quantitative data can be the rate of product adoption (a percentage), conversions (a number), or page load speed (a unit of time), or other metrics. In the context of shopping, quantitative data could be how many customers bought a certain item. Regarding vehicles, quantitative data might be how much horsepower a car has. 

What are examples of quantitative data? 

Quantitative data is anything that can be counted in definite units and numbers . So, among many, many other things, some examples of quantitative data include: 

Revenue in dollars

Weight in kilograms or pounds

Age in months or years

Distance in miles or kilometers

Time in days or weeks

Experiment results

Website conversion rates

Website page load speed

What is the difference between quantitative and qualitative data? 

There are many differences between qualitative and quantitative data —each represents very different data sets and are used in different situations. Often, too, they’re used together to provide more comprehensive insights.

As we’ve described, quantitative data relates to numbers ; it can be definitively counted or measured.  Qualitative data, on the other hand, is descriptive data that are expressed in words or visuals. So, where quantitative data is used for statistical analysis, qualitative data is categorized according to themes. 

Examples of qualitative vs. quantitative data

As mentioned above, examples of quantitative data include distance in miles or age in years. 

Qualitative data , however, is expressed by describing or labeling certain attributes, such as “chocolate milk,” “blue eyes,” and “red flowers.” In these examples, the adjectives chocolate, blue, and red are qualitative data because they tell us something about the objects that cannot be quantified. 

Further reading: The differences between categorical and quantitative Data and examples of qualitative data

Characteristics of quantitative data 

Quantitative data is made up of numerical values has numerical properties, and can easily undergo math operations like addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using math techniques. 

Specific types of quantitative data

All quantitative data can be measured numerically, as shown above. But these data types can be broken down into more specific categories, too.

There are two types of quantitative data: discrete and continuous . Continuous data can be further divided into interval data and ratio data . 

Discrete data

In reference to quantitative data, discrete data is information that can only take certain fixed values. While discrete data doesn’t have to be represented by whole numbers, there are limitations to how it can be expressed. 

Examples of discrete data:

The number of players on a team

The number of employees at a company

The number of items eggs broken when you drop the carton

The number of outs a hitter makes in a baseball game

The number of right and wrong questions on a test

A website's bounce rate (percentages can be no less than 0 or greater than 100)

Discrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, as shown below.

Continuous data 

Continuous data , on the other hand, can take any value and varies over time. This type of data can be infinitely and meaningfully broken down into smaller and smaller parts. 

Examples of continuous data:

Website traffic

Water temperature

The time it takes to complete a task

Because continuous data changes over time, its insights are best expressed with a line graph or grouped into categories, as shown below.

Continuous data can be further broken down into two categories: interval data and ratio data. 

Interval data

Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. Interval data is always expressed in numbers where the distance between two points is standardized and equal. These numbers can also be called integers. 

Examples of interval data include temperature since it can move below and above 0.

Ratio data has all the properties of interval data, but unlike interval data, ratio data also has a true zero. For example, weight in grams is a type of ratio data because it is measured along a continuous scale with equal space between each value, and the scale starts at 0.0.

Other examples of ratio data are weight, length, height, and concentration. 

Interval data vs. ratio data

Ratio data gets its name because the ratio of two measurements can be interpreted meaningfully, whereas two measurements cannot be directly compared with intervals.

For example, something that weighs six pounds is twice as heavy as something that weighs three pounds. However, this rule does not apply to interval data, which has no zero value. An SAT score of 700, for instance, is not twice as good as an SAT score of 350, because the scale does not begin at zero.

Similarly, 40º is not twice as hot as 20º. Saying uses 0º as a reference point to compare the two temperatures, which is incorrect.

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When should I use quantitative or qualitative research? 

Quantitative and qualitative research can both yield valuable findings, but it’s important to choose which type of data to collect based on the nature and objectives of your research. 

When to use quantitative research

Quantitative research is likely most appropriate if the thing you are trying to study or measure can be counted and expressed in numbers. For example, quantitative methods are used to calculate a city’s demographics—how many people live there, their ages, their ethnicities, their incomes, and so on. 

When to use qualitative research

Qualitative data is defined as non-numerical data such as language, text, video, audio recordings, and photographs. This data can be collected through qualitative methods and research such as interviews, survey questions, observations, focus groups, or diary accounts. 

Conducting qualitative research involves collecting, analyzing, and interpreting qualitative non-numerical data (like color, flavor, or some other describable aspect). Methods of qualitative analysis include thematic analysis, coding, and content analysis.

If the thing you want to understand is subjective or measured along a scale, you will need to conduct qualitative research and qualitative analysis.

To use our city example from above, determining why a city's population is happy or unhappy—something you would need to ask them to describe—requires qualitative data. 

In short: The goal of qualitative research is to understand how individuals perceive their own social realities. It's commonly used in fields like psychology, social sciences and sociology, educational research, anthropology, political science, and more. 

In some instances, like when trying to understand why users are abandoning your website, it’s helpful to assess both quantitative and qualitative data . Understanding what users are doing on your website—as well as why they’re doing it (or how they feel when they’re doing it)—gives you the information you need to make your website’s experience better. 

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What are the pros and cons of quantitative data? 

Quantitative data is most helpful when trying to understand something that can be counted and expressed in numbers. 

Pros of quantitative data: 

Quantitative data is less susceptible to selection bias than qualitative data.

It can be tested and checked, and anyone can replicate both an experiment and its results.

Quantitative data is relatively quick and easy to collect. 

Cons of quantitative data: 

Quantitative data typically lacks context. In other words, it tells you what something is but not why it is.

Conclusions drawn from quantitative research are only applicable to the particular case studied, and any generalized conclusions are only hypotheses.

How do you collect quantitative data? 

There are many ways to collect quantitative data , with common methods including surveys and questionnaires. These can generate both quantitative data and qualitative data, depending on the questions asked. 

Once the data is collected and analyzed, it can be used to examine patterns, make predictions about the future, and draw inferences. 

For example, a survey of 100 consumers about where they plan to shop during the holidays might show that 45 of them plan to shop online, while the other 55 plan to shop in stores. 

Questionnaires and surveys 

Surveys and questionnaires are commonly used in quantitative research and qualitative research because they are both effective and relatively easy to create and distribute. With a wide array of simple-to-use tools, conducting surveys online is a quick and convenient research method. 

These research types are useful for gathering in-depth feedback from users and customers, particularly for finding out how people feel about a certain product, service, or experience. For example, many e-commerce companies send post-purchase surveys to find out how a customer felt about the transaction — and if any areas could be improved. 

Another common way to collect quantitative data is through a consumer survey, which retailers and other businesses can use to get customer feedback, understand intent, and predict shopper behavior . 

Open-source online datasets 

There are many public datasets online that are free to access and analyze. In some instances, rather than conducting original research through the methods mentioned above, researchers analyze and interpret this previously collected data in the way that suits their own research project. Examples of public datasets include: 

The Bureau of Labor Statistics Data

The Census Bureau Data

World Bank Open Data

The CIA World Factbook  

Experiments

An experiment is another common method that usually involves a  control group  and an  experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

Controlled experiments,  A/B tests ,  blind experiments , and many others fall under this category.

With large data pools, a survey of each individual person or data point may be infeasible. In this instance, sampling is used to conduct quantitative research. Sampling is the process of selecting a representative sample of data , which can save time and resources. There are two types of sampling : random sampling (also known as probability sampling) and non-random sampling (also known as non-probability sampling). 

Probability sampling allows for the randomization of the sample selection, meaning that each sample has the same probability of being selected for survey as any other sample. 

In non-random sampling, each sample unit does not have the same probability of being included in the sample. This type of sampling relies on factors other than random chance to select sample units, such as the researcher’s own subjective judgment. Non-random sampling is most commonly used in qualitative research. 

Typically, data analysts and data scientists use a variety of special tools to gather and analyze quantitative data from different sources. 

For example, many web analysts and marketing professionals use Google Analytics (pictured below) to gather data about their website’s traffic and performance. This tool can reveal how many visitors come to your site in a day or week, the length of an average session, where traffic comes from, and more. In this example, the goal of this quantitative analysis is to understand and optimize your site’s performance. 

Google Analytics is just one example of the many quantitative analytics tools available for different research professionals. 

Other quantitative data tools include…

Microsoft Excel

Microsoft Power BI

Apache Spark

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Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

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Frequently asked questions about quantitative data

Is quantitative data objective.

Quantitative researchers do everything they can to ensure data’s objectivity by eliminating bias in the collection and analysis process. However, there are factors that can cause quantitative data to be biased.

