Have a language expert improve your writing

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

  • Knowledge Base

Methodology

  • What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

  • What is the demographic makeup of Singapore in 2020?
  • How has the average temperature changed globally over the last century?
  • Does environmental pollution affect the prevalence of honey bees?
  • Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

Prevent plagiarism. Run a free check.

Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

  • Academic style
  • Vague sentences
  • Style consistency

See an example

what is importance of quantitative research across fields

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

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

Cite this Scribbr article

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

Bhandari, P. (2023, June 22). What Is Quantitative Research? | Definition, Uses & Methods. Scribbr. Retrieved March 8, 2024, from https://www.scribbr.com/methodology/quantitative-research/

Is this article helpful?

Pritha Bhandari

Pritha Bhandari

Other students also liked, descriptive statistics | definitions, types, examples, inferential statistics | an easy introduction & examples, what is your plagiarism score.

  • Privacy Policy

Buy Me a Coffee

Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

What is Quantitative Research

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

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

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Case Study Research

Case Study – Methods, Examples and Guide

Qualitative Research

Qualitative Research – Methods, Analysis Types...

Descriptive Research Design

Descriptive Research Design – Types, Methods and...

Qualitative Research Methods

Qualitative Research Methods

Basic Research

Basic Research – Types, Methods and Examples

Exploratory Research

Exploratory Research – Types, Methods and...

IdeaScale Logo

What is Quantitative Research? Definition, Examples, Key Advantages, Methods and Best Practices

By Nick Jain

Published on: May 17, 2023

What is Quantitative Research

Table of Contents

What is Quantitative Research?

Quantitative research examples, quantitative research: key advantages, quantitative research methodology, 7 best practices to conduct quantitative research.

Quantitative research stands as a powerful research methodology dedicated to the systematic collection and analysis of measurable data. Through rigorous statistical and mathematical techniques, this method extracts insights from structured surveys, controlled experiments, or other defined data-gathering methods.

The primary objective of quantitative research is to measure and quantify variables, relationships, and patterns within the dataset. By testing hypotheses, making predictions, and drawing generalizable conclusions, it plays a crucial role in fields such as psychology, sociology, economics, and education. This approach often involves significant sample sizes, ensuring robust results.

Explore the depth of quantitative research with this comprehensive guide, offering practical examples and applications to demonstrate its real-world impact. Stay updated with the latest trends and developments in quantitative research as we continually refine our insights to provide you with the most relevant and cutting-edge information.

Quantitative Research: Key Characteristics

Below are the key characteristics of quantitative research:

  • Objectivity: Quantitative research is grounded in the principles of objectivity and empiricism, which means that the research is focused on observable and measurable phenomena, rather than personal opinions or experiences.
  • Structured approach: Quantitative research follows a structured and systematic approach to data collection and analysis, using clearly defined variables, hypotheses, and research questions.
  • Numeric data: Quantitative research uses numerical data to describe and analyze the phenomena under study, such as statistical analysis, surveys, and experiments.
  • Large sample size: Quantitative research often involves large sample sizes to ensure statistical significance and to generalize findings to a larger population.
  • Standardized data collection: Quantitative research typically involves standardized data collection methods, such as surveys or experiments, to minimize potential sources of bias and increase reliability.
  • Deductive reasoning: Quantitative research uses deductive reasoning, where the researcher tests a specific hypothesis based on prior knowledge and theory.
  • Replication: Quantitative research emphasizes the importance of replication, where other researchers can reproduce the study’s methods and obtain similar results.
  • Statistical analysis: Quantitative research involves statistical analysis to analyze the data and test the research hypotheses, often using software programs to assist with data analysis.
  • Precision: Quantitative research aims to be precise in its measurement and analysis of data. It seeks to quantify and measure the specific aspects of a phenomenon being studied.
  • Generalizability: Quantitative research aims to generalize findings from a sample to a larger population. It seeks to draw conclusions that apply to a broader group beyond the specific sample being studied.

Below are 3 examples of quantitative research:

Example 1: Boosting Employee Performance with Innovative Training Programs

In this quantitative study, we delve into the transformative impact of a cutting-edge training program on employee productivity within corporate environments. Employing a quasi-experimental framework, we meticulously analyze the outcomes of a cohort undergoing innovative training against a control group. Through advanced statistical methodologies, we unveil actionable insights into performance enhancements, arming organizations with data-driven strategies for workforce development and competitive advantage.

Example 2: Unveiling the Power of Physical Exercise on Mental Well-being

Unlocking the correlation between physical exercise and mental health, this quantitative inquiry stands at the forefront of holistic wellness research. Through meticulous data collection and rigorous statistical analyses, we dissect the nuanced relationship between exercise regimens and mental well-being indicators. Our findings not only underscore the profound impact of exercise on psychological resilience but also provide actionable insights for healthcare professionals and individuals striving for optimal mental health.

Example 3: Revolutionizing Education with Innovative Teaching Methodologies

In this groundbreaking study, we embark on a quantitative exploration of the transformative potential of innovative teaching methods on student learning outcomes. Utilizing a quasi-experimental design, we meticulously evaluate the efficacy of novel pedagogical approaches against conventional teaching methodologies. Through rigorous statistical analyses of pre-test and post-test data, we unearth compelling evidence of enhanced academic performance, paving the way for educational institutions to embrace innovation and elevate learning experiences.

Learn more: What is Quantitative Market Research?

Quantitative Research: Key Advantages

The advantages of quantitative research make it a valuable research method in a variety of fields, particularly in fields that require precise measurement and testing of hypotheses.

  • Precision: Quantitative research aims to be precise in its measurement and analysis of data. This can increase the accuracy of the results and enable researchers to make more precise predictions.
  • Test hypotheses: Quantitative research is well-suited for testing specific hypotheses or research questions, allowing researchers to draw clear conclusions and make predictions based on the data.
  • Quantify relationships: Quantitative research enables researchers to quantify and measure relationships between variables, allowing for more precise and quantitative comparisons.
  • Efficiency: Quantitative research often involves the use of standardized procedures and data collection methods, which can make the research process more efficient and reduce the amount of time and resources required.
  • Easy to compare: Quantitative research often involves the use of standardized measures and scales, which makes it easier to compare results across different studies or populations.
  • Ability to detect small effects: Quantitative research is often able to detect small effects that may not be observable through qualitative research methods, due to the use of statistical analysis and large sample sizes.

Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research:

1. Primary Method

There are several methods of primary quantitative research, each with its own strengths and limitations.

Surveys: Surveys are a common method of quantitative research and involve collecting data from a sample of individuals using standardized questionnaires or interviews. Surveys can be conducted in various ways, such as online, by mail, by phone, or in person. Surveys can be used to study attitudes, behaviors, opinions, and demographics.

One of the main advantages of surveys is that they can be conducted on a large scale, making it possible to obtain representative data from a population. However, surveys can suffer from issues such as response bias, where participants may not provide accurate or truthful answers, and nonresponse bias, where certain groups may be less likely to participate in the survey.

Experiments: Experiments involve manipulating one or more variables to determine their effects on an outcome of interest. Experiments can be carried out in controlled laboratory settings or in real-world field environments. Experiments can be used to test causal relationships between variables and to establish cause-and-effect relationships.

One of the main advantages of experiments is that they provide a high level of control over the variables being studied, which can increase the internal validity of the study. However, experiments can suffer from issues such as artificiality, where the experimental setting may not accurately reflect real-world situations, and demand characteristics, where participants may change their behavior due to the experimental setting.

Observational studies: Observational studies involve observing and recording data without manipulating any variables. Observational studies can be conducted in various settings, such as naturalistic environments or controlled laboratory settings. Observational studies can be used to study behaviors, interactions, and phenomena that cannot be manipulated experimentally.

One of the main advantages of observational studies is that they can provide rich and detailed data about real-world phenomena. However, observational studies can suffer from issues such as observer bias, where the observer may interpret the data in a subjective manner, and reactivity, where the presence of the observer may change the behavior being observed.

Content analysis: Content analysis involves analyzing media or communication content, such as text, images, or videos, to identify patterns or trends. Content analysis can be used to study media representations of social issues or to identify patterns in social media data.

One of the main advantages of content analysis is that it can provide insights into the cultural and social values reflected in media content. However, content analysis can suffer from issues such as the subjectivity of the coding process and the potential for errors or bias in the data collection process.

Psychometrics: Psychometrics involves the development and validation of standardized tests or measures, such as personality tests or intelligence tests. Psychometrics can be used to study individual differences in psychological traits and to assess the validity and reliability of psychological measures.

One of the main advantages of psychometrics is that it can provide a standardized and objective way to measure psychological constructs. However, psychometrics can suffer from issues such as the cultural specificity of the measures and the potential for response bias in self-report measures.

2. Secondary Method

Secondary quantitative research methods involve analyzing existing data that was collected for other purposes. This can include data from government records, public opinion polls, or market research studies. Secondary research is often quicker and less expensive than primary research, but it may not provide data that is as specific to the research question.

One of the main advantages of secondary data analysis is that it can be a cost-effective way to obtain large amounts of data. However, secondary data analysis can suffer from issues such as the quality and relevance of the data, and the potential for missing or incomplete data.

Learn more: What is Quantitative Observation?

7 Best Practices to Conduct Quantitative Research

Here are the key best practices that should be followed when conducting quantitative research:

1. Clearly define the research question: The research question should be specific, measurable, and focused on a clear problem or issue.

2. Use a well-designed research design: The research design should be appropriate for the research question, and should include a clear sampling strategy, data collection methods, and statistical analysis plan.

3. Use validated and reliable instruments: The instruments used to collect data should be validated and reliable to ensure that the data collected is accurate and consistent.

4. Ensure informed consent: Participants should be fully informed about the purpose of the research, their rights, and how their data will be used. Informed consent should be obtained before data collection begins.

5. Minimize bias: Researchers should take steps to minimize bias in all stages of the research process, including study design, data collection, and data analysis.

6. Ensure data security and confidentiality: Data should be kept secure and confidential to protect the privacy of participants and prevent unauthorized access.

7. Use appropriate statistical analysis: Statistical analysis should be appropriate for the research question and the data collected. Accurate and clear reporting of results is imperative in quantitative research.

Learn more: What is Qualitative Research?

Enhance Your Research

Collect feedback and conduct research with IdeaScale’s award-winning software

Elevate Research And Feedback With Your IdeaScale Community!

IdeaScale is an innovation management solution that inspires people to take action on their ideas. Your community’s ideas can change lives, your business and the world. Connect to the ideas that matter and start co-creating the future.

Copyright © 2024 IdeaScale

Privacy Overview

  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • QuestionPro

survey software icon

  • Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
  • Resources Blog eBooks Survey Templates Case Studies Training Help center

what is importance of quantitative research across fields

Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research

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:

what is importance of quantitative research across fields

  • 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:

advantages-of-quantitative-research

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:

Tips to conduct 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:

what is importance of quantitative research across fields

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.

FREE TRIAL         LEARN MORE

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.

MORE LIKE THIS

AI in Healthcare

AI in Healthcare: Exploring ClinicAI + FREE eBook

Mar 6, 2024

HRIS Integration

HRIS Integration: What it is, Benefits & How to Approach It?

Mar 4, 2024

social listening tools

Top 10 Social Listening Tools for Brand Reputation

Mar 1, 2024

knowledge management software

16 Best Knowledge Management Software 2024

Feb 29, 2024

Other categories

  • Academic Research
  • Artificial Intelligence
  • Assessments
  • Brand Awareness
  • Case Studies
  • Communities
  • Consumer Insights
  • Customer effort score
  • Customer Engagement
  • Customer Experience
  • Customer Loyalty
  • Customer Research
  • Customer Satisfaction
  • Employee Benefits
  • Employee Engagement
  • Employee Retention
  • Friday Five
  • General Data Protection Regulation
  • Insights Hub
  • Life@QuestionPro
  • Market Research
  • Mobile diaries
  • Mobile Surveys
  • New Features
  • Online Communities
  • Question Types
  • Questionnaire
  • QuestionPro Products
  • Release Notes
  • Research Tools and Apps
  • Revenue at Risk
  • Survey Templates
  • Training Tips
  • Uncategorized
  • Video Learning Series
  • What’s Coming Up
  • Workforce Intelligence

Library Home

A Quick Guide to Quantitative Research in the Social Sciences

(10 reviews)

what is importance of quantitative research across fields

Christine Davies, Carmarthen, Wales

Copyright Year: 2020

Last Update: 2021

Publisher: University of Wales Trinity Saint David

Language: English

Formats Available

Conditions of use.

Attribution-NonCommercial

Learn more about reviews.

what is importance of quantitative research across fields

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

Comprehensiveness rating: 3 see less

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

Clarity rating: 5

The text is very clear and accessible.

Consistency rating: 5

The text is internally consistent.

Modularity rating: 5

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

Organization/Structure/Flow rating: 5

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Interface rating: 5

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

Grammatical Errors rating: 5

There were no noticeable grammatical errors.

Cultural Relevance rating: 4

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing... read more

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

Content Accuracy rating: 4

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

Organization/Structure/Flow rating: 4

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

Cultural Relevance rating: 5

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

Comprehensiveness rating: 4 see less

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text.

