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Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

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Data Descriptor
Open access
Published: 03 May 2024

A dataset for measuring the impact of research data and their curation

Libby Hemphill ORCID: orcid.org/0000-0002-3793-7281 1 , 2 ,
Andrea Thomer 3 ,
Sara Lafia 1 ,
Lizhou Fan 2 ,
David Bleckley ORCID: orcid.org/0000-0001-7715-4348 1 &
Elizabeth Moss 1

Scientific Data volume 11 , Article number: 442 ( 2024 ) Cite this article

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Metrics details

Research data
Social sciences

Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

SciSciNet: A large-scale open data lake for the science of science research

Data, measurement and empirical methods in the science of science

Interdisciplinarity revisited: evidence for research impact and dynamism

Background & summary.

Recent policy changes in funding agencies and academic journals have increased data sharing among researchers and between researchers and the public. Data sharing advances science and provides the transparency necessary for evaluating, replicating, and verifying results. However, many data-sharing policies do not explain what constitutes an appropriate dataset for archiving or how to determine the value of datasets to secondary users 1 , 2 , 3 . Questions about how to allocate data-sharing resources efficiently and responsibly have gone unanswered 4 , 5 , 6 . For instance, data-sharing policies recognize that not all data should be curated and preserved, but they do not articulate metrics or guidelines for determining what data are most worthy of investment.

Despite the potential for innovation and advancement that data sharing holds, the best strategies to prioritize datasets for preparation and archiving are often unclear. Some datasets are likely to have more downstream potential than others, and data curation policies and workflows should prioritize high-value data instead of being one-size-fits-all. Though prior research in library and information science has shown that the “analytic potential” of a dataset is key to its reuse value 7 , work is needed to implement conceptual data reuse frameworks 8 , 9 , 10 , 11 , 12 , 13 , 14 . In addition, publishers and data archives need guidance to develop metrics and evaluation strategies to assess the impact of datasets.

Several existing resources have been compiled to study the relationship between the reuse of scholarly products, such as datasets (Table 1 ); however, none of these resources include explicit information on how curation processes are applied to data to increase their value, maximize their accessibility, and ensure their long-term preservation. The CCex (Curation Costs Exchange) provides models of curation services along with cost-related datasets shared by contributors but does not make explicit connections between them or include reuse information 15 . Analyses on platforms such as DataCite 16 have focused on metadata completeness and record usage, but have not included related curation-level information. Analyses of GenBank 17 and FigShare 18 , 19 citation networks do not include curation information. Related studies of Github repository reuse 20 and Softcite software citation 21 reveal significant factors that impact the reuse of secondary research products but do not focus on research data. RD-Switchboard 22 and DSKG 23 are scholarly knowledge graphs linking research data to articles, patents, and grants, but largely omit social science research data and do not include curation-level factors. To our knowledge, other studies of curation work in organizations similar to ICPSR – such as GESIS 24 , Dataverse 25 , and DANS 26 – have not made their underlying data available for analysis.

This paper describes a dataset 27 compiled for the MICA project (Measuring the Impact of Curation Actions) led by investigators at ICPSR, a large social science data archive at the University of Michigan. The dataset was originally developed to study the impacts of data curation and archiving on data reuse. The MICA dataset has supported several previous publications investigating the intensity of data curation actions 28 , the relationship between data curation actions and data reuse 29 , and the structures of research communities in a data citation network 30 . Collectively, these studies help explain the return on various types of curatorial investments. The dataset that we introduce in this paper, which we refer to as the MICA dataset, has the potential to address research questions in the areas of science (e.g., knowledge production), library and information science (e.g., scholarly communication), and data archiving (e.g., reproducible workflows).

We constructed the MICA dataset 27 using records available at ICPSR, a large social science data archive at the University of Michigan. Data set creation involved: collecting and enriching metadata for articles indexed in the ICPSR Bibliography of Data-related Literature against the Dimensions AI bibliometric database; gathering usage statistics for studies from ICPSR’s administrative database; processing data curation work logs from ICPSR’s project tracking platform, Jira; and linking data in social science studies and series to citing analysis papers (Fig. 1 ).

Steps to prepare MICA dataset for analysis - external sources are red, primary internal sources are blue, and internal linked sources are green.

Enrich paper metadata

The ICPSR Bibliography of Data-related Literature is a growing database of literature in which data from ICPSR studies have been used. Its creation was funded by the National Science Foundation (Award 9977984), and for the past 20 years it has been supported by ICPSR membership and multiple US federally-funded and foundation-funded topical archives at ICPSR. The Bibliography was originally launched in the year 2000 to aid in data discovery by providing a searchable database linking publications to the study data used in them. The Bibliography collects the universe of output based on the data shared in each study through, which is made available through each ICPSR study’s webpage. The Bibliography contains both peer-reviewed and grey literature, which provides evidence for measuring the impact of research data. For an item to be included in the ICPSR Bibliography, it must contain an analysis of data archived by ICPSR or contain a discussion or critique of the data collection process, study design, or methodology 31 . The Bibliography is manually curated by a team of librarians and information specialists at ICPSR who enter and validate entries. Some publications are supplied to the Bibliography by data depositors, and some citations are submitted to the Bibliography by authors who abide by ICPSR’s terms of use requiring them to submit citations to works in which they analyzed data retrieved from ICPSR. Most of the Bibliography is populated by Bibliography team members, who create custom queries for ICPSR studies performed across numerous sources, including Google Scholar, ProQuest, SSRN, and others. Each record in the Bibliography is one publication that has used one or more ICPSR studies. The version we used was captured on 2021-11-16 and included 94,755 publications.

To expand the coverage of the ICPSR Bibliography, we searched exhaustively for all ICPSR study names, unique numbers assigned to ICPSR studies, and DOIs 32 using a full-text index available through the Dimensions AI database 33 . We accessed Dimensions through a license agreement with the University of Michigan. ICPSR Bibliography librarians and information specialists manually reviewed and validated new entries that matched one or more search criteria. We then used Dimensions to gather enriched metadata and full-text links for items in the Bibliography with DOIs. We matched 43% of the items in the Bibliography to enriched Dimensions metadata including abstracts, field of research codes, concepts, and authors’ institutional information; we also obtained links to full text for 16% of Bibliography items. Based on licensing agreements, we included Dimensions identifiers and links to full text so that users with valid publisher and database access can construct an enriched publication dataset.

Gather study usage data

ICPSR maintains a relational administrative database, DBInfo, that organizes study-level metadata and information on data reuse across separate tables. Studies at ICPSR consist of one or more files collected at a single time or for a single purpose; studies in which the same variables are observed over time are grouped into series. Each study at ICPSR is assigned a DOI, and its metadata are stored in DBInfo. Study metadata follows the Data Documentation Initiative (DDI) Codebook 2.5 standard. DDI elements included in our dataset are title, ICPSR study identification number, DOI, authoring entities, description (abstract), funding agencies, subject terms assigned to the study during curation, and geographic coverage. We also created variables based on DDI elements: total variable count, the presence of survey question text in the metadata, the number of author entities, and whether an author entity was an institution. We gathered metadata for ICPSR’s 10,605 unrestricted public-use studies available as of 2021-11-16 ( https://www.icpsr.umich.edu/web/pages/membership/or/metadata/oai.html ).

To link study usage data with study-level metadata records, we joined study metadata from DBinfo on study usage information, which included total study downloads (data and documentation), individual data file downloads, and cumulative citations from the ICPSR Bibliography. We also gathered descriptive metadata for each study and its variables, which allowed us to summarize and append recoded fields onto the study-level metadata such as curation level, number and type of principle investigators, total variable count, and binary variables indicating whether the study data were made available for online analysis, whether survey question text was made searchable online, and whether the study variables were indexed for search. These characteristics describe aspects of the discoverability of the data to compare with other characteristics of the study. We used the study and series numbers included in the ICPSR Bibliography as unique identifiers to link papers to metadata and analyze the community structure of dataset co-citations in the ICPSR Bibliography 32 .

Process curation work logs

Researchers deposit data at ICPSR for curation and long-term preservation. Between 2016 and 2020, more than 3,000 research studies were deposited with ICPSR. Since 2017, ICPSR has organized curation work into a central unit that provides varied levels of curation that vary in the intensity and complexity of data enhancement that they provide. While the levels of curation are standardized as to effort (level one = less effort, level three = most effort), the specific curatorial actions undertaken for each dataset vary. The specific curation actions are captured in Jira, a work tracking program, which data curators at ICPSR use to collaborate and communicate their progress through tickets. We obtained access to a corpus of 669 completed Jira tickets corresponding to the curation of 566 unique studies between February 2017 and December 2019 28 .

To process the tickets, we focused only on their work log portions, which contained free text descriptions of work that data curators had performed on a deposited study, along with the curators’ identifiers, and timestamps. To protect the confidentiality of the data curators and the processing steps they performed, we collaborated with ICPSR’s curation unit to propose a classification scheme, which we used to train a Naive Bayes classifier and label curation actions in each work log sentence. The eight curation action labels we proposed 28 were: (1) initial review and planning, (2) data transformation, (3) metadata, (4) documentation, (5) quality checks, (6) communication, (7) other, and (8) non-curation work. We note that these categories of curation work are very specific to the curatorial processes and types of data stored at ICPSR, and may not match the curation activities at other repositories. After applying the classifier to the work log sentences, we obtained summary-level curation actions for a subset of all ICPSR studies (5%), along with the total number of hours spent on data curation for each study, and the proportion of time associated with each action during curation.