For example, selection bias can occur when certain individuals are more likely to be selected for study than others. Other types of bias include reporting bias , attrition bias , recall bias , observer bias , and others. 

Who uses quantitative data?

Quantitative research is used in many fields of study, including psychology, digital experience intelligence , economics, demography, marketing, political science, sociology, epidemiology, gender studies, health, and human development. Quantitative research is used less commonly in fields such as history and anthropology. 

Many people who are seeking advanced degrees in a scientific field use quantitative research as part of their studies.

What is quantitative data in statistics?

Statistics is a branch of mathematics that is commonly used in quantitative research. To conduct quantitative research with statistical methods, a researcher would collect data based on a hypothesis, and then that data is manipulated and studied as part of hypothesis testing, proving the accuracy or reliability of the hypothesis.

Is quantitative data better than qualitative data?

It depends on the researcher’s goal. If the researcher wants to measure something—for example, to understand “how many” or “how often,”—quantitative data is appropriate. However, if a researcher wants to learn the reason behind something—to understand “why” something is—qualitative research methods will better answer these questions.

Further reading: Qualitative vs. quantitative data — what's the difference?

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Qualitative and quantitative data differ on what they emphasize—qualitative focuses on meaning, and quantitative emphasizes statistical analysis.

Categorical & quantitative variables both provide vital info about a data set. But each is important for different reasons and has its own pros/cons.

Quantitative data is used for calculations or obtaining numerical results. Learn about the different types of quantitative data uses cases and more.

Discover how just-in-time data, explained by Lane Greer, enhances customer insights and decision-making beyond real-time analytics.

Jordan Morrow shares how AI-driven decision-making can revolutionize your business by harnessing data and enhancing your decision-making processes.

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• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

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

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

All research questions should be:

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

Table of contents

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

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

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

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

Using your research problem to develop your research question

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

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

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

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

Good sub-questions should be:

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

Here are a few examples of descriptive and framing questions:

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

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

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

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

Methodology

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

 Statistics

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

Research bias

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

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

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

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

This involves:

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

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

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

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

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

• Researchability
• Feasibility and specificity
• Relevance and originality

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7 Kinesiology Research Topics for a Dissertation

Kinesiology, the scientific study of human movement and physical performance, offers a broad array of research topics for dissertations. The field is evolving rapidly, with new findings and developments in exercise science, making it a rich ground for scholarly exploration. In this article, we’ll explore seven emerging kinesiology research topics, discuss how to formulate a dissertation topic, and outline what makes for a successful dissertation or research paper.

Research Topics for Kinesiology PhD Students

In a PhD program, students are expected to craft a unique dissertation that contributes to the field’s body of knowledge. Below are some detailed research topics for kinesiology PhD students, starting with biomechanics of injury prevention in sports.

1. Biomechanics of Injury Prevention in Sports

Injury prevention is a critical area in kinesiology. With the rise of high-intensity sports and physical exercise training programs, understanding the biomechanics behind injuries is crucial. Research can focus on identifying risk factors, developing new techniques or equipment to reduce injuries, or analyzing movement patterns that predispose athletes to specific injuries.

Formulation Tip: Narrow your focus to a particular sport or type of injury. For instance, “Biomechanical Analysis of ACL Injury Prevention in Female Soccer Players.”

2. Neuromuscular Adaptations to Strength Training

Neuromuscular adaptations are essential for improving athletic performance. This topic investigates how muscles and nerves adapt to different training regimens. Research can explore the differences in adaptations based on age, gender, or the type of strength and resistance training performed.

Formulation Tip: Consider a specific population or training method. For example, “Neuromuscular Adaptations in Elderly Adults Engaged in High-Intensity Interval Training.”

3. The Impact of Wearable Technology on Physical Activity and Health

Wearable technology, such as fitness trackers and smartwatches, has become ubiquitous in sport science. Research in this area can assess the accuracy of these devices, their impact on physical activity levels, and their potential to improve health outcomes.

Formulation Tip: Focus on a particular demographic or health outcome. For instance, “The Effectiveness of Wearable Fitness Trackers in Promoting Physical Activity Among Sedentary Office Workers.”

4. Psychological Factors in Athletic Performance

Exercise psychology plays a significant role in athletic performance. This research topic can delve into how mental health factors such as motivation, stress, and anxiety influence performance. It can also explore psychological interventions to enhance performance.

Formulation Tip: Choose a specific psychological factor and sport. For example, “The Role of Mental Toughness in Endurance Sports Performance.”

5. Exercise Physiology and Chronic Disease Management

Exercise physiology’s role in managing chronic diseases such as diabetes, cardiovascular disease, and obesity is a growing research area. Studies can take an in-depth look at how different types of exercise affect disease progression and management.

Formulation Tip: Target a specific chronic disease and exercise type. For instance, “The Impact of Aerobic Exercise on Blood Glucose Control in Type 2 Diabetes Patients.”

6. Gender Differences in Sports Performance

Gender differences in sports performance remain a contentious and highly researched topic. This area can explore physiological, biomechanical, and psychological differences between male and female athletes and how these differences influence training and performance.

Formulation Tip: Focus on a specific aspect of performance. For example, “Gender Differences in Recovery Rates Following High-Intensity Interval Training.”

7. The Role of Nutrition in Athletic Performance

Nutrition is fundamental to athletic performance. Research can investigate the effects and health benefits of various diets, sports supplements, and hydration strategies on performance and recovery. This area is particularly relevant given the proliferation of new dietary trends and supplements.

Formulation Tip: Focus on a specific nutrient or dietary approach. For instance, “The Effects of Ketogenic Diet on Endurance Performance in Long-Distance Runners.”

Formulating Your Dissertation Topic

When formulating your dissertation topic, consider the following steps during the research process:

• Identify Your Research Interests: Choose a topic that genuinely interests you, as you will be dedicating a significant amount of time and effort to this research.
• Conduct an Existing Literature Review: This helps you understand the current state of research and identify gaps that your dissertation can fill.
• Define Your Research Question: Your research question should be specific, measurable, achievable, relevant, and time-bound (SMART).
• Consult with Advisors: Seek input from your academic advisors and mentors to refine your potential kinesiology research paper topics to ensure their feasibility.
• Consider the Scope: Ensure that your topic is neither too broad nor too narrow. It should be manageable within the time frame and resources available.

What Makes for a Successful Dissertation?

A successful dissertation in kinesiology should possess the following qualities:

• Originality: Your research should contribute new knowledge or insights to the field.
• Relevance: The topic should address a significant issue or gap in current research.
• Methodological Rigor: Use appropriate and robust research methods to ensure the validity and reliability of your findings.
• Clarity: Your writing should be clear, concise, and well-organized. Each section should logically flow into the next.
• Comprehensive Literature Review: Demonstrate a thorough understanding of the existing research and how your work fits into the broader context.
• Strong Data Analysis: Employ rigorous data analysis techniques and clearly present your findings.
• Implications and Recommendations: Discuss the implications of your findings for practice, policy, and future research. Provide practical recommendations based on your results.

Contribute to the Field with CSP Global’s Kinesiology Program 

Choosing a dissertation topic in kinesiology can be challenging but immensely rewarding. By focusing on emerging and relevant research areas, you can contribute valuable insights to the field. Whether it’s the biomechanics of injury prevention or the psychological factors influencing athletic performance, your research can have a significant impact. Remember to formulate your topic carefully, ensure methodological rigor, and contribute original knowledge to make your dissertation a success.

CSP Global offers 100% online doctoral programs in kinesiology. Earn your PhD or EdD in our student-centered online programs. CSP Global’s kinesiology doctoral programs are taught by world-class instructors and provide students with opportunities to gain real-world experience in the field of health sciences. Students can expect to learn deep research methods, complete a research dissertation, and graduate ready to advance their careers in kinesiology.

Level up by earning your doctorate from CSP Global. If you’re interested in learning more about earning a Doctorate in Kinesiology please contact us .

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______________ research methods allow researchers to investigate topics and research questions that would be impossible with __________________ approaches.

Qualitative research methods allow researchers to investigate topics and research questions that would be impossible with quantitative approaches.