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

Relevance/Longevity rating: 4

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of... read more

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive research study as an Appendix after section 7 (page 26) to help readers comprehend information better.

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

Clarity rating: 4

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and... read more

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

Modularity rating: 4

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

Consistency rating: 4

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable).

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new... read more

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Table of Contents

  • Section 1: What will this resource do for you?
  • Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
  • Section 3: An overview of the Research Process and Research Designs
  • Section 4: Quantitative Research Methods
  • Section 5: the data obtained from quantitative research
  • Section 6: Analysis of data
  • Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.  

About the Contributors

Christine Davies , Ph.D

Contribute to this Page

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Methodology
  • What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

  • What is the demographic makeup of Singapore in 2020?
  • How has the average temperature changed globally over the last century?
  • Does environmental pollution affect the prevalence of honey bees?
  • Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Prevent plagiarism, run a free check.

Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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

Cite this Scribbr article

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

Bhandari, P. (2022, October 10). What Is Quantitative Research? | Definition & Methods. Scribbr. Retrieved 8 March 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-quantitative-research/

Is this article helpful?

Pritha Bhandari

Pritha Bhandari

Home • Knowledge hub • What is quantitative research?

What is quantitative research?

what is importance of quantitative research across fields

Quantitative research is an important part of market research that relies on hard facts and numerical data to gain as objective a picture of people’s opinions as possible.

It’s different from qualitative research in a number of important ways and is a highly useful tool for researchers.

Quantitative research is a systematic empirical approach used in the social sciences and various other fields to gather, analyze, and interpret numerical data. It focuses on obtaining measurable data and applying statistical methods to generalize findings to a larger population.

Researchers use structured instruments such as surveys, questionnaires, or experiments to collect data from a representative sample in quantitative research. The data collected is typically numerical values or categorical responses that can be analyzed using statistical techniques. These statistical analyses help researchers identify patterns, relationships, trends, or associations among variables.

Quantitative research aims to generate objective and reliable information about a particular phenomenon, population, or group. It aims to better understand the subject under investigation by employing statistical measures such as means, percentages, correlations, or regression analyses.

Quantitative research provides:

  • A quantitative understanding of social phenomena.
  • Allowing researchers to make generalizations.
  • Predictions.
  • Comparisons based on numerical data.

It is widely used in psychology, sociology, economics, marketing, and many other disciplines to explore and gain insights into various research questions.

In this article, we’ll take a deep dive into quantitative research, why it’s important, and how to use it effectively.

How is quantitative research different from qualitative research?

Although they’re both extremely useful, there are a number of key differences between quantitative and qualitative market research strategies. A solid market research strategy will make use of both qualitative and quantitative research.

  • Quantitative research relies on gathering numerical data points. Qualitative research on the other hand, as the name suggests, seeks to gather qualitative data by speaking to people in individual or group settings. 
  • Quantitative research normally uses closed questions, while qualitative research uses open questions more frequently.
  • Quantitative research is great for establishing trends and patterns of behavior, whereas qualitative methods are great for explaining the “why” behind them.

Why is quantitative research useful?

Quantitative research has a crucial role to play in any market research strategy for a range of reasons:

  • It enables you to conduct research at scale
  • When quantitative research is conducted in a representative way, it can reveal insights about broader groups of people or the population as a whole
  • It enables us to easily compare different groups (e.g. by age, gender or market) to understand similarities or differences 
  • It can help businesses understand the size of a new opportunity 
  •  It can be helpful for reducing a complex problem or topic to a limited number of variables

Get regular insights

Keep up to date with the latest insights from our research as well as all our company news in our free monthly newsletter.

  • First Name *
  • Last Name *
  • Business Email *

what is importance of quantitative research across fields

Quantitative Research Design

Quantitative research design refers to the overall plan and structure that guides the collection, analysis, and interpretation of numerical data in a quantitative research study. It outlines the specific steps, procedures, and techniques used to address research questions or test hypotheses systematically and rigorously. A well-designed quantitative research study ensures that the data collected is reliable, valid, and capable of answering the research objectives.

There are several key components involved in designing a quantitative research study:

  • Research Questions or Hypotheses: The research design begins with clearly defined research questions or hypotheses articulating the study’s objectives. These questions guide the selection of variables and the development of research instruments.
  • Sampling: A critical aspect of quantitative research design is selecting a representative sample from the target population. The sample should be carefully chosen to ensure it adequately represents the population of interest, allowing for the generalizability of the findings.
  • Variables and Operationalization: Quantitative research involves the measurement of variables. In the research design phase, researchers identify the variables they will study and determine how to operationalize them into measurable and observable forms. This includes defining the indicators or measures used to assess each variable.
  • Data Collection Methods: Quantitative research typically involves collecting data through structured instruments, such as surveys, questionnaires, or tests. The research design specifies the data collection methods, including the procedures for administering the instruments, the timing of data collection, and the strategies for maximizing response rates.
  • Data Analysis: Quantitative research design includes decisions about the statistical techniques and analyses applied to the collected data. This may involve descriptive statistics (e.g., means, percentages) and inferential statistics (e.g., t-tests, regression analyses) to examine variables’ relationships, differences, or associations.
  • Validity and Reliability: Ensuring the validity and reliability of the data is a crucial consideration in quantitative research design. Validity refers to the extent to which a measurement instrument or procedure accurately measures what it intends to measure. Reliability refers to the consistency and stability of the measurement over time and across different conditions. Researchers employ pilot testing, validity checks, and statistical measures to enhance validity and reliability.
  • Ethical Considerations: Quantitative research design also includes ethical considerations, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring the study adheres to ethical guidelines and regulations.

By carefully designing a quantitative research study, researchers can ensure their investigations are methodologically sound, reliable, and valid. 

Well-designed research provides a solid foundation for collecting and analyzing numerical data, allowing researchers to draw meaningful conclusions and contribute to the body of knowledge in their respective fields.

Quantitative research data collection methods

When collecting and analyzing the data you need for quantitative research, you have a number of possibilities available to you. Each has its own pros and cons, and it might be best to use a mix. Here are some of the main research methods:

Survey research

This involves sending out surveys to your target audience to collect information before statistically analyzing the results to draw conclusions and insights. It’s a great way to better understand your target customers or explore a new market and can be turned around quickly. 

There are a number of different ways of conducting surveys, such as:

  • Email — this is a quick way of reaching a large number of people and can be more affordable than the other methods described below.
  • Phone — not everyone has access to the internet so if you’re looking to reach a particular demographic that may struggle to engage in this way (e.g. older consumers) telephone can be a better approach. That said, it can be expensive and time-consuming.
  • Post or Mail — as with the phone, you can reach a wide segment of the population, but it’s expensive and takes a long time. As organizations look to identify and react to changes in consumer behavior at speed, postal surveys have become somewhat outdated. 
  • In-person — in some instances it makes sense to conduct quantitative research in person. Examples of this include intercepts where you need to collect quantitative data about the customer experience in the moment or taste tests or central location tests , where you need consumers to physically interact with a product to provide useful feedback. Conducting research in this way can be expensive and logistically challenging to organize and carry out.

Survey questions for quantitative research usually include closed-ended questions rather than the open-ended questions used in qualitative research. For example, instead of asking

“How do you feel about our delivery policy?”

You might ask…

“How satisfied are you with our delivery policy? “Very satisfied / Satisfied / Don’t Know / Dissatisfied / Very Dissatisfied” 

This way, you’ll gain data that can be categorized and analyzed in a quantitative, numbers-based way.

Correlational Research

Correlational research is a specific type of quantitative research that examines the relationship between two or more variables. It focuses on determining whether there is a statistical association or correlation between variables without establishing causality. In other words, correlational research helps to understand how changes in one variable correspond to changes in another.

One of the critical features of correlational research is that it allows researchers to analyze data from existing sources or collect data through surveys or questionnaires. By measuring the variables of interest, researchers can calculate a correlation coefficient, such as Pearson’s, to quantify the strength and direction of the relationship. The correlation coefficient ranges from -1 to +1, where a positive value indicates a positive relationship, a negative value indicates a negative relationship and a value close to zero suggests no significant relationship. Correlational research is valuable in various fields, such as psychology, sociology, and economics, as it helps researchers explore connections between variables that may not be feasible to manipulate in an experimental setting. For example, a psychologist might use correlational research to investigate the relationship between sleep duration and student academic performance. By collecting data on these variables, they can determine whether there is a correlation between the two factors and to what extent they are related. It is important to note that correlational research does not imply causation. While a correlation suggests an association between variables, it does not provide evidence for a cause-and-effect relationship. Other factors, known as confounding variables, may be influencing the observed relationship. Therefore, researchers must exercise caution in interpreting correlational findings and consider additional research methods, such as experimental studies, to establish causality. Correlational research is vital in quantitative research and analysis by investigating relationships between variables. It provides valuable insights into the strength and direction of associations and helps researchers generate hypotheses for further investigation. By understanding the limitations of correlational research, researchers can use this method effectively to explore connections between variables in various disciplines.

Experimental Research

Experimental research is a fundamental approach within quantitative research that aims to establish cause-and-effect relationships between variables. It involves the manipulation of an independent variable and measuring its effects on a dependent variable while controlling for potential confounding variables. Experimental research is highly regarded for its ability to provide rigorous evidence and draw conclusions about causal relationships. The hallmark of experimental research is the presence of at least two groups: the experimental and control groups. The experimental group receives the manipulated variable, the independent variable, while the control group does not. By comparing the outcomes or responses of the two groups, researchers can attribute any differences observed to the effects of the independent variable. Several key components are employed to ensure the reliability and validity of experimental research. Random assignment is a crucial step that involves assigning participants to either the experimental or control group in a random and unbiased manner. This minimizes the potential for pre-existing differences between groups and strengthens the study’s internal validity. Another essential feature of experimental research is the ability to control extraneous variables. By carefully designing the study environment and procedures, researchers can minimize the influence of factors other than the independent variable on the dependent variable. This control enhances the ability to isolate the manipulated variable’s effects and increases the study’s internal validity. Quantitative data is typically collected in experimental research through objective and standardized measurements. Researchers use instruments such as surveys, tests, observations, or physiological measurements to gather numerical data that can be analyzed statistically. This allows for applying various statistical techniques, such as t-tests or analysis of variance (ANOVA), to determine the significance of the observed effects and draw conclusions about the relationship between variables. Experimental research is widely used across psychology, medicine, education, and the natural sciences. It enables researchers to test hypotheses, evaluate interventions or treatments, and provide evidence-based recommendations. Experimental research offers valuable insights into the effectiveness or impact of specific variables, interventions, or strategies by establishing cause-and-effect relationships. Despite its strengths, experimental research also has limitations. The artificial nature of laboratory settings and the need for control may reduce the generalizability of findings to real-world contexts. Ethical considerations also play a crucial role in experimental research, as researchers must ensure participants’ well-being and informed consent. Experimental research is a powerful tool in the quantitative research arsenal. It enables researchers to establish cause-and-effect relationships, control extraneous variables, and gather objective numerical data. Experimental research contributes to evidence-based decision-making and advances knowledge in various fields by employing rigorous methods.

Analyzing results

Once you have your results, the next step — and one of the most important overall — is to categorize and analyze them.

There are many ways to do this. One powerful method is cross-tabulation, where you separate your results into categories based on demographic subgroups. For example, of the people who answered ‘yes’ to a question, how many of them were business leaders and how many were entry-level employees?

You’ll also need to take time to clean the data (for example removing people who sped through the survey, selecting the same answer) to make sure you can confidently draw conclusions. This can all be taken care of by the right team of experts.

The importance of quantitative research

Quantitative research is a powerful tool for anyone looking to learn more about their market and customers. It allows you to gain reliable, objective insights from data and clearly understand trends and patterns.

Where quantitative research falls short is in explaining the ‘why’. This is where you need to turn to other methods, like qualitative research, where you’ll actually talk to your audience and delve into the more subjective factors driving their decision-making.

At Kadence, it’s our job to help you with every aspect of your research strategy. We’ve done this with countless businesses, and we’d love to do it with you. To find out more, get in touch with us .

Helping brands uncover valuable insights

We’ve been working with Kadence on a couple of strategic projects, which influenced our product roadmap roll-out within the region. Their work has been exceptional in providing me the insights that I need. Senior Marketing Executive Arla Foods
Kadence’s reports give us the insight, conclusion and recommended execution needed to give us a different perspective, which provided us with an opportunity to relook at our go to market strategy in a different direction which we are now reaping the benefits from. Sales & Marketing Bridgestone
Kadence helped us not only conduct a thorough and insightful piece of research, its interpretation of the data provided many useful and unexpected good-news stories that we were able to use in our communications and interactions with government bodies. General Manager PR -Internal Communications & Government Affairs Mitsubishi
Kadence team is more like a partner to us. We have run a number of projects together and … the pro-activeness, out of the box thinking and delivering in spite of tight deadlines are some of the key reasons we always reach out to them. Vital Strategies
Kadence were an excellent partner on this project; they took time to really understand our business challenges, and developed a research approach that would tackle the exam question from all directions.  The impact of the work is still being felt now, several years later. Customer Intelligence Director Wall Street Journal

Get In Touch

" (Required) " indicates required fields

Privacy Overview

Banner Image

Quantitative and Qualitative Research

  • I NEED TO . . .