Data Records

The MICA dataset 27 connects records for each of ICPSR’s archived research studies to the research publications that use them and related curation activities available for a subset of studies (Fig. 2 ). Each of the three tables published in the dataset is available as a study archived at ICPSR. The data tables are distributed as statistical files available for use in SAS, SPSS, Stata, and R as well as delimited and ASCII text files. The dataset is organized around studies and papers as primary entities. The studies table lists ICPSR studies, their metadata attributes, and usage information; the papers table was constructed using the ICPSR Bibliography and Dimensions database; and the curation logs table summarizes the data curation steps performed on a subset of ICPSR studies.

Studies (“ICPSR_STUDIES”): 10,605 social science research datasets available through ICPSR up to 2021-11-16 with variables for ICPSR study number, digital object identifier, study name, series number, series title, authoring entities, full-text description, release date, funding agency, geographic coverage, subject terms, topical archive, curation level, single principal investigator (PI), institutional PI, the total number of PIs, total variables in data files, question text availability, study variable indexing, level of restriction, total unique users downloading study data files and codebooks, total unique users downloading data only, and total unique papers citing data through November 2021. Studies map to the papers and curation logs table through ICPSR study numbers as “STUDY”. However, not every study in this table will have records in the papers and curation logs tables.

Papers (“ICPSR_PAPERS”): 94,755 publications collected from 2000-08-11 to 2021-11-16 in the ICPSR Bibliography and enriched with metadata from the Dimensions database with variables for paper number, identifier, title, authors, publication venue, item type, publication date, input date, ICPSR series numbers used in the paper, ICPSR study numbers used in the paper, the Dimension identifier, and the Dimensions link to the publication’s full text. Papers map to the studies table through ICPSR study numbers in the “STUDY_NUMS” field. Each record represents a single publication, and because a researcher can use multiple datasets when creating a publication, each record may list multiple studies or series.

Curation logs (“ICPSR_CURATION_LOGS”): 649 curation logs for 563 ICPSR studies (although most studies in the subset had one curation log, some studies were associated with multiple logs, with a maximum of 10) curated between February 2017 and December 2019 with variables for study number, action labels assigned to work description sentences using a classifier trained on ICPSR curation logs, hours of work associated with a single log entry, and total hours of work logged for the curation ticket. Curation logs map to the study and paper tables through ICPSR study numbers as “STUDY”. Each record represents a single logged action, and future users may wish to aggregate actions to the study level before joining tables.

Entity-relation diagram.

Technical Validation

We report on the reliability of the dataset’s metadata in the following subsections. To support future reuse of the dataset, curation services provided through ICPSR improved data quality by checking for missing values, adding variable labels, and creating a codebook.

All 10,605 studies available through ICPSR have a DOI and a full-text description summarizing what the study is about, the purpose of the study, the main topics covered, and the questions the PIs attempted to answer when they conducted the study. Personal names (i.e., principal investigators) and organizational names (i.e., funding agencies) are standardized against an authority list maintained by ICPSR; geographic names and subject terms are also standardized and hierarchically indexed in the ICPSR Thesaurus 34 . Many of ICPSR’s studies (63%) are in a series and are distributed through the ICPSR General Archive (56%), a non-topical archive that accepts any social or behavioral science data. While study data have been available through ICPSR since 1962, the earliest digital release date recorded for a study was 1984-03-18, when ICPSR’s database was first employed, and the most recent date is 2021-10-28 when the dataset was collected.

Curation level information was recorded starting in 2017 and is available for 1,125 studies (11%); approximately 80% of studies with assigned curation levels received curation services, equally distributed between Levels 1 (least intensive), 2 (moderately intensive), and 3 (most intensive) (Fig. 3 ). Detailed descriptions of ICPSR’s curation levels are available online 35 . Additional metadata are available for a subset of 421 studies (4%), including information about whether the study has a single PI, an institutional PI, the total number of PIs involved, total variables recorded is available for online analysis, has searchable question text, has variables that are indexed for search, contains one or more restricted files, and whether the study is completely restricted. We provided additional metadata for this subset of ICPSR studies because they were released within the past five years and detailed curation and usage information were available for them. Usage statistics including total downloads and data file downloads are available for this subset of studies as well; citation statistics are available for 8,030 studies (76%). Most ICPSR studies have fewer than 500 users, as indicated by total downloads, or citations (Fig. 4 ).

ICPSR study curation levels.

ICPSR study usage.

A subset of 43,102 publications (45%) available in the ICPSR Bibliography had a DOI. Author metadata were entered as free text, meaning that variations may exist and require additional normalization and pre-processing prior to analysis. While author information is standardized for each publication, individual names may appear in different sort orders (e.g., “Earls, Felton J.” and “Stephen W. Raudenbush”). Most of the items in the ICPSR Bibliography as of 2021-11-16 were journal articles (59%), reports (14%), conference presentations (9%), or theses (8%) (Fig. 5 ). The number of publications collected in the Bibliography has increased each decade since the inception of ICPSR in 1962 (Fig. 6 ). Most ICPSR studies (76%) have one or more citations in a publication.

ICPSR Bibliography citation types.

ICPSR citations by decade.

Usage Notes

The dataset consists of three tables that can be joined using the “STUDY” key as shown in Fig. 2 . The “ICPSR_PAPERS” table contains one row per paper with one or more cited studies in the “STUDY_NUMS” column. We manipulated and analyzed the tables as CSV files with the Pandas library 36 in Python and the Tidyverse packages 37 in R.

The present MICA dataset can be used independently to study the relationship between curation decisions and data reuse. Evidence of reuse for specific studies is available in several forms: usage information, including downloads and citation counts; and citation contexts within papers that cite data. Analysis may also be performed on the citation network formed between datasets and papers that use them. Finally, curation actions can be associated with properties of studies and usage histories.

This dataset has several limitations of which users should be aware. First, Jira tickets can only be used to represent the intensiveness of curation for activities undertaken since 2017, when ICPSR started using both Curation Levels and Jira. Studies published before 2017 were all curated, but documentation of the extent of that curation was not standardized and therefore could not be included in these analyses. Second, the measure of publications relies upon the authors’ clarity of data citation and the ICPSR Bibliography staff’s ability to discover citations with varying formality and clarity. Thus, there is always a chance that some secondary-data-citing publications have been left out of the bibliography. Finally, there may be some cases in which a paper in the ICSPSR bibliography did not actually obtain data from ICPSR. For example, PIs have often written about or even distributed their data prior to their archival in ICSPR. Therefore, those publications would not have cited ICPSR but they are still collected in the Bibliography as being directly related to the data that were eventually deposited at ICPSR.

In summary, the MICA dataset contains relationships between two main types of entities – papers and studies – which can be mined. The tables in the MICA dataset have supported network analysis (community structure and clique detection) 30 ; natural language processing (NER for dataset reference detection) 32 ; visualizing citation networks (to search for datasets) 38 ; and regression analysis (on curation decisions and data downloads) 29 . The data are currently being used to develop research metrics and recommendation systems for research data. Given that DOIs are provided for ICPSR studies and articles in the ICPSR Bibliography, the MICA dataset can also be used with other bibliometric databases, including DataCite, Crossref, OpenAlex, and related indexes. Subscription-based services, such as Dimensions AI, are also compatible with the MICA dataset. In some cases, these services provide abstracts or full text for papers from which data citation contexts can be extracted for semantic content analysis.

Code availability

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Acknowledgements

We thank the ICPSR Bibliography staff, the ICPSR Data Curation Unit, and the ICPSR Data Stewardship Committee for their support of this research. This material is based upon work supported by the National Science Foundation under grant 1930645. This project was made possible in part by the Institute of Museum and Library Services LG-37-19-0134-19.

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L.H. and A.T. conceptualized the study design, D.B., E.M., and S.L. prepared the data, S.L., L.F., and L.H. analyzed the data, and D.B. validated the data. All authors reviewed and edited the manuscript.

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Hemphill, L., Thomer, A., Lafia, S. et al. A dataset for measuring the impact of research data and their curation. Sci Data 11 , 442 (2024). https://doi.org/10.1038/s41597-024-03303-2

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Acknowledgments

The analysis in this report is based on a self-administered web survey conducted from Sept. 26 to Oct. 23, 2023, among a sample of 1,453 dyads, with each dyad (or pair) comprised of one U.S. teen ages 13 to 17 and one parent per teen. The margin of sampling error for the full sample of 1,453 teens is plus or minus 3.2 percentage points. The margin of sampling error for the full sample of 1,453 parents is plus or minus 3.2 percentage points. The survey was conducted by Ipsos Public Affairs in English and Spanish using KnowledgePanel, its nationally representative online research panel.

The research plan for this project was submitted to an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants. The IRB thoroughly vetted this research before data collection began. Due to the risks associated with surveying minors, this research underwent a full board review and received approval (Approval ID Pro00073203).

KnowledgePanel members are recruited through probability sampling methods and include both those with internet access and those who did not have internet access at the time of their recruitment. KnowledgePanel provides internet access for those who do not have it and, if needed, a device to access the internet when they join the panel. KnowledgePanel’s recruitment process was originally based exclusively on a national random-digit dialing (RDD) sampling methodology. In 2009, Ipsos migrated to an address-based sampling (ABS) recruitment methodology via the U.S. Postal Service’s Delivery Sequence File (DSF). The Delivery Sequence File has been estimated to cover as much as 98% of the population, although some studies suggest that the coverage could be in the low 90% range. 4

Panelists were eligible for participation in this survey if they indicated on an earlier profile survey that they were the parent of a teen ages 13 to 17. A random sample of 3,981 eligible panel members were invited to participate in the study. Responding parents were screened and considered qualified for the study if they reconfirmed that they were the parent of at least one child ages 13 to 17 and granted permission for their teen who was chosen to participate in the study. In households with more than one eligible teen, parents were asked to think about one randomly selected teen, and that teen was instructed to complete the teen portion of the survey. A survey was considered complete if both the parent and selected teen completed their portions of the questionnaire, or if the parent did not qualify during the initial screening.