Qualitative research is an exploratory approach that focuses on understanding and interpreting human behavior, experiences, and attitudes. This type of research is open-ended, flexible, and allows for a more in-depth exploration of complex phenomena that cannot be easily quantified. Qualitative research methods include techniques such as interviews, observations, focus groups, and case studies. These methods provide rich, detailed data that can help researchers develop a deeper understanding of the social, cultural, and psychological factors that influence human behavior. In contrast, quantitative research methods use numerical data and statistical analysis to answer research questions. While quantitative research provides precise measurements and generalizable findings, it can be limited in its ability to capture the complexity and nuances of human experiences. In summary, qualitative research methods offer a valuable alternative to quantitative approaches, allowing researchers to investigate topics and research questions that would be impossible to explore with numerical data alone.

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Related Questions

Using the material provided in these Georgia Perimeter College class notes, what is the approximate temperature and depth of formation of most slate

Slate is formed under moderate temperatures and pressures, typically in the range of 200 to 400 degrees Celsius (392 to 752 degrees Fahrenheit) and at depths of a few kilometers (typically 1 to 10 kilometers).

These conditions are associated with regional metamorphism , which occurs over large areas due to tectonic forces and the burial of sedimentary rocks . During the metamorphic process, the original clay minerals in shale or mudstone recrystallize and align, forming fine-grained foliation that gives slate its characteristic texture and cleavage. Slate is known for its ability to split into thin, flat sheets, making it a popular material for roofing tiles and other construction purposes.

It's important to note that the specific temperature and depth of slate formation can vary depending on the local geologic context and the original composition of the parent rock.

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A circular cylinder is to be inscribed in a sphere of radius 12. Find the height of thatinscribed cylinder whose volume is as large as possible. Justify your choice.

The height of the inscribed cylinder whose volume is as large as possible is 24 units.To find the height of the inscribed cylinder with the largest volume, we can use the following approach. Let's consider a cross-section of the cylinder and the sphere. The diameter of the sphere is equal to the height of the cylinder, denoted as 2h. The radius of the sphere is 12 units. By drawing a line from the center of the sphere to one end of the cylinder's base, we can form a right-angled triangle. The hypotenuse of this triangle is the radius of the sphere (12 units), and one of the legs is the radius of the cylinder (r). The other leg is half of the cylinder's height, h. Applying the Pythagorean theorem, we have: (12)^2 = (r)^2 + (h/2)^2. To maximize the volume of the cylinder, we need to maximize the radius (r). The maximum value for r occurs when r = 12, which means the cylinder's base is tangent to the sphere. Substituting r = 12 into the equation above, we can solve for h: (12)^2 = (12)^2 + (h/2)^2, 0 = (h/2)^2. The equation tells us that h/2 = 0, which implies h = 0. Therefore, the height of the inscribed cylinder with the largest volume is 24 units. The height of the inscribed cylinder with the largest volume is 24 units. This occurs when the cylinder is tangent to the sphere at its base.

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FILL IN THE BLANK. The __________ model of mental illness holds that abnormal behavior is caused by physiological malfunction that is often attributable to hereditary factors.

The biomedical model of mental illness holds that abnormal behavior is caused by a physiological malfunction that is often attributable to hereditary factors

The biomedical model of mental illness holds that abnormal behavior is caused by a physiological malfunction that is often attributable to hereditary factors. This model suggests that mental illnesses are similar to physical illnesses, with an underlying biological cause that can be identified and treated. According to the biomedical model, mental disorders result from issues such as genetic predisposition, chemical imbalances in the brain, or structural abnormalities.

The biomedical model has been influential in shaping how society views mental illness and has led to significant advancements in treatment, including the development of psychotropic medications. This model has also encouraged research into the genetic and neurobiological underpinnings of mental disorders. However, critics argue that the biomedical model may be too reductionist, as it does not consider other factors that can contribute to mental illness, such as psychological, social, and environmental influences. Despite these criticisms, the biomedical model remains an essential framework for understanding the complex nature of mental illness and its potential causes.

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The common characteristic possessed by all assets is.

The common characteristic possessed by all assets is economic value or the ability to provide future economic benefits.

Assets are resources or items of value that are owned or controlled by an individual, organization, or entity. While assets can take various forms, such as cash, property, investments , or intellectual property, they all share a common characteristic: economic value.

Economic value refers to the potential of an asset to generate future economic benefits. Assets have the ability to generate income, be sold, or provide value through their use or appreciation over time. They contribute to the overall wealth and financial position of an individual or organization.

For example, cash is an asset because it has value and can be used to purchase goods and services or make investments. Similarly, a property or real estate asset has economic value as it can be rented or sold, generating income or capital gains.

Whether tangible or intangible, assets are recognized as valuable resources that can contribute to wealth creation, financial stability, and economic growth. They are essential for individuals, businesses, and economies to thrive and achieve their goals.

The common characteristic possessed by all assets is their economic value or the ability to provide future economic benefits. Assets are valuable resources that can generate income, be sold, or provide value through their use or appreciation over time. Recognizing and effectively managing assets is crucial for individuals and organizations to build wealth, achieve financial goals, and contribute to overall economic prosperity.

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Policymakers who control monetary and fiscal policy and want to offset the effects on output of an economic contraction caused by a shift in aggregate supply could use policy to shift Group of answer choices aggregate demand to the left. aggregate supply to the right. aggregate demand to the right. aggregate supply to the left.

Aggregate supply is the total amount of goods and services that firms are willing and able to produce in an economy.

Policymakers who want to offset the effects of an economic contraction caused by a shift in aggregate supply can use policy to shift aggregate demand to the right. By implementing expansionary monetary and fiscal policies, such as lowering interest rates, increasing government spending, or reducing taxes, policymakers aim to stimulate consumer and business spending, thereby increasing aggregate demand in the economy.

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A 150 g copper bowl contains 220 g of water, both at 20.0 C.A very hot 300 g copper cylinder is dropped into the water, causing the water to boil, with 5.00 g being converted to steam. The final temperature of the system is 100 C. Neglect energy transfers with the environment. (a) How much energy (in calories) is transferred to the water as heat

The amount of energy (in calories) is transferred to the water as heat is given as 20295 calories.

Because heat is limited to the transfer of energy, it is incorrect to refer to a body's heat. Heat is not the same as energy stored in a body, nor is work, since work is also energy in transit. However, the terms "sensible" and " latent " heat are commonly used. The idle intensity, likewise called the intensity of vaporization, is how much energy important to change a fluid to a fume at steady temperature and tension .

The energy expected to liquefy a strong to a fluid is known as the intensity of combination , and the intensity of sublimation is the energy important to change a strong straightforwardly to a fume, these progressions likewise occurring under states of steady temperature and tension.

The amount of energy transferred as heat when the water undergoes a phase change is given by

Substitute the givens and the heat of evaporation

Q1 =5 (g) · 539 (cal/g)

The heat transferred to the water because of the temperature change is

Substitute the givens

Q2 = 220 (g) ·1 (cal/g.K) · (100 °C – 20 °C) =

= 17600 cal

The total heat transferred to the water

Qw = Q1 + Q2

= 1795 cal + 17600 cal

= 20295 cal.

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according to tamir and nadler (2007), field-dependent people . a. have strong social skills b. tend to be highly creative c. are skillful at analyzing complex situations d. make better progress when learning a second language

According to Tamir and Nadler (2007), field-dependent people have strong social skills . Therefore, the correct option is A.

The primary reason behind this is that field-dependent people prefer working with others rather than being alone. Thus, they tend to develop strong social skills over time. Field-dependent people are those who rely on sensory information obtained from the environment around them, particularly social cues, to perceive and make meaning of the world. They are sensitive to social influences and tend to base their decision-making processes on external cues and feedback.

In contrast, field-independent people rely more on their own internal frameworks and have less concern for external feedback and social cues. They are more individualistic in nature. Field-dependent people tend to have strong social skills because they tend to work well with others. They prefer working with others rather than working alone. They are more cooperative and participative in group activities.

Since they rely more on external cues and feedback, they tend to be more sensitive to the needs and feelings of others. They are more attuned to the social dynamics of a group and can navigate complex social situations more effectively than field-independent people.

In conclusion, according to Tamir and Nadler (2007), field-dependent people have strong social skills because they prefer working with others rather than being alone. They are more sensitive to social cues and feedback and can navigate complex social situations more effectively. Hence, the correct answer is option A.