What is Quantitative Research?

  • What is Qualitative Research?
  • Quantitative vs Qualitative
  • Step 1: Accessing CINAHL
  • Step 2: Create a Keyword Search
  • Step 3: Create a Subject Heading Search
  • Step 4: Repeat Steps 1-3 for Second Concept
  • Step 5: Repeat Steps 1-3 for Quantitative Terms
  • Step 6: Combining All Searches
  • Step 7: Adding Limiters
  • Step 8: Save Your Search!
  • What Kind of Article is This?
  • More Research Help This link opens in a new window

Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

  • << Previous: I NEED TO . . .
  • Next: What is Qualitative Research? >>
  • Last Updated: Dec 8, 2023 10:05 PM
  • URL: https://libguides.uta.edu/quantitative_and_qualitative_research

University of Texas Arlington Libraries 702 Planetarium Place · Arlington, TX 76019 · 817-272-3000

  • Internet Privacy
  • Accessibility
  • Problems with a guide? Contact Us.

Quantitative research in education : Background information

  • Background information
  • SAGE researchmethods SAGE Research Methods is a tool created to help researchers, faculty and students with their research projects. Users can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and other areas of research.

Cover Art

  • The American freshman, national norms for ... From the Higher Education Research Institute, University of California, Los Angeles
  • Education at a glance : OECD indicators
  • Global education digest From UNESCO
  • Next: Recent e-books >>
  • Recent e-books
  • Recent print books
  • Connect to Stanford e-resources

Profile Photo

  • Last Updated: Jan 23, 2024 12:46 PM
  • URL: https://guides.library.stanford.edu/quantitative_research_in_ed

what is importance of quantitative research across fields

PHILO-notes

Free Online Learning Materials

Importance of Quantitative Research Across Fields

First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).

Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.

In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.

Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.

First, on the importance of quantitative research in psychology .

We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.

It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.

Second, on the importance of quantitative research in economics .

In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.

Third, on the importance of quantitative research in education .

Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.

In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.

Fourth, on the importance of quantitative research in Environmental Science and Sustainability.

Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.

But how does quantitative research provide the context for environmental science and sustainability?

Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.

And fifth, on the importance of quantitative research in business .

As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a  market research  assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.

Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.

what is importance of quantitative research across fields

Provide details on what you need help with along with a budget and time limit. Questions are posted anonymously and can be made 100% private.

what is importance of quantitative research across fields

Studypool matches you to the best tutor to help you with your question. Our tutors are highly qualified and vetted.

what is importance of quantitative research across fields

Your matched tutor provides personalized help according to your question details. Payment is made only after you have completed your 1-on-1 session and are satisfied with your session.

what is importance of quantitative research across fields

  • Homework Q&A
  • Become a Tutor

what is importance of quantitative research across fields

All Subjects

Mathematics

Programming

Health & Medical

Engineering

Computer Science

Foreign Languages

what is importance of quantitative research across fields

Access over 20 million homework & study documents

Importance of quantitative research across fields.

what is importance of quantitative research across fields

Sign up to view the full document!

what is importance of quantitative research across fields

24/7 Homework Help

Stuck on a homework question? Our verified tutors can answer all questions, from basic  math  to advanced rocket science !

what is importance of quantitative research across fields

Similar Documents

what is importance of quantitative research across fields

working on a homework question?

Studypool, Inc., Tutoring, Mountain View, CA

Studypool is powered by Microtutoring TM

Copyright © 2024. Studypool Inc.

Studypool is not sponsored or endorsed by any college or university.

Ongoing Conversations

what is importance of quantitative research across fields

Access over 20 million homework documents through the notebank

what is importance of quantitative research across fields

Get on-demand Q&A homework help from verified tutors

what is importance of quantitative research across fields

Read 1000s of rich book guides covering popular titles

what is importance of quantitative research across fields

Sign up with Google

what is importance of quantitative research across fields

Sign up with Facebook

Already have an account? Login

Login with Google

Login with Facebook

Don't have an account? Sign Up

This paper is in the following e-collection/theme issue:

Published on 8.3.2024 in Vol 26 (2024)

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Authors of this article:

Author Orcid Image

  • Yan Chen * , PhD   ; 
  • Pouyan Esmaeilzadeh * , PhD  

Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States

*all authors contributed equally

Corresponding Author:

Pouyan Esmaeilzadeh, PhD

Department of Information Systems and Business Analytics

College of Business

Florida International University

Modesto A Maidique Campus

11200 SW 8th St, RB 261 B

Miami, FL, 33199

United States

Phone: 1 3053483302

Email: [email protected]

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.

Introduction

Artificial intelligence (AI) is transforming many industries, including health care. AI has the potential to revolutionize health care by enabling the detection of signs, patterns, diseases, anomalies, and risks. From administrative automation to clinical decision support, AI holds immense potential to improve patient outcomes, lower costs, and accelerate medical discoveries [ 1 ]. An especially promising subset of AI is generative models, which are algorithms that can synthesize new data, imagery, text, and other content with humanlike creativity and nuance based on patterns learned from existing data [ 2 ]. Generative AI could power clinical practices in health care, from generating synthetic patient data to augmenting rare disease research to creating AI-assisted drug discovery systems [ 3 ]. Generative AI has the potential to detect signs, patterns, diseases, anomalies, and risks and assist in screening patients for various chronic diseases, making more accurate and data-driven diagnoses and improving clinical decision-making [ 4 ]. Generative AI also has the potential to transform patient care with generative AI virtual health assistants [ 5 ].

However, generative AI systems pose acute privacy and security risks along with their transformative potential because of their vast data requirements and opacity [ 6 ]. Generative AI models can be trained on sensitive, multimodal patient data, which could be exploited by malicious actors. Therefore, the collection and processing of sensitive patient data, along with tasks such as model training, model building, and implementing generative AI systems, present potential security and privacy risks. Given the sensitive nature of medical data, any compromise can have dire consequences, not just in data breaches but also in patients’ trust and the perceived reliability of medical institutions. As these AI systems move from laboratory to clinical deployment, a measured approach is required to map and mitigate their vulnerabilities. Another challenge of using generative AI models is that they can be biased, which could lead to inaccurate diagnoses and treatments [ 7 ].

Despite the growing interest in generative AI in health care, there is a gap in the literature regarding a comprehensive examination of the unique security and privacy threats associated with generative AI systems. Our study attempts to provide insights into the different categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support. This study also aims to address the gap by identifying security and privacy threats and mapping them to the life cycle of various generative AI systems in health care, from data collection through model building to clinical implementation. By identifying and analyzing these threats, we can gain insights into the vulnerabilities and risks associated with the use of generative AI in health care. We also seek to contribute to theory and practice by highlighting the importance of addressing these threats and proposing mitigation strategies.

The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks of using these systems. The significance of this study lies in its potential to inform policy makers, health care organizations, and AI developers about the security and privacy challenges associated with generative AI in health care. The findings of this study can guide the development of robust data governance frameworks, secure infrastructure, and ethical guidelines to ensure the safe and responsible use of generative AI in health care. With careful governance, the benefits of generative models can be realized while safeguarding patient data and public trust. Ultimately, this study contributes to the advancement of knowledge in the field of AI in health care and supports the development of secure and privacy-preserving generative AI systems for improved patient care and outcomes.

Generative AI Applications in Health Care

Generative AI models use neural networks to identify patterns and structures within existing data to generate new and original content. Generative AI refers to techniques such as generative adversarial networks (GANs) and large language models (LLMs) that synthesize novel outputs such as images, text, and molecular structures [ 8 ]. GANs use 2 neural networks, a generator and a discriminator, that compete against each other to become better at generating synthetic data [ 9 ]. LLMs such as GPT-4 (OpenAI) are trained on massive text data and can generate synthetic natural language text, code, and so on [ 10 ].

Generative AI has spurred a wide range of applications in health care. This subset of AI has the potential to make a breakthrough in medical diagnostic applications, given its capability to build models using multimodal medical data [ 5 ]. Generative AI also promises to accelerate drug discovery by inventing optimized molecular candidates [ 11 ]. In research settings, these generative AI techniques can hypothesize promising new directions by creatively combining concepts [ 12 ]. Generative AI also has applications in engaging patients through natural conversation powered by LLMs [ 2 ]. When integrated into clinical workflows, it may also provide physicians with patient-specific treatment suggestions [ 13 ].

The classification of generative AI systems presented in Table 1 was developed based on a careful analysis of the various factors that differentiate these technologies.

Differentiating Factors

The goal was to provide a framework for better understanding the diversity of generative AI across health care settings. We leverage several key factors to differentiate the applications and provide insights into this emerging field, described in the following sections.

The clinical setting categorizes where in the health care workflow the generative AI system is applied, such as diagnostics, treatment planning, drug discovery, clinical decision support, and patient education [ 14 ]. This provides insights into the breadth of health care contexts leveraging these technologies.

Generative AI tools are tailored to different types of users in health care, from clinicians to researchers to patients [ 15 ]. Categorization by intended user groups reveals how generative AI penetrates various stakeholder groups and which user groups may adopt and interact with generative AI applications.

The data sources powering generative AI systems vary significantly, from electronic health records (EHRs) and medical imaging to biomedical literature, laboratory tests, and patient-provided data [ 16 ]. Categorization by data inputs illustrates how different data fuel different categories of applications.

Output Data

The outputs produced by the system, such as images, care planning, prescription advice, treatment options, drug molecules, text, risk scores, and education materials [ 17 ], demonstrate the wide range of generative AI capabilities in health care.

Personalization Level

The level of personalization to individual patients reveals the precision of the outputs, from generalized to fully patient specific. This provides a perspective on the customizability of the generative AI system.

Workflow Integration

Some generative AI systems are designed as stand-alone applications, whereas others are integrated into clinical workflows via EHRs, order sets, and so on. Categorization by workflow integration sheds light on the level of adoption, implementation practices, and integration of these tools.

Validation Needs

The extent of validation required, from noncritical outputs to those needing rigorous US Food and Drug Administration approval [ 18 ], highlights differences in oversight and impact levels.

Impact: profiling the benefits and use cases served by the generative AI technology, such as improving diagnostics, reducing medication errors, or accelerating drug discovery, provides insights into the varied impacts.

Discussing risks and limitations provides a balanced view of concerns such as algorithmic bias, privacy concerns, security issues, system vulnerability, and clinical integration challenges.

Human-AI Collaboration

Generative AI systems differ in the level of human involvement required, from fully automated to human-in-the-loop (human engagement in overseeing and interacting with the AI’s operational process) [ 19 ]. Categorization by human-AI partnership provides insights into the changing dynamics between humans and AI across health care.

This study aims to reveal crucial differences, use cases, adoption levels, various risks, and implementation practices by developing categories based on these key attributes of generative AI systems. The proposed framework clarifies the heterogeneous landscape of generative AI in health care and enables a trend analysis across categories. These factors provide a perspective on how generative AI manifests distinctly for various users, data types, workflows, risk factors, and human-AI partnerships within health care. By systematically analyzing the diverse range of generative AI systems across health care settings using the key factors discussed previously, we can classify the heterogeneous landscape of generative AI in health care into 5 overarching categories: medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support.

Medical Diagnostics

Generative AI techniques can analyze data from wearables, EHRs, and medical images (eg, x-rays, magnetic resonance imaging, and computed tomography scans) to detect signs, patterns, diseases, anomalies, and risks and generate descriptive findings to improve diagnoses. Systems such as AI-Rad Companion leverage natural language generation models to compose radiology reports automatically, highlighting potential abnormalities and issues for clinician review [ 20 ]. This assists radiologists by providing initial draft findings more rapidly. However, clinicians must thoroughly validate any generative AI outputs before clinical use. Ongoing challenges include reducing false positives and negatives [ 21 ].

Drug Discovery

Generative AI shows promise for expediting and enhancing drug discovery through inventing optimized molecular structures de novo. Techniques such as GANs combined with reinforcement learning allow the intelligent generation of molecular graph representations [ 22 ]. Companies such as Insilico Medicine are using these generative chemistry techniques to propose novel target-specific drug candidates with desired properties. This accelerates preclinical pharmaceutical research. However, validating toxicity and efficacy remains critical before human trials.

Virtual Health Assistants

Generative models such as LLMs can power conversational agents that understand and respond to patient questions and concerns [ 23 ]. Companies such as Sensely and Woebot Health leverage these techniques to create virtual assistants that explain symptoms, provide health information, and offer screening triage advice through natural dialogue [ 24 ]. This increases access and engagement for patients. However, challenges remain around privacy, information accuracy, and integration into provider workflows [ 25 ].

Medical Research

In research settings, generative AI can formulate novel hypotheses by making unexpected combinations of concepts, mimicking human creativity and intuition. Claude from Anthropic can read research papers and propose unexplored directions worth investigating [ 26 ]. This unique generative capacity could accelerate scientific advancement. However, corroboration by human researchers is crucial to prevent the blind acceptance of AI-generated findings [ 27 ].

Clinical Decision Support

Integrating generative AI into clinical workflows could provide patient-specific suggestions to assist physicians in decision-making. Glass AI leverages LLMs such as GPT-3 to generate tailored treatment options based on patient data for physicians to review [ 15 ]. This could improve outcomes and reduce errors. However, bias mitigation and high validation thresholds are critical before real-world adoption [ 28 ].