Of the sampled panelists, 1,763 (excluding break-offs) responded to the invitation and 1,453 qualified, completed the parent portion of the survey, and had their selected teen complete the teen portion of the survey, yielding a final stage completion rate of 44% and a qualification rate of 82%. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 2.2%. The break-off rate among those who logged on to the survey (regardless of whether they completed any items or qualified for the study) is 26.9%.

Upon completion, qualified respondents received a cash-equivalent incentive worth $10 for completing the survey. To encourage response from non-Hispanic Black panelists, the incentive was increased from $10 to $20 on Oct 5, 2023. The incentive was increased again on Oct. 10, from $20 to $40; then to $50 on Oct. 17; and to $75 on Oct. 20. Reminders and notifications of the change in incentive were sent for each increase.

All panelists received email invitations and any nonresponders received reminders, shown in the table. The field period was closed on Oct. 23, 2023.

A table showing Invitation and reminder dates

The analysis in this report was performed using separate weights for parents and teens. The parent weight was created in a multistep process that begins with a base design weight for the parent, which is computed to reflect their probability of selection for recruitment into the KnowledgePanel. These selection probabilities were then adjusted to account for the probability of selection for this survey, which included oversamples of non-Hispanic Black and Hispanic parents. Next, an iterative technique was used to align the parent design weights to population benchmarks for parents of teens ages 13 to 17 on the dimensions identified in the accompanying table, to account for any differential nonresponse that may have occurred.

To create the teen weight, an adjustment factor was applied to the final parent weight to reflect the selection of one teen per household. Finally, the teen weights were further raked to match the demographic distribution for teens ages 13 to 17 who live with parents. The teen weights were adjusted on the same teen dimensions as parent dimensions with the exception of teen education, which was not used in the teen weighting.

Sampling errors and tests of statistical significance take into account the effect of weighting. Interviews were conducted in both English and Spanish.

In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.

The following table shows the unweighted sample sizes and the error attributable to sampling that would be expected at the 95% level of confidence for different groups in the survey:

A table showing the unweighted sample sizes and the error attributable to sampling

Sample sizes and sampling errors for subgroups are available upon request.

Dispositions and response rates

The tables below display dispositions used in the calculation of completion, qualification and cumulative response rates. 5

AAPOR Task Force on Address-based Sampling. 2016. “AAPOR Report: Address-based Sampling.” ↩
For more information on this method of calculating response rates, refer to: Callegaro, Mario, and Charles DiSogra. 2008. “Computing response metrics for online panels.” Public Opinion Quarterly. ↩

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Dark blue boxes indicate RRs, and horizontal bars indicate 95% CIs. Sizes of dark blue boxes are proportional to the inverse variance. The light blue diamond indicates the pooled RR estimate and 95% CI in the random-effects model meta-analysis. RRs are maximally adjusted estimates as reported by studies (see eTable 1 in Supplement 1 for adjustment variables). Badr et al 32 for RR of all-cause death and Postigo et al 28 for RR of MACE were crude estimates calculated by this study’s authors based on number of participants and number of events reported for patients living with HIV and control groups. The definition of MACE for Shitole et al 18 and Postigo et al 28 was death or cardiovascular admissions.

RRs are shown for recurrent acute coronary syndrome (ACS) (A), heart failure (HF) admission (B), cardiovascular (CV) death (C), and restenosis (D). Dark blue boxes indicate RRs, and horizontal bars indicate 95% CIs. Sizes of dark blue boxes are proportional to the inverse variance. The light blue diamond indicates the pooled RR estimate and 95% CI in the random-effects model meta-analysis.

eTable 1. Additional Patient Characteristics by Study for Patients Living With HIV and Patients in Control Groups

eTable 2. Comparison of Patient Characteristics Between Patients Living With HIV and Patients in Control Groups

eTable 3. Clinical Outcomes, Relative Risks, and Adjustment Variables by Study

eTable 4. Sensitivity Analysis of Pooled Relative Risks Calculated Using Knapp-Hartung Method for Random-Effects Model Meta-Analysis

eTable 5. Quality Assessment of Included Studies With Newcastle-Ottawa Scale

eFigure 1. Study Flow Sheet

eFigure 2. Pooled Relative Risks for Patients Living With HIV vs Patients in Control Groups for TLR and TVR

eFigure 3. Pooled Unadjusted Relative Risks for Patients Living With HIV vs Patients in Control Groups for All-Cause Mortality, MACE, and Recurrent ACS

eFigure 4. Funnel Plot of Relative Risks for All-Cause Mortality and MACE

eMethods. Detailed Description of Statistical Analysis

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Haji M , Capilupi M , Kwok M, et al. Clinical Outcomes After Acute Coronary Syndromes or Revascularization Among People Living With HIV : A Systematic Review and Meta-Analysis . JAMA Netw Open. 2024;7(5):e2411159. doi:10.1001/jamanetworkopen.2024.11159

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Clinical Outcomes After Acute Coronary Syndromes or Revascularization Among People Living With HIV : A Systematic Review and Meta-Analysis

1 Department of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island
2 Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri
3 Department of Medicine, Duke Global Health Institute and Duke Clinical Research Institute, Duke University, Durham, North Carolina
4 Global Health Institute, University of Washington, Seattle
5 Infectious Disease Section, Michael E. DeBakey VA Medical Center, Houston, Texas
6 Department of Medicine, Baylor College of Medicine, Houston, Texas
7 Pharmacy Service, Michael E. DeBakey VA Medical Center, Houston, Texas
8 Center of Innovation, Providence VA Medical Center, Providence, Rhode Island
9 Evidence Synthesis Program Center, Providence VA Health Care System, Providence, Rhode Island
10 Department of Medicine, Providence VA Medical Center, Providence, Rhode Island
11 Department of Pharmacy, University of Rhode Island, Providence
12 Department of Health Services, Policy and Practice, Brown University, Providence, Rhode Island

Question What are the postdischarge outcomes for patients living with HIV after acute coronary syndromes or coronary revascularization?

Findings In this systematic review and meta-analysis of 15 studies involving 9499 patients living with HIV and 1 531 117 patients without HIV, patients living with HIV had a higher risk of all-cause mortality, major adverse cardiovascular events, recurrent acute coronary syndromes, and admission for heart failure after the index event, despite being approximately 11 years younger at the time of the event. Patients living with HIV were more likely to be current smokers and engage in illicit drug use and had higher triglyceride and lower high-density lipoprotein cholesterol levels than those without HIV.

Meaning This analysis highlights the need for attention toward secondary prevention strategies to address poor outcomes of cardiovascular disease among patients living with HIV.

Importance Clinical outcomes after acute coronary syndromes (ACS) or percutaneous coronary interventions (PCIs) in people living with HIV have not been characterized in sufficient detail, and extant data have not been synthesized adequately.

Objective To better characterize clinical outcomes and postdischarge treatment of patients living with HIV after ACS or PCIs compared with patients in an HIV-negative control group.

Data Sources Ovid MEDLINE, Embase, and Web of Science were searched for all available longitudinal studies of patients living with HIV after ACS or PCIs from inception until August 2023.

Study Selection Included studies met the following criteria: patients living with HIV and HIV-negative comparator group included, patients presenting with ACS or undergoing PCI included, and longitudinal follow-up data collected after the initial event.

Data Extraction and Synthesis Data extraction was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Clinical outcome data were pooled using a random-effects model meta-analysis.

Main Outcome and Measures The following clinical outcomes were studied: all-cause mortality, major adverse cardiovascular events, cardiovascular death, recurrent ACS, stroke, new heart failure, total lesion revascularization, and total vessel revascularization. The maximally adjusted relative risk (RR) of clinical outcomes on follow-up comparing patients living with HIV with patients in control groups was taken as the main outcome measure.

Results A total of 15 studies including 9499 patients living with HIV (pooled proportion [range], 76.4% [64.3%-100%] male; pooled mean [range] age, 56.2 [47.0-63.0] years) and 1 531 117 patients without HIV in a control group (pooled proportion [range], 61.7% [59.7%-100%] male; pooled mean [range] age, 67.7 [42.0-69.4] years) were included; both populations were predominantly male, but patients living with HIV were younger by approximately 11 years. Patients living with HIV were also significantly more likely to be current smokers (pooled proportion [range], 59.1% [24.0%-75.0%] smokers vs 42.8% [26.0%-64.1%] smokers) and engage in illicit drug use (pooled proportion [range], 31.2% [2.0%-33.7%] drug use vs 6.8% [0%-11.5%] drug use) and had higher triglyceride (pooled mean [range], 233 [167-268] vs 171 [148-220] mg/dL) and lower high-density lipoprotein-cholesterol (pooled mean [range], 40 [26-43] vs 46 [29-46] mg/dL) levels. Populations with and without HIV were followed up for a pooled mean (range) of 16.2 (3.0-60.8) months and 11.9 (3.0-60.8) months, respectively. On postdischarge follow-up, patients living with HIV had lower prevalence of statin (pooled proportion [range], 53.3% [45.8%-96.1%] vs 59.9% [58.4%-99.0%]) and β-blocker (pooled proportion [range], 54.0% [51.3%-90.0%] vs 60.6% [59.6%-93.6%]) prescriptions compared with those in the control group, but these differences were not statistically significant. There was a significantly increased risk among patients living with HIV vs those without HIV for all-cause mortality (RR, 1.64; 95% CI, 1.32-2.04), major adverse cardiovascular events (RR, 1.11; 95% CI, 1.01-1.22), recurrent ACS (RR, 1.83; 95% CI, 1.12-2.97), and admissions for new heart failure (RR, 3.39; 95% CI, 1.73-6.62).