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How many ways are there to assign 5 tasks to 7 employees if each employee can be given more than one task

The total number of ways to assign 5 tasks to 7 employees is 16,807

There are several ways to approach this problem, but one common method is to use the concept of combinations with repetitions . First, we can think of each task as a slot that can be filled by any of the 7 employees. There are 7 choices for the first task, 7 choices for the second task, and so on, for a total of 7 choices for each of the 5 tasks. To find the total number of possible assignments, we can use the multiplication principle and multiply the number of choices for each task together: 7 x 7 x 7 x 7 x 7 = 7⁵ However, this counts each assignment multiple times, since the order in which the tasks are assigned doesn't matter. For example, assigning tasks A, B, C, D, E to employees 1, 2, 3, 4, 5 is the same as assigning them to employees 5, 3, 2, 4, 1 (just in a different order). To account for this overcounting, we need to divide by the number of ways to arrange 5 tasks. This is simply 5!, or 120, since there are 5 choices for the first task, 4 choices for the second task, and so on. Therefore, the total number of ways to assign 5 tasks to 7 employees, where each employee can be given more than one task, is: 7^5 / 5! = 16,807

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Customer discrimination occurs when Group of answer choices customers prefer to spend money at stores where they interact with a certain type of worker. workers don't have direct contact with customers. workers discriminate against customers, by providing better service to certain types of customers. employers have a distaste for hiring a certain type of worker.

Customer discrimination occurs when customers prefer to spend money at stores where they interact with a certain type of worker. It involves customer preferences based on the characteristics or attributes of the workers they interact with.

Customer discrimination refers to situations where customers exhibit preferences or biases based on the characteristics of the workers they interact with. This discrimination can be based on factors such as race, gender, age, appearance , or other personal attributes. Customers may consciously or unconsciously prefer to spend money at stores or establishments where they perceive workers with certain characteristics to be more desirable or trustworthy.

For example, customers may choose to shop at stores where they believe they will receive better service from workers who match their own demographic profile or have similar cultural backgrounds. This can result in unequal treatment of workers based on characteristics unrelated to their job performance or qualifications.

Customer discrimination can have significant implications for employment opportunities and can perpetuate inequalities in the labor market. It may lead to workers from certain demographic groups facing barriers in terms of hiring, promotions, and earning potential. Employers may also be influenced by customer preferences and exhibit their own biases when making hiring decisions.

Addressing customer discrimination requires efforts to promote inclusivity, diversity, and equal treatment in the marketplace . Businesses can work towards creating a culture of fairness and non-discrimination by implementing policies that promote equal opportunity and by fostering an inclusive and respectful environment for both workers and customers.

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justices who practice ____________ tend to view the constitution as a document that was intended to be flexible so that it could evolve along with changing social and political conditions.

Justices who practice judicial activism tend to view the Constitution as a document that was intended to be flexible so that it could evolve along with changing social and political conditions.

Judicial activism refers to an approach where judges interpret the Constitution in a broad and dynamic manner, actively shaping legal precedents and making decisions that align with their own values and social goals. Proponents of judicial activism believe that the Constitution is a living document that should adapt to the needs of contemporary society, allowing for progressive changes and interpretations that reflect the evolving values and circumstances of the times.

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Ethical decisions in an organization are influenced by individuals' moral standards, the influence of managers and coworkers, and Group of answer choices informal ethical policies or rules individual religious values. ethical values of clients. family influence. the opportunity to engage in misconduct.

A culture that promotes ethical behavior, provides clear guidelines and policies, encourages open communication, and supports individuals in making ethical decisions.

Ethical decisions in an organization can be influenced by various factors, including:

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who or what typically owns airports in other countries than the usa?

In most countries outside the USA, airports are typically owned by either the government or a combination of public and private entities. Government ownership can be at the national, regional, or local level, depending on the country and airport size. In some cases, airports are owned and operated by state-owned enterprises or authorities, which manage airport operations on behalf of the government. Public-private partnerships (PPPs) are also common in airport ownership , where private companies invest in and manage airport infrastructure alongside government entities. These partnerships often aim to improve efficiency and provide additional funding for airport development projects. In some instances, airports are completely privatized, with private companies owning and operating them. This model is less common but can be found in countries like the United Kingdom, where some major airports are owned by private corporations. Overall, airport ownership varies globally based on the country's governance structure , economic model , and local regulations. While the USA predominantly has municipal or regional government ownership, other countries may opt for national government ownership , public-private partnerships , or privatization to best meet their airport infrastructure and management needs.

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harry's roommate is very concerned about his behavior. there are times when harry uses a foreign accent, is aggressive, and says his name is eddie. when questioned, harry acts like he doesn't know what his roommate is talking about and denies any change in his behavior. which of the following could explain harry's behavior?

Harry's behavior can be potentially explained by a psychological condition called Dissociative Identity Disorder (DID).

DID is characterized by the presence of two or more distinct personality states, which may have different names, accents, and behaviors. In this case, Harry seems to be alternating between his regular personality and an aggressive personality named Eddie with a foreign accent. DID usually stems from a history of trauma or extreme stress, which causes the individual to develop alternate personalities as a coping mechanism. When Harry is confronted about his behavior, he may genuinely not remember acting as Eddie, as each personality state can have its own memory, feelings, and sense of identity. This is known as amnesia, which is a common symptom in DID. It is crucial for Harry's roommate to approach the situation with empathy and support. Encouraging Harry to seek professional help from a mental health professional, such as a psychologist or psychiatrist, would be an appropriate step. Treatment options for DID include psychotherapy , which focuses on integrating the different personality states into a unified sense of self and improving overall functioning. Remember that mental health concerns should always be taken seriously and addressed with care and understanding.

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juveniles tend to commit crimes _________ a. on the weekends b. in groups c. at night d. alone

Juveniles tend to commit crimes in groups (option b).

Research and studies on juvenile delinquency have consistently shown that juveniles are more likely to engage in criminal activities when they are in groups. This tendency to commit crimes in groups can be influenced by various factors, including peer pressure, a desire for social acceptance, and the influence of delinquent peers .

Group dynamics play a significant role in shaping the behavior of juveniles. When individuals come together in a group, they may experience a diffusion of responsibility, reduced fear of consequences, and a heightened sense of anonymity. These factors can contribute to increased risk-taking and engagement in criminal behavior .

Committing crimes in groups can also provide juveniles with a sense of social support, reinforcement, and validation from their peers. It can create a shared identity or sense of belonging within the delinquent group , further encouraging involvement in criminal activities.

While it is important to note that not all juvenile crimes occur exclusively in groups, the research suggests that the influence of group dynamics is a significant contributing factor to juvenile delinquency.

Therefore, out of the options provided, juveniles tend to commit crimes in groups (option b).

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The nurse assesses the bp in both arms of a newly admitted patient. Why would the nurse do this?.

The nurse assesses the blood pressure (BP) in both arms of a newly admitted patient to determine if there is any significant difference in readings between the two arms.

Measuring the BP in both arms allows the nurse to identify any discrepancies that could indicate underlying vascular abnormalities or conditions such as arterial stenosis, aortic dissection, or coarctation of the aorta. The normal systolic BP difference between the two arms is usually less than 10 mmHg, with the left arm often having slightly higher readings. However, a significant difference of 15 mmHg or more between arms suggests a potential vascular problem.

To assess the BP, the nurse uses a sphygmomanometer and a stethoscope or an automated blood pressure monitor. The process involves wrapping the cuff around the upper arm, inflating it to temporarily occlude the brachial artery, and slowly releasing the pressure while listening for Korotkoff sounds with the stethoscope or relying on the digital display of the automated monitor. The nurse performs this procedure on both arms to compare the readings.

Assessing the BP in both arms is a vital component of the initial assessment for a newly admitted patient. Identifying a significant difference in BP between the arms prompts further investigation and evaluation by the healthcare team. Early detection of vascular abnormalities or conditions can lead to timely interventions, improving patient outcomes and preventing potential complications.

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according to the configural-cue approach, overshadowing occurs because ___

According to the configural-cue approach, overshadowing occurs because the more salient cue or feature in a compound stimulus becomes the dominant predictor of the outcome.

In other words, when two cues are presented together, one may be more noticeable or attention-grabbing than the other, leading to an overreliance on that cue for predicting the outcome. This can result in a reduced ability to learn the predictive value of the overshadowed cue . For example, if a rat is trained to associate a light and a tone with a shock, but the light is brighter and more noticeable than the tone, the rat may learn to associate the shock more strongly with the light than with the tone. This overshadowing effect can occur even if the tone is a more reliable predictor of the shock. Overall, the configural-cue approach emphasizes the importance of the overall pattern of cues and features in determining the strength of learning, rather than simply looking at individual cues in isolation.

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why do i have to try so hard to get good grades while my friends don't even have to try and get good grades

Your friends might be able to hold and remember more information in their brain, but there is nothing wrong if you can’t remember things as well as they can. I know that I am the same way as you and to help me better prepare for tests I just study.