By holistically examining all the key factors, we can see how each one contributes to delineating these 5 high-level categories that provide a comprehensive snapshot of the generative AI landscape in health care. Analyzing these 5 categories through the lens of the proposed factors enables our study to reveal crucial differences, use cases, benefits, limitations, and implementation practices of generative AI technologies across major health care domains.

Literature Review

The adoption of AI (powered by various models) is accelerating across health care for applications ranging from medical imaging to virtual assistants. However, the data-intensive nature and complexity of these systems introduce acute privacy and security vulnerabilities that must be addressed to ensure safe and ethical deployment in clinical settings. This literature review covers 2 topics. First, we highlight the dual nature of technological advancements in generative AI within health care, its benefits, and its risks, particularly in terms of privacy and security that it entails. Second, we explain AI regulation and compare the key aspects of the European Union (EU) AI Act and the US AI Bill of Rights.

Generative AI: Balancing Benefits and Risks

The use of generative AI systems in medicine holds promise for improvements in areas such as patient education and diagnosis support. However, recent studies highlight that privacy and security concerns may slow user adoption. A survey explores the application of GANs toward ensuring privacy and security [ 29 ]. It highlights how GANs can be used to address increasing privacy concerns and strengthen privacy regulations in various applications, including medical image analysis. The unique feature of GANs in this context is their adversarial training characteristic, which allows them to investigate privacy and security issues without predetermined assumptions about opponents’ capabilities. This is crucial because these capabilities are often complex to determine with traditional attack and defense mechanisms. In the privacy and security models using GANs, the generator can be modeled in two ways: (1) as an attacker aiming to fool a defender (the discriminator) to simulate an attack scenario and (2) as a defender resisting a powerful attacker (the discriminator) to simulate a defense scenario.

Examples of defense models include generative adversarial privacy [ 30 ], privacy-preserving adversarial networks [ 31 ], compressive adversarial privacy [ 32 ], and reconstructive adversarial network [ 33 ]. These GAN-based mechanisms offer innovative ways to enhance privacy and security in various machine learning and data processing scenarios. The examples are described in the subsequent sections.

Protection of Preimage Privacy

The compressive privacy GAN is designed to preprocess private data before the training stage in machine learning as a service scenarios [ 34 ]. It includes 3 modules: a generator module (G) as a privatization mechanism for generating privacy-preserving data, a service module (S) providing prediction services, and an attacker module (A) that mimics an attacker aiming to reconstruct the data. The objective is to ensure optimal performance of the prediction service, even in the face of strong attackers, by intentionally increasing the reconstruction error. This method defends against preimage privacy attacks in machine learning as a service by ensuring that the input data of a service module contains no sensitive information.

Privacy in Distributed Learning Systems

In decentralized learning systems, such as distributed selective stochastic gradient descent [ 35 ] and federated learning (FL) [ 36 ], data are trained locally by different participants without data sharing. This setup can protect data privacy to some extent, but it is not perfect. The GAN-based models in these systems can mimic data distribution and potentially threaten data privacy. The potential risks associated with the application of GAN-based models in decentralized learning systems are multifaceted, highlighting the need for robust privacy protection measures. These risks are explained as the following: an attacker might use GANs to recover sensitive information within the distributed training system, and a malicious server can reveal user-level privacy in distributed learning systems by training a multitask GAN with auxiliary identification.

Protection mechanisms include embedding a “buried point layer” in local models to detect abnormal changes and block attackers and integrating GAN with FL to produce realistic data without privacy leakage.

Differential Privacy in GANs

To address the problem of privacy leakage in the models, two solutions have been proposed: (1) adding a regularization term in a loss function to avoid overfitting and improve robustness; for example, this method can be applied to defend against membership inference attacks, [ 37 ] and (2) adding acceptable noise into the model parameters to hinder privacy inference attacks. Such methods have been used for privacy protection, particularly the combination of differential privacy and neural networks [ 38 ].

In medical research, the widespread use of medical data, particularly in image analysis, raises significant concerns about the potential exposure of individual identities. An innovative adversarial training method focused on identity-obfuscated segmentation has been proposed to address this challenge [ 39 ]. This method is underpinned by a deep convolutional GAN-based framework comprising three key components: (1) a deep encoder network, functioning as the generator, efficiently obscuring identity markers in medical images by incorporating additional noise; (2) a binary classifier serves as the discriminator, ensuring that the transformed images retain a resemblance to their original counterparts; and (3) a convolutional neural network–based network dedicated to medical image analysis, acting as an alternate discriminator responsible for analyzing the segmentation details of the images. This framework integrates an encoder, a binary classifier, and a segmentation analysis network to form a robust approach to safeguard medical data privacy while preserving the integrity and efficacy of medical image segmentation.

The use of EHR medical records has significantly advanced medical research while simultaneously amplifying concerns regarding the privacy of this sensitive information. In response, Choi et al [ 40 ] devised the medical GAN (medGAN), an innovative adaptation of the standard GAN framework, aimed at producing synthetic patient records that respect privacy. The medGAN excels at generating high-dimensional discrete variables. Its architecture uses an autoencoder as the generator, which creates synthetic medical data augmented with noise. A binary classifier functions as the discriminator, ensuring the resemblance of these data to real records. The outcome is synthetic medical data suitable for various uses, such as distribution analysis, predictive modeling, and medical expert evaluations, minimizing the privacy risks associated with both identity and attributes. Furthering these advancements, Yale et al [ 41 ] conducted an in-depth evaluation of medGAN’s ability to protect privacy in medical records. In a parallel development, Torfi and Fox [ 42 ] introduced Correlation-Capturing Convolutional Generative Adversarial Networks (CorGAN), which focuses on the correlations within medical records. Unlike medGAN, CorGAN uses a dual autoencoder in its generator, enabling the creation of sequential EHRs rather than discrete entries. This approach enhances predictive accuracy, providing more effective assistance to medical professionals [ 43 ].

Similarly, Nova [ 14 ] discusses the transformative impact of generative AI on EHRs and medical language processing, underlining the accompanying privacy concerns. It examines the balance between the utility of GANs in generating health care data and the preservation of privacy. Rane [ 44 ] explores the wider privacy and security implications of using generative AI models, such as ChatGPT, in health care within the context of Industry 4.0 and Industry 5.0 transformation. The impact of generative content on individual privacy is further explored by Bale et al [ 45 ], emphasizing the ethical considerations in health care.

Ghosheh et al [ 46 ] suggest that the use of GANs to create synthetic EHRs creates many privacy challenges (eg, reidentification and membership attacks). Hernandez et al [ 47 ] discuss privacy concerns related to synthetic tabular data generation in health care. Various methods and evaluation metrics are used to assess the privacy dimension of the synthetic tabular data generation approaches. These methods include identity disclosure, attribute disclosure, distance to the closest record, membership attack, maximum real-to-synthetic similarity, differential privacy cost, and GANs. For instance, differential privacy is an approach that adds noise to the data to prevent the identification of individuals. GANs can create new and nonreal data points. Other advanced statistical and machine learning techniques attempt to balance data utility and privacy. Each method has its strengths and limitations, and the choice depends on the specific requirements of the health care application and the sensitivity of the data involved.

The applications and challenges of generative AI in health care, including privacy issues and AI-human collaboration, are explored by Fui-Hoon et al [ 48 ]. They discuss several privacy issues related to generative AI, such as the potential disclosure of sensitive or private information by generative AI systems, the widening of the digital divide, and the collection of personal and organizational data by these systems, which raises concerns about security and confidentiality. In addition, they highlight regulatory and policy challenges, such as issues with copyright for AI-generated content, the lack of human control over AI behavior, data fragmentation, and information asymmetries between technology giants and regulatory authorities.

A study discusses the potential of FL as a privacy-preserving approach in health care AI applications [ 49 ]. FL is a distributed AI paradigm that offers privacy preservation in smart health care systems by allowing models to be trained without accessing the local data of participants. It provides privacy to end users by only sharing gradients during training. The target of FL in health care AI applications is to preserve the privacy of sensitive patient information communicated between hospitals and end users, particularly through Internet of Medical Things (IoMT) devices. The approach incorporates advanced techniques such as reinforcement learning, digital twin, and GANs to detect and prevent privacy threats in IoMT networks. The potential beneficiaries of FL in health care include patients, health care providers, and organizations involved in collaborative health care research and analysis. However, implementing FL in IoMT networks presents challenges, such as the need for robust FL for diffused health data sets, the integration of FL with next-generation IoMT networks, and the use of blockchain for decentralized and secure data storage. Furthermore, incentive mechanisms are being explored to encourage the participation of IoMT devices in FL, and digital twin technology is being leveraged to create secure web-based environments for remote patient monitoring and health care research. Overall, FL in health care AI applications aims to address privacy and security concerns while enabling collaborative and efficient health care systems.

Another study emphasizes the need for secure and robust machine learning techniques in health care, particularly focusing on privacy and security [ 50 ]. Finally, a study addresses the vulnerabilities of generative models to adversarial attacks (eg, evasion attacks and membership inference attacks), highlighting a significant area of concern in health care data security [ 51 ]. These studies collectively underscore the need for a balanced approach to leveraging the benefits of AI-driven health care innovations while ensuring robust privacy and security measures.

AI, Legal Challenges, and Regulation

AI, especially generative AI, has presented many legal challenges, raising many profound questions on how AI can be legally, securely, and safely used by businesses and individuals [ 52 ]. The EU AI Act, passed in 2023, is the first comprehensive legal framework to specifically regulate AI systems [ 53 ]. It categorizes systems by risk level and introduces mandatory requirements for high-risk AI related to data and documentation, transparency, human oversight, accuracy, cybersecurity, and so on. As stated in the act, national authorities will oversee compliance.

The US AI Bill of Rights, unveiled in 2023, takes a different approach as a nonbinding set of principles to guide AI development and use focused on concepts such as algorithmic discrimination awareness, data privacy, notice and explanation of AI, and human alternatives and oversight [ 54 ]. Rather than authoritative regulation, it promotes voluntary adoption by organizations.

Although the EU law institutes enforceable accountability around risky AI, the US bill espouses aspirational AI ethics principles. Both identify important issues such as potential bias, privacy risks, and the need for human control but tackle them differently—the EU through compliance requirements and the United States through voluntary principles. Each seeks more responsible AI but via divergent methods that fit their governance models. Despite differences in methods, there is a consensus on fundamental issues such as ensuring transparency, maintaining accuracy, minimizing adverse effects, and providing mechanisms for redressal.

Specifically, for generative AI such as ChatGPT, the EU AI Act mandates transparency requirements, such as disclosing AI-generated content, designing models to prevent illegal content generation, and publishing training data summaries. Although the principles mentioned in the US AI Bill of Rights do not specifically address generative AI, they provide a framework for the ethical and responsible use of all AI technologies, including generative AI. The principles emphasize safety, nondiscrimination, privacy, transparency, and human oversight, all of which are relevant to developing and deploying generative AI systems.

Ultimately, the EU legislates binding rules that companies must follow, whereas the United States issues guidance that organizations may freely adopt. Despite this schism, both highlight growing policy makers’ concern over AI’s societal impacts and the emergence of either compulsory or optional frameworks aimed at accountability. As leading AI powers craft different but related policy solutions, ongoing collaboration around shared values while allowing varied implementations will be important for setting global AI standards.

Security and Privacy Threats in the Life Cycle of a Generative AI in Health Care System

Although generative AI in health care holds great promise, substantial validation is required before real-world deployment. Ethical risks around reliability, accountability, algorithmic bias, and data privacy as well as security risks related to confidentiality, integrity, and availability must be addressed through a human-centric approach [ 55 ]. Liu et al [ 56 ] surveyed the security and privacy attacks related to machine learning and developed a taxonomy. The taxonomy classifies those attacks into three categories: (1) attacks targeting classifiers; (2) attacks violating integrity, availability, and privacy (ie, part of confidentiality); and (3) attacks with or without specificity. They also summarize the defense techniques in the training phase and the testing and inferring phase of the life cycle of machine learning, for example, data sanitization techniques against data poisoning attacks in the training phase and privacy-preserving techniques against privacy attacks in the testing or inferring phase. Similarly, Hu et al [ 57 ] present an overall framework of attacks and defense strategies based on the following five phases of the AI life cycle: (1) data collection phase—main security threats include databases, fake data, data breaches, and sensor attacks; defense strategies include data sanitization and data government; (2) data processing phase—image scaling is the main threat; recommended defense strategies include image reconstruction and data randomization; (3) training phase—data poisoning is the main threat; defense strategies focus on techniques that can identify and remove poisoned data (eg, the certified defense technique proposed by Tang et al [ 58 ]) and provide robust and reliable AI models; (4) inference phase—this phase mainly faces adversarial example attacks such as white-box, gray-box, and black-box attacks depending on how much the attacker knows about the target model; a variety of defense strategies can be implemented to tackle such attacks, such as adopting strategies in phases 1 to 3 to modify data (eg, data reconstruction and randomization) or modify or enhance models with newer model construction methods resistant to adversarial example attacks (eg, using deep neural networks and GAN-based networks [ 58 , 59 ]); (5) integration phase—AI models face AI biases, confidentiality attacks (eg, model inversion, model extraction, and various privacy attacks), and code vulnerability exploitation; defense strategies in this phase should be comprehensive via integrating various solutions such as fuzz testing and blockchain-based privacy protection.