Conclusions and Relevance These findings suggest the need for attention toward secondary prevention strategies to address poor outcomes of cardiovascular disease among patients living with HIV.

The widespread use of effective antiretroviral therapies (ARTs) has led to increased survivorship among people living with HIV. Therefore, people living with HIV are experiencing an increased prevalence of age-related disease, such as cardiovascular disease (CVD). 1 , 2 The increase in CVD in this population has been attributed to multiple factors, including increasing age, the increase in burden of traditional CVD factors and psychosocial risk factors, the long-term metabolic effects of ART, and the low-grade immune activation of chronic HIV. 1 , 3 - 8

Epidemiological studies have shown that compared with populations without HIV, people living with HIV have a higher risk of coronary artery disease, acute coronary syndromes (ACS), and heart failure, with onset at younger ages. 4 , 9 - 12 Given this earlier emergence of CVD among people living with HIV, there has been significant attention and evidence generated for primary prevention strategies involving statins. 13 , 14 In conjunction with these studies, characterization of longitudinal CVD outcomes is important to identify strategies for secondary prevention and further improve survivorship among people living with HIV. Studies on clinical outcomes after ACS and percutaneous coronary interventions (PCIs) among patients living with HIV have shown higher rates of recurrent coronary disease and mortality compared with patients in HIV-negative control groups. 11 , 15 - 17 However, this association has not been characterized in sufficient detail in current literature, and extant data have not been adequately synthesized. We conducted a systematic review and meta-analysis of longitudinal studies of patients living with HIV after ACS or PCIs to better characterize clinical outcomes and postdischarge treatment compared with patients in HIV-negative control groups.

We report this systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) reporting guideline. This study was not preregistered. Please see the eMethods in Supplement 1 for a detailed description of methods used in this meta-analysis, as recommended by the International Committee of Medical Journal Editors.

We searched Ovid MEDLINE, Embase, and Web of Science for all available articles from inception to August 2023 for the key terms coronary artery disease , myocardial infarction , non-fatal myocardial infarction , acute coronary syndrome , revascularization , percutaneous coronary intervention , and secondary prevention . We also reviewed references of relevant articles.

Articles were screened by 2 reviewers (M.H. and M.C.) by title and abstract and later by full text. We included studies if they fulfilled the following criteria: patients living with HIV and a comparator group of patients without HIV (control group) included, patients with obstructive coronary artery disease presenting with ACS or undergoing revascularization through PCI included, and longitudinal follow-up data on clinical outcomes after initial event collected. We initially also searched for studies that discussed outcomes after stroke and peripheral artery disease.

We extracted the following data where available using standardized forms: study characteristics, baseline demographics (ie, age, sex, and race and ethnicity) and other characteristics (ie, underlying comorbidities, revascularization strategies, and postdischarge medications) of HIV-positive and HIV-negative control populations, HIV-specific characteristics (use of ART, CD4 count, and viral load), number of events by group and hazard ratios (HRs) of clinical outcomes (ie, all-cause mortality, major adverse cardiovascular events [MACE], cardiovascular death, recurrent ACS, stroke, total lesion revascularization, total vessel revascularization, and admission for heart failure). We extracted maximally adjusted HRs where available, as well as unadjusted (crude) or minimally adjusted HRs for clinical outcomes. We captured data on race and ethnicity to help assess the full scope of diversity among patients living with HIV and how applicable our data may be within the global population of people living with HIV. Race and ethnicity were self-reported in the study by Shitole et al. 18 In the other studies reporting this information, data were obtained from review of medical records, including electronic health records. Reported race and ethnicity categories included African American, American Indian, Asian, Hispanic, Pacific Islander, White, and other. We primarily report aggregated data for Black, White, and Hispanic populations only given that there were limited data available on other races and ethnicities.

We combined summary study characteristics (eg, mean age, percentage male and female, percentage Black and White, and percentage Hispanic) across studies using study sizes as analytical weights to provide estimates of pooled means or percentages. The δ and P values comparing summary study-level characteristics (means or prevalences pooled across studies) between HIV-positive and HIV-negative groups were calculated from a linear regression model of each variable on HIV status weighted by the number of participants for each study (ie, a fixed-effects meta-regression). When HRs were not reported, we calculated crude risk ratios from the number of events in each group. In 2 studies, 15 , 19 data were reported as odds ratios. We pooled HRs of clinical outcomes across studies using a random-effects model meta-analysis, estimating between-study heterogeneity using the DerSimonian-Laird method. 20 As a sensitivity analysis, we also estimated between-study heterogeneity using the residual maximum likelihood method and calculated variances ( P values and CIs) of pooled relative risk (RR) estimates using modifications proposed by Knapp and Hartung. 21 For the purpose of the meta-analysis, we considered odds ratios, risk ratios, and HRs as equivalent measures of RR.

We assessed between-study heterogeneity using the Cochran Q statistic and I 2 statistic, which estimates the percentage of total variation across studies due to true between-study difference rather than chance. 22 , 23 We did not explore heterogeneity further owing to the limited numbers of studies available for most comparisons.

The quality of included studies was assessed using the Newcastle-Ottawa Scale for cohort studies. 24 We visually inspected funnel plots to assess the risk of publication bias. We also performed the Egger test for small study bias, although this was limited by the small number of studies that were generally available for investigated outcomes. Where there were P values trending toward small study bias, we performed trim and fill analyses to help assess the impact of the bias on pooled estimates (even if Egger test P values did not reach statistical significance). A 2-sided P value less than .05 was considered statistically significant. For the meta-analysis of RRs, we report point estimates and 95% CIs. All analyses were performed using Stata software statistical software version 15 (StataCorp).

An initial search yielded 3263 studies, which were screened using titles, abstracts, and full texts. Studies reviewing patient outcomes after diagnoses and interventions of peripheral artery disease and stroke were limited, reporting mainly in-hospital outcomes, short-term follow-up, or results without non-HIV comparator groups, and were not further considered in this meta-analysis. We identified 15 studies 11 , 15 , 16 , 18 , 25 - 35 of post-ACS or revascularization outcomes from 2003 to 2023 that met inclusion criteria (eFigure 1 in Supplement 1 ). Of identified studies, 2 were abstracts. 30 , 31 All were retrospective cohort studies except for 3 prospective studies ( Table 1 ). 11 , 26 , 30

Details of patient characteristics and outcomes by study are presented in Table 1 and eTable 1 in Supplement 1 . A total of 9499 patients living with HIV (pooled proportion [range], 76.4% [64.3%-100%] male; pooled mean [range] age, 56.2 [47.0-63.0] years; pooled proportion [range], 10.1% [95% CI, 7.0%-62.5%] Black; 8.1% [95% CI, 0.4%-54.6%] Hispanic, and 13.1% [95% CI, 7.2%-64.0%] White) and 1 531 117 patients in control groups without HIV (pooled proportion [range], 61.7% [59.7%-100%] male; pooled mean [range] age, 67.7 [42.0-69.4] years; pooled proportion [range], 3.3% [95% CI, 2.5%-21.4%] Black, 3.6% [95% CI, 0.7%-36.3%] Hispanic, and 21.1% [95% CI, 14.3%-68.0%] White) who experienced ACS or underwent coronary revascularization were included in the meta-analysis. Summary baseline characteristics of study participants and comparisons of patients living with HIV with patients in control groups are presented in Table 2 and eTable 2 in Supplement 1 . The mean age of patients living with HIV was 11.1 years (95% CI, 6.2-16.0 years) less than that of patients in HIV-negative control groups ( P < .001). HIV-positive and control populations were similarly male dominant. Patients living with HIV were statistically significantly more likely to be current smokers (pooled proportion [range], 59.1% [24.0%-75.0%] smokers vs 42.8% [26.0%-64.1%] smokers; P < .001) and engage in illicit drug use (pooled proportion [range], 31.2% [2.0%-33.7%] drug use vs 6.8% [0%-11.5%] drug use; P < .001) and had significantly higher pooled mean (range) triglyceride (233 [167-268] vs 171 [148-220] mg/dL; P = .01) and lower pooled mean (range) high-density lipoprotein cholesterol (40 [26-43] vs 46 [29-46] mg/dL; P = .03) levels. (To convert triglycerides and cholesterol to millimoles per liter, multiply by 0.0113 and 0.0259, respectively.) There were similar proportions of patients with diabetes, hypertension, and a family history of coronary artery disease in the 2 groups ( Table 2 ; eTable 2 in Supplement 1 ).

Patients with HIV had been diagnosed with HIV for a pooled mean (range) of 11.2 (8.5-12.0) years. From 9 studies 11 , 16 , 18 , 26 , 28 , 29 , 31 , 34 , 35 that provided these data, a pooled proportion (range) of 75.2% (50.0%-94.1%) of patients living with HIV were receiving ART and 47.6% (25.0%-85.6%) had previously received protease inhibitor therapy. The pooled mean (range) CD4 count was 377 (318-462) cells/mm 3 among patients living with HIV, and most of these patients (pooled proportion [range], 77.8% [63.3%-94.6%]) had a viral load less of than 200 copies per mL ( Table 2 ).