Stock Splits and Stock Dividends [LO3] Simmons Mineral Operations, Inc. (SMO currently has 530,000 shares of stock outstanding that sell for $68 per share. Assuming no market imperfections or tax effects exist, what will the share price be after: a. SMO has a five-for-three stock split

After a five-for-three stock split, the share price of Simmons Mineral Operations, Inc. (SMO) can be calculated by dividing the previous share price by the split ratio, which in this case is 5/3.

A stock split is a corporate action that increases the number of shares outstanding while proportionally reducing the share price. In the case of a five-for-three stock split, for every three shares held, shareholders will receive an additional five shares. To calculate the post-split share price, we divide the previous share price by the split ratio. In this scenario, the previous share price is $68 and the split ratio is 5/3. Therefore, the post-split share price can be calculated as follows:

Post-split share price = Previous share price / Split ratio

Post-split share price = $68 / (5/3)

Post-split share price = $68 * (3/5)

Post-split share price = $40.80

Thus, after the five-for-three stock split, the share price of SMO will be $40.80. It's important to note that stock splits do not change the overall value of an investor's holdings, as the number of shares owned increases proportionally to the decrease in share price.

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A company has beginning inventory of 18 units at a cost of $18 each on February 1. On February 3, it purchases 28 units at $20 each. 23 units are sold on February 5. Using the FIFO periodic inventory method, what is the cost of the 23 units that are sold

The FIFO ( First-In, First-Out ) periodic inventory method assumes that the first units purchased are the first ones sold. To calculate the cost of the 23 units sold, we need to determine the cost of the earliest units available in inventory.

Given the information provided:

Beginning inventory on February 1: 18 units at $18 each

Purchase on February 3: 28 units at $20 each

To determine the cost of the 23 units sold, we need to consider the earliest units available in inventory. In this case, we will consider the 18 units from the beginning inventory and 5 units from the February 3 purchase, totaling 23 units.

Cost of 18 units from beginning inventory: 18 units * $18/unit = $324

Cost of 5 units from February 3 purchase: 5 units * $20/unit = $100

Therefore, the cost of the 23 units that were sold using the FIFO periodic inventory method is $324 + $100 = $424.

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a ____ is any entire collection of people, animals, plants or things from which we may collect data. it is the entire group we are interested in, which we wish to describe or draw conclusions about.

A population is any entire collection of people, animals, plants, or things from which we may collect data. It is the entire group we are interested in, and we wish to describe or draw conclusions about it.

In research, understanding the population is crucial as it helps to accurately represent the characteristics of the group under study. To gather information about a population, we may conduct a survey or use sampling methods.

Sampling involves selecting a smaller subset, called a sample, from the population to make inferences about the larger group. This way, researchers can draw conclusions about the entire population based on the findings from the sample.

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A professor is delivering a lecture and some students begin to whisper back and forth, the professor stops the lecture, remains silent, and stares at the students. The students violated a ________________.

The students violated a code of conduct or established classroom etiquette by whispering during the lecture, leading the professor to stop and address the issue.

The act of whispering back and forth during a lecture disrupts the learning environment and shows a lack of respect for the professor and fellow students. In an academic setting, there are generally established rules or codes of conduct that students are expected to adhere to, including maintaining a respectful and focused atmosphere during lectures. By engaging in whispered conversations, the students not only distract themselves but also disrupt the concentration of others in the class. The professor's decision to stop the lecture, remain silent, and stare at the students is a non-verbal way of reprimanding them and drawing attention to their disruptive behavior. This action serves as a reminder to the students that their conduct is not acceptable and that they should maintain proper decorum during the lecture.

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black entertainment television (bet) is an example of what concept? group of answer choices highbrow culture racist aesthetic narrowcasting cultural appropriation

Black Entertainment Television (BET) is an example of the concept of narrowcasting

Narrowcasting refers to the practice of targeting specific audiences or demographics with specialized content or programming.

Black entertainment television (bet) specifically caters to African American viewers and focuses on providing entertainment and programming that reflects their culture and interests. by targeting a specific demographic, BET aims to appeal to a particular audience and meet their entertainment needs.

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an activator that turns the operon on by binding to DNA. a repressor that is inactive when it binds to lactose. a repressor that is active when it binds to tryptophan. a repressor that is active when it is alone.

The correct option is C, The expression of the tryptophan operon is controlled by a repressor that is active when it binds to tryptophan.

Tryptophan is an essential amino acid, meaning it cannot be produced by the human body and must be obtained through diet. It is crucial for various physiological functions, including protein synthesis and the production of neurotransmitters such as serotonin and melatonin. Tryptophan is found in many protein-rich foods, such as poultry, eggs, dairy products, and certain nuts and seeds.

Tryptophan plays a vital role in regulating mood, sleep, and appetite. It is often associated with the feeling of relaxation and drowsiness after consuming a Thanksgiving turkey meal, although the effect is usually attributed to a combination of factors rather than solely tryptophan. Supplementing with tryptophan has been explored as a potential treatment for conditions such as depression, anxiety, and insomnia.

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Complete Question:

The expression of the tryptophan operon is controlled by

A). an activator that turns the operon on by binding to DNA.

B). a repressor that is inactive when it binds to lactose.

C). a repressor that is active when it binds to tryptophan.

D). a repressor that is active when it is alone.

An activator is a protein that turns on the transcription of genes by binding to DNA. An operon is a group of structural genes that are transcribed together under the control of a single promoter. The activation of the operon is carried out by the activator that binds to the DNA region upstream of the promoter. It enhances the binding of RNA polymerase and thereby facilitates the transcription of the operon. A repressor is a protein that turns off the transcription of genes by binding to DNA . It prevents the binding of RNA polymerase to the promoter region of the operon. In the absence of the inducer molecule, the repressor protein binds to the operator site and the operon remains inactive. There are different types of repressors that are activated by different molecules. The repressor that is inactive when it binds to lactose is the lac repressor. It is a regulatory protein that binds to the operator site of the lac operon in the absence of lactose. When lactose is present, it binds to the repressor protein and changes its shape, making it unable to bind to the operator. This releases the blockage and allows RNA polymerase to transcribe the operon. The repressor that is active when it binds to tryptophan is the trp repressor. It is a regulatory protein that binds to the operator site of the trp operon in the presence of tryptophan. When tryptophan is scarce, the repressor is unable to bind to the operator site, and the operon is transcribed. In the presence of tryptophan, the repressor binds to the operator and prevents transcription of the operon. The repressor that is active when it is alone is the arginine repressor . It is a regulatory protein that binds to the operator site of the arginine operon in the absence of arginine. In the presence of arginine, the repressor undergoes a conformational change and loses its ability to bind to the operator site, allowing the operon to be transcribed.

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true or false? according to the public interest model, government tries to enact laws, regulations, and policies that benefit the public.select one:truefalse

Explanation: According to the public interest model, government tries to enact laws, regulations, and policies that benefit the public - true

According to this model, the government must intervene in the marketplace to protect consumers, keep equal competition, improve public health and safety, and fix market failures. The aim is to find a balance between financial interests and the health of the community as a whole. The government's goal in establishing rules and policies is to promote public interest and increase the general well-being of its population.

How many games were played in the first world series?.

The first World Series was played in 1903. It was a best-of-nine series , meaning the first team to win five games would be declared the winner.

However, the series ended after only eight games. The Boston Americans (now the Red Sox) defeated the Pittsburgh Pirates five games to three to win the championship. The first World Series was played in 1903. It was a best-of-nine series, meaning the first team to win five games would be declared the winner. However, the series ended after only eight games. The Boston Americans (now the Red Sox) defeated the Pittsburgh Pirates five games to three to win the championship. Hence, 8 games were played in the first World Series.

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Tech A says that the natural angle of a V6 is 90 degrees. Tech B says that the natural angle of a V10 is 60 degrees. Who is correct

Among Tech A and Tech B, only Tech A is correct, as angle of a V6 engines are 90 degrees.

A V6 engine is said to have a natural angle of 90 degrees. On the other hand, a V10 engine's natural angle is approximately 72 degrees.

In the automotive industry, engines are categorized by the number of cylinders they possess. Most vehicles have a 4, 6, or 8-cylinder engine. It's worth mentioning that the higher the number of cylinders, the greater the engine's power output.