Generative AI is built upon machine learning and AI techniques and hence faces similar security and privacy threats, as summarized in the studies by Liu et al [ 56 ] and Hu et al [ 57 ]. Nevertheless, because generative AI, such as LLMs, often requires large volumes of data (eg, large volumes of patient data) to train, it faces many existing and new security and privacy threats. If deployed carelessly, generative models increase the avenues for protected health information (PHI) to be leaked, stolen, or exposed in a breach. For example, deidentifying data for LLMs is challenging [ 60 ]. Even anonymized patterns in data could potentially reidentify individuals if models are improperly handled after training. One example is medical image analysis, as deidentified medical images could be reidentified in medical image analysis because of the massive amount of image data used in training [ 39 ]. LLMs in health care also face data quality and bias issues, similar to any machine learning model, leading to erroneous medical conclusions or recommendations [ 61 ].

Furthermore, hackers could also exploit vulnerabilities in systems hosting generative models to access the sensitive health data used for training. Skilled hackers may be able to feed prompts to models to obtain outputs of specific patient details that allow reidentification even from anonymized data. For example, improperly secured LLMs could enable bad actors to generate fake patient data or insurance claims [ 62 ]. In general, generative AI in health care encounters many of the same security and privacy threats as general AI and machine learning systems, along with new threats stemming from its unique context. On the basis of the life cycle in the studies by Liu et al [ 56 ] and Hu et al [ 57 ], our study presents a 3-phase life cycle for generative AI. It also identifies security and privacy threats and maps them to the life cycle of various generative AI systems in health care ( Figure 1 ). It should be noted that although this study primarily discusses various security and privacy threats associated with generative AI in health care (such as AI hallucination in health care), many of these threats are not unique to generative AI systems and are also prevalent in broader AI systems and machine learning models in health care and other fields.

what is importance of quantitative research across fields

Data Collection and Processing Phase

Similar to AI systems in other fields, almost all types of generative AI in health care face integrity threats. The main integrity threats in this phase are traditionally owing to errors and biases. Unintentionally, the increased data volume and complexity of generative AI threatens data integrity because errors and biases are prone to occur [ 63 ]. Errors and biases also depend on the data sources for different types of generative AI in health care. For example, assembling genomic databases and chemical compound or protein structure databases for drug discovery is extremely challenging and could be error ridden because many genomic and protein databases lack necessary annotations, are inconsistent in formats, and may be poor in data quality [ 64 ].

Intentionally, data poisoning can occur when data are collected from various software packages by tampering with data. For example, malicious insiders can tamper with data intentionally when gathering data from various software sources. For example, malicious actors can internationally submit mislabeled genomic sequences and chemical compound protein structures to tamper genomic databases and chemical compound or protein structure databases, leading to fault training models and AI hallucination.

In addition to data poisoning from software, in health care, data may be gathered from sensors embedded in medical devices and equipment. Sensor data can be spoofed [ 65 , 66 ], tampered with, and thus poisoned. Furthermore, medical data contains a large number of images. Adversaries can exploit the difference in cognitive processes between AI and humans and tamper with images during the data collection and processing phase. Image-scaling attacks, in which an adversary manipulates images so that changes are imperceptible to the human eye but recognizable by AI after downscaling, represent one such form of attack [ 67 , 68 ]. Other attacks on data sources of medical images include, but are not limited to, copy-move tampering (ie, copying an area and moving it to another area), classical inpainting tampering (ie, patching a missing area with tampered image slices), deep inpainting tampering (ie, similar to classical inpainting tampering but using highly realistic image slides generated by GANs), sharpening, blurring, and resampling [ 69 ]. In scenarios where AI in imaging diagnostics is targeted by such attacks, the image data can be poisoned with malicious information. Furthermore, generative AI, such as GANs, has empowered hackers to generate or change the attributes or content of medical images with high visual realism, making the detection of tampered images extremely difficult [ 69 ].

Moreover, many generative AI applications in health care rely on LLMs and are trained on large amounts of internet data without being properly screened and filtered [ 70 ]. Adversaries can use AI technologies to automatically generate large quantities of fake data to poison data to be fed into LLMs, resulting in deteriorated performance of the models (eg, accuracy and fairness) and eventually AI hallucination, misinformation or disinformation, and deepfakes. Although some of these threats are not unique to generative AI in health care, they can be particularly risky if false information is used for medical decision-making. Generative AI also carries unique integrity risks. As mentioned before, its capability to create synthetic data leads to a unique integrity risk—AI hallucination. In the health care context, generative AI in health care could be used to create fake medical records or alter existing ones. Fabricated medical data can be fed again into LLMs, further threatening the integrity of medical information. For instance, the malicious use of deepfakes generated by deep generative models could fabricate a patient’s medical history to falsely claim insurance or lead to incorrect treatments. Another example is that a generative AI model may create synthetic radiology reports to diagnose nonexistent medical conditions, leading to misdiagnosis or unnecessary treatment.

By contrast, research has used synthetic data in AI for medicine and health care to address the scarcity of annotated medical data in the real world [ 71 ]. For instance, deep generative models are used to create synthetic images such as skin lesions, pathology slides, colon mucosa, and chest x-rays, thereby greatly improving the reproducibility of medical data [ 71 ]. With the development of generative AI, researchers have increasingly used GANs to synthesize realistic training data for data imputation when the data lacks distribution. Noise-to-image and image-to-image GANs have been used to synthesize realistic training magnetic resonance imaging images to boost the performance of convolutional neural networks for image diagnostic AI [ 39 , 72 ]. CorGAN [ 42 ] synthesizes discrete and continuous health care records for model training. From a broader perspective, generative AI is projected to build and use next-generation synthetic gene networks for various AI applications in health care, including medical diagnostics, drug discovery, and medical research [ 73 ]. The growth in the use of synthetic data by generative AI also creates new concerns about data integrity and AI hallucination. Nevertheless, given that health care is a heavily regulated field in terms of patient privacy and safety, researchers even claim that synthetic medical data might be promising to overcome data sharing obstacles for health care AI and free developers from sensitive patient information [ 74 ]. These applications indicate that there is a fine line between harmful AI hallucinations or deepfakes and beneficial synthetic data use by generative AI in health care. Nevertheless, even the benevolent use of synthetic medical data faces privacy and security challenges as well as integrity challenges. Deep-faked patient face images could violate patient privacy and lead to the leakage or exploitation of PHI [ 75 ]. How to navigate this fine line is both a policy and research blind spot. Currently, there are just insufficient use cases, especially for rare use cases, to establish clinical reference standards such as clinical quality measures and evaluation metrics to assess risks and benefits.

Similar to generative AI applications in other fields, almost all types of generative AI in health care face confidentiality threats. Deidentified data may become identifiable during the data collection and processing phase, and confidential proprietary medical information, such as drug development and treatment plans, may be inferred during the data collection and processing phase [ 76 ], leading to data and privacy breaches. Research has found that genomic databases are prone to privacy violations. For example, legit researchers obtain or recover the whole or partial genomic sequence of a target individual (privacy violation through reference), link the sequence to a target individual (ie, reidentifying), and identify the group of interest of a target individual (privacy violation through membership reference) when processing data from multiple sources. In addition, the growth of synthetic medical data in health AI systems raises concerns about the vulnerabilities of such systems and the challenges of the current regulations and policies.

Table 2 summarizes the data sources and security or privacy threats for each type of generative AI in health care in the data collection and processing phase.

a AI: artificial intelligence.

b CT: computed tomography.

c MRI: magnetic resonance imaging.

d EHR: electronic health record.

e NIH: National Institutes of Health.

Again, it should be noted that although all AI and machine learning systems face many similar threats, as listed in Table 2 , generative AI amplifies them because of its generating nature and data source volume and complexity. For example, generative medical research AI may update knowledge and literature databases with “wrong inputs” based on wrong findings in these databases or with synthesized but hallucinated findings. Similarly, generative virtual health assistants may put dangerous advice into knowledge databases based on erroneous data from sources or again put synthesized but hallucinated advice into such databases.

Model Training and Building Phase

Generative AI also encounters integrity issues, leading to phenomena such as AI hallucinations during model training and development phases. This is especially true for generative AI in health care. Prior research found that generative AI created nonfactual or unfaithful data and outputs [ 72 , 77 ]. The growing use of highly synthetic data or images by generative AI, such as CorGAN, exacerbates the situation as it becomes increasingly challenging for human professionals to detect unfaithful data and outputs [ 69 ]. This can be a serious integrity and authenticity issue, as both patients and clinicians expect factual, scientific answers or outputs with consistency from such models. Technically speaking, similar to all other AI models, generative AI models in health care, particularly those based on deep learning, are often seen as “black boxes” [ 78 ]. The lack of interpretability and explainability can be a significant challenge in health care, where understanding the reasoning behind a diagnosis or treatment recommendation is crucial for integrity and accountability.

Adversarial training is a method to check for the integrity and accountability of AI models. The method uses carefully crafted adversarial examples to attack the training model to check for the integrity and robustness of outputs [ 57 , 79 ]. It is an active AI research area in the health care field. Adversarial training is used to check for fake or realistic features in synthetic medical images created by GANs to avoid fabrication and misleading in the model training process. By contrast, malicious parties also intensively explore this method and use adversarial examples to attack training models to generate incorrect outcomes [ 57 ]. Technically, all types of generative AI using GANs and LLMs, particularly those in health care, can be attacked with adversarial examples that compromise the integrity of the training model. For example, adversaries can use image-scaling attacks to feed human-invisible data into an AI model to force it to make a mistake [ 67 , 68 ].

Another example is to feed an AI model with carefully crafted relabeled data to create the wrong classification [ 80 ]. When being trained with adversarial examples, a diagnostic AI could make an incorrect diagnosis, a conversational virtual assistant could offer harmful advice to patients, and a clinical decision support AI could make the wrong recommendations, to list a few. Moreover, feeding an AI model with adversarial training examples and other poisonous data can also deteriorate the performance of AI, eventually making the AI model useless and thus unavailable. In general, adversarial attacks can pose long-term risks, such as thwarting AI innovation in health care because of concerns about misdiagnosis, mistreatment, and patient safety.

Implementation Phase

In practice, generative AI systems in health care have been found to experiencing integrity threats, such as generating disinformation and misinformation, and making biased decisions [ 81 ]. AI hallucination is a newly-coined term describing the phenomenon wherein generative AI generates fake information that appears authentic [ 82 ]. If generative AI in health care is used for diagnostics, personalized medicine, or clinical assistance, AI hallucination can be extremely dangerous and may even harm patients’ lives [ 83 ]. As discussed before, because GANs and LLMs need large annotated medical data for training, the difficulty of acquiring such data (eg, unwillingness to share because of legal compliance requirements and data paucity resulting from rare medical conditions) leads to the proliferation of synthetic medical data creation. The relationship between AI hallucination by GANs and LLMs and synthetic data use is an unknown territory in research and practice, leading to unknown vulnerabilities such as adversarial attacks.

Privacy attacks are a grave concern at this stage. The use of GANs for creating synthetic EHRs and its associated privacy challenges are analyzed by Ghosheh et al [ 46 ]. Such privacy challenges are as follows: (1) risk of reidentification—although the data are synthetic, there might be a risk of reidentifying individuals if the synthetic data closely resemble real patient data; (2) data leakage—ensuring that the synthetic data do not leak sensitive information from the original data set; (3) model inversion attacks—potential for attackers to use the GAN model to infer sensitive information about the original data set. In this attack, attackers aim to reconstruct the training data using their ability to constantly query the model [ 84 ]; (4) membership inference attacks—an attacker gains access to a set of real patient records and tries to determine whether any of the real patients are included in the training set of the GAN model [ 85 ]; and (5) attribute disclosure attacks—an attacker can infer additional attributes about a patient by learning a subset of other attributes about the same patient [ 86 ].

Generative medical diagnosis and drug discovery AI involving genomic databases and chemical compound or protein structure databases are extremely susceptible to privacy attacks. Fernandes et al [ 87 ] pointed out that genomic data such as DNA data are susceptible to inference attacks, reidentification attacks, membership attacks, and recovery attacks. It is extremely concerning when such attacks target high-profile individuals. Moreover, generative AI enhances the ability to profile patients, thereby increasing the risk of privacy violations and attacks, although this capability is not unique to AI.

In addition to AI-specific security and privacy threats, AI systems interfacing with other hardware and software may face new security and privacy threats that have never existed before [ 57 ]. Malicious use and exploitation may also threaten the integrity of AI systems. Similar to other AI systems, health care AI systems, especially generative AI systems, are susceptible to code extraction and information extraction (eg, black-box, gray-box, and white-box attacks), leading to security and privacy breaches [ 57 ]. The excessive use of prompts may reveal copyright-protective data, proprietary research findings (eg, chemical compounds of a new drug), and training models or algorithms.

Table 3 summarizes the previously discussed security and privacy threats associated with each category of generative AI systems throughout their life cycle in health care.

Again, it should be noted that some of these threats are unique to generative AI systems, but many of the threats are prevalent in broader AI systems in health care and other fields.