Among 13 studies 11 , 15 , 16 , 18 , 25 - 29 , 31 - 34 that reported data on ACS, patients living with HIV and those in control groups presented similarly with ST-segment elevation myocardial infarction, non–ST-segment elevation myocardial infarction, and unstable angina. Additionally, the groups received PCIs or coronary artery bypass graft surgery at similar proportions. After revascularization, pooled mean (range) left ventricular ejection fraction values were similar between groups (49.4% [44.0%-55.4%] vs 50.9% [48.0%-54.8%]). On postdischarge follow up, patients living with HIV had a lower proportion (range) of statin (53.3% [45.8%-96.1%] vs 59.9% [58.4%-99.0%]) and β-blocker (54.0% [51.3%-90.0%] vs 60.6% [59.6%-93.6%]) prescription compared with patients in control groups, but these differences were not statistically significant ( Table 2 ; eTable 2 in Supplement 1 ).

Over a pooled mean (range) follow-up of a mean of 16.2 (3.0-60.8) months after ACS or revascularization, patients living with HIV had a significantly higher adjusted risk of all-cause mortality (pooled adjusted RR, 1.64; 95% CI, 1.32-2.04), MACE (RR, 1.11; 95% CI, 1.01-1.22), recurrent ACS (RR, 1.83; 95% CI, 1.12-2.97), and heart failure readmission (RR, 3.39; 95% CI, 1.73-6.62) ( Figure 1 ), as well as restenosis (RR, 2.40; 95% CI, 1.13-5.09) ( Figure 2 ) compared with patients in HIV-negative control groups (pooled mean [range] follow-up, 11.9 [3.0-60.8] months). For CV death, total vessel revascularization, and total lesion revascularization, pooled HRs showed no significantly higher risk among patients living with HIV compared with patients in control groups (eFigure 2 in Supplement 1 ). RRs of clinical outcomes and adjustment variables included in multivariate models that were reported by each study are presented in eTable 3 in Supplement 1 . Sensitivity analyses specifying an alternative method for the random-effects model yielded comparable results (eTable 4 in Supplement 1 ). In a separate subsidiary analysis, there was no association between HIV status and risk of post–ACS or PCI mortality, recurrent ACS, or MACE outcomes in the unadjusted (minimally adjusted in some studies) model (eFigure 3 in Supplement 1 ).

There was generally low heterogeneity across studies for most outcomes ( Figure 1 and Figure 2 ). Visual inspection of the funnel plot for publication bias assessment and Egger tests did not suggest the presence of significant publication bias (eFigure 4 in Supplement 1 ). For the all-cause mortality outcome, the Egger test for bias was borderline, and so we performed trim and fill analysis; this yielded similar results (RR, 1.61; 95% CI, 1.30-2.00). Included studies were of moderate to high quality based on the Newcastle-Ottawa Scale, indicating a low to moderate risk of bias (eTable 5 in Supplement 1 ).

We performed a literature-based systematic review and meta-analysis of 15 studies of longitudinal clinical outcomes after ACS or revascularization from 2003 to 2023, comprising a total of 9499 patients living with HIV and 1 531 117 patients without HIV in control groups. We found that patients living with HIV were younger and had a higher risk of all-cause mortality, MACE, recurrent ACS, and heart failure after the index event. We also noted lower rates of statin and β-blocker prescription after discharge among patients living with HIV. Overall, these findings highlight the need to develop and implement strategies for secondary prevention of CVD among patients living with HIV.

The increased mortality, recurrence of ACS, and heart failure admissions among patients living with HIV may be attributed to increased traditional CVD risk factors, psychosocial factors, HIV-related chronic inflammation, and long-term effects of ART. 11 , 16 These factors are equally difficult to control after an initial coronary event. 19 , 35 , 36 The study by Boccara et al 11 from 2020 compared its findings with those of their first, 2011 study 37 and noted an increased rate of recurrence of ACS in patients living with HIV; the authors also noted persistent smoking and chronic inflammation as factors associated with some of the greatest increases in risk for recurrent disease. This further reinforces the need for a multifaceted approach to secondary prevention.

Of note, our study found suboptimal statin prescription in patients living with HIV after ACS or revascularization, which is consistent with results of other retrospective studies. 11 , 18 , 19 , 26 , 28 , 38 - 42 These findings and those of the Evaluating the Use of Pitavastatin to Reduce the Risk of Cardiovascular Disease in HIV-Infected Adults (REPRIEVE) trial, 14 which demonstrated the benefits of pitavastatin for primary prevention of atherosclerotic cardiovascular disease among patients living with HIV, highlight the need for a concerted effort to improve guideline-directed statin prescription and adherence among these patients. 43 Additionally, the higher prevalence of smoking and higher triglyceride levels we found among patients living with HIV highlight areas for optimization, with the goal of improving secondary prevention of atherosclerotic cardiovascular disease. Differences in statin and β-blocker prescriptions on follow-up were not statistically significant, although patients living with HIV had numerically lower percentages for both outcomes.

Our pooled estimates for postdischarge antiplatelet therapy are influenced by the study from Parks et al, 33 which defined antiplatelet use as a filled prescription for clopidogrel, ticagrelor, prasugrel, or ticlopidine and as a retrospective observational study, could not reliably exclude patients with type 2 myocardial infarctions who would not typically qualify for these therapies. In that study’s sensitivity analyses of patients who received coronary angiography, percentages of patients with postdischarge antiplatelet therapies were significantly higher. We performed an analysis of aggregate postdischarge antiplatelet therapy rates excluding data from Parks et al, 33 and aggregate data for postdischarge antiplatelet therapy was much higher.

Few studies reported race or ethnicity of participants, leading to overall low aggregate percentages of White and Black patients living with HIV in our analysis, which is not representative of the global population of these patients. Race and ethnicity in most studies were obtained from review of electronic health records, except in the study by Shitole et al, 18 in which race and ethnicity were self-reported. The analysis of race and ethnicity was skewed by 2 studies; in 1 study, 44 most of the population’s race and ethnicity was unknown, and in the other study, 19 the population was mainly Hispanic. Likewise, the percentage of patients who underwent PCIs was lower than expected for a typical population presenting with ACS. This was also contributed by the Parks et al study, 33 which included patients with type 2 myocardial infarctions, who were not candidates for PCIs in their analysis.

Most studies in our analysis included patients receiving ART with low viral loads and CD4 counts greater than 200 cells/mm 3 , indicating patients with good control of their HIV disease, who are representative of people living with HIV in the current era. 1 , 4 , 7 , 45 We found 8 studies 11 , 16 , 26 - 28 , 31 , 34 , 35 that reported use of protease inhibitors among approximately 50% of patients living with HIV (47.6%). Protease inhibitors are known to have metabolic effects associated with CVD, presenting a plausible explanation for the difference in hypertriglyceridemia between patients living with HIV and patients without HIV in our study. 46 Modern ART regimens have transitioned away from the use of protease inhibitors and now include integrase inhibitors. 7 Conflicting data have emerged around the possible association of integrase inhibitors with increased incidence of CVD. 47 , 48 Therefore, further research on long-term outcomes associated with ART will be essential to primary and secondary prevention of CVD among patients living with HIV.

The period after ACS or PCI provides additional opportunity to introduce aggressive interventions to improve CVD risk factors in patients living with HIV, and these interventions may involve multidisciplinary teams. Ensuring access to and engagement of cardiologists for patients living with HIV will be important to improve outcomes, especially among underrepresented racial and ethnic minorities. 49 Input from pharmacists can also help with optimal selection of statin types, other lipid-lowering agents, and dosages to avoid drug interactions and drug-related adverse effects and maximize adherence to these therapies. Additionally, input from addiction medicine specialists and psychologists can help address underlying mental health disorders (eg, depression and anxiety) and behavioral risk factors (eg, smoking, alcohol use, and cocaine use). In our study, patients living with HIV were more likely to be smokers and engage in illicit drug use, similar to contemporary studies that also show that these behaviors are associated with an overall increased mortality in patients living with HIV despite adequate control of their underlying infection. 50 Likewise, assistance from social workers can help to mitigate social determinants associated with diet and the ability to afford crucial medications. 36 , 51 - 53 Addressing this latter aspect is critically important to improve secondary outcomes of CVD in patients living with HIV because despite increased prescription rates for cardioprotective medications, patients living with HIV have been found to be less likely to fill these medications. 38 , 42 , 52 A multifaceted or multidisciplinary intervention to address psychosocial barriers to cardiovascular care may have the potential to limit mortality and morbidity after ACS or PCI for patients living with HIV.

The findings of this meta-analysis should be considered in context of several limitations. First, given that this was a literature based meta-analysis of aggregate published data, we were unable to compare the association between HIV status and CVD outcomes by clinically important subgroup, such as age, race and ethnicity, or sex. Second, the degree of adjustment for confounders in RR estimates is limited to what is reported in individual studies, is not consistent across studies, and may be inadequate overall. For instance, very few studies accounted for HIV-specific characteristics. However, the goal of the meta-analysis was to understand the difference in secondary CVD outcomes stratified by HIV status regardless of factors that may be contributing to them. We also performed a comparison between maximally adjusted and unadjusted or minimally adjusted RRs to provide further insight into the association. Our analysis showed that there was no association between HIV status and post-ACS or -PCI mortality, recurrent ACS, or MACE outcomes in the unadjusted model. This is likely due to the reverse confounding effect of age given that patients living with HIV were significantly younger than patients in control groups, with a difference of 11 years in pooled mean age across studies. Third, most studies included in this review evaluated patients living with HIV who lived in high-income countries, which may limit generalizability to the global population of patients living with HIV. Fourth, we were not able to perform subgroup analyses of patients who had ACS and were treated medically vs PCI, as well as those who received PCI for stable coronary disease, because these data were not reported separately. Future assessment of outcomes within these subgroups would be important for preventative efforts. Fifth, we were unable to identify timelines for prescription of or adherence to ART or cardioprotective medications based on these aggregate data. Understanding these trends will also be an important focus for secondary prevention in future studies.

In this literature based systematic review and meta-analysis of longitudinal studies from 2000 to 2023, we found that patients living with HIV were significantly younger than patients in control groups. Patients living with HIV had a significantly higher risk of all-cause mortality, MACE, recurrent ACS, and admission for heart failure after the index event compared with patients in control groups.

Patients living with HIV were also significantly more likely to be current smokers and engage in illicit drug use and had higher triglyceride levels at baseline. As more data emerge for primary prevention, this analysis highlights the need for optimization of secondary prevention strategies to address poor outcomes of CVD among patients living with HIV. Future studies can focus on assessing the role of aggressive interventions, including use of multidisciplinary teams to target important risk factors and improve prescription of and adherence to cardioprotective medications among patients living with HIV after ACS or PCI.

Accepted for Publication: March 7, 2024.

Published: May 14, 2024. doi:10.1001/jamanetworkopen.2024.11159

Corresponding Author: Sebhat Erqou, MD, PhD, Department of Medicine, Providence VA Medical Center, 830 Chalkstone Ave, Providence, RI 02908 ( [email protected] ).

Author Contributions: Drs Haji and Erqou had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Haji, Ashong, Richard, Wu, Erqou.

Acquisition, analysis, or interpretation of data: Haji, Capilupi, Kwok, Ibrahim, Bloomfield, Longenecker, Rodriguez-Barradas, Jutkowitz, Taveira, Sullivan, Rudolph, Wu, Erqou.

Drafting of the manuscript: Haji, Capilupi, Kwok, Taveira, Erqou.

Critical review of the manuscript for important intellectual content: Capilupi, Kwok, Ibrahim, Bloomfield, Longenecker, Rodriguez-Barradas, Ashong, Jutkowitz, Taveira, Richard, Sullivan, Rudolph, Wu.

Statistical analysis: Kwok, Erqou.

Obtained funding: Erqou.

Administrative, technical, or material support: Haji, Capilupi, Kwok, Jutkowitz, Sullivan, Rudolph, Wu.

Supervision: Bloomfield, Taveira, Richard, Rudolph, Wu, Erqou.

Conflict of Interest Disclosures: Dr Longenecker reported receiving personal fees from Theratechnologies advisory board outside the submitted work. Dr Jutkowitz reported receiving grants from the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development during the conduct of the study. Dr Rudolph reported receiving grants from the National Institute on Aging during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported by a VISN 1 Career Development Award from the Department of Veterans Affairs, Veterans Health Administration, to Dr Erqou. Dr Erqou was also funded by the Center for Aids Research, Rhode Island Foundation, and Lifespan Cardiovascular Institute. Drs Sullivan, Rudolph, and Wu were funded by grants CIN 13-419 and C19-20-213 from the VA Health Services Research and Development Center of Innovation in Long Term Services and Supports.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government.

Data Sharing Statement: See Supplement 2 .

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Measurements-Based Radar Signature Modeling : An Analysis Framework

Joseph T. Mayhan is a Senior Staff Member at MIT Lincoln Laboratory, where he has worked for fifty years, formerly as Group Leader of the Sensor Systems and Measurements Group.

The late John A. Tabaczynski served as Assistant, Associate, and then Leader of the Ballistic Missile Defense Analysis division at MIT Lincoln Laboratory, where he worked for over fifty years.

A high-level text that synthesizes diverse research areas for characterizing objects (targets) from radar data and establishes a novel analysis framework for a class of signal processing techniques useful for high-resolution radar signature modeling.

The only text to integrate a diverse body of work on characterizing objects (targets) from radar data into a common analysis framework, this book brings together the results of research papers and technical reports providing improved resolution and precision in radar target signature modeling and target motion solutions. It offers comprehensive coverage related to basic radar concepts, signal representation, and radar measurements; the development of advanced analysis tools essential for high-resolution signature modeling; the development of novel wideband and narrowband radar imaging techniques; the application of 2D spectral estimation theory to wideband signal processing; ultra-wideband scattering phenomenology and sparse-band sensor data fusion; and the integration of field measurements into the radar signature modeling process. The analysis techniques developed in the text provide the framework for a novel approach, called measurements-based modeling (MBM), to model target signatures by incorporating measurement data into the signature model of the target. Extensive examples throughout compare the performance of the new techniques with that of conventional analysis techniques.

• The first systematic, comprehensive synthesis of wide-ranging research areas for characterizing targets from radar data

• A deeply researched, lucid presentation enriched by extensive illustrations and examples

• An essential reference for experts in radar and signal processing, professional engineers in related fields, and graduate students

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Measurements-Based Radar Signature Modeling : An Analysis Framework By: Joseph T. Mayhan, John A. Tabaczynski https://doi.org/10.7551/mitpress/10736.001.0001 ISBN (electronic): 9780262374538 Publisher: The MIT Press Published: 2024

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[ Front Matter ] Doi: https://doi.org/10.7551/mitpress/10736.003.0001 Open the PDF Link PDF for [ Front Matter ] in another window
Preface Doi: https://doi.org/10.7551/mitpress/10736.003.0002 Open the PDF Link PDF for Preface in another window
Introduction Doi: https://doi.org/10.7551/mitpress/10736.003.0003 Open the PDF Link PDF for Introduction in another window
1: Background Doi: https://doi.org/10.7551/mitpress/10736.003.0005 Open the PDF Link PDF for 1: Background in another window
2: Target Signature Modeling and the Role of Sequential Estimation Processing Doi: https://doi.org/10.7551/mitpress/10736.003.0006 Open the PDF Link PDF for 2: Target Signature Modeling and the Role of Sequential Estimation Processing in another window
3: Acceleration Estimation: Extending the All-Pole Model Doi: https://doi.org/10.7551/mitpress/10736.003.0007 Open the PDF Link PDF for 3: Acceleration Estimation: Extending the All-Pole Model in another window
4: Autocorrelation Measures and Signal Coherence Doi: https://doi.org/10.7551/mitpress/10736.003.0008 Open the PDF Link PDF for 4: Autocorrelation Measures and Signal Coherence in another window
5: A Solution Framework for the Joint Target-Motion Estimation Problem Doi: https://doi.org/10.7551/mitpress/10736.003.0010 Open the PDF Link PDF for 5: A Solution Framework for the Joint Target-Motion Estimation Problem in another window
6: Narrowband Signature Modeling Techniques Doi: https://doi.org/10.7551/mitpress/10736.003.0011 Open the PDF Link PDF for 6: Narrowband Signature Modeling Techniques in another window
7: Interferrometric ISAR Doi: https://doi.org/10.7551/mitpress/10736.003.0012 Open the PDF Link PDF for 7: Interferrometric ISAR in another window
8: Motion Estimation Techniques Doi: https://doi.org/10.7551/mitpress/10736.003.0013 Open the PDF Link PDF for 8: Motion Estimation Techniques in another window
9: Joint Motion and 2D/3D Characterization of Tumbling Targets Doi: https://doi.org/10.7551/mitpress/10736.003.0014 Open the PDF Link PDF for 9: Joint Motion and 2D/3D Characterization of Tumbling Targets in another window
10: Joint Target-Motion Solution from Range-Only Data Doi: https://doi.org/10.7551/mitpress/10736.003.0015 Open the PDF Link PDF for 10: Joint Target-Motion Solution from Range-Only Data in another window
11: Multisensor Fusion and Mutual Coherence Doi: https://doi.org/10.7551/mitpress/10736.003.0017 Open the PDF Link PDF for 11: Multisensor Fusion and Mutual Coherence in another window
12: Data Extrapolation and the Composite Target Space Mapping Doi: https://doi.org/10.7551/mitpress/10736.003.0018 Open the PDF Link PDF for 12: Data Extrapolation and the Composite Target Space Mapping in another window
13: Colocated Sensors: Sparse Frequency Band Processing Doi: https://doi.org/10.7551/mitpress/10736.003.0019 Open the PDF Link PDF for 13: Colocated Sensors: Sparse Frequency Band Processing in another window
14: Signature Modeling Using Sparse Angle Data Doi: https://doi.org/10.7551/mitpress/10736.003.0020 Open the PDF Link PDF for 14: Signature Modeling Using Sparse Angle Data in another window
15: An Integrated Predictive/Measurements-Based RCS Signature Model Doi: https://doi.org/10.7551/mitpress/10736.003.0022 Open the PDF Link PDF for 15: An Integrated Predictive/Measurements-Based RCS Signature Model in another window
16: Component Modeling Using Measurement Data Doi: https://doi.org/10.7551/mitpress/10736.003.0023 Open the PDF Link PDF for 16: Component Modeling Using Measurement Data in another window
Acknowledgments Doi: https://doi.org/10.7551/mitpress/10736.003.0024 Open the PDF Link PDF for Acknowledgments in another window
Appendix A: Characterization of Torque-Free Euler Rotational Motion Doi: https://doi.org/10.7551/mitpress/10736.003.0025 Open the PDF Link PDF for Appendix A: Characterization of Torque-Free Euler Rotational Motion in another window
Appendix B: 2D Spectral Estimation: A State-Space Approach Doi: https://doi.org/10.7551/mitpress/10736.003.0026 Open the PDF Link PDF for Appendix B: 2D Spectral Estimation: A State-Space Approach in another window
Appendix C: 2D Spectral Estimation: An ESPRIT Approach Doi: https://doi.org/10.7551/mitpress/10736.003.0027 Open the PDF Link PDF for Appendix C: 2D Spectral Estimation: An ESPRIT Approach in another window
Appendix D: Location Estimation Using Target Space Filters for {Rn,Rn} Observables Doi: https://doi.org/10.7551/mitpress/10736.003.0028 Open the PDF Link PDF for Appendix D: Location Estimation Using Target Space Filters for {Rn,Rn} Observables in another window
Appendix E: Acceleration Estimation MATLAB Code and Input Parameters Doi: https://doi.org/10.7551/mitpress/10736.003.0029 Open the PDF Link PDF for Appendix E: Acceleration Estimation MATLAB Code and Input Parameters in another window
Appendix F: Integrating Static Range and Field Test Measurements into a Computational, Measurements-Based Signature Model Doi: https://doi.org/10.7551/mitpress/10736.003.0030 Open the PDF Link PDF for Appendix F: Integrating Static Range and Field Test Measurements into a Computational, Measurements-Based Signature Model in another window
Appendix G: A Polynomial Filter Estimate of Scattering Center Acceleration Doi: https://doi.org/10.7551/mitpress/10736.003.0031 Open the PDF Link PDF for Appendix G: A Polynomial Filter Estimate of Scattering Center Acceleration in another window
References Doi: https://doi.org/10.7551/mitpress/10736.003.0032 Open the PDF Link PDF for References in another window
Contributors Doi: https://doi.org/10.7551/mitpress/10736.003.0033 Open the PDF Link PDF for Contributors in another window
About the Authors Doi: https://doi.org/10.7551/mitpress/10736.003.0034 Open the PDF Link PDF for About the Authors in another window
Index Doi: https://doi.org/10.7551/mitpress/10736.003.0035 Open the PDF Link PDF for Index in another window
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Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices

ORIGINAL PAPER
Published: 13 May 2024

Cite this article

Dongling Ma 1 ,
Qingji Huang 1 ,
Qian Zhang 1 ,
Qian Wang 1 ,
Hailong Xu 2 &
Yingwei Yan 3

The ecological environment of the Yellow River Delta is undergoing serious degradation due to the pressures of economic development and population growth. To improve and protect the ecological environment, it is crucial to accurately assess and monitor its eco-environmental quality. With consideration of the characteristics of terrestrial salinization in the region and the need for long-term ecological monitoring, we first utilized Google Earth Engine (GEE) to construct the Improved Remote Sensing Ecological Index (IRSEI). The IRSEI is based on the Remote Sensing Ecological Index (RSEI), which consists of the Normalized Difference Vegetation Index (NDVI), WET, Land Surface Temperature (LST), and Normalized Difference Built-Up and Soil Index (NDBSI), as well as the Net Primary Productivity (NPP) index. The entropy weighting method was employed to construct the IRSEI for assessing the eco-environmental quality of the Yellow River Delta. The validity of the index was verified through image entropy and contrast assessment. We then employed the Hurst exponent, Sen's slope estimation, and Coefficient of Variation (CV) to calculate the range of variation of the IRSEI in the Yellow River Delta over a 20-year period to analyze the spatio-temporal evolution of the ecological quality and its distribution pattern. Furthermore, we conducted a comprehensive analysis combining the Geographically and Temporally Weighted Regression (GTWR) model and Geodetector to understand the influence of drivers such as topography, soil, and climate on the IRSEI, considering both the temporal and spatial dimensions. The results indicate that: (1) The proposed IRSEI demonstrates higher reliability, adaptability, and sensitivity compared to RSEI in monitoring the eco-environmental quality of the Yellow River Delta. (2) From 2000 to 2020, the eco-environmental quality of the Yellow River Delta remained generally stable, with a spatial distribution resembling a "Y" shape, showing significant improvement, particularly in Lijin County and its surrounding areas. However, the middle and eastern estuary exhibited a declining trend in eco-environmental quality. (3) The impact of driving factors on the eco-environmental quality varied across the four subordinate regions of the Yellow River Delta, indicating spatial heterogeneity. Factors such as FVC, Soil, LST, JS, and Srad significantly influenced and explained the spatial differentiation of eco-environmental quality in the region. The proposed IRSEI demonstrates better monitoring capabilities in the Yellow River Delta compared to RSEI, providing a scientific basis for land use planning and ecological protection in the area.

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This research was funded by the National Natural Science Foundation of China (grant number 42171435), the Natural Science Foundation of Shandong Province (grant number ZR2020MD025), the Science and Technology Research Program for Colleges and Universities in Shandong Province (grant number J18KA183), the Key Topics of Art and Science in Shandong Province (grant number 2014082), and the Doctoral Fund Projects in Shandong Jianzhu University (grant number X21079Z).

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Department of Geography, National University of Singapore, Singapore, Singapore

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Dongling Ma: Conceptualization, Data curation, Funding acquisition. Qingji Huang: Formal analysis, Investigation, Writing-original draft. Qian Zhang: Supervision, Project administration, Resources, Writing-review & editing. Qian Wang: Investigation, Validation. Hailong Xu: Methodology, Visualization. Yingwei Yan: Software.

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Ma, D., Huang, Q., Zhang, Q. et al. Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02740-0

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Blue carbon: The potential of coastal and oceanic climate action

The oceans and coasts are the Earth’s climate regulators. Covering 72 percent of the planet’s surface, they have absorbed around 40 percent of carbon emitted by human activities since 1850. 1 Pierre Friedlingstein et al., “Global carbon budget 2019,” Earth System Science Data , 2019, Volume 11, Number 4. Coastal ecosystems such as mangroves, tidal marshes, and seagrass meadows act as deep carbon reservoirs, while marine ecosystems absorb and sequester greenhouse gases (GHG) through the carbon cycle. 2 International Union for Conservation of Nature issues brief , International Union for Conservation of Nature (IUCN), November 2017. The bad news for humankind is that both oceans and coasts are under pressure, amid atmospheric and marine warming, habitat destruction, pollution, and the impacts of overfishing and industrial activity. These destructive factors are undermining the role of oceanic systems in slowing climate change.

Humankind’s impact on coastal and offshore ecosystems is a double-edged sword. While we are responsible for significant destruction, we also have agency over potential outcomes. Through our efforts, we can avert damage to or restore the oceans. This would increase carbon absorption from the atmosphere and move the world toward the net-zero emissions envisaged by the Paris Agreement on climate change. Companies that are seeking to offset their carbon emissions through voluntary and compliance carbon markets, and in particular those whose activities are connected to the oceans, such as the fishing industry, would have a key role to play in facilitating this process.

One of the key tools to tackle climate change is the carbon markets, through which organizations can trade emissions allowances to achieve reduction targets. The vast majority of funding provided by carbon markets is allocated to so-called nature-based solutions (NBS). These are focused on the protection, restoration, and management of natural and modified ecosystems. On land, the most recognizable NBS is planting of trees to restore forests. In this report, we analyze the potential of so-called blue carbon NBS, which are designed to protect or enhance ecosystems on coasts and in the oceans. We consider three categories of blue carbon solutions, which we rank according to their scientific and economic maturity:

Established solutions: We consider blue carbon NBS to be “established” when they meet minimum standards of scientific understanding and implementation potential. These relatively mature solutions are focused on the protection and restoration of mangroves, salt marshes, and seagrass meadows. They are more widely understood than many less mature blue carbon solutions, offer scientifically verifiable levels of carbon abatement, and are amenable to funding through the carbon markets. 3 Carlos M. Duarte and Catherine E. Lovelock, “Dimensions of blue carbon and emerging perspectives,” Biology Letters , 2019, Volume 15, Number 3.
Emerging solutions: Emerging solutions are those for which there is an existing body of peer-reviewed research to quantify CO 2 abatement potential, but for which further research is required to align with funding frameworks such as the Core Carbon Principles, published by the Taskforce on Scaling Voluntary Carbon Markets. The emerging category includes the protection and restoration of seaweed forests, extension of seaweed forests, and strategies to reduce bottom trawling.

Nascent solutions: The nascent and potentially largest blue carbon NBS category focuses on the protection or restoration of marine fauna populations. This category is the most challenging in terms of understanding impacts, establishing permanence (preventing leakage), and proving the vital concept of additionality—meaning the benefit would not have accrued anyway, for example, for economic or legal reasons. Fish themselves are not considered a form of carbon sequestration, but they contribute to the effectiveness of the biological carbon pump and therefore to exportation of carbon into the deep sea. Also in the nascent category are reef-based solutions. Healthy reefs may contribute to carbon sequestration through their support for a range of organisms and shell fish.

Due to the scientific challenges around quantification, the nascent category is not yet financeable through carbon markets.

Assessing blue carbon solutions

McKinsey’s new report, Blue Carbon: The potential of coastal and economic climate action , sizes blue carbon NBS and measures their impacts, costs, and likely access to future funding. It highlights the latest scientific research and leverages McKinsey analysis to estimate abatement or conservation potential on a 2050 timeline. Deep dives on kelp reforestation and bottom trawling show how economies of scale in these emerging solutions could help reduce costs.

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Join us for a series of dynamic virtual events during COP28. Discover new research, practical strategies, and collaborations across sectors that propel climate action and growth towards net-zero.