A V6 engine is a six-cylinder engine in which the cylinders are positioned in a V shape. The V6 engine is composed of two rows of three cylinders each, with each row slanted towards the other at a 60-degree angle. Because the angle between the cylinders is 60 degrees, the engine is naturally balanced. The V6 engine is often seen in midsize sedans and smaller SUVs due to its size, weight, and fuel efficiency . It is also used in high-performance vehicles, where it can produce up to 400 horsepower and more. Tech A is correct when he claims that the natural angle of a V6 engine is 90 degrees.

Tech B is incorrect when he claims that the natural angle of a V10 engine is 60 degrees. The natural angle of a V10 engine is roughly 72 degrees. The V10 engine is composed of two banks of five cylinders each, with each bank forming a 90-degree angle. Because the banks are 18 degrees apart, the natural angle of the V10 engine is around 72 degrees. The V10 engine is a high-performance engine that is commonly seen in race cars. The V10 engine is a powerful engine that is capable of producing up to 600 horsepower.

Therefore, Tech A is correct.

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Highlander Inc. uses state-of-the-art equipment to manufacture products for its customers. Each machine costs $1,520,000, plus an additional $48,000 a year to operate. The machines have a five-year life. What is the equivalent annual cost of one these machines if the cost of capital is 15.5 percent

The equivalent annual cost of one of these machines, considering a cost of capital of 15.5 percent, is approximately $444,919.

To calculate the equivalent annual cost, we need to consider the initial cost of the machine, the operating cost per year, the machine's life, and the cost of capital. The formula to calculate the equivalent annual cost is as follows:

Equivalent Annual Cost = Initial Cost + (Operating Cost per Year / (1 + Cost of Capital)^(Machine Life)).

Initial Cost = $1,520,000

Operating Cost per Year = $48,000

Machine Life = 5 years

Cost of Capital = 15.5% or 0.155 (as a decimal)

Plugging these values into the formula, we have:

Equivalent Annual Cost = $1,520,000 + ($48,000 / (1 + 0.155)^5)

Equivalent Annual Cost ≈ $444,919.

Therefore, the equivalent annual cost of one of these machines, considering the cost of capital, is approximately $444,919. This represents the annualized cost of owning and operating the machine over its five-year life.

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20% ofA common rule is that housing expenses plus other debt payments should not be more than 36% of your monthly income. Your monthly income is $4,300, and you have student loan payments of $321 per month. How much can you spend on housing

To determine how much you can spend on housing, we need to calculate 36% of your monthly income and subtract your monthly debt payments.

First, calculate 36% of your monthly income:

36% of $4,300 = 0.36 * $4,300 = $1,548

Next, subtract your monthly debt payments from the result:

$1,548 - $321 = $1,227

Therefore, you can spend up to $1,227 on housing while adhering to the common rule that housing expenses plus other debt payments should not exceed 36% of your monthly income.

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A new technology allows users to bypass Wi-Fi security and use any network they want. However, if too many people use the same Wi-Fi network, network speed will slow significantly. In this scenario: a. In this scenario, is Wi-Fi excludable? Explain. b. In this scenario, is Wi-Fi rival in consumption? Explain. c. Is Wi-Fi a public good? Explain.

a. In this scenario, Wi-Fi is excludable. b. In this scenario, Wi-Fi is rival in consumption. c. Wi-Fi, in this scenario, does not meet the definition of a public good.

a. In this scenario, Wi-Fi is excludable because the use of the network can be controlled or restricted by implementing security measures. The technology that allows users to bypass Wi-Fi security indicates that there are means to exclude certain individuals from accessing the network.

The network owner or administrator can utilize encryption, password protection, or other access control mechanisms to limit access to authorized users. By implementing these measures, they can prevent unauthorized individuals from using the network.

b. In this scenario, Wi-Fi is rival in consumption because the network speed is significantly impacted when too many people use the same network. As more users connect to the Wi-Fi network and consume bandwidth, the available capacity is shared among them.

This sharing of limited resources creates congestion, resulting in slower network speeds for all users. The decrease in speed indicates that the use of Wi-Fi by one individual affects the ability of others to use it simultaneously. Therefore, the consumption of Wi-Fi is rivalrous, as one user's consumption diminishes the available resources for others.

c. Wi-Fi, in this scenario, does not meet the definition of a public good . A public good is non-excludable, meaning that it is difficult or impossible to prevent individuals from accessing or using it.

Additionally, the rivalrous nature of Wi-Fi consumption indicates that it does not possess the characteristic of non-rivalry , another key aspect of public goods.

When too many users utilize the same network, the network speed slows down, suggesting that one person's use diminishes the ability of others to use it effectively. Therefore, Wi-Fi does not meet the criteria of being a public good in this scenario.

In summary, in this scenario, Wi-Fi is excludable due to the ability to control access through security measures. It is rival in consumption because network speed decreases as more users connect to the same network. Therefore, Wi-Fi does not qualify as a public good.

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Before a new product arrives in the store, a manufacturer goes through several stages including designing, building, testing a prototype, setting up a production line, and so on. This approach is called _____.

The approach of going through several stages including building, testing a prototype , setting up a production line, and more before a new product arrives in the store is known as the product development process .

The product development process involves a series of systematic steps that a manufacturer follows to bring a new product from concept to market. These steps typically include ideation , market research, design, prototyping, testing, production, and launch. Each stage has its specific goals and activities aimed at ensuring the product's quality, functionality, and market success. By following this approach, manufacturers can mitigate risks, refine their product ideas , and create a well-developed and market-ready product.

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Research on the spatiotemporal coupling characteristics between urban population and land in china based on the improved coupling model of polar coordinates.

1. Introduction

2. materials and methods, 2.1. research methods, 2.2. improved coupling model of polar coordinates, 2.3. data sources, 3.1. overall evolution characteristics, 3.1.1. scale characteristics, 3.1.2. coupling characteristics, 3.2. spatial pattern characteristics, 3.2.1. scale characteristics, 3.2.2. coupling characteristics, 4. discussion, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Zhang, L.; Zhu, D. The coordination degree between urban construction land scale expansion and urban population increase: Taking the Yangtze River economic belt as an example. China Dev. 2018 , 18 , 14–19. [ Google Scholar ]
• Deng, X.; Huang, J.; Rozelle, S.; Uchida, E. Growth, population and industrialization, and urban land expansion of China. J. Urban. Econ. 2008 , 63 , 96–115. [ Google Scholar ] [ CrossRef ]
• Long, H. Land use policy in China: Introduction. Land Use Policy 2014 , 40 , 1–5. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Fang, F.; Li, Y. Key issues of land use in China and implications for policy making. Land Use Policy 2014 , 40 , 6–12. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Fang, C. Growth of Urban Construction Land: Progress and Prospect. Progr. Geog. 2011 , 30 , 1440–1448. [ Google Scholar ]
• Su, M.; Guo, R.; Hong, W. Institutional transition and implementation path for cultivated land protection in highly urbanized regions: A case study of Shenzhen, China. Land Use Policy 2019 , 81 , 493–501. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Lin, J.; Yang, L. Thoughts on the management of annual land use plan under land and space planning. China Land 2022 , 41 , 28–30. [ Google Scholar ]
• Fang, L.; Tian, C. Construction land quotas as a tool for managing urban expansion. Landscape. Urban. Plan. 2020 , 195 , 103727. [ Google Scholar ] [ CrossRef ]
• Zhang, W. Discussion on a Policy of Linking Human to the Land what it means to the New Urbanization Construction. Nat. Resour. Econ. China 2013 , 26 , 17–20. [ Google Scholar ]
• Lu, D.; Chen, M. Several viewpoints on the background of compiling the “National New Urbanization Planning (2014–2020)”. Acta Geogr. Sin. 2015 , 70 , 179–185. [ Google Scholar ]
• Bierwagen, B.G.; Theobald, D.M.; Pyke, C.R.; Choate, A.; Groth, P.; Thomas, J.V.; Morefield, P. National housing and impervious surface scenarios for integrated climate impact assessments. Proc. Natl. Acad. Sci. USA 2010 , 107 , 20887–20892. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Frolking, S.; Milliman, T.; Seto, K.C.; Friedl, M.A. A global fingerprint of macro–scale changes in urban structure from 1999 to 2009. Environ. Res. Lett. 2013 , 8 , 24004. [ Google Scholar ] [ CrossRef ]
• Song, L.; Kong, X.; Cheng, P. Supply-demand matching assessment of the public service facilities in 15-minute community life circle based on residents’ behaviors. Cities 2024 , 144 , 104637. [ Google Scholar ] [ CrossRef ]
• Kong, W.; Xu, H.; Zhang, F. Study on the Decoupling between Urban and Rural Population and Construction Land Expansion in China. J. Huaiyin Teach. Coll. (Nat. Sci. Ed.) 2019 , 18 , 142–149. [ Google Scholar ]
• Zhang, L.; Shi, X. Study on the Coordinated Development of Urban and Rural Population and Construction Land in China. Urban Dev. Stud. 2019 , 26 , 30–36. [ Google Scholar ]
• Zeng, P.; Xu, J.; Chen, J. Research on construction land expansion and population performance of urban agglomerations in China. Stat. Decis. 2021 , 37 , 64–68. [ Google Scholar ]
• Zhou, Y.; Zhong, Z.; Cheng, G. Cultivated land loss and construction land expansion in China: Evidence from national land surveys in 1996, 2009 and 2019. Land Use Policy 2023 , 125 , 106496. [ Google Scholar ] [ CrossRef ]
• Ning, Q.; Ouyang, X.; Liu, S. Spatio-Temporal Evolution and Quality Analysis of Construction Land in Urban Agglomerations in Central China. Front. Ecol. Evol. 2022 , 10 , 912127. [ Google Scholar ] [ CrossRef ]
• Li, S.; Wang, X.; Ji, X.; Zhang, Y. Spatial change and influencing factors of population aging in Shandong Province at the township scale. Progr. Geog. 2019 , 38 , 567–576. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Luo, T.; Liu, Z.; Kong, X.; Li, J.; Tan, R. A comparative analysis of urban and rural construction land use change and driving forces: Implications for urban–rural coordination development in Wuhan, Central China. Habitat. Int. 2015 , 47 , 113–125. [ Google Scholar ] [ CrossRef ]
• van Vliet, J.; Verburg, P.H.; Grădinaru, S.R.; Hersperger, A.M. Beyond the urban-rural dichotomy: Towards a more nuanced analysis of changes in built-up land. Comput. Environ. Urban. 2019 , 74 , 41–49. [ Google Scholar ] [ CrossRef ]
• Moreno-Monroy, A.I.; Schiavina, M.; Veneri, P. Metropolitan areas in the world. Delineation and population trends. J. Urban. Econ. 2020 , 125 , 103242. [ Google Scholar ] [ CrossRef ]
• Ouyang, D.; Zhu, X.; Liu, X.; He, R.; Wan, Q. Spatial Differentiation and Driving Factor Analysis of Urban Construction Land Change in County-Level City of Guangxi, China. Land 2021 , 10 , 691. [ Google Scholar ] [ CrossRef ]
• Lei, K.; Li, M.; Yi, L. Comparing the driving mechanisms of different types of urban construction land expansion: A case study of the Beijing-Tianjin-Hebei region. J. Geogr. Sci. 2024 , 34 , 722–744. [ Google Scholar ]
• Wu, R.; Li, Z. The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis. Sci. Total. Environ. 2020 , 766 , 142591. [ Google Scholar ] [ CrossRef ]
• Fu, B.; Wang, S.; Shen, Y.; Cheng, C.; Li, Y.; Feng, X.; Liu, Y. Mechanisms of Human-natural System Coupling and Optimization of the Yellow River Basin. Fundam. Res. 2021 , 35 , 504–509. [ Google Scholar ]
• Yang, Y.; Wei, Q.; Li, M.; Liu, Y. Spatial Optimization Strategies of Population Function in China’s World-class Urban Agglomerations During 14th Five-Year Plan Period. Bull. Chin. Acad. Sci. 2020 , 35 , 835–843. [ Google Scholar ]
• Peng, W.; Liu, N. Optimization Strategy of Population Distribution in Capital Metropolitan Region: Based on Land Resources Carrying Capacity Evaluation. Sci. Geogr. Sin. 2015 , 35 , 558–564. [ Google Scholar ]
• Mahmoud, H. Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city. Egypt. J. Remote Sens. Space Sci. 2019 , 22 , 59–66. [ Google Scholar ] [ CrossRef ]
• Gwan, A.S.; Kimengsi, J.N. Urban Expansion and the Dynamics of Farmers’ Livelihoods: Evidence from Bamenda, Cameroon. Sustainability 2020 , 12 , 5788. [ Google Scholar ] [ CrossRef ]
• Han, C.; Lu, B.; Zheng, J. Analysis and Prediction of Land Resources’ Carrying Capacity in 31 Provinces of China from 2008 to 2016. Sustainability 2021 , 13 , 13383. [ Google Scholar ] [ CrossRef ]
• Sun, M.; Wang, J.; He, K. Analysis on the urban land resources carrying capacity during urbanization—A case study of Chinese YRD. Appl. Geogr. 2020 , 116 , 102170. [ Google Scholar ] [ CrossRef ]
• Luo, W.; Ren, Y.; Shen, L.; Zhu, M.; Jiang, Y.; Meng, C.; Zhang, P. An evolution perspective on the urban land carrying capacity in the urbanization era of China. Sci. Total. Environ. 2020 , 744 , 140827. [ Google Scholar ] [ CrossRef ]
• Shi, J.; Liu, M.; Duan, K. Spatiotemporal evolution of coupling coordination in population-land relationship and its influencing factors: A case study of 26 main cities in Yangtze River Delta, China. Phys. Chem. Earth 2022 , 128 , 103247. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Kong, X.; Zhu, Z. Multiscale analysis of the correlation patterns between the urban population and construction land in China. Sustain. Cities. Soc. 2020 , 61 , 102326. [ Google Scholar ] [ CrossRef ]
• Lv, M.; Chen, Z.; Yao, L.; Dang, X.; Li, P.; Cao, X. Potential Zoning of Construction Land Consolidation in the Loess Plateau Based on the Evolution of Human–Land Relationship. Int. J. Environ. Res. Public Health 2022 , 19 , 14927. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shao, J.; Kong, X. Sustainable urban expansion and human development in China: An analysis using an environmentally improved human development index. Sustain. Dev. 2024 . [ Google Scholar ] [ CrossRef ]
• Zhu, C.; Zhang, X.; Wang, K.; Yuan, S.; Yang, L.; Skitmore, M. Urban–rural construction land transition and its coupling relationship with population flow in China’s urban agglomeration region. Cities 2020 , 101 , 102701. [ Google Scholar ] [ CrossRef ]
• Cai, E.; Zhang, S.; Chen, W.; Li, L. Spatio–temporal dynamics and human–land synergistic relationship of urban expansion in Chinese megacities. Heliyon 2023 , 9 , e19872. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Liao, Q.; Liang, Y.; Li, Z.; Huang, C. Spatio–Temporal Heterogeneity of Urban Expansion and Population Growth in China. Int. J. Environ. Res. Public Health 2021 , 18 , 13031. [ Google Scholar ] [ CrossRef ]
• Luo, J.; Xing, X.; Wu, Y.; Zhang, W.; Chen, R.S. Spatio-temporal analysis on built-up land expansion and population growth in the Yangtze River Delta Region, China: From a coordination perspective. Appl. Geogr. 2018 , 96 , 98–108. [ Google Scholar ] [ CrossRef ]
• Yang, Y.; Feng, Z.; Zhao, Y.; You, Z. Study on the coordination between urban land expansion and population growth in China. Geogr. Res.-Aust. 2013 , 32 , 1668–1678. [ Google Scholar ]
• Pan, F.; Sun, Y.; Jiang, N.; Jian, Z.; Mei, Y.; Liu, Q. Analysis of the Coordination of Built-Up Area Expansion and Population Growth in the Shandong Peninsula Urban Agglomeration. Sustainability 2023 , 15 , 7763. [ Google Scholar ] [ CrossRef ]
• Zhou, K.; Zhang, X.; Xia, Y.; Zhang, R.; Zeng, P. Optimizing major function-oriented zone of Hunan Province from the perspective of population-land coordination. Resour. Sci. 2024 , 46 , 421–433. [ Google Scholar ]
• Wang, S.; Kong, W.; Ren, L.; Zhi, D.; Dai, B. Research on misuses and modification of coupling coordination degree model in China. J. Nat. Resour. 2021 , 36 , 793. [ Google Scholar ] [ CrossRef ]
• Cai, E.; Zhao, X.; Zhang, S.; Li, L. Spatial Agglomeration and Coupling Coordination of Population, Economics, and Construction Land in Chinese Prefecture-Level Cities from 2010 to 2020. Land 2023 , 12 , 1561. [ Google Scholar ] [ CrossRef ]
• Zou, X.; Xie, M.; Xiao, Z.; Chen, H. The Coupling and Spatial Coordination Characteristics Between Agricultural Population Migration and Urban Construction Land Increase in Jiangxi Province. China Land Sci. 2019 , 33 , 31–38. [ Google Scholar ]
• Zhang, X.; Wang, J.; Song, W.; Wang, F.; Gao, X.; Liu, L.; Dong, K.; Yang, D. Decoupling Analysis between Rural Population Change and Rural Construction Land Changes in China. Land 2022 , 11 , 231. [ Google Scholar ] [ CrossRef ]
• Yuan, J.; Li, Z. Decoupling Analysis between Rural Population Change and Rural Construction Land Changes in China. Yunnan Geogr. Environ. Res. 2018 , 30 , 10–15. [ Google Scholar ]
• Li, Z.; Luan, W.; Zhang, Z.; Su, M. Relationship between urban construction land expansion and population/economic growth in Liaoning Province, China. Land Use Policy 2020 , 99 , 105022. [ Google Scholar ] [ CrossRef ]
• Tapio, P. Towards a theory of decoupling: Degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transp. Policy 2005 , 12 , 137–151. [ Google Scholar ] [ CrossRef ]
• Liu, S.; Ye, Y.; Lin, Y. Evolution characteristics and decomposition of driving factors on rural residential land based on decoupling theory and LMDI model. Trans. Chin. Soc. Agric. Eng. 2019 , 35 , 272–280. [ Google Scholar ]
• Zang, Y.; Zhu, J.; Han, X.; Lv, L. Dynamics between Population Growth and Construction Land Expansion: Evidence from the Yangtze River Economic Belt of China. Land 2023 , 12 , 1288. [ Google Scholar ] [ CrossRef ]
• Li, J.; Ye, Q.; Chen, W.; Kong, X.; Bi, Q.; Lu, J.; Cai, E.; Wei, H.; Feng, X.; Guo, Y. An Analysis Method of Quantitative Coupling Rationality between Urban-Rural Construction Land and Population: A Case Study of Henan Province in China. Land 2022 , 11 , 735. [ Google Scholar ] [ CrossRef ]
• OECD. Indicators to Measure Decoupling of Environmental Pressure from Economic Growth ; OECD: Paris, France, 2002. [ Google Scholar ]
• Jia, H.; Li, T.; Wang, A.; Liu, G.; Guo, X. Decoupling analysis of economic growth and mineral resources consumption in China from 1992 to 2017: A comparison between tonnage and exergy perspective. Resour Policy 2021 , 74 , 102448. [ Google Scholar ] [ CrossRef ]
• Shao, J.; Ge, J. Investigation into Relationship between Intensive Land Use and Urban Heat Island Effect in Shijiazhuang City Based on the Tapio Decoupling Theory. J. Urban Plan. 2020 , 146 , 4020043. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Song, W.; Wang, J.; Wen, B.; Yang, D.; Jiang, S.; Wu, Y. Analysis on Decoupling between Urbanization Level and Urbanization Quality in China. Sustainability 2020 , 12 , 6835. [ Google Scholar ] [ CrossRef ]
• Ministry of Housing and Urban-Rural Development of China. Code for Classification of Urban Land Use and Planning Standards of Development Land (GB 50137–2011) ; China Architecture & Building Press: Beijing, China, 2011. [ Google Scholar ]
• Huang, L.; Yang, P.; Zhang, B.; Hu, W. Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt. Land 2021 , 10 , 400. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Chen, Y.; Zhang, S.; Ye, Q.; Chen, W.; Li, Y.; Cai, E.; Kong, X.; Guo, L.; Li, J. Research on the Spatiotemporal Coupling Characteristics between Urban Population and Land in China Based on the Improved Coupling Model of Polar Coordinates. Land 2024 , 13 , 1101. https://doi.org/10.3390/land13071101

Chen Y, Zhang S, Ye Q, Chen W, Li Y, Cai E, Kong X, Guo L, Li J. Research on the Spatiotemporal Coupling Characteristics between Urban Population and Land in China Based on the Improved Coupling Model of Polar Coordinates. Land . 2024; 13(7):1101. https://doi.org/10.3390/land13071101

Chen, Yihao, Shuai Zhang, Qingqing Ye, Weiqiang Chen, Yingchao Li, Enxiang Cai, Xuesong Kong, Long Guo, and Jiwei Li. 2024. "Research on the Spatiotemporal Coupling Characteristics between Urban Population and Land in China Based on the Improved Coupling Model of Polar Coordinates" Land 13, no. 7: 1101. https://doi.org/10.3390/land13071101

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How to Integrate Cloud, Data, and AI Technologies — and Make Your Company More Adaptable

• Paul R. Daugherty,
• H. James Wilson,
• Karthik Narain,
• Prashant Shukla

Companies that invested in this “digital core” saw more growth and higher returns.

Building a strong, flexible “digital core” that integrates cloud, data, and AI technologies to serve as an interconnected foundation for your company is the key to future growth. It is your means of supporting the current business drive toward efficiency and effectiveness, while remaining flexible enough to respond to the new needs of the organization and quickly adopt and scale the latest technology innovations. According to new research from Accenture on more than 1,500 large, global companies across industries, those that built a reinvention-ready digital core achieved what the researchers call the “60:40 effect.” Compared to a sample baseline, these leaders experienced a 60% acceleration in their revenue growth rate (from 7.1% to 11.1% on average) while increasing profitability by 40% (from 14.2 to 19.4 percentage points on average). The researchers found that the digital core has vastly expanded in importance, driven by the pandemic and explosion of gen AI. No matter your industry, the strategic shift toward continuous reinvention is fueling an urgent need for a digital core that amplifies machines, humans, and the interaction of the two in significant new ways.

We’ve entered an era of radical disruption, where waves of new technologies make continuous reinvention the default business strategy. Generative AI is only the latest innovation of many more to come that organizations will need to accommodate at pace and scale, or risk falling behind their competitors. That means companies must create a “digital core” that integrates cloud, data, and AI technologies to build an interconnected foundation for the enterprise that balances growth and reinvention, instead of focusing merely on cost.

• Paul R. Daugherty is Accenture’s chief technology and innovation officer. He is coauthor, with H. James Wilson, of Human + Machine: Reimagining Work in the Age of AI   (HBR Press, New and Expanded Edition, 2024). pauldaugh
• H. James Wilson is the global managing director of technology research and thought leadership at Accenture Research. He is coauthor, with Paul R. Daugherty, of  Human + Machine: Reimagining Work in the Age of AI   (HBR Press, New and Expanded Edition, 2024). hjameswilson
• Karthik Narain is group chief executive of Technology at Accenture. He oversees cloud, data & AI, enterprise & industry technologies, security and ecosystem partnerships. KarthikSNarain
• Prashant Shukla is principal director at Accenture Research. PrashShukla

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ANA Ethics Code of Marketing Best Practices

July 18, 2024    

T he ANA Ethics Code of Best Marketing Practices is a framework of high-level principles, guidelines, resources and examples of ethical marketing and advertising best practices applicable to all entities engaged in marketing, advertising, and fundraising. The Code has been shaped by your peers on the ANA Ethics Code Steering Committee and is designed to be a resource to guide the industry towards ethical business practices with the overarching goal of rebuilding and maintaining consumer trust.

The Code covers the following sections:

• Member Principles
• Advertising Offers
• Audiences (Children, Older adults, Disabled)
• Diversity and Inclusion
• Data Privacy, Security and Stewardship
• Regulated Products (Tobacco, Alcohol, and others)
• Digital Innovation (AI, Machine Learning, and Automated Processing)

This is a living, evolving document. If you are interested in participating in the drafting of further enhancements to the Code, contact us at [email protected] . We will be reconvening the Ethics Code Steering Committee periodically to review and consider further enhancements. The Code cannot and does not cover every nuanced topic. It provides broad guidance on issue areas identified as timely and relevant that will continue to be reviewed to reflect changes in the legislative, regulatory, technological, and business landscape.

Our vision is to advance an accountable and ethical marketing ecosystem for brand growth through a central Code of Ethics, alongside complementary efforts that help implement the spirit of that code, managed by the Center for Ethical Marketing and ANA's leadership as a force for responsible growth. The Code is voluntary and matched with a foundational self-regulatory program to help educate companies on implementing acceptable industry standard practices. These efforts stave off regulation at the state and federal levels. It serves as the baseline of best practices for our industry through ethical accountability and elevates ANA and its members as leaders in this area.

"ANA Ethics Code of Marketing Best Practices." ANA, July 2024.