Recommendations

As security and privacy threats exist in the life cycle of various generative AI systems in health care, from data collection through model building to clinical implementation, a systematic approach to safeguard them is critical. This section provides some recommendations on safeguards. In doing so, we rely on the National Institute of Standards and Technology Privacy Framework and the National Institute of Standards and Technology AI Risk Management Framework as well as the regulatory guidance discussed in the Literature Review section. It should be noted that although the security and privacy threats discussed in this study are significant and some are unique in the context of generative AI in health care, many are also common in other types of AI models and other AI application contexts. Hence, many of the recommendations we propose in the subsequent section can be applied to AI in non–health care contexts.

Development Protocols of Risk Assessment for Generative AI in Health Care

AI risks, including those of generative AI in health care, can emerge in a variety of ways at any phase of an AI project. Health care organizations need to learn from managing risks for other technologies to develop risk assessment protocols for generative AI in health care, along with risk assessment metrics.

AI Risk Assessment Protocols

To systematically manage AI risks, health care organizations must develop risk assessment protocols that include risk assessment procedures and methodologies by following industrial standards and frameworks as well as best practices [ 63 ]. A total of 3 main risk assessment activities are involved in the protocol development: risk identification, risk prioritization, and risk controls. All 3 activities must be conducted throughout the life cycle of a generative AI system in health care.

In the data collection and processing phase, health care organizations can use several methods to identify, prioritize, and control AI risks. As discussed before, health care data are messy and tend to have organic biases (eg, a hospital specializes in serving a particular patient demographic, attending to gender-specific health requirements or offering dedicated care for rare diseases). When collecting data or using GANs to generate synthetic data, the health care field needs to be extremely diligent. One recommendation is to establish data collection or generation policies and procedures. The separation of clinical and nonclinical data is necessary, given the significantly different risks in these 2 types of data. Similarly, the establishment of the metrics and methods to check training data on biases for clinical and nonclinical data is also important. Data provenance and authentication metrics can be used to prevent collecting data from untrustworthy sources; detecting and filtering methods can be used to identify and filter poisoned data; and data standardization improves the quality of data collection [ 57 ]. As the frontline defense, these prevention mechanisms can prevent integrity and availability attacks during this phase. Nevertheless, regardless of the mechanisms, data collected from medical sources or generated by GANs should reflect the comprehensive overview of a medical domain and the complexity of the physical and digital dimensions in such a domain to prevent biases and test for risks.

In the model training and building phase, detecting and filtering are also important for identifying and removing adversary training examples. Robustness, generalizability, and other vulnerability tests (eg, black-box and white-box tests) can further prevent integrity and availability attacks and data breaches [ 88 ]. Input reconstruction is another mechanism to pinpoint sources of adversary training [ 89 ]. Modifying training processes and models as well as training methods may also help to control AI risks in this phase [ 57 ]. Given the complexity and variety of AI models in reasoning and learning, we suggest a taxonomy approach. For example, a deep learning model can carry significantly different risks than a probabilistic learning model. By building a taxonomy of AI models and their risks, researchers can systematically identify and control security and privacy risks based on the AI model.

In the model implementation phase, routine verification and validation are key to identifying and controlling AI risks [ 63 ]. The implementation contexts of generative AI also matter. In some cases, verification and validation are about not only factual accuracy but also communications and perceptions as well as cultures. A medical chatbot that was thoroughly tested in adult populations may not be very useful in teenage populations. Gesture and face recognition AI for medical diagnosis may need to be culturally sensitive to be useful. When generative AI is integrated and interacts with other systems, for example, to create multiagent systems or medical robotics (eg, companion robots), security tests along with social, philosophical, and ethical tests are a must.

AI Risk Assessment Metrics

Given the complexity of AI security and privacy risks, health care organizations should develop risk assessment metrics for each of the 3 phases of the life cycle of a generative AI project. The following subsections highlight some measures for AI risk assessment metrics.

Security Objectives

AI risk assessment metrics should include well-established security and privacy objectives such as confidentiality, integrity, availability, nonrepudiation, authentication, and privacy protection. In the data collection and processing phase, collection technologies should be evaluated regardless of software- or hardware-based collection to ensure that they meet the security and privacy objectives. The use of synthetic medical data should follow the same security and privacy objectives to ensure that such data capture the factual and scientific truth. In the model training and building phase, vulnerability tests should be conducted to identify known and unknown threats based on security objectives. For example, availability attacks such as denial of service can be used to flood conversational health AI applications to assess their resilience and availability before deployment, and integrity attacks with poisoned data can be used to test the stability of model performance and generalizability [ 57 ]. In the implementation phase, all security objectives should be routinely assessed.

Generative AI–Specific Metrics

Ai inscrutability.

AI inscrutability refers to the lack of understandability of an AI model and its outcomes [ 63 ]. Although AI inscrutability is not directly related to security and privacy, it adds obfuscations to AI risk assessment to identify threats and vulnerabilities as well as biases owing to the lack of transparency and explainability in AI, especially in generative AI based on deep learning. Although we have identified AI inscrutability as a key metric for generative AI assessment, we acknowledge that the challenge of inscrutability is not unique to generative AI and has been a long-standing issue in the broader field of AI, particularly in health care. Various algorithms used in patient matching, diagnosis, and other proprietary applications often lack transparency because of their closed nature or intellectual property constraints. Therefore, many of them, even those that are not based on generative techniques, face similar scrutiny regarding their lack of transparency. Hence, the call for greater openness and explainability applies broadly across AI applications in health care, reflecting a growing demand for accountable and interpretable AI systems.

Nevertheless, the problem of inscrutability becomes pronounced in the context of generative AI because of its complex and often opaque decision-making processes, which can amplify the challenges already faced in health care AI. Generative AI models, especially when based on deep learning, can operate as “black boxes,” making it even more difficult for practitioners to understand how conclusions or recommendations are derived. This opacity is a critical concern in health care, where explainability and trust as well as accountability are paramount for clinical acceptance and ethical practice.

To address these concerns, there is a need for concerted efforts toward developing more interpretable AI models and regulatory frameworks that mandate transparency in AI applications, including those used in patient care. These efforts should be complemented by initiatives to educate health care professionals about the workings and limitations of AI tools, enabling them to make informed decisions while using these technologies in clinical settings. Therefore, although the inscrutability of generative AI presents specific challenges owing to the complexity and novelty of these models, it is a continuation of the broader issue of transparency in health care AI. Recognizing this, our discussion of AI inscrutability not only highlights the unique aspects of generative AI but also situates it within the ongoing discourse on the need for greater transparency and accountability in all AI applications in health care.

AI Trustworthiness

AI trustworthiness is defined as the degree to which stakeholders of an AI system have confidence in its various attributes [ 63 , 90 ]. Trust has been a significant factor in IT adoption. The fundamental argument is that if an IT system automatically runs behind the scenes to assist the work and decisions of human users, a trusting relationship must be established for users to interact with and rely on the system [ 91 ]. Nevertheless, trust is a complex concept and is built upon human users’ interaction and consequent assessment of the system from cognitive, emotional, and social dimensions [ 91 - 93 ]. Since the emergence of AI, AI trustworthiness has caught significant attention in research, given the foreseeable complexity of human-AI interaction. The rise of generative AI has stimulated more discussions on this topic. The current consensus is that AI trustworthiness itself is a complex measurement with multiple dimensions, such as reliability, resilience, accuracy, and completeness [ 63 , 90 ]. Many other AI metrics or factors, such as transparency, explainability, robustness, fairness, and user interactions or perceptions, can be the antecedents of AI trustworthiness. AI trustworthiness can also be context dependent. For example, explainability and interaction experience can be the determinants of the AI trustworthiness of a chatbot application on the patient portal, whereas reliability, accuracy, and completeness are significant factors in the AI trustworthiness of a radiology diagnosis AI for radiologists. Given the complexity of measuring AI trustworthiness, we recommend developing context-specific AI trustworthiness metrics. Similar to AI inscrutability, although AI trustworthiness is not a direct measure of security and privacy risks, it helps reduce the probability and magnitude of such risks throughout the life cycle of generative AI in health care. For instance, accuracy and reliability help to improve the integrity of an AI system.

AI Responsibility

AI responsibility is another key measure in AI risk assessment. Again, although this measure does not directly evaluate security and privacy risks, it endorses responsible AI practices that facilitate the discovery of the negative consequences and risks of AI, including the security and privacy risks of generative AI. Moreover, this measure is centered on the uniqueness of AI, especially generative AI, in “human centricity, social responsibility, and sustainability” [ 63 ]. In other words, AI responsibility is a multifaceted measure depending on many other metrics and factors such as the ethical framework (eg, biases, fairness, and transparency) and legal perspective (eg, accountability and traceability). This is also an emerging concept that is under development. The development and deployment of generative AI add complexity to this measure owing to its possible, unintended, but profound negative consequences and risks to human society. In health care, there is a legal ambiguity related to AI responsibility. Hospitals are still unclear about their legal liability when facing an AI incident. Despite such legal uncertainty, responsible AI use should be the baseline. We recommend that health care organizations use AI for consultation and assistance instead of replacement, given legal ambiguity and uncertainty, while intensively exploring generative AI from the perspectives of patient centricity and social responsibility and asking serious questions. For example, a generative drug discovery AI may find a new molecular formula for a biochemical weapon. How can we responsibly use such AI without crossing the line of no harm to human beings? Such a question leads to another key measure for AI risk assessment—AI harm.

AI harm can occur to individuals, organizations, and societies. For example, AI may cause physical harm to individual patients, damage a hospital’s reputation owing to AI incidents, and even endanger society if it is weaponized (eg, being used to disrupt the global drug manufacturing and supply chain). Hence, AI harm is a risk measure highly related to AI responsibility and trustworthiness. Developing trustworthy AI and following responsible AI practices can reduce or avoid AI harm.

It is worth mentioning that some of the metrics we proposed here pass some human characteristics into AI. A crucial philosophical distinction must be made regarding the attribution of human characteristics such as trustworthiness and responsibility to generative AI systems versus the health care organizations and technology partners developing these algorithms. Although metrics aim to make models appear more trustworthy and responsible in reality, trust emerges from human-centered institutional processes, and responsibility stems from human accountability. It may be challenging to humanize AI systems and transfer attributes such as trustworthiness to the algorithms themselves. Indicators of model transparency, reliability, or accuracy may engender confidence among stakeholders, but public trust fundamentally arises from the ethical data governance, risk communication, and oversight procedures instantiated by organizations. Without robust governance and review processes overseeing development, data practices, and risk monitoring, claims of AI trustworthiness lack substantiation. Similarly, although algorithmic outputs highlighting potential issues such as biases or errors increase awareness, this does not intrinsically amount to AI responsibility. True accountability involves diligent human investigation of problems that surface, enacting appropriate recourse, and continuous authority oversight. Metrics may aim for AI to appear more responsible, but responsibility mainly manifests in organizational commitment to discovering issues, working with experts to properly assess AI harms, and instituting robust redress processes with stakeholder input. Thus, trustworthiness and responsibility are contingent on extensive institutional support structures rather than innate model capabilities. Although progress indicators may serve as signals for these desired attributes, establishing genuine public trust and accountability in health care ultimately falls on the shoulders of health care administrators, innovators, and engaged communities, rather than solely on the algorithms themselves. Clarifying this distinction enables us to properly set expectations and delineate responsibilities as generative AI becomes increasingly prevalent in critical medical settings.

Conclusions

Integrating generative AI systems into health care offers immense potential to transform medical diagnostics, research, treatment planning, and patient care. However, deploying these data-intensive technologies also introduces complex privacy and security challenges that must be proactively addressed to ensure the safe and effective use of these systems. Examining diverse applications of generative AI across medical domains (ie, medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support) helps this study uncover vulnerabilities and threats across the life cycle of these systems, from data collection to model development to clinical implementation. Although generative AI enables innovative use cases, adequate safeguards are needed to prevent breaches of PHI and to maintain public trust. Strategies such as developing AI risk assessment protocols; formulating specific metrics for generative AI such as inscrutability, trustworthiness, responsibility, and harm; and ongoing model monitoring can help mitigate risks. However, developing robust governance frameworks and updates to data privacy regulations are also required to oversee these rapidly evolving technologies. By analyzing the use cases, impacts, and risks of generative AI across diverse domains within health care, this study contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. Future research and development in generative AI systems should emphasize security and privacy to ensure the responsible and trustworthy use of these AI models in health care. Moreover, the security and privacy concerns highlighted in this analysis should serve as a call to action for both the AI community and health care organizations looking to integrate generative AI. Collaborative efforts between AI developers, health care providers, policy makers, and domain experts will be critical to unlocking the benefits of generative AI while also prioritizing ethics, accountability, and safety. By laying the groundwork to make security and privacy the central pillars of generative AI in medicine, stakeholders can work to ensure that these transformative technologies are harnessed responsibly for patients worldwide.

Conflicts of Interest

None declared.

  • Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med. Jul 2019;132(7):795-801. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. Mar 06, 2023;9:e46885. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. Feb 9, 2023;2(2):e0000198. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Li X, Jiang Y, Rodriguez-Andina JJ, Luo H, Yin S, Kaynak O. When medical images meet generative adversarial network: recent development and research opportunities. Discov Artif Intell. Sep 22, 2021;1(1):1-20. [ FREE Full text ] [ CrossRef ]
  • Topol EJ. As artificial intelligence goes multimodal, medical applications multiply. Science. Sep 15, 2023;381(6663):adk6139. [ CrossRef ] [ Medline ]
  • Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, et al. Opinion paper: “so what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manage. Aug 2023;71:102642. [ CrossRef ]
  • Thirunavukarasu AJ, Ting DS, Elangovan K, Gutierrez L, Tan TF, Ting DS. Large language models in medicine. Nat Med. Aug 17, 2023;29(8):1930-1940. [ CrossRef ] [ Medline ]
  • Alqahtani H, Kavakli-Thorne M, Kumar G. Applications of generative adversarial networks (GANs): an updated review. Arch Computat Methods Eng. Dec 19, 2019;28(2):525-552. [ CrossRef ]
  • Jain S, Seth G, Paruthi A, Soni U, Kumar G. Synthetic data augmentation for surface defect detection and classification using deep learning. J Intell Manuf. Nov 18, 2020;33(4):1007-1020. [ CrossRef ]
  • Arora A, Arora A. The promise of large language models in health care. Lancet. Feb 2023;401(10377):641. [ CrossRef ]
  • Zeng X, Wang F, Luo Y, Kang S, Tang J, Lightstone FC, et al. Deep generative molecular design reshapes drug discovery. Cell Rep Med. Dec 20, 2022;3(12):100794. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Jiang S, Hu J, Wood KL, Luo J. Data-driven design-by-analogy: state-of-the-art and future directions. J Mech Des. 2022;144(2):020801. [ CrossRef ]
  • Javaid M, Haleem A, Singh RP. ChatGPT for healthcare services: an emerging stage for an innovative perspective. TBench. Feb 2023;3(1):100105. [ CrossRef ]
  • Nova K. Generative AI in healthcare: advancements in electronic health records, facilitating medical languages, and personalized patient care. J Adv Anal Healthc Manag. 2023;7(1):115-131. [ FREE Full text ]
  • Zhang P, Kamel Boulos MN. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet. Aug 24, 2023;15(9):286. [ CrossRef ]
  • Byrne DW. Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward with Rigorous Science. Philadelphia, PA. Lippincott Williams & Wilkins; 2022.
  • Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare. Amsterdam, The Netherlands. Elsevier Academic Press; 2020.
  • Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. Jan 2021;26(1):80-93. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. Aug 17, 2022;56(4):3005-3054. [ CrossRef ]
  • Martín-Noguerol T, Oñate Miranda MO, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, et al. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol. Apr 2023;161:110726. [ CrossRef ] [ Medline ]
  • Ellis RJ, Sander RM, Limon A. Twelve key challenges in medical machine learning and solutions. Intell Based Med. 2022;6:100068. [ CrossRef ]
  • Martinelli DD. Generative machine learning for de novo drug discovery: a systematic review. Comput Biol Med. Jun 2022;145:105403. [ CrossRef ] [ Medline ]
  • Kasirzadeh A, Gabriel I. In conversation with artificial intelligence: aligning language models with human values. Philos Technol. Apr 19, 2023;36(2):1-24. [ CrossRef ]
  • van Bussel MJ, Odekerken-Schröder GJ, Ou C, Swart RR, Jacobs MJ. Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study. BMC Health Serv Res. Jul 09, 2022;22(1):890. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Xu L, Sanders L, Li K, Chow JC. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer. Nov 29, 2021;7(4):e27850. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Summerfield C. Natural General Intelligence: How Understanding the Brain Can Help Us Build AI. Oxford, UK. Oxford University Press; 2022.
  • Gesk TS, Leyer M. Artificial intelligence in public services: when and why citizens accept its usage. Gov Inf Q. Jul 2022;39(3):101704. [ CrossRef ]
  • Wang Z, Qinami K, Karakozis IC, Genova K, Nair P, Hata K. Towards fairness in visual recognition: effective strategies for bias mitigation. In: Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020 Presented at: CVPR '20; June 13-19, 2020, 2020;8916-8925; Seattle, WA. URL: https://ieeexplore.ieee.org/document/9156668 [ CrossRef ]
  • Cai Z, Xiong Z, Xu H, Wang P, Li W, Pan Y. Generative adversarial networks: a survey toward private and secure applications. ACM Comput Surv. Jul 13, 2021;54(6):1-38. [ CrossRef ]
  • Huang C, Kairouz P, Chen X, Sankar L, Rajagopal R. Context-aware generative adversarial privacy. Entropy. Dec 01, 2017;19(12):656. [ CrossRef ]
  • Tripathy A, Wang Y, Ishwar P. Privacy-preserving adversarial networks. In: Proceedings of the 57th Annual Allerton Conference on Communication, Control, and Computing. 2019 Presented at: ALLERTON '19; September 24-27, 2019, 2019;495-505; Monticello, IL. URL: https://ieeexplore.ieee.org/document/8919758 [ CrossRef ]
  • Chen CS, Chang SF, Liu CH. Understanding knowledge-sharing motivation, incentive mechanisms, and satisfaction in virtual communities. Soc Behav Pers. May 01, 2012;40(4):639-647. [ CrossRef ]
  • Liu S, Shrivastava A, Du J, Zhong L. Better accuracy with quantified privacy: representations learned via reconstructive adversarial network. arXiv Preprint posted online January 25, 2019. 2019. [ FREE Full text ] [ CrossRef ]
  • Tseng BW, Wu PY. Compressive privacy generative adversarial network. IEEE Trans Inf Forensics Secur. 2020;15:2499-2513. [ CrossRef ]
  • Shokri R, Shmatikov V. Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 2015 Presented at: CCS '15; October 12-16, 2015, 2015;1310-1321; Denver, CO. URL: https://dl.acm.org/doi/10.1145/2810103.2813687 [ CrossRef ]
  • McMahan B, Moore E, Ramage D, Hampson S. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 2017. Presented at: AISTATS '17; April 20-22, 2017, 2017; Fort Lauderdale, FL. URL: https://proceedings.mlr.press/v54/mcmahan17a?ref=https://githubhelp.com
  • Nasr M, Shokri R, Houmansadr A. Machine learning with membership privacy using adversarial regularization. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 2018 Presented at: CCS '18; October 15-19, 2018, 2018;634-646; Toronto, ON. URL: https://dl.acm.org/doi/10.1145/3243734.3243855 [ CrossRef ]
  • Abadir PM, Chellappa R, Choudhry N, Demiris G, Ganesan D, Karlawish J, et al. The promise of AI and technology to improve quality of life and care for older adults. Nat Aging. Jun 25, 2023;3(6):629-631. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Kim BN, Dolz J, Jodoin PM, Desrosiers C. Privacy-net: an adversarial approach for identity-obfuscated segmentation of medical images. IEEE Trans Med Imaging. Jul 27, 2021;40(7):1737-1749. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Choi E, Biswal S, Malin B, Duke J, Stewart WF, Sun J. Generating multi-label discrete patient records using generative adversarial networks. In: Proceedings of the 2017 Machine Learning for Health Care Conference. 2017 Presented at: MLHC '17; August 18-19, 2017, 2017;1-20; Boston, MA. URL: https://proceedings.mlr.press/v68/choi17a/choi17a.pdf
  • Yale A, Dash S, Dutta R, Guyon I, Pavao A, Bennett KP. Generation and evaluation of privacy preserving synthetic health data. Neurocomput. Nov 2020;416:244-255. [ CrossRef ]
  • Torfi A, Fox EA. CorGAN: correlation-capturing convolutional generative adversarial networks for generating synthetic healthcare records. arXiv Preprint posted online January 25, 2020. 2020. [ FREE Full text ]
  • Lee D, Yu H, Jiang X, Rogith D, Gudala M, Tejani M, et al. Generating sequential electronic health records using dual adversarial autoencoder. J Am Med Inform Assoc. Jul 01, 2020;27(9):1411-1419. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Rane N. ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges and opportunities for industry 4.0, industry 5.0 and society 5.0. SSRN J. 2023. [ FREE Full text ] [ CrossRef ]
  • Bale AS, Dhumale R, Beri N, Lourens M, Varma RA, Kumar V, et al. The impact of generative content on individuals privacy and ethical concerns. Int J Intell Syst Appl Eng. 2023;12(1):697-703. [ FREE Full text ]
  • Ghosheh GO, Li J, Zhu T. A survey of generative adversarial networks for synthesizing structured electronic health records. ACM Comput Surv. Jan 22, 2024;56(6):1-34. [ CrossRef ]
  • Hernandez M, Epelde G, Alberdi A, Cilla R, Rankin D. Synthetic data generation for tabular health records: a systematic review. Neurocomput. Jul 2022;493:28-45. [ CrossRef ]
  • Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L. Generative AI and ChatGPT: applications, challenges, and AI-human collaboration. J Inf Technol Case Appl Res. Jul 21, 2023;25(3):277-304. [ CrossRef ]
  • Ali M, Naeem F, Tariq M, Kaddoum G. Federated learning for privacy preservation in smart healthcare systems: a comprehensive survey. IEEE J Biomed Health Inform. Feb 2023;27(2):778-789. [ CrossRef ]
  • Khan S, Saravanan V, Lakshmi TJ, Deb N, Othman NA. Privacy protection of healthcare data over social networks using machine learning algorithms. Comput Intell Neurosci. Mar 24, 2022;2022:9985933-9985938. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Sun H, Zhu T, Zhang Z, Jin D, Xiong P, Zhou W. Adversarial attacks against deep generative models on data: a survey. IEEE Trans Knowl Data Eng. Apr 1, 2023;35(4):3367-3388. [ CrossRef ]
  • The legal issues presented by generative AI. MIT Sloan School of Management. URL: https://mitsloan.mit.edu/ideas-made-to-matter/legal-issues-presented-generative-ai [accessed 2024-01-29]
  • EU AI Act: first regulation on artificial intelligence. European Parliament. 2023. URL: https:/​/www.​europarl.europa.eu/​topics/​en/​article/​20230601STO93804/​eu-ai-act-first-regulation-on-artificial-intelligence [accessed 2024-02-16]
  • Blueprint for an AI bill of rights: making automated systems work for the American people. The White House. URL: https://www.whitehouse.gov/ostp/ai-bill-of-rights/ [accessed 2024-02-19]
  • Ahmad K, Maabreh M, Ghaly M, Khan K, Qadir J, Al-Fuqaha A. Developing future human-centered smart cities: critical analysis of smart city security, Data management, and Ethical challenges. Comput Sci Rev. Feb 2022;43:100452. [ CrossRef ]
  • Liu Q, Li P, Zhao W, Cai W, Yu S, Leung VC. A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access. 2018;6:12103-12117. [ CrossRef ]
  • Hu Y, Kuang W, Qin Z, Li K, Zhang J, Gao Y, et al. Artificial intelligence security: threats and countermeasures. ACM Comput Surv. Nov 23, 2021;55(1):1-36. [ CrossRef ]
  • Tang X, Yin P, Zhou Z, Huang D. Adversarial perturbation elimination with GAN based defense in continuous-variable quantum key distribution systems. Electronics. May 27, 2023;12(11):2437. [ CrossRef ]
  • Gu S, Rigazio L. Towards deep neural network architectures robust to adversarial examples. arXiv Preprint posted online December 11, 2014. 2014. [ FREE Full text ]
  • Brown H, Lee K, Mireshghallah F, Shokri R, Tramèr F. What does it mean for a language model to preserve privacy? In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022. Presented at: FAccT '22; June 21-24, 2022, 2022; Seoul, Republic of Korea. URL: https://dl.acm.org/doi/fullHtml/10.1145/3531146.3534642 [ CrossRef ]
  • Albahri A, Duhaim AM, Fadhel MA, Alnoor A, Baqer NS, Alzubaidi L, et al. A systematic review of trustworthy and explainable artificial intelligence in healthcare: assessment of quality, bias risk, and data fusion. Inf Fusion. Aug 2023;96:156-191. [ CrossRef ]
  • Hacker P, Engel A, Mauer M. Regulating ChatGPT and other large generative AI models. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 2023 Presented at: FAccT '23; June 12-15, 2023, 2023;1112-1113; Chicago, IL. URL: https://dl.acm.org/doi/abs/10.1145/3593013.3594067 [ CrossRef ]
  • Artificial Intelligence Risk Management Framework (AIRMF1.0). National Institute of Standards and Technology. 2023. URL: https://doi.org/10.6028/NIST.AI.100-1 [accessed 2023-09-20]
  • Learned K, Durbin A, Currie R, Kephart ET, Beale HC, Sanders LM, et al. Barriers to accessing public cancer genomic data. Sci Data. Jun 20, 2019;6(1):98. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Park Y, Son Y, Shin H, Kim D. This ain't your dose: sensor spoofing attack on medical infusion pump. In: Proceedings of the 10th USENIX Workshop on Offensive Technologies. 2016. Presented at: WOOT '16; August 8-9, 2016, 2016; Austin, TX. URL: https://www.usenix.org/system/files/conference/woot16/woot16-paper-park_0.pdf
  • Shoukry Y, Martin P, Tabuada P, Srivastava M. Non-invasive spoofing attacks for anti-lock braking systems. In: Proceedings of the 15th International Workshop on Cryptographic Hardware and Embedded Systems. 2013 Presented at: CHES '13; August 20-23, 2013, 2013;55-72; Santa Barbara, CA. URL: https://link.springer.com/chapter/10.1007/978-3-642-40349-1_4 [ CrossRef ]
  • Quiring E, Klein D, Arp D, Johns M, Rieck K. Adversarial preprocessing: understanding and preventing image-scaling attacks in machine learning. In: Proceedings of the 29th USENIX Security Symposium. 2020 Presented at: USS '20; August 12-14, 2020, 2020;1363-1380; Boston, MA. URL: https://www.usenix.org/conference/usenixsecurity20/presentation/quiring
  • Xiao Q, Chen Y, Shen C, Chen Y, Li K. Seeing is not believing: camouflage attacks on image scaling algorithms. In: Proceedings of the 28th USENIX Security Symposium. 2019. Presented at: USENIXS '19; August 14-16, 2019, 2019; Santa Clara, CA. URL: https://www.usenix.org/conference/usenixsecurity19/presentation/xiao
  • Reichman B, Jing L, Akin O, Tian Y. Medical image tampering detection: a new dataset and baseline. In: Proceedings of the 2021 Workshops and Challenges on Pattern Recognition. 2021 Presented at: ICPR '21; January 10-15, 2021, 2021;266-277; Virtual Event. URL: https://link.springer.com/chapter/10.1007/978-3-030-68763-2_20 [ CrossRef ]
  • Harrer S. Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine. Apr 2023;90:104512. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Chen RJ, Lu MY, Chen TY, Williamson DF, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. Jun 15, 2021;5(6):493-497. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Han C, Rundo L, Araki R, Nagano Y, Furukawa Y, Mauri G, et al. Combining noise-to-image and image-to-image GANs: brain MR image augmentation for tumor detection. IEEE Access. 2019;7:156966-156977. [ CrossRef ]
  • Lu TK, Khalil AS, Collins JJ. Next-generation synthetic gene networks. Nat Biotechnol. Dec 9, 2009;27(12):1139-1150. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Synthetic data is enabling better healthcare tools - here’s how. Particle Health. URL: https://www.particlehealth.com/blog/synthetic-data-healthcare-tools [accessed 2024-01-29]
  • Chen D, Yu N, Zhang Y, Fritz M. GAN-Leaks: a taxonomy of membership inference attacks against generative models. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. 2020 Presented at: CCS '20; November 9-13, 2020, 2020;343-362; Virtual Event. URL: https://dl.acm.org/doi/10.1145/3372297.3417238 [ CrossRef ]
  • Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H. Beyond inferring class representatives: user-level privacy leakage from federated learning. In: Proceedings of the 2019 IEEE Conference on Computer Communications. 2019 Presented at: IEEE INFOCOM '19; April 29-May 2, 2019, 2019;2512-2520; Virtual Event. URL: https://dl.acm.org/doi/abs/10.1109/infocom.2019.8737416 [ CrossRef ]
  • Xie Q, Schenck EJ, Yang HS, Chen Y, Peng Y, Wang F. Faithful AI in medicine: a systematic review with large language models and beyond. Res Sq. Dec 04, 2023.:2023. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • McCoy LG, Brenna CT, Chen SS, Vold K, Das S. Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. J Clin Epidemiol. Feb 2022;142:252-257. [ CrossRef ] [ Medline ]
  • Mahmood F, Chen R, Durr NJ. Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans Med Imaging. Dec 2018;37(12):2572-2581. [ CrossRef ]
  • Shafahi A, Huang W, Najibi M, Suciu O, Studer C, Dumitras TA, et al. Poison frogs! targeted clean-label poisoning attacks on neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018 Presented at: NIPS'18; December 3-8, 2018, 2018;6106-6116; Montréal, QC. URL: https://dl.acm.org/doi/10.5555/3327345.3327509
  • Walker HL, Ghani S, Kuemmerli C, Nebiker CA, Müller BP, Raptis DA, et al. Reliability of medical information provided by ChatGPT: assessment against clinical guidelines and patient information quality instrument. J Med Internet Res. Jun 30, 2023;25:e47479. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Athaluri S, Manthena SV, Kesapragada VK, Yarlagadda V, Dave T, Duddumpudi RT. Exploring the boundaries of reality: investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references. Cureus. Apr 2023;15(4):e37432. [ FREE Full text ] [ CrossRef ] [ Medline ]
  • Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI Chatbot for medicine. N Engl J Med. Mar 30, 2023;388(13):1233-1239. [ CrossRef ]
  • Fredrikson M, Jha S, Ristenpart T. Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 2015 Presented at: CCS '15; October 12-16, 2015, 2015;1322-1323; Denver, CO. [ CrossRef ]
  • Shokri R, Stronati M, Song C, Shmatikov V. Membership inference attacks against machine learning models. In: Proceedings of the 38th IEEE Symposium on Security and Privacy. 2017 Presented at: SSP '17; May 22-24, 2017, 2017;3-18; San Jose, CA. URL: https://www.computer.org/csdl/proceedings-article/sp/2017/07958568/12OmNBUAvVc [ CrossRef ]
  • Matwin S, Nin J, Sehatkar M, Szapiro T. A review of attribute disclosure control. In: Advanced Research in Data Privacy. Thousand Oaks, CA. Springer; 2015.
  • Fernandes M, Decouchant J, Couto FM. Security, privacy, and trust management in DNA computing. Adv Comput. 2023.:129. [ CrossRef ]
  • Mopuri KR, Uppala PK, Babu VR. Ask, acquire, and attack: data-free UAP generation using class impressions. In: proceedings of the 15th European Conference on Computer Vision. 2018 Presented at: ECCV '18; September 8-14, 2018, 2018;20-35; Munich, Germany. URL: https://link.springer.com/chapter/10.1007/978-3-030-01240-3_2 [ CrossRef ]
  • Song Y, Kim T, Nowozin S, Ermon S, Kushman N. Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. arXiv Preprint posted online October 30, 2017. 2017. [ FREE Full text ] [ CrossRef ]
  • Mattioli J, Sohier H, Delaborde A, Amokrane-Ferka K, Awadid A, Chihani Z, et al. Towards a holistic approach for AI trustworthiness assessment based upon aids for multi-criteria aggregation. In: Proceedings of the Safe AI 2023-The AAAI’s Workshop on Artificial Intelligence Safety. 2023. Presented at: SafeAI '23; February 13-14, 2023, 2023; Washington, DC. URL: https://hal.science/hal-04086455
  • Chen Y, Zahedi FM, Abbasi A, Dobolyi D. Trust calibration of automated security IT artifacts: a multi-domain study of phishing-website detection tools. Inf Manag. Jan 2021;58(1):103394. [ CrossRef ]
  • Lankton N, McKnight DH, Tripp J. Technology, humanness, and trust: rethinking trust in technology. J Assoc Inf Syst. Oct 2015;16(10):880-918. [ FREE Full text ] [ CrossRef ]
  • Mcknight DH, Carter M, Thatcher JB, Clay PF. Trust in a specific technology: an investigation of its components and measures. ACM Trans Manag Inf Syst. Jul 2011;2(2):1-25. [ CrossRef ]