If fully implemented, the established class of solutions would offer 0.4 to 1.2 metric gigatons (Gt) of annual CO 2 abatement, or between 1 and 3 percent of total current annual emissions (Exhibit 1). That potential jumps to approximately 3 GtCO 2 of annual abatement (about 7 percent of total current annual emissions) if the solutions in the emerging category, such as large-scale seaweed farming and bottom-trawling management, were to be fully confirmed and implemented. Nascent solutions might add another 1 to 2 GtCO 2 of annual abatement potential in the longer term, but the science remains highly uncertain. 4 Estimate based on emerging and evolving science and the assumptions we outline in this report; $18/tCO 2 based on opportunity cost of lower-end estimate of bottom trawling impact (approximately 0.4 Gt) in emerging category. If bottom trawling is confirmed at full potential (approximately 1.5 Gt), price viability for large portion of abatement potential could drop to approximately $11/tCO 2 . To put these numbers into context, annual human emissions are currently around 40 GtCO 2 . 5 Myles R. Allen et al., Special report: Global warming of 1.5°C: Summary for policymakers , Intergovernmental Panel on Climate Change, 2018.

Alongside the climate case for blue carbon solutions, there are potentially significant ecosystem benefits. For example, as mangroves recover, fish and marine-fauna populations will expand, supporting both fisheries and nature-based tourism, as well as bolstering coastal protection and filtering runoff. 6 Michael Getzner and Muhammad Shariful Islam, “Ecosystem services of mangrove forests: Results of a meta-analysis of economic values,” International Journal of Environmental Research and Public Health , 2020, Volume 17, Number 16.

When it comes to costs, preliminary analysis suggests that around one third of the total abatement potential would be viable below $18 per tCO 2 . This is more than the $5 to $15 per tCO 2 average price paid in the voluntary carbon markets but below the $40 to 100 per tCO 2 paid in the European compliance markets over the past year (February 2021–2022) (Exhibit 2). 7 Voluntary carbon markets offer entities or individuals the opportunity to buy GHG or carbon credits to offset their emissions and to finance the avoidance or reduction of emissions from other sources; the $18 per tCO 2 estimate is based on the opportunity cost of the lower-end estimate of bottom-trawling impact (approximately 0.4Gt) in the emerging category. If bottom trawling is confirmed at full potential (approximately 1.5Gt), price viability for a large portion of abatement potential could drop to approximately $11 per tCO 2 ; Kate Abnett, Nina Chestney, Susanna Twidale, “Europe’s carbon price nears the 100 euro milestone,” Reuters , February 6, 2022.

Significant hurdles

While blue carbon solutions are an increasingly viable option to help companies and organizations get to net zero, many promising ideas face significant hurdles. Scientific research into many solutions remains at an early stage, creating uncertainty over the impacts of abatement. For example, it is scientifically unclear how seaweed farming or avoided bottom trawling reduces atmospheric CO 2 (complex biogeochemical cycles in seawater and ocean currents influence net exchange of CO 2 with the atmosphere 8 Peter Macreadie et al. “The future of blue carbon science,” Nature Communications , 2019, Volume 10, Number 3998. ). In addition, there is insufficient modeling of how terrestrial processes such as agricultural runoff and climate change may impact the ocean’s continued ability to sequester carbon. 9 Peter Macreadie et al. “The future of blue carbon science,” Nature Communications , 2019, Volume 10, Number 3998.

Beyond scientific uncertainty, matters of coastal and marine law are often complex or opaque. Estuarine and coastal environments, which are subject to national jurisdictions, are often governed by numerous subnational regulatory and administrative regimes. Offshore ocean environments are mainly overseen by the consensus-oriented United Nations Convention on the Law of the Sea and UN Environment Programme. However, individual nations retain rights to resources up to 200 nautical miles from their coastlines. Nearer to shore, disputes over land tenure are common. Finally, in many countries, the practical path to implementation is likely to be bumpy. Coastal blue carbon project developers will need to engage with local communities, respecting traditional access and tenure rights and supporting marine-resource stewardship. We show how some organizations are working to tackle challenges in these areas.

Apple’s blue carbon initiative

Despite varying levels of practicality and scientific certainty, there are viable arguments to suggest that blue carbon solutions present a net opportunity. Indeed, companies are starting to roll out projects as part of their journeys toward net-zero emissions. Apple is working with nonprofit Conservation International to preserve a 27,000-acre mangrove forest in Colombia, the first fully accounted carbon offset credit for a mangrove, expected to sequester one million metric tons of CO 2 over its lifetime. Procter & Gamble, meanwhile, has partnered with the same organization to safeguard 31 species of mangroves in the Philippines.

Another tailwind is the ongoing development of methodologies to report and quantify project impacts. In 2020, standards setter Verra published the first blue carbon conservation methodology approved under any major carbon-offset program. The methodology, which is a revision to the VCS REDD+ Methodology Framework (VM0007), adds blue carbon conservation and restoration activities as eligible project types, and is expected to unlock new sources of funding for tidal wetland conservation and restoration. 10 This methodology provides a set of modules for various components of a methodology for reducing emissions from deforestation and forest degradation (REDD). The modules, when used together, quantify GHG emission reductions and removals from avoiding unplanned and planned deforestation and forest degradation. This methodology is applicable to forest lands, forested wetlands, forested peatlands, and tidal wetlands that would be deforested or degraded in the absence of the project activity.

Actions to support funding

There is no escaping the fact that blue carbon solutions are, for the most part, in their infancy. Just a trickle of projects have qualified for carbon markets to date, and there are significant financial, practical, and legal hurdles to scaling in ocean and coastal environments. In short, there are deficits in both supply and demand, resulting in a challenging risk-return profile. That said, the science that supports established blue carbon sequestration is sound, and there is clear opportunity for corporations to consider blue carbon opportunities. Moreover, given their beneficial impact on biodiversity and coastal communities, blue carbon solutions are particularly rich in “cobenefits” beyond their abatement profiles. Therefore, amid narrow pathways toward a 1.5°C outcome, the solutions merit serious consideration across financial markets, corporates, and governments .

Financial markets

As in any nascent technology, a key early requirement is to get to sufficient scale to achieve critical mass. At financial institutions, current investment in blue carbon projects is rooted in a broader mismatch between climate ambition and operational resources. Outside the top tier, many banks and investors lack the strategy and capabilities to commit to a relatively marginal asset class. Ticket sizes tend to be small compared with the effort required, and there is often a gap to cost parity with incumbent technologies. To resolve these challenges, financial institutions need to find ways to layer blue carbon into portfolio allocation frameworks and source the knowledge resources that can help them navigate new markets. Even then, there are doubts around returns profiles and timelines. These present significant barriers that need to be overcome if blue carbon is to become established as an alternative to terrestrial solutions.

Corporate scaling opportunities

Companies looking to offset their carbon emissions face similar challenges to those faced by financial institutions. In comparison with more readily available terrestrial credits, blue carbon offset opportunities may appear high risk, subscale, and expensive. Still, Apple and others have shown there are opportunities, particularly in the established class of solutions. For companies focused on the ocean, such as expedition cruise lines, there is also the chance to align their net-zero programs with their real-world activities. Tackling the challenge of scaling both supply and demand, the recently announced Blue Carbon Buyers Alliance aims to aggregate and educate buyers around a clear demand signal, with members committing to funding or purchasing credits from high-quality blue carbon projects. 11 Blue Carbon Buyers Alliance: Scaling blue carbon markets to conserve and restore coastal ecosystems , Business Alliance to Scale Climate Solutions, 2021. These collective, early mover signals could have a significant impact on supply, potentially bringing down prices in the process.

Project leads and governments

To support financial and corporate initiatives, blue carbon project leads have an important role to play. They must seek out more risk-tolerant financing and then design, pilot, and demonstrate project feasibility. This will establish the track record that will support more capital inflows. To create early momentum, they should share their early successes as widely and as comprehensively as possible.

Finally, governments will be critical in scaling participation and funding. A good blueprint is the work of the US Advanced Research Projects Agency, which is tasked with promoting and funding research into advanced energy technologies. In addition, multilateral and development assistance agencies can fund innovative and scalable programs. Progress at the COP26 summit in Glasgow on drafting the terms of a future structure for carbon markets under the revised Article 6 of the Paris Agreement was a positive step, and more progress is expected over the coming year. Governments could also signal support by including blue carbon solutions in nationally determined contributions (NDCs) under the Paris Agreement. Through these kinds of initiatives, they could nudge blue carbon toward the mainstream, and the world toward a promising new abatement opportunity.

Julien Claes is an associate partner in McKinsey’s Brussels office, Duko Hopman is a partner in the New Jersey office, Gualtiero Jaeger is a consultant in the Miami office, and Matt Rogers is a senior partner emeritus in the Bay Area office.

The authors wish to thank Joe Roman at the University of Vermont, Amy Schmid at Verra, and David Wigan at Perceptive Communications, as well as our McKinsey colleagues Urs Binggeli, Caroline De Vit, Hauke Engel, Kartik Jayaram, Laurent Kinet, Peter Mannion, Sébastien Marlier, Erik Ringvold, Ignus Rocher, Robin Smale, Antoine Stevens, and Matt Stone for their contributions to this article.

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Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
Criterion sampling-selection based on pre-identified factors.
Convenience sampling- selection based on availability.
Snowball sampling- the selection is by referral from other participants or people who know potential participants.
Extreme case sampling- targeted selection of rare cases.
Typical case sampling-selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

Internal validity--- Credibility
External validity---Transferability
Reliability---Dependability
Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc.

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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