Abbreviations

Edited by T de Azevedo Cardoso, G Eysenbach; submitted 22.09.23; peer-reviewed by P Williams, M Noman; comments to author 27.11.23; revised version received 12.12.23; accepted 31.01.24; published 08.03.24.

©Yan Chen, Pouyan Esmaeilzadeh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

IMAGES

  1. Lesson 2- The importance of Quantitative Research across fields-2.pdf

    what is importance of quantitative research across fields

  2. Importance of Quantitative Research Across Different Fields

    what is importance of quantitative research across fields

  3. Importance of Quantitative Research Across Fields

    what is importance of quantitative research across fields

  4. SOLUTION: Importance of quantitative research across fields

    what is importance of quantitative research across fields

  5. Chapter 3

    what is importance of quantitative research across fields

  6. Importance of Quantitative Research Across Fields

    what is importance of quantitative research across fields

VIDEO

  1. Quantitative research process

  2. Statistical Foundations

  3. Importance of Quantitative Research Across Fields

  4. Qualitative research vs Quantitative research explained in hindi

  5. Quantitative and Qualitative research in research psychology

  6. Types of Research / Exploratory/ Descriptive /Quantitative/qualitative /Applied /Basic Research

COMMENTS

  1. Why Is Quantitative Research Important?

    The purpose of quantitative research is to attain greater knowledge and understanding of the social world. Researchers use quantitative methods to observe situations or events that affect people. 1 Quantitative research produces objective data that can be clearly communicated through statistics and numbers. We do this in a systematic scientific ...

  2. What Is Quantitative Research?

    Revised on June 22, 2023. Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and analyzing ...

  3. What is quantitative research?

    Quantitative research is a powerful tool for anyone looking to learn more about their market and customers. It allows you to gain reliable, objective insights from data and clearly understand trends and patterns. Where quantitative research falls short is in explaining the 'why'. This is where you need to turn to other methods, like ...

  4. Quantitative Research

    Quantitative research is a method of inquiry that uses numbers and mathematical operations to explore questions about reality. ... Quantitative research has many applications across a wide range of fields. Here are some common examples: ... It is important to clearly communicate the research question, methods, results, and conclusions to ensure ...

  5. Quantitative research

    Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.. Associated with the natural, applied, formal, and social sciences this research strategy promotes the objective empirical investigation of ...

  6. What is Quantitative Research? Definition, Examples, Key ...

    Quantitative Research: Key Advantages. The advantages of quantitative research make it a valuable research method in a variety of fields, particularly in fields that require precise measurement and testing of hypotheses. Precision: Quantitative research aims to be precise in its measurement and analysis of data.

  7. 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.

  8. A Quick Guide to Quantitative Research in the Social Sciences

    About the Book. This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed ...

  9. What Is Quantitative Research?

    Quantitative research is the opposite of qualitative research, which involves collecting and analysing non-numerical data (e.g. text, video, or audio). Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc. Quantitative research question examples.

  10. Definition of quantitative research and its importance

    Quantitative research is a systematic empirical approach used in the social sciences and various other fields to gather, analyze, and interpret numerical data. It focuses on obtaining measurable data and applying statistical methods to generalize findings to a larger population. Researchers use structured instruments such as surveys ...

  11. Quantitative and Qualitative Research

    What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns. Quantitative research gathers a range of numeric data.

  12. The Importance of Quantitative Research Across Fields ...

    Full transcript on this video lecture is available at: https://philonotes.com/2022/05/importance-of-quantitative-research-across-fields*****See also:How to F...

  13. Quantitative research in education : Background information

    Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Commonly referred to as quantitative methods, these techniques cover a range of statistical tests and tools. The Sage encyclopedia of educational research, measurement, and evaluation by Bruce B. Frey (Ed.)

  14. Importance of Quantitative Research Across Fields

    Importance of Quantitative Research Across Fields. First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people ...

  15. Importance of Quantitative Research Across Fields

    Module 1 Lesson 2 Illustrates the importance of quantitative research across fields (CS_RS12-LA-C-2)Basically, quantitative research is important because it ...

  16. PDF Quantitative Research Across Field

    Value of Research across Field. Discover the unknown and improve underlying conditions Can influence leaders and law-makers' decisions Helps determine and better understand relationships between ...

  17. IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS

    ‼️SHS PRACTICAL RESEARCH 2‼️🟣 GRADE 11: IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS‼️GRADE 11 PLAYLISTS ‼️General MathematicsFirst Quarter: https://t...

  18. Module 2-Importance of Quantitative Research across

    ##### quantitative research is important across the field and NO if not. In marketing, quantitative research gives customer incorporates a survey-based ##### approach to gain feedback in relation to a populations ideas and opinions. ##### 2. Quantitative Research will negate positive results after data was gathered in math. ##### 3.

  19. Chapter 3

    IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS. People do research to find solutions, even tentative ones, to problems, in order to improve or enhance ways of doing things, to disprove or provide a new hypothesis, or simply to find answers to questions or solutions to problems in daily life.

  20. (PDF) Quantitative Research Across Field

    Quantitative Research Across Field. September 2021 ... research is important to this field. 11. ... Quantitative research that aims to identify factors that most contributed to academic ...

  21. Lesson 2 importance of quant r

    importance-of-research-across-fields.pptx. Lesson 2-quanti (1) Types of researches. The Nature of Qualitative Research. Slide 1 what is social science social science is about examinin. Lesson 2 characteristics, purpose and types of research. Research-Methods.pptx. Single-System Studies Mark A. Mattaini ocial work pr.docx.

  22. Importance of quantitative research across fields

    Unformatted Attachment Preview. Importance of Quantitative Research across Fields Ø ABM - Since ABM strand focuses more on sales, business, and especially marketing then the importance of research is basically to have survey and to gather information about what people more likely to buy, and strategic goals for a company to reach their goal.

  23. importance-of-research-across-fields.pptx

    IMPORTANCE OF QUANTITATIVE RESEARCH ACROSS FIELDS. 5. ANTHROPOLOGY - is the scientific study of humans, their behavior, and societies in the past. • Quantitative research has given huge contributions to the improvement of human life.

  24. Journal of Medical Internet Research

    As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient ...