what is research analysis meaning

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data analysis , the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data , generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making . Data analysis techniques are used to gain useful insights from datasets, which can then be used to make operational decisions or guide future research . With the rise of “ big data ,” the storage of vast quantities of data in large databases and data warehouses, there is increasing need to apply data analysis techniques to generate insights about volumes of data too large to be manipulated by instruments of low information-processing capacity.

Datasets are collections of information. Generally, data and datasets are themselves collected to help answer questions, make decisions, or otherwise inform reasoning. The rise of information technology has led to the generation of vast amounts of data of many kinds, such as text, pictures, videos, personal information, account data, and metadata, the last of which provide information about other data. It is common for apps and websites to collect data about how their products are used or about the people using their platforms. Consequently, there is vastly more data being collected today than at any other time in human history. A single business may track billions of interactions with millions of consumers at hundreds of locations with thousands of employees and any number of products. Analyzing that volume of data is generally only possible using specialized computational and statistical techniques.

The desire for businesses to make the best use of their data has led to the development of the field of business intelligence , which covers a variety of tools and techniques that allow businesses to perform data analysis on the information they collect.

For data to be analyzed, it must first be collected and stored. Raw data must be processed into a format that can be used for analysis and be cleaned so that errors and inconsistencies are minimized. Data can be stored in many ways, but one of the most useful is in a database . A database is a collection of interrelated data organized so that certain records (collections of data related to a single entity) can be retrieved on the basis of various criteria . The most familiar kind of database is the relational database , which stores data in tables with rows that represent records (tuples) and columns that represent fields (attributes). A query is a command that retrieves a subset of the information in the database according to certain criteria. A query may retrieve only records that meet certain criteria, or it may join fields from records across multiple tables by use of a common field.

Frequently, data from many sources is collected into large archives of data called data warehouses. The process of moving data from its original sources (such as databases) to a centralized location (generally a data warehouse) is called ETL (which stands for extract , transform , and load ).

• The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
• The transformation step is the process of cleaning the data so that they fit the analytical need for the data and the schema of the data warehouse. This may involve changing formats for certain fields, removing duplicate records, or renaming fields, among other processes.
• Finally, the clean data are loaded into the data warehouse, where they may join vast amounts of historical data and data from other sources.

After data are effectively collected and cleaned, they can be analyzed with a variety of techniques. Analysis often begins with descriptive and exploratory data analysis. Descriptive data analysis uses statistics to organize and summarize data, making it easier to understand the broad qualities of the dataset. Exploratory data analysis looks for insights into the data that may arise from descriptions of distribution, central tendency, or variability for a single data field. Further relationships between data may become apparent by examining two fields together. Visualizations may be employed during analysis, such as histograms (graphs in which the length of a bar indicates a quantity) or stem-and-leaf plots (which divide data into buckets, or “stems,” with individual data points serving as “leaves” on the stem).

Data analysis frequently goes beyond descriptive analysis to predictive analysis, making predictions about the future using predictive modeling techniques. Predictive modeling uses machine learning , regression analysis methods (which mathematically calculate the relationship between an independent variable and a dependent variable), and classification techniques to identify trends and relationships among variables. Predictive analysis may involve data mining , which is the process of discovering interesting or useful patterns in large volumes of information. Data mining often involves cluster analysis , which tries to find natural groupings within data, and anomaly detection , which detects instances in data that are unusual and stand out from other patterns. It may also look for rules within datasets, strong relationships among variables in the data.

Research vs Analysis: What's the Difference and Why It Matters

When it comes to data-driven business decisions, research and analysis are often used interchangeably. However, these terms are not synonymous, and understanding the difference between them is crucial for making informed decisions.

Here are our five key takeaways:

• Research is the process of finding information, while analysis is the process of evaluating and interpreting that information to make informed decisions.
• Analysis is a critical step in the decision-making process, providing context and insights to support informed choices.
• Good research is essential to conducting effective analysis, but research alone is not enough to inform decision-making.
• Analysis requires a range of skills, including data modeling, statistics, and critical thinking.
• While analysis can be time-consuming and resource-intensive, it is a necessary step for making informed decisions based on data.

In this article, we'll explore the key differences between research and analysis and why they matter in the decision-making process.

Table of Contents

Understanding research vs analysis, why analysis matters in the decision-making process, the role of research in analysis, skills needed for effective analysis, the time and resource requirements for analysis, the unified stack for modern data teams, get a personalized platform demo & 30-minute q&a session with a solution engineer, introduction.

This is a guest post by Bill Inmon. Bill Inmon is a pioneer in data warehousing, widely known as the “Father of Data Warehousing.” He is also the author of more than 50 books and over 650 articles on data warehousing, data management, and information technology.

The search vendors will tell you that there is no difference. Indeed, when you do analysis you have to do research. But there are some very real and very important differences.

When it comes to the methodology of data science, understanding the main difference between research and analysis is crucial.

What is Research?

Research is the process of collecting and analyzing data, information, or evidence to answer a specific question or to solve a problem. It involves identifying a research question, designing a study or experiment, collecting and analyzing data, and drawing conclusions based on the results.

Research is typically focused on gathering information through various qualitative research methods, in order to develop an understanding of a particular topic or phenomenon.

In its simplest form, it means we go look for something. We go to a library and we find some books. Or we go to the Internet and find a good restaurant to go to. Or we go to the Bible and look up the story of Cain and Abel. To research means to go to a body of elements and find the one or two that we need for our purposes.

What are some common research methods?

There are many research methods, but some common ones include surveys, experiments, observational studies, case studies, and interviews. Each method has its strengths and weaknesses, and the choice of method depends on the research question, the type of data needed, and the available resources.

What is Analysis?

Analysis is the process of breaking down complex information into smaller parts to gain a better understanding of it. Then take that information and apply statistical analysis and other methods to draw conclusions and make predictions.

Somewhat similar to research, we go to a body of elements and find one or two that are of interest to us. Then after finding what we are looking for we do further investigation. 

That further investigation may take many forms. 

• We may compare and contrast the elements
• We may simply count and summarize the elements
• We may look at many elements and qualify some of them and disqualify the others 

The goal of analysis is to answer questions or solve problems. Analysis often involves examining and interpreting data sets, identifying patterns and trends, and drawing predictive conclusions based on the evidence.

In contrast to research, which is focused on gathering data, analysis is focused on making sense of the data that has already been collected.

What are some common analysis methods?

In the analysis process, data scientists use a variety of techniques and tools to explore and analyze the data, such as regression analysis, clustering, and machine learning algorithms. These techniques are used to uncover patterns, relationships, and trends in the data that can help inform business decisions and strategies.

There are many analysis methods, but some common ones include descriptive statistics, inferential statistics, content analysis, thematic analysis, and discourse analysis. Each method has its strengths and weaknesses, and the choice of method depends on the type of data collected, the research question, and the available resources.

Analysis is a critical step in the decision-making process. It provides context and insights to support informed choices. Without analysis, decision-makers risk making choices based on incomplete or inaccurate information, leading to poor outcomes. Effective analysis helps decision-makers understand the impact of different scenarios, identify potential risks, and identify opportunities for improvement.

In almost every case, the analysis starts with quantitative research. So it’s almost like differentiating between baiting a hook and catching a fish. If you are going to catch a fish, you have to start by baiting a hook.

Although that might not be the best analogy, the role of research in analysis works in the same order. Good research is essential to conducting effective analysis. It provides a foundation of knowledge and understanding, helping analysts identify patterns, trends, and relationships in data collection. However, research alone is not enough to inform decision-making. Just like baiting a hook alone is not enough to catch a fish. 

Effective analysis requires a range of skills, including data modeling, statistics, and critical thinking. Data modeling involves creating a conceptual framework for understanding the data, while statistics helps data analysts identify patterns and relationships in the data sets. Critical thinking is essential for evaluating data analytics and drawing insights that support informed decision-making.

Related Reading : The Best Data Modeling Tools: Advice & Comparison

Just because you search for something does not mean you are going to analyze it.

Analysis can be time-consuming and resource-intensive, requiring significant investments in technology, talent, and infrastructure. However, It is necessary to analyze something when you need to extract meaningful insights or draw conclusions based on big data or information gathered through quantitative research.

Whether you're conducting research or performing statistical analysis, having a solid understanding of your data and how to interpret it is essential for success. Data scientists play a critical role in this process, as they have the skills and expertise to apply statistical methods and other techniques to make sense of complex data sets.

Organizations that invest in effective analysis capabilities are better positioned to make predictive data-driven business decisions that support their strategic goals. Without quantitative analysis, research may remain incomplete or inconclusive, and the data gathered may not be effectively used.

Related Reading : 7 Best Data Analysis Tools

How Integrate.io Can Help

When it comes to search and analysis, having access to accurate and reliable data is essential for making informed decisions. This is where Integrate.io comes in - as a big data integration platform, it enables businesses to connect and combine data from a variety of sources, making it easier to search for and analyze the information that's most relevant to their needs. By streamlining the data integration process, Integrate.io helps businesses get the most out of their data collection, enabling them to make more informed decisions and gain a competitive edge in their respective industries.

In conclusion, the main difference between research and analysis lies in the approach to data collection and interpretation. While research is focused on gathering information through qualitative research methods, analysis is focused on drawing predictive conclusions based on statistical analysis and other techniques. By leveraging the power of data science and tools like Integrate.io , businesses can make better decisions based on data-driven insights.

Tags: big data, data-analytics, Versus

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

QuestionPro offers solutions for every issue and industry, making it more than just survey software. For handling data, we also have systems like our InsightsHub research library.

You may make crucial decisions quickly while using QuestionPro to understand your clients and other study subjects better. Make use of the possibilities of the enterprise-grade research suite right away!

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What is Research? – Definition, Objectives & Types of Research

Introduction: Research is a systematic and structured investigation that seeks to expand knowledge, uncover new insights, and provide evidence-based understanding in various fields. It is vital in advancing human understanding, addressing complex problems, and driving innovation. Research encompasses a wide range of methodologies, including empirical studies, experiments, surveys, and theoretical analyses, conducted by researchers across academic, scientific, and professional domains. New discoveries are made through research, theories are developed and tested, and practical solutions are generated. The impact of research is far-reaching, influencing advancements in technology, healthcare, social sciences, environmental conservation, and more. It drives progress, informs policy decisions, and shapes the future by providing a solid foundation of reliable and verified knowledge. The importance of research cannot be overstated, as it drives human knowledge forward and fosters societal development and improvement. Types of Research

What is Research?

The primary objective of the research is to contribute to the existing body of knowledge by uncovering new insights, validating existing theories, or challenging prevailing assumptions. It is driven by the pursuit of truth, accuracy, and evidence-based understanding.

Research can take various forms, depending on the discipline and the nature of the inquiry. It can be empirical, involving the collection and analysis of data through experiments, surveys, observations, or interviews. It can also be theoretical, involving the critical analysis of existing literature and concepts to develop new frameworks or models.

The research process is characterized by systematic and organized steps. It begins with identifying a research problem or topic of interest, followed by an extensive literature review to understand the existing knowledge and identify gaps. Research questions or hypotheses are formulated, and a research design is developed to guide data collection and analysis.

Data collection methods can vary widely, ranging from quantitative approaches such as surveys or experiments to qualitative approaches such as interviews or case studies. Researchers analyze the collected data using appropriate statistical or qualitative analysis techniques to draw meaningful conclusions.

One of the key aspects of research is its emphasis on objectivity and rigor. Researchers strive to minimize bias, ensure the reliability and validity of findings, and maintain ethical standards in their research practices.

The impact of research extends far beyond the academic realm. Research findings inform decision-making processes in various sectors, including healthcare, policy development, business strategies, environmental conservation, and social sciences. It drives technological advancements, fosters innovation, and provides the foundation for evidence-based practices.

Furthermore, research is an iterative process, with new studies building upon and refining existing knowledge. It is a collaborative endeavor, often involving interdisciplinary collaborations and the exchange of ideas among researchers worldwide.

Definitions of Research:

Research is a systematic and organized investigation conducted to expand knowledge, gain a deeper understanding, and generate new insights in a specific field. It involves rigorous and organized data collection, analysis, and interpretation to address research questions or hypotheses. The pursuit of new information drives research, the validation of existing theories, or the exploration of new perspectives. It employs various methodologies to gather and analyze data, including empirical studies, experiments, surveys, interviews, or theoretical analyses. The ultimate goal of the research is to contribute to the existing body of knowledge, advance understanding, and inform decision-making processes across academic, scientific, and professional domains.

Kasi (2009) 1 defines “Research is, therefore, a method for investigating and collecting information aimed at the discovery of new facts or interpretation of existing information, to discover or revise facts, theories, and applications.”

Research is stated by Gina Wisker 1 as “Research is about asking and beginning to answer questions, seeking knowledge and understanding of the world and its processes, and testing assumptions and beliefs.”

Redman and Mory define research as a “systematized effort to gain new knowledge.” 2

Burns (1997) defines research as “a systematic investigation to find answers to a problem.” 2

“The word research is composed of two syllables, re and search. The dictionary defines the former as a prefix meaning again, anew, or over again and the latter as a verb meaning to examine closely and carefully, to test and try, or to probe. Together, they form a noun describing a careful, systematic, patient study and investigation in some field of knowledge undertaken to establish facts or principles.” (Grinnell 1993) 2

Objectives of Research:

The research objectives can vary depending on the specific field of study, the nature of the research, and the researcher’s goals. However, some common purposes of the research include:

• Answer questions: Research aims to provide answers to specific questions or hypotheses. It seeks to investigate and uncover information, data, or insights about a particular topic or issue.
• Solve problems: Research is often conducted to address real-world issues or challenges. It aims to identify innovative solutions, strategies, or approaches that can help overcome obstacles and improve existing systems or practices.
• Generate new knowledge: Research endeavors to contribute to the existing body of knowledge by uncovering new information, theories, or perspectives. It involves exploring uncharted territory or expanding upon existing knowledge in various fields of study.
• Improve understanding: Research aims to deepen our understanding of complex phenomena, processes, or concepts. It seeks to clarify misconceptions, explore underlying mechanisms, or uncover relationships between variables, leading to a more comprehensive and accurate understanding of the subject.
• Add value: Research brings value by providing practical or theoretical benefits. It can lead to technological advancements, policies or practices, enhanced decision-making processes, or the development of new products, services, or theories.

Types of Research:

C.R. Kothari, a renowned Indian researcher and author, has proposed several types of research in his book Research Methodology: Methods and Techniques . According to Kothari, research can be categorized into the following types:

• Descriptive Research: Descriptive research is a method of investigation that provides an accurate and comprehensive description of a specific phenomenon, situation, or population. It involves collecting data through various methods, such as surveys, interviews, or observations, and analyzing the data to identify patterns, characteristics, or trends. Descriptive research does not aim to establish causal relationships or manipulate variables but instead aims to answer questions about what is happening or the current state of the research subject. This type of research is valuable in generating a foundational understanding of a topic, informing decision-making processes, and providing a basis for further research in various fields of study.
• Analytical Research: Analytical research focuses on critically examining and interpreting existing data, information, or theories to gain deeper insights and understanding. It involves analyzing and evaluating data or literature to identify patterns, relationships, or underlying causes. Analytical research aims to go beyond descriptive findings and delves into the reasons and explanations behind observed phenomena. This type of research often involves rigorous statistical analysis, comparative studies, or theoretical frameworks to draw conclusions and make inferences. Analytical research is crucial in advancing knowledge, refining theories, and providing evidence-based insights that can inform decision-making and policy development in various fields of study.
• Applied Research: Applied research is a type of research that is conducted to address practical problems or improve existing practices. It focuses on directly applying knowledge and theories to real-world situations and aims to provide actionable solutions. Applied research often involves collaborating with stakeholders, such as industry professionals or policymakers, to ensure the research outcomes have practical relevance. This type of research emphasizes implementing and evaluating interventions, strategies, or technologies to solve specific issues. The results of applied research can potentially impact society, leading to advancements in technology, policy improvements, or enhanced practices in various domains, including healthcare, education, business, and engineering.
• Fundamental Research: Fundamental research, also known as basic research or pure research, is a type of inquiry that aims to expand knowledge and understanding in a particular field. It explores theoretical concepts, principles, and fundamental laws without immediate practical application. Fundamental research is driven by curiosity and the desire to explore new frontiers of knowledge. It often involves the formulation of hypotheses, experimentation, and rigorous data analysis. The fundamental research findings may not have immediate or direct practical implications. Still, they lay the groundwork for applied research and can lead to significant breakthroughs, innovations, and advancements in various scientific disciplines. Fundamental research is essential for pushing the boundaries of knowledge and fostering a deeper understanding of the world around us.
• Qualitative Research: Qualitative research is an exploratory approach to understanding individuals’ or groups’ meaning, context, and subjective experiences. It involves collecting and analyzing non-numerical data, such as interviews, observations, or textual analysis, to gain deep insights into complex social phenomena. Qualitative research focuses on uncovering underlying motivations, beliefs, attitudes, and cultural influences that shape human behavior. It emphasizes the richness, depth, and complexity of human experiences and seeks to provide a detailed and holistic understanding of a research topic. Qualitative research methods allow for flexibility and adaptability, enabling researchers to capture nuances and explore emerging themes. This type of research is valuable in fields such as anthropology, sociology, psychology, and education, where a deep understanding of human behavior and social processes is sought.
• Quantitative Research: Quantitative research systematically gathers and analyzes numerical data to uncover patterns, trends, and relationships. It involves collecting structured data through surveys, experiments, or observations and applying statistical techniques for data analysis. Quantitative research aims to quantify variables, measure phenomena, and draw objective conclusions based on statistical evidence. This type of research focuses on obtaining precise and measurable results, often using large sample sizes to increase the generalizability of findings. Quantitative research is prevalent in social sciences, economics, psychology, and market research, where numerical data and statistical analysis provide a rigorous and quantifiable approach to understanding and explaining phenomena.

Significance of Research:

The significance of research cannot be overstated, as it serves as the cornerstone of progress and development in various fields. Whether in science, technology, social sciences, or humanities, research is vital in advancing knowledge, addressing problems, and shaping society.

One of the primary significances of research is its ability to expand our understanding and knowledge base. Through rigorous investigation, research uncovers new information, theories, and insights that contribute to the existing body of knowledge. It allows us to delve deeper into complex phenomena, explore uncharted territories, and uncover hidden connections. This expansion of knowledge forms the basis for innovation, development, and the evolution of society.

Research also serves as a powerful tool for problem-solving. It enables us to identify and address pressing issues, whether they are in healthcare, education, economics, or any other field. By systematically examining problems, collecting and analyzing relevant data, and developing evidence-based solutions, research provides the means to overcome challenges and improve existing practices. It empowers us to make informed decisions, develop effective strategies, and allocate resources wisely.

Furthermore, research plays a critical role in informing decision-making processes. Policymakers, business leaders, and organizations rely on research findings to guide their choices, shape policies, and plan for the future. Research provides reliable and credible information, allowing decision-makers to navigate complex issues more confidently and accurately. It serves as a bridge between theory and practice, translating abstract concepts into tangible outcomes that benefit society.

Innovation and improvement are other significant outcomes of research. Research drives innovation by exploring new ideas, pushing boundaries, and challenging established norms. It leads to the developing of new technologies, products, and services that improve our quality of life. Research also fosters improvements in existing practices and processes by identifying inefficiencies, gaps, and areas for enhancement. Through research, we continuously strive to find better, more efficient ways of doing things.

Research has a profound impact on society as a whole. It addresses social issues, informs public policies, and promotes positive social change. Research provides evidence-based solutions that address societal challenges, from healthcare interventions to educational reforms. It influences public opinion, shapes cultural norms, and contributes to communities’ well-being and progress.

Moreover, research plays a crucial role in validating and challenging existing knowledge. It provides empirical evidence that supports or challenges established theories and concepts. Through rigorous scrutiny and critical analysis, research ensures that knowledge constantly evolves, grows, and adapts to new information. It encourages intellectual discourse, promotes healthy skepticism, and encourages a culture of lifelong learning.

References: 

• Kasi, P. (2009). Research: What, Why and How? AuthorHouse.
• Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New Age International.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

What is a Research Analyst?

Types of research analysts, what does a research analyst do, what skills/personality do you need, jobs and career opportunities, proper ra training, types of companies that hire analysts, additional resources, research analyst.

A professional who performs research and analysis

A research analyst is responsible for researching, analyzing, interpreting, and presenting data related to markets, operations, finance/accounting, economics, customers, and other information related to the field they work in.  A research analyst is typically very quantitative, analytical, logical, and good at managing numbers and data.  This guide will break down the main aspects of being an analyst in different industries, with a focus on the finance industry.

Research analysts exist in just about every industry but are more commonly found in some industries – such as the financial services industry – than in others.  Within a company, they might be found in a number of departments, with a number of different job titles.

The most common research analyst job titles are:

• Market Research Analyst (Marketing)
• Operations Research Analyst
• Economic Research analyst
• Financial Analyst
• Equity Research Analyst

A Financial Analyst is primarily concerned with performing financial forecasting, evaluating operational metrics, analyzing financial data, and creating financial models and presentations to assist executive management in its decision making and reporting on the financial performance of the company.

Job Responsibilities may include any or all of the following:

• Analyze past results and perform variance analysis
• Identify trends and make recommendations for improvements
• Provide analysis of trends and forecasts and recommend actions for optimization
• Identify and drive process improvements, including the creation of standard and ad-hoc reports
• Use Excel functions to organize and analyze data
• Create charts, graphs, and presentations for leadership teams
• Develop recommendations to improve business operations going forward

There are several key skills you should have in order to be successful in the field of research analysis. While everyone is different and all sorts of people can be successful as an analyst, there are some skills and traits that nearly all RAs share.

The most commonly found research analyst skills and personality traits are:

• Good with numbers
• High attention to detail
• Inquisitive
• Ability to distill large amounts of information into specific takeaways

One of the best ways to find job opportunities for analysts is by using the LinkedIn “job search” function and generating a list of research analyst jobs on LinkedIn .

You can refine your search by specifying a geographic location, industry, company size, or other criteria.  You can then easily apply for positions directly through LinkedIn, and also check to see if you have any 1st, 2nd, or 3rd-degree LinkedIn connections at the company.

Getting the proper training and mastering the necessary skills to be a research analyst is critical for both landing an analyst job and succeeding in it. Many analysts get their formal training at a university or through studying to obtain a professional designation like the FMVA  (Financial Modeling Valuation Analyst) certification program that we offer here at CFI.

Analysts are increasingly turning to online training programs such as CFI’s to master the practical, hands-on skills they need for professional success.

Specifically, some of the most important areas of research analysis training include:

• Excel training
• Accounting training
• Financial analysis training

Launch All CFI online courses to start financial modeling training now!

There is a broad range of industries and companies that hire analysts to perform research.

Some of the most common types of companies include:

• Insurance companies
• Governments
• Health Care providers
• Pharmaceutical companies
• Marketing agencies
• Manufacturing companies

Thank you for reading the CFI guide to Research Analyst. CFI’s mission is to help you advance your career. With that goal in mind, these additional resources will help you on our way toward becoming a top-tier financial professional:

• The Analyst Trifecta eBook
• Advanced Excel Formulas
• Types of Charts and Graphs
• Career Resources
• See all career resources
• See all capital markets resources

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Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

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Documentary Analysis – Methods, Applications and Examples

Table of Contents

Documentary Analysis

Definition:

Documentary analysis, also referred to as document analysis , is a systematic procedure for reviewing or evaluating documents. This method involves a detailed review of the documents to extract themes or patterns relevant to the research topic .

Documents used in this type of analysis can include a wide variety of materials such as text (words) and images that have been recorded without a researcher’s intervention. The domain of document analysis, therefore, includes all kinds of texts – books, newspapers, letters, study reports, diaries, and more, as well as images like maps, photographs, and films.

Documentary analysis provides valuable insight and a unique perspective on the past, contextualizing the present and providing a baseline for future studies. It is also an essential tool in case studies and when direct observation or participant observation is not possible.

The process usually involves several steps:

• Sourcing : This involves identifying the document or source, its origin, and the context in which it was created.
• Contextualizing : This involves understanding the social, economic, political, and cultural circumstances during the time the document was created.
• Interrogating : This involves asking a series of questions to help understand the document better. For example, who is the author? What is the purpose of the document? Who is the intended audience?
• Making inferences : This involves understanding what the document says (either directly or indirectly) about the topic under study.
• Checking for reliability and validity : Just like other research methods, documentary analysis also involves checking for the validity and reliability of the documents being analyzed.

Documentary Analysis Methods

Documentary analysis as a qualitative research method involves a systematic process. Here are the main steps you would generally follow:

Defining the Research Question

Before you start any research , you need a clear and focused research question . This will guide your decision on what documents you need to analyze and what you’re looking for within them.

Selecting the Documents

Once you know what you’re looking for, you can start to select the relevant documents. These can be a wide range of materials – books, newspapers, letters, official reports, diaries, transcripts of speeches, archival materials, websites, social media posts, and more. They can be primary sources (directly from the time/place/person you are studying) or secondary sources (analyses created by others).

Reading and Interpreting the Documents

You need to closely read the selected documents to identify the themes and patterns that relate to your research question. This might involve content analysis (looking at what is explicitly stated) and discourse analysis (looking at what is implicitly stated or implied). You need to understand the context in which the document was created, the author’s purpose, and the audience’s perspective.

Coding and Categorizing the Data

After the initial reading, the data (text) can be broken down into smaller parts or “codes.” These codes can then be categorized based on their similarities and differences. This process of coding helps in organizing the data and identifying patterns or themes.

Analyzing the Data

Once the data is organized, it can be analyzed to make sense of it. This can involve comparing the data with existing theories, examining relationships between categories, or explaining the data in relation to the research question.

Validating the Findings

The researcher needs to ensure that the findings are accurate and credible. This might involve triangulating the data (comparing it with other sources or types of data), considering alternative explanations, or seeking feedback from others.

Reporting the Findings

The final step is to report the findings in a clear, structured way. This should include a description of the methods used, the findings, and the researcher’s interpretations and conclusions.

Applications of Documentary Analysis

Documentary analysis is widely used across a variety of fields and disciplines due to its flexible and comprehensive nature. Here are some specific applications:

Historical Research

Documentary analysis is a fundamental method in historical research. Historians use documents to reconstruct past events, understand historical contexts, and interpret the motivations and actions of historical figures. Documents analyzed may include personal letters, diaries, official records, newspaper articles, photographs, and more.

Social Science Research

Sociologists, anthropologists, and political scientists use documentary analysis to understand social phenomena, cultural practices, political events, and more. This might involve analyzing government policies, organizational records, media reports, social media posts, and other documents.

Legal Research

In law, documentary analysis is used in case analysis and statutory interpretation. Legal practitioners and scholars analyze court decisions, statutes, regulations, and other legal documents.

Business and Market Research

Companies often analyze documents to gather business intelligence, understand market trends, and make strategic decisions. This might involve analyzing competitor reports, industry news, market research studies, and more.

Media and Communication Studies

Scholars in these fields might analyze media content (e.g., news reports, advertisements, social media posts) to understand media narratives, public opinion, and communication practices.

Literary and Film Studies

In these fields, the “documents” might be novels, poems, films, or scripts. Scholars analyze these texts to interpret their meaning, understand their cultural context, and critique their form and content.

Educational Research

Educational researchers may analyze curricula, textbooks, lesson plans, and other educational documents to understand educational practices and policies.

Health Research

Health researchers may analyze medical records, health policies, clinical guidelines, and other documents to study health behaviors, healthcare delivery, and health outcomes.

Examples of Documentary Analysis

Some Examples of Documentary Analysis might be:

• Example 1 : A historian studying the causes of World War I might analyze diplomatic correspondence, government records, newspaper articles, and personal diaries from the period leading up to the war.
• Example 2 : A policy analyst trying to understand the impact of a new public health policy might analyze the policy document itself, as well as related government reports, statements from public health officials, and news media coverage of the policy.
• Example 3 : A market researcher studying consumer trends might analyze social media posts, customer reviews, industry reports, and news articles related to the market they’re studying.
• Example 4 : An education researcher might analyze curriculum documents, textbooks, and lesson plans to understand how a particular subject is being taught in schools. They might also analyze policy documents to understand the broader educational policy context.
• Example 5 : A criminologist studying hate crimes might analyze police reports, court records, news reports, and social media posts to understand patterns in hate crimes, as well as societal and institutional responses to them.
• Example 6 : A journalist writing a feature article on homelessness might analyze government reports on homelessness, policy documents related to housing and social services, news articles on homelessness, and social media posts from people experiencing homelessness.
• Example 7 : A literary critic studying a particular author might analyze their novels, letters, interviews, and reviews of their work to gain insight into their themes, writing style, influences, and reception.

When to use Documentary Analysis

Documentary analysis can be used in a variety of research contexts, including but not limited to:

• When direct access to research subjects is limited : If you are unable to conduct interviews or observations due to geographical, logistical, or ethical constraints, documentary analysis can provide an alternative source of data.
• When studying the past : Documents can provide a valuable window into historical events, cultures, and perspectives. This is particularly useful when the people involved in these events are no longer available for interviews or when physical evidence is lacking.
• When corroborating other sources of data : If you have collected data through interviews, surveys, or observations, analyzing documents can provide additional evidence to support or challenge your findings. This process of triangulation can enhance the validity of your research.
• When seeking to understand the context : Documents can provide background information that helps situate your research within a broader social, cultural, historical, or institutional context. This can be important for interpreting your other data and for making your research relevant to a wider audience.
• When the documents are the focus of the research : In some cases, the documents themselves might be the subject of your research. For example, you might be studying how a particular topic is represented in the media, how an author’s work has evolved over time, or how a government policy was developed.
• When resources are limited : Compared to methods like experiments or large-scale surveys, documentary analysis can often be conducted with relatively limited resources. It can be a particularly useful method for students, independent researchers, and others who are working with tight budgets.
• When providing an audit trail for future researchers : Documents provide a record of events, decisions, or conditions at specific points in time. They can serve as an audit trail for future researchers who want to understand the circumstances surrounding a particular event or period.

Purpose of Documentary Analysis

The purpose of documentary analysis in research can be multifold. Here are some key reasons why a researcher might choose to use this method:

• Understanding Context : Documents can provide rich contextual information about the period, environment, or culture under investigation. This can be especially useful for historical research, where the context is often key to understanding the events or trends being studied.
• Direct Source of Data : Documents can serve as primary sources of data. For instance, a letter from a historical figure can give unique insights into their thoughts, feelings, and motivations. A company’s annual report can offer firsthand information about its performance and strategy.
• Corroboration and Verification : Documentary analysis can be used to validate and cross-verify findings derived from other research methods. For example, if interviews suggest a particular outcome, relevant documents can be reviewed to confirm the accuracy of this finding.
• Substituting for Other Methods : When access to the field or subjects is not possible due to various constraints (geographical, logistical, or ethical), documentary analysis can serve as an alternative to methods like observation or interviews.
• Unobtrusive Method : Unlike some other research methods, documentary analysis doesn’t require interaction with subjects, and therefore doesn’t risk altering the behavior of those subjects.
• Longitudinal Analysis : Documents can be used to study change over time. For example, a researcher might analyze census data from multiple decades to study demographic changes.
• Providing Rich, Qualitative Data : Documents often provide qualitative data that can help researchers understand complex issues in depth. For example, a policy document might reveal not just the details of the policy, but also the underlying beliefs and attitudes that shaped it.

Advantages of Documentary Analysis

Documentary analysis offers several advantages as a research method:

• Unobtrusive : As a non-reactive method, documentary analysis does not require direct interaction with human subjects, which means that the research doesn’t affect or influence the subjects’ behavior.
• Rich Historical and Contextual Data : Documents can provide a wealth of historical and contextual information. They allow researchers to examine events and perspectives from the past, even from periods long before modern research methods were established.
• Efficiency and Accessibility : Many documents are readily accessible, especially with the proliferation of digital archives and databases. This accessibility can often make documentary analysis a more efficient method than others that require data collection from human subjects.
• Cost-Effective : Compared to other methods, documentary analysis can be relatively inexpensive. It generally requires fewer resources than conducting experiments, surveys, or fieldwork.
• Permanent Record : Documents provide a permanent record that can be reviewed multiple times. This allows for repeated analysis and verification of the data.
• Versatility : A wide variety of documents can be analyzed, from historical texts to contemporary digital content, providing flexibility and applicability to a broad range of research questions and fields.
• Ability to Cross-Verify (Triangulate) Data : Documentary analysis can be used alongside other methods as a means of triangulating data, thus adding validity and reliability to the research.

Limitations of Documentary Analysis

While documentary analysis offers several benefits as a research method, it also has its limitations. It’s important to keep these in mind when deciding to use documentary analysis and when interpreting your findings:

• Authenticity : Not all documents are genuine, and sometimes it can be challenging to verify the authenticity of a document, particularly for historical research.
• Bias and Subjectivity : All documents are products of their time and their authors. They may reflect personal, cultural, political, or institutional biases, and these biases can affect the information they contain and how it is presented.
• Incomplete or Missing Information : Documents may not provide all the information you need for your research. There may be gaps in the record, or crucial information may have been omitted, intentionally or unintentionally.
• Access and Availability : Not all documents are readily available for analysis. Some may be restricted due to privacy, confidentiality, or security considerations. Others may be difficult to locate or access, particularly historical documents that haven’t been digitized.
• Interpretation : Interpreting documents, particularly historical ones, can be challenging. You need to understand the context in which the document was created, including the social, cultural, political, and personal factors that might have influenced its content.
• Time-Consuming : While documentary analysis can be cost-effective, it can also be time-consuming, especially if you have a large number of documents to analyze or if the documents are lengthy or complex.
• Lack of Control Over Data : Unlike methods where the researcher collects the data themselves (e.g., through experiments or surveys), with documentary analysis, you have no control over what data is available. You are reliant on what others have chosen to record and preserve.

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• Research and Analysis

When writing an analytical essay, you will likely have to conduct research. Research is the process of investigating a topic in an in-depth, systematic manner. You will then have to analyze that research to examine its implications and support a defensible claim about the topic. Sometimes writers do not conduct research when writing an analytical essay, but they usually still analyze sources that have used research. Learning how to conduct and analyze research is thus a critical part of strengthening analytical writing skills.  

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Research and Analysis Definition

When people are interested in a topic and want to learn more about it, they conduct research. In academic and professional settings, research follows systematic, critical processes.

Analysis is the process of critically examining research. When analyzing a source, researchers reflect on many elements, including the following:

How the information is presented

The author's main point

The evidence the author uses

The credibility of the author and the evidence

The potential for bias

The implications of the information

Research and Analysis Types

The type of research people conduct depends on what they are interested in learning about. When writing analytical essays about literature, authors typically consult primary sources, secondary sources, or both. Then they craft an analytical argument in which they make a claim about the sources supported with direct evidence.

Analyzing Primary Sources

Writers who write about literature often have to analyze primary sources.

A primary source is an original document or first-hand account.

For instance, plays, novels, poems, letters, and journal entries are all examples of primary sources. Researchers can find primary sources in libraries, archives, and online. To analyze primary sources , researchers should follow the following st eps:

1. Observe the Source

Take a look at the source at hand and preview it. How is it structured? How long is it? What is the title? Who is the author? What are some defining details about it?

For example, imagine a student is faced with the following prompt:

Pick an 18th-century English poet to research. Evaluate how their personal lives shaped the themes of their poetry.

To address this prompt, the researcher might analyze a letter their chosen poet sent to a friend. When observing the letter, they might note that the writing is neat cursive and includes salutations such as "faithfully yours." Without even reading the letter, the researcher can already tell that this is a formal letter and infer that the writer is trying to come across as respectful.

2. Read the Source

Next, researchers should read the entire primary source. Developing the skill of active reading (discussed later in this article) will help readers engage with a primary source. While reading, readers should take notes about the most important details in the text and what they suggest about the research topic.

For instance, the researcher analyzing the historical letter should note what the main purpose of the letter is. Why was it written? Is the writer asking for anything? Does the writer recount any important stories or pieces of information that are central to the text?

Sometimes primary sources are not written texts. For example, photographs can also be primary sources. If you can't read a source, observe it and ask analytical questions.

3. Reflect on the Source

When analyzing a primary source, readers should reflect on what it shows about the research topic. Questions for analysis include:

What is the main idea of this text?

What is the purpose of the text?

What is the historical, social, or political context of this text?

How might the context shape the meaning of the text?

Who is the intended audience of the text?

What does this text reveal about the research topic?

The precise questions a reader should ask when analyzing a primary source depend on the research topic. For example, when analyzing the letter from the poet, the student should compare the main ideas in the letter to the main ideas in some of the writer's poems. This will help them develop an argument about how elements of the poet's personal life shaped the themes of their poetry.

When analyzing literary primary sources, writers should examine and reflect on the elements such as characters, dialogue, plot, narrative structure, point of view, setting, and tone. They should also analyze how the author uses literary techniques like figurative language to convey messages. For instance, you might identify an important symbol in a novel. To analyze it, you could argue that the author uses it to develop a particular theme.

Analyzing Secondary Sources

When researchers consult a source that is not original, they are consulting a secondary source. For example, scholarly journal articles, newspaper articles, and textbook chapters are all secondary sources.

A secondary source is a document that interprets information from a primary source.

Secondary sources can help researchers understand primary sources. Authors of secondary sources analyze primary sources. The elements they analyze might be elements other readers of the primary source might not have noticed. Using secondary sources also makes for credible analytical writing because writers can show their audience that other credible scholars support their points of view.

To analyze secondary sources, researchers should follow the same steps as analyzing primary sources. However, they should ask slightly different analytical questions, such as the following:

Where was this source published?

What sources does the author use? Are they credible?

Who is the intended audience?

Is it possible that this interpretation is biased?

What is the author's claim?

Is the author's argument convincing?

How does the author use their sources to support their claim?

What does this source suggest about the research topic?

For example, a writer analyzing the themes of a particular poet's body of work should search for secondary sources in which other writers interpret the poet's work. Reading other scholars' interpretations can help writers better understand the poetry and develop their own perspectives.

To find credible secondary sources, writers can consult academic databases. These databases often have trustworthy articles from peer-reviewed scholarly journals, newspaper articles, and book reviews.

Research and Analysis Writing

After conducting research, writers must then craft a cohesive argument using relevant analysis. They can use primary and secondary sources to support an analytical argument by making use of the following strategies:

Summarize Each Source

Researchers should reflect on all of the sources they consulted during the research process . Creating a short summary of each source for themselves can help them identify patterns and make connections between ideas. This will then ensure they craft a strong claim about the research topic.

Taking notes about the main ideas of each source while reading can make summarizing each source quite simple!

Develop an Argument

After making connections between sources, researchers should craft a claim about the argument that addresses the prompt. This claim is called a thesis statement, a defensible statement that the writer can support with evidence from the research process .

Synthesize the Sources

Once writers have fine-tuned the essay's thesis, they should synthesize the sources and decide how to use information from multiple sources to support their claims. For instance, perhaps three of the sources help prove one supporting point, and another three support a different one. Writers must decide how each source is applicable, if at all.

Discuss Quotations and Details

Once researchers have decided what pieces of evidence to use, they should incorporate short quotes and details to prove their point. After each quote, they should explain how that evidence supports their thesis and include a citation.

Research and Analysis Skills

To strengthen the ability to conduct research and analysis, researchers should work on the following skills :

Active Reading

Readers should actively read the texts that they research, as this will ensure they notice important elements for analysis.

Active reading is engaging with a text while reading it for a specific purpose.

In the case of research and analysis, the purpose is to investigate the research topic. Active reading involves the following steps.

1. Preview the Text

First, readers should skim the text and understand how the author structured it. This will help readers know what to expect when they dive in.

2. Read and Annotate the Text

Readers should read the text attentively, with a pencil or pen in hand, ready to note important elements and jot down thoughts or questions. While reading, they should also ask questions, make predictions and connections, and check for clarification by summarizing important points.

3. Recall and Review the Text

To make sure they understood the text, readers should ask themselves what the main idea was and what they learned.

Writing down a mini summary of a text's main points is useful in the research process because it will help researchers keep track of the point of all of their sources.

Critical Thinking

Researchers need to think critically in order to analyze sources. Critical thinking is the process of thinking analytically. Researchers who are critical thinkers are always ready to make connections, comparisons, evaluations, and arguments. Thinking critically allows researchers to draw conclusions from their work.

Organization

Collecting large amounts of data can be overwhelming! Creating an organized system to keep track of all of the information will streamline the research process .

Research and Analysis Example

Imagine a student is given the following prompt.

Analyze how William Shakespeare uses the image of blood to develop a theme in Macbeth (1623).

To analyze this prompt, the student should use Macbeth as well as secondary sources about the play to support an original analytical argument that addresses the prompt.

When reading Macbeth , the student should actively read, paying careful attention to instances of bloody images and what they might mean. They should also consult an academic database and search for articles about the images and themes in Macbeth . These secondary sources can provide insight into the potential meanings behind the images they are looking up.

Once the student has all of their sources, they should look them all over and consider what they suggest about the image of blood in the play. It is important that they do not repeat an argument that they found in secondary sources, and instead use those sources to come up with their own perspective on the topic. For instance, the student might state:

In Macbeth , William Shakespeare uses images of blood to represent the theme of guilt.

The student can then synthesize information from the sources in their research process and identify three supporting points for their thesis. They should carefully select short but significant quotes that prove each point and explain the implications of those points. For example, they might write something like the following:

As Lady Macbeth scrubs the hallucination of blood off her hands, she shouts, "Out, damned spot; out, I say" (Act V, Scene i). As English professor John Smith says, "her desperation is evident in the tone of the writing" (Smith, 2018). Her desperation emphasizes the guilt she feels. It is as if the murder is a stain on her soul.

Note how the student drew from both primary and secondary sources to inform their interpretation of the writing.

Finally, the student should make sure that they cited their sources from the research process to avoid plagiarism and give the original authors proper credit.

Research and Analysis - Key Takeaways

• Research is the process of investigating a topic in an in-depth, systematic manner.
• Analysis is the critical interpretation of research.
• Researchers can collect and analyze primary sources, which are first-hand accounts or original documents.
• Researchers can also collect and analyze secondary sources, which are interpretations of primary sources.
• Readers should actively read their sources, note the main ideas, and reflect on how information from the sources supports a claim in response to the research topic.

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Frequently Asked Questions about Research and Analysis

What is meant by research analysis?

Research is the process of formally investigating a topic and analysis is the process of interpreting what is found in the research process. 

What is the difference between research and analysis?

Research is the process of investigating a topic. Analysis is the process of using critical thinking skills to interpret sources found during research. 

What is the research and analysis process?

Research involves searching for relevant information, closely reading and engaging with that information, and then analyzing that information. 

What are the types of research methods?

Researchers can collect primary or secondary sources. 

What is an example of analysis?

An example of analysis is identifying the intended audience of a primary source and inferring what this suggests about the author's intentions. 

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What is Research?

Research is a process of systematic inquiry that entails collection of data; documentation of critical information; and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines.

Research is conducted to...

• Evaluate the validity of a hypothesis or an interpretive framework.
• To assemble a body of substantive knowledge and findings for sharing them in appropriate manners.
• To help generate questions for further inquiries.

If you would like further examples of specific ways different schools at Hampshire think about research, see: School Definitions of Research » What is "research" that needs to be reviewed and approved by the Institutional Review Board at Hampshire before proceeding?  Research should be reviewed by the IRB only when human subjects are involved, and the term research should be considered under a more narrow definition. Specifically, when the researcher is conducting research as outlined above AND has direct interaction with participants or data linked to personal identifiers , it should always fall under the purview of the IRB. Even if you have not directly collected the data yourself, as the researcher, your research may fall under the purview of the IRB. In reviewing such research, the IRB is concerned with the methodology of data collection in the "field" (e.g. collection, experimentation, interview, participant observation, etc.) and the use of the data.  The broader validity of the hypotheses or research questions, and the quality of inferences that may result (unless, of course, the research methodologies severely compromise the data collection and data usage directly), is not something they will be evaluating.

What if I am using information that is already available?

If you are doing research that is limited to secondary analysis of data, records, or specimens that are either publicly available, de-identified, or otherwise impossible to be linked to personal identities, you may still need IRB approval to do your project. Sometimes a data use agreement between the researcher and the data custodian may still be required to verify that the researcher will not have access to identifying codes.  This "de-linking" of data from personal identifiers  allows the IRB to make this determination. Regardless, you should submit an IRB proposal so the IRB can determine whether your project needs IRB review, and if so, the type of review required. For specifics of what research should be reviewed by the IRB and the category of review required, see the flow chart and examples provided .

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What Is a Research Analyst?

• Qualifications

Financial Analyst vs. Research Analyst

The bottom line.

• Degrees & Certifications

What Is a Research Analyst? What They Do and Qualifications

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

A research analyst is a professional who prepares investigative reports on securities or assets for in-house or client use. Other titles for this function include securities analyst, investment analyst, equity analyst, rating analyst, or simply " analyst ."

The work conducted by a research analyst is in an effort to inquire into, examine, find, or revise facts, principles, and theories for internal use by a financial institution or an external financial client. The report an analyst prepares entails the examination of public securities records of companies or industries, and often concludes with a "buy," "sell," or "hold" recommendation.

If a research analyst is involved with an investment bank or a securities firm controlled by a member organization of the Financial Industry Regulatory Authority (FINRA) , they may be required to register with a self-regulatory organization (SRO) and/or take certain exams.

Key Takeaways

• A research analyst is a professional who prepares investigative reports on securities or assets for in-house or client use.
• These reports examine individual companies' or industries' public securities records and often make recommendations to buy, sell, or hold.
• The main differences between buy-side and sell-side analysts are the type of firm that employs them and the people to whom they make their recommendations.

The Basics of Being a Research Analyst

Research analysts are usually divided into two groups: "buy-side" and "sell-side" analysts. A buy-side (brokerage) research analyst is typically employed by an asset management company and recommends securities for investment to the money managers of the fund that employs them. The research of a sell-side (investment firm) analyst tends to be sold to the buy-side. Sell-side research is also given to clients for free for consideration, in an attempt to win business, for example. Such research can be used to promote companies.

A buy-side analyst usually works for institutional investors such as hedge funds, pension funds, or mutual funds. Buy-side research analysts are often considered more professional, academic, and reputable compared to the sell-side. Sell-side research jobs are often likened to marketing and sometimes pay higher salaries.

Buy-side analysts will determine how promising an investment seems and how well it coincides with the fund's investment strategy. Sell-side analysts are those who issue recommendations of "strong buy," "outperform," "neutral," or "sell."

Research analysts can work at a variety of companies, such as at asset management companies, investment banks, insurance companies, hedge funds, pension funds, brokerages or any business that needs to crunch data to spot trends or decide on a valuation, make an investment decision, or forecast the outlook of a company or asset. According to Glassdoor, the average base salary for a research analyst is $90,838, ranging anywhere between $68,000 and $125,000.

Research Analyst Qualifications

Companies that employ research analysts sometimes require a master's degree in finance or a chartered financial analyst (CFA) designation on top of several regulatory hurdles. Research analysts might be required to take the Series 86/87 exams if they are involved with a member organization.

Other required securities licenses often include the  Series 7  general securities representative license and the Series 63  uniform securities agent license. FINRA licenses are typically associated with the selling of specific securities as a firm’s registered representative. Investment analysts may also seek to obtain the chartered financial analyst (CFA) certification.

Financial firms in the United States do not really present a unified definition of either of these job titles. Some financial analysts are really just researchers who collect and organize market data, while others put together specific proposals for securities investments with large institutional clients. Similarly, some research analysts are glorified marketing specialists, while others apply socioeconomic or political insights and are probably better classified as management consultants.

It's possible to narrow the differences between research analysts and financial analysts. Generally speaking, financial analysts focus on analyzing investments and market performance. They rely on a  fundamental understanding of business valuation  and economic principles to create reports and make recommendations; they are the behind-the-scenes experts. Research analysts occupy a less prescriptive role than financial analysts. Instead of looking through the lens of broad economic principles, they focus more on mathematical models to produce objective answers about historical data.

Financial analysts collect and analyze data but always within the context of a prior deductive understanding of how markets should function. Their thinking is systemic and, particularly at more senior levels, subjective. Research analysts tend to be operations-focused. Give a research analyst a series of inputs, and they can calculate the most efficient way to maximize output. If the research analyst works in the securities business, it's likely that recommendations may be made based on some predetermined criteria.

What Do You Need to Become a Research Analyst?

Research analysts gather, analyze, and work with data to prepare reports for internal use by a financial institution or an external financial client. For this work, strong mathematics and statistics skills are required. Typically, a research analyst will have a bachelor's degree in a business-related field, and a master's degree in finance or a chartered financial analyst's certification may be required. Depending on the requirements of their job, they also may need to gain securities licenses.

What Is a Research Analyst's Salary?

In 2024, the average base salary for a research analyst is $90,838, anad ranges anywhere between $68,000 and $125,000, according to Glassdoor.

Is a Research Analyst a Stressful Job?

It can be. Being a research analyst requires constant learning, problem-solving, and good communication skills. There also may be tight deadlines, complex challenges, and high expectations, which can make this type of work pressured and stressful.

Research analysts are finance professionals who analyze securities data to make recommendations to their own firms or outside clients. They may be buy-side or sell-side analysts, which are distinguished by what types of companies they work for. Qualifications may include a master's degree in finance or certification as a chartered financial analyst (CFA). Sometimes they may be required to take certain tests for licensure. Base salaries hover around $90,000.

FINRA. " FINRA Rules: 1220. Registration Categories ."

Corporate Finance Institute. " What’s the Difference between the Buy Side vs Sell Side? " Accessed Sept. 11, 2020.

Glassdoor. " Research Analyst Salaries ."

FINRA. " Qualification Exams ."

CFA Institute. " CFA Program ."

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

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Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Title & authors

Raganit, Ariel A. "A Comparative Analysis of Mean Percentage Scores (MPS) of Senior High School Classes." International Journal of Multidisciplinary: Applied Business and Education Research , vol. 2, no. 7, 2021, pp. 587-590, doi: 10.11594/ijmaber.02.07.06 .

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This comparative analysis on the Mean Percentage Scores (MPS) results of senior high school classes for Quarters I, II, and III was a good practice that can be emulated by educators in order to give meaning to the data. MPS is not for compilation only. It must be used for decision making in lesson delivery enhancements, learning resources utilization and school improvement plan and adjustment. The design used was descriptive research. This is a comparative analysis of the results of Quarterly Assessments of senior high school classes in a public school in Leyte with the sample of five sections determined by stratified sampling technique. Data were analyzed using Mean, standard deviation, dependent sample t-test, independent sample t-test, two-way Analysis of Variance (ANOVA) and thematic analysis of interviews of subject teachers. Descriptive study is scientific research that methodically deals with a certain region or population and characterizes an occurrence, phenomenon, or fact (Modato, 2017).   Based from interviews and document analysis, extracurricular activities, socio-cultural involvements, the difficulty of learning competences, and examination schedules and the school calendar in general all had an influence on students' performance. In addition, test structures, test layout, and clear directions and instructions have an impact on test results.

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• U.S. Department of Health and Human Services
• National Institutes of Health

Probiotics: Usefulness and Safety

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What are probiotics?

Probiotics are live microorganisms that are intended to have health benefits when consumed or applied to the body. They can be found in yogurt and other fermented foods, dietary supplements , and beauty products. Cases of severe or fatal infections have been reported in premature infants who were given probiotics, and the U.S. Food and Drug Administration (FDA) has warned health care providers about this risk.

Although people often think of bacteria and other microorganisms as harmful “germs,” many are actually helpful. Some bacteria help digest food, destroy disease-causing cells, or produce vitamins. Many of the microorganisms in probiotic products are the same as or similar to microorganisms that naturally live in our bodies.

What types of bacteria are in probiotics?

Probiotics may contain a variety of microorganisms. The most common are bacteria that belong to groups called Lactobacillus and Bifidobacterium . Other bacteria may also be used as probiotics, and so may yeasts such as Saccharomyces boulardii .

Different types of probiotics may have different effects. For example, if a specific kind of Lactobacillus helps prevent an illness, that doesn’t necessarily mean that another kind of Lactobacillus or any of the Bifidobacterium probiotics would do the same thing.

Are prebiotics the same as probiotics?

No, prebiotics aren’t the same as probiotics. Prebiotics are nondigestible food components that selectively stimulate the growth or activity of desirable microorganisms.

What are synbiotics?

Synbiotics are products that combine probiotics and prebiotics.

How popular are probiotics?

The 2012 National Health Interview Survey (NHIS) showed that about 4 million (1.6 percent) U.S. adults had used probiotics or prebiotics in the past 30 days. Among adults, probiotics or prebiotics were the third most commonly used dietary supplement other than vitamins and minerals. The use of probiotics by adults quadrupled between 2007 and 2012. The 2012 NHIS also showed that 300,000 children age 4 to 17 (0.5 percent) had used probiotics or prebiotics in the 30 days before the survey.

How might probiotics work?

Probiotics may have a variety of effects in the body, and different probiotics may act in different ways.

Probiotics might:

• Help your body maintain a healthy community of microorganisms or help your body’s community of microorganisms return to a healthy condition after being disturbed
• Produce substances that have desirable effects
• Influence your body’s immune response.

How are probiotics regulated in the United States?

Government regulation of probiotics in the United States is complex. Depending on a probiotic product’s intended use, the FDA might regulate it as a dietary supplement, a food ingredient, or a drug.

Many probiotics are sold as dietary supplements, which don’t require FDA approval before they are marketed. Dietary supplement labels may make claims about how the product affects the structure or function of the body without FDA approval, but they aren’t allowed to make health claims, such as saying the supplement lowers your risk of getting a disease, without the FDA’s consent.

If a probiotic is going to be marketed as a drug for treatment of a disease or disorder, it has to meet stricter requirements. It must be proven safe and effective for its intended use through clinical trials and be approved by the FDA before it can be sold.

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The community of microorganisms that lives on us and in us is called the “microbiome,” and it’s a hot topic for research. The Human Microbiome Project, supported by the National Institutes of Health (NIH) from 2007 to 2016, played a key role in this research by mapping the normal bacteria that live in and on the healthy human body. With this understanding of a normal microbiome as the basis, researchers around the world, including many supported by NIH, are now exploring the links between changes in the microbiome and various diseases. They’re also developing new therapeutic approaches designed to modify the microbiome to treat disease and support health.

The National Center for Complementary and Integrative Health (NCCIH) is among the many agencies funding research on the microbiome. Researchers supported by NCCIH are studying the interactions between components of food and microorganisms in the digestive tract. The focus is on the ways in which diet-microbiome interactions may lead to the production of substances with beneficial health effects.

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A great deal of research has been done on probiotics, but much remains to be learned about whether they’re helpful and safe for various health conditions.

Probiotics have shown promise for a variety of health purposes, including prevention of antibiotic-associated diarrhea (including diarrhea caused by Clostridium difficile ), prevention of necrotizing enterocolitis and sepsis in premature infants, treatment of infant colic , treatment of periodontal disease , and induction or maintenance of remission in ulcerative colitis .

However, in most instances, we still don’t know which probiotics are helpful and which are not. We also don’t know how much of the probiotic people would have to take or who would be most likely to benefit. Even for the conditions that have been studied the most, researchers are still working toward finding the answers to these questions.

The following sections summarize the research on probiotics for some of the conditions for which they’ve been studied.

Gastrointestinal Conditions

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• Probiotics have been studied for antibiotic-associated diarrhea in general, as well as for antibiotic-associated diarrhea caused by one specific bacterium, Clostridium difficile . This section discusses the research on antibiotic-associated diarrhea in general. C. difficile is discussed in a separate section below.
• A 2017 review of 17 studies (3,631 total participants) in people who were not hospitalized indicated that giving probiotics to patients along with antibiotics was associated with a decrease of about half in the likelihood of antibiotic-associated diarrhea. However, this conclusion was considered tentative because the quality of the studies was only moderate. Patients who were given probiotics had no more side effects than patients who didn’t receive them.
• Probiotics may be helpful for antibiotic-associated diarrhea in young and middle-aged people, but a benefit has not been demonstrated in elderly people, according to a 2016 review of 30 studies (7,260 participants), 5 of which focused on people age 65 or older. It’s uncertain whether probiotics actually don’t work in elderly people or whether no effect was seen because there were only a few studies of people in this age group.
• A review of 23 studies (with 3,938 participants) of probiotics to prevent antibiotic-associated diarrhea in children provided moderate quality evidence that probiotics had a protective effect. No serious side effects were observed in children who were otherwise healthy, except for the infection for which they were being treated.

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• The bacterium Clostridium difficile can infect the colon (large intestine) of patients who have received antibiotics, causing diarrhea that can range from mild to severe. C. difficile infection is difficult to treat and sometimes comes back after treatment. It’s more common in people who take antibiotics long-term and in elderly people, and it can spread in hospitals and nursing homes. C. difficile infection affects about half a million people a year in the United States and causes about 15,000 deaths.
• A 2017 analysis of 31 studies (8,672 total patients) concluded that it is moderately certain that probiotics can reduce the risk of C. difficile diarrhea in adults and children who are receiving antibiotics. Most of these studies involved hospital patients. The analysis also concluded that the use of probiotics along with antibiotics appears to be safe, except for patients who are very weak or have poorly functioning immune systems.
• The types of probiotics that would be most useful in reducing the risk of C. difficile diarrhea, the length of time for which they should be taken, and the most appropriate doses are uncertain.

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• A 2014 review of 14 studies (1,182 participants) of probiotics for constipation in adults showed some evidence of benefit, especially for Bifidobacterium lactis .
• A 2017 evaluation of 9 studies (778 participants) of probiotics for constipation in elderly people indicated that probiotics produced a small but meaningful benefit. The type of bacteria most often tested was Bifidobacterium longum . The researchers who performed the evaluation suggested that probiotics might be helpful for chronic constipation in older people as an addition to the usual forms of treatment.
• A 2017 review looked at 7 studies of probiotics for constipation in children (515 participants). The studies were hard to compare because of differences in the groups of children studied, the types of probiotics used, and other factors. The reviewers did not find evidence that any of the probiotics tested in the children were helpful. A second 2017 review, which included 4 of the same studies and 2 others (498 total participants in the 6 studies examined), took a more optimistic view of the evidence, noting that overall, probiotics did increase stool frequency, and that the effect was more noticeable in Asian than European children.

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• Diarrhea is a common side effect of chemotherapy or radiotherapy for cancer. It’s been suggested that probiotics might help prevent or treat this type of diarrhea. However, a 2018 review of 12 studies (1,554 participants) found that the evidence for a beneficial effect of probiotics was inconclusive.

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• In diverticulosis, small pouches develop at weak spots in the wall of the colon (large intestine). In most cases, this does not cause any symptoms. If symptoms (such as bloating, constipation, diarrhea, or cramping) do occur, the condition is called diverticular disease. If any of the pouches become inflamed, the condition is called diverticulitis. Patients with diverticulitis can have severe abdominal pain and may develop serious complications.
• A 2016 review of 11 studies (764 participants) of probiotics for diverticular disease was unable to reach conclusions on whether the probiotics were helpful because of the poor quality of the studies.

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• Inflammatory bowel disease is a term for a group of conditions that cause a portion of the digestive system to become inflamed; the most common types are ulcerative colitis and Crohn’s disease. Symptoms may include abdominal pain, diarrhea (which may be bloody), loss of appetite, weight loss, and fever. The symptoms can range from mild to severe, and they may come and go. Treatment includes medicines and in some cases, surgery.
• A 2014 review of 21 studies in patients with ulcerative colitis (1,700 participants) indicated that adding probiotics, prebiotics, or synbiotics to conventional treatment could be helpful in inducing or maintaining remission of the disease. The same review also looked at 14 studies (746 participants) of probiotics, prebiotics, or synbiotics for Crohn’s disease and did not find evidence that they were beneficial.

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• A 2018 review of 53 studies (5,545 total participants) of probiotics for irritable bowel syndrome (IBS) concluded that probiotics may have beneficial effects on global IBS symptoms and abdominal pain, but it was not possible to draw definite conclusions about their effectiveness or to identify which species, strains, or combinations of probiotics are most likely to be helpful.

For more information, see the NCCIH fact sheet on irritable bowel syndrome .

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• A 2018 review evaluated 11 studies (5,143 participants) of probiotics or prebiotics for prevention of traveler’s diarrhea and found evidence that they may be helpful. However, the review didn’t assess the quality of the studies and didn’t include data on side effects.
• A 2017 clinical practice guideline by the International Society of Travel Medicine stated that there’s insufficient evidence to recommend probiotics or prebiotics to prevent or treat traveler’s diarrhea. The guidelines acknowledged that there’s evidence suggesting a small benefit but pointed out that studies vary greatly in terms of factors such as the probiotic strains used, the causes of the diarrhea, and geographic locations. Also, some studies had weaknesses in their design.

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.header_greentext{color:greenimportant;font-size:24pximportant;font-weight:500important;}.header_bluetext{color:blueimportant;font-size:18pximportant;font-weight:500important;}.header_redtext{color:redimportant;font-size:28pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;font-size:28pximportant;font-weight:500important;}.header_purpletext{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.header_yellowtext{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.header_blacktext{color:blackimportant;font-size:22pximportant;font-weight:500important;}.header_whitetext{color:whiteimportant;font-size:22pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;}.green_header{color:greenimportant;font-size:24pximportant;font-weight:500important;}.blue_header{color:blueimportant;font-size:18pximportant;font-weight:500important;}.red_header{color:redimportant;font-size:28pximportant;font-weight:500important;}.purple_header{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.yellow_header{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.black_header{color:blackimportant;font-size:22pximportant;font-weight:500important;}.white_header{color:whiteimportant;font-size:22pximportant;font-weight:500important;} infant colic.

• Colic is excessive, unexplained crying in young infants. Babies with colic may cry for 3 hours a day or more, but they eat well and grow normally. The cause of colic is not well understood, but studies have shown differences in the microbial community in the digestive tract between infants who have colic and those who don’t, which suggests that microorganisms may be involved.
• A 2018 review of 7 studies (471 participants) of probiotics for colic, 5 of which involved the probiotic Lactobacillus reuteri DSM 17938, found that this probiotic was associated with successful treatment (defined as a reduction of more than half in daily crying time). However, the effect was mainly seen in exclusively breastfed infants.
• No harmful effects were seen in a review of 4 studies (345 participants) of L. reuteri DSM 17938 for colic or in a small NCCIH-funded study that included repeated physical examinations and blood tests in infants with colic who were given this probiotic, as well as parents’ reports of symptoms.

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• Necrotizing enterocolitis is a serious, sometimes fatal disease that occurs in premature infants. It involves injury or damage to the intestinal tract, causing death of intestinal tissue. Its exact cause is unknown, but an abnormal reaction to food components and the microorganisms that live in a premature baby’s digestive tract may play a role.
• A 2017 review of 23 studies (7,325 infants) showed that probiotics helped to prevent necrotizing enterocolitis in very-low-birth-weight infants. However, the results of individual studies varied; not all showed a benefit. Probiotics that included both Lactobacillus and Bifidobacterium seemed to produce the best results, but it was not possible to identify the most beneficial strains within these large groups of bacteria.
• None of the infants in the studies described above developed harmful short-term side effects from the probiotics. However, the long-term effects of receiving probiotics at such a young age are uncertain. Outside of these studies, there have been instances when probiotics did have harmful effects in newborns. In 2023, the FDA warned health care providers that premature infants who are given probiotics are at risk of severe, potentially fatal infections caused by the microorganisms in the products.

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• Sepsis is a serious illness in which the body has a harmful, overwhelming response to an infection. It can cause major organs and body systems to stop working properly and can be life threatening. The risk of sepsis is highest in infants, children, the elderly, and people with serious medical problems. One group particularly at risk for sepsis is premature infants.
• A review of 37 studies (9,416 participants) found that probiotics were helpful in reducing the risk of sepsis in premature infants.

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.header_greentext{color:greenimportant;font-size:24pximportant;font-weight:500important;}.header_bluetext{color:blueimportant;font-size:18pximportant;font-weight:500important;}.header_redtext{color:redimportant;font-size:28pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;font-size:28pximportant;font-weight:500important;}.header_purpletext{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.header_yellowtext{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.header_blacktext{color:blackimportant;font-size:22pximportant;font-weight:500important;}.header_whitetext{color:whiteimportant;font-size:22pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;}.green_header{color:greenimportant;font-size:24pximportant;font-weight:500important;}.blue_header{color:blueimportant;font-size:18pximportant;font-weight:500important;}.red_header{color:redimportant;font-size:28pximportant;font-weight:500important;}.purple_header{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.yellow_header{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.black_header{color:blackimportant;font-size:22pximportant;font-weight:500important;}.white_header{color:whiteimportant;font-size:22pximportant;font-weight:500important;} dental caries (tooth decay).

• A small amount of research, all in infants and young children, has examined the possibility that probiotics might be helpful in preventing dental caries (also called cavities or tooth decay). A review of 7 studies (1,715 total participants) found that the use of probiotics was associated with fewer cavities in 4 of the 7 studies, but the quality of the evidence was low, and no definite conclusions about the effectiveness of probiotics could be reached.

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• Periodontal diseases result from infections and inflammation of the gums and bone that surround and support the teeth. If the disease is severe, the gums can pull away from the teeth, bone can be lost, and teeth may loosen or fall out.
• A 2016 review of 12 studies (452 participants) that evaluated probiotics for periodontal disease found evidence that they could be a helpful addition to treatment by reducing disease-causing bacteria and improving clinical signs of the disease. However, effects may differ for different probiotics.

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.header_greentext{color:greenimportant;font-size:24pximportant;font-weight:500important;}.header_bluetext{color:blueimportant;font-size:18pximportant;font-weight:500important;}.header_redtext{color:redimportant;font-size:28pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;font-size:28pximportant;font-weight:500important;}.header_purpletext{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.header_yellowtext{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.header_blacktext{color:blackimportant;font-size:22pximportant;font-weight:500important;}.header_whitetext{color:whiteimportant;font-size:22pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;}.green_header{color:greenimportant;font-size:24pximportant;font-weight:500important;}.blue_header{color:blueimportant;font-size:18pximportant;font-weight:500important;}.red_header{color:redimportant;font-size:28pximportant;font-weight:500important;}.purple_header{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.yellow_header{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.black_header{color:blackimportant;font-size:22pximportant;font-weight:500important;}.white_header{color:whiteimportant;font-size:22pximportant;font-weight:500important;} allergic rhinitis (hay fever).

• A review of 23 studies (1,919 participants) in which probiotics were tested for treating allergic rhinitis found some evidence that they may be helpful for improving symptoms and quality of life. However, because the studies tested different probiotics and measured different effects, no recommendations about the use of probiotics could be made. Few side effects of probiotics were reported in these studies.

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• A review of 11 studies (910 participants) of probiotics for asthma in children had inconclusive results.

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• Atopic dermatitis is an itchy chronic skin disorder that’s associated with allergies but not caused by them. It’s most common in infants and may start as early as age 2 to 6 months. Many people outgrow it by early adulthood. Atopic dermatitis is one of several types of eczema.
• A 2017 review of 13 studies (1,271 participants) of probiotics for the treatment of atopic dermatitis in infants and children did not find consistent evidence of a beneficial effect. A review of 9 studies (269 participants) in adults provided preliminary evidence that some strains of probiotics might be beneficial for symptoms of atopic dermatitis.

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• It’s been suggested that changes in people’s lifestyles and environment may have led to reduced contact with microorganisms early in life, and that this decrease may have contributed to an increase in allergies. This is sometimes called the “hygiene hypothesis,” although factors unrelated to hygiene, such as smaller family size and the use of antibiotics, may also play a role. Studies have been done in which probiotics were given to pregnant women and/or young infants in the hope of preventing the development of allergies.
• A 2015 review of 17 studies (4,755 participants) that evaluated the use of probiotics during pregnancy or early infancy found that infants exposed to probiotics had a lower risk of developing atopic dermatitis, especially if they were exposed to a mixture of probiotics. However, probiotics did not have an effect on the risks of asthma, wheezing, or hay fever (allergic rhinitis).

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.header_greentext{color:greenimportant;font-size:24pximportant;font-weight:500important;}.header_bluetext{color:blueimportant;font-size:18pximportant;font-weight:500important;}.header_redtext{color:redimportant;font-size:28pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;font-size:28pximportant;font-weight:500important;}.header_purpletext{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.header_yellowtext{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.header_blacktext{color:blackimportant;font-size:22pximportant;font-weight:500important;}.header_whitetext{color:whiteimportant;font-size:22pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;}.green_header{color:greenimportant;font-size:24pximportant;font-weight:500important;}.blue_header{color:blueimportant;font-size:18pximportant;font-weight:500important;}.red_header{color:redimportant;font-size:28pximportant;font-weight:500important;}.purple_header{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.yellow_header{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.black_header{color:blackimportant;font-size:22pximportant;font-weight:500important;}.white_header{color:whiteimportant;font-size:22pximportant;font-weight:500important;} acne.

• Research has identified mechanisms by which probiotics, either taken orally or used topically (applied to the skin), might influence acne. However, there has been very little research in people on probiotics for acne, and the American Academy of Dermatology’s 2016 guidelines for managing acne state that the existing evidence isn’t strong enough to justify any recommendations about the use of probiotics.

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• When the liver is damaged and unable to remove toxic substances from the blood, the toxins can build up in the bloodstream and affect the nervous system. This may lead to impairments of brain function called hepatic encephalopathy.
• A 2017 review looked at 21 studies (1,420 participants) of probiotics for hepatic encephalopathy and concluded that they were generally of low quality. There was evidence that compared with a placebo (an inactive substance) or no treatment, probiotics probably had beneficial effects on hepatic encephalopathy, but it was uncertain whether probiotics were better than lactulose, a conventional treatment for liver disease.

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• Probiotics have been tested for their effects against upper respiratory infections (a group that includes the common cold, middle ear infections, sinusitis, and various throat infections). A 2015 evaluation of 12 studies with 3,720 total participants indicated that people taking probiotics may have fewer and shorter upper respiratory infections. However, the quality of the evidence was low because some of the studies were poorly conducted.

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• A 2015 review of 9 studies (735 participants) of probiotics for the prevention of urinary tract infection did not find evidence of a beneficial effect.

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• Probiotics have an extensive history of apparently safe use, particularly in healthy people. However, few studies have looked at the safety of probiotics in detail, so there’s a lack of solid information on the frequency and severity of side effects.
• The risk of harmful effects from probiotics is greater in people with severe illnesses or compromised immune systems. When probiotics are being considered for high-risk individuals, such as premature infants or seriously ill hospital patients, the potential risks of probiotics should be carefully weighed against their benefits. Cases of severe or fatal infections have been reported in premature infants who were given probiotics, and the U.S. Food and Drug Administration (FDA) has warned health care providers about this risk.
• Possible harmful effects of probiotics include infections, production of harmful substances by the probiotic microorganisms, and transfer of antibiotic resistance genes from probiotic microorganisms to other microorganisms in the digestive tract.
• Some probiotic products have been reported to contain microorganisms other than those listed on the label. In some instances, these contaminants may pose serious health risks.

NCCIH-Funded Research

NCCIH sponsors a variety of research projects related to probiotics or the microbiome. In addition to the previously mentioned studies on diet-microbiome interactions in the digestive tract, recent topics include:

• The mechanisms by which probiotics may help to reduce postmenopausal bone loss
• Engineering probiotics to synthesize natural substances for microbiome-brain research
• The mechanisms by which certain probiotics may relieve chronic pelvic pain
• The effects of a specific Bifidobacterium strain on changes in short-chain fatty acid production in the gut that may play a role in antibiotic-associated diarrhea.

More To Consider

• Don’t use probiotics as a reason to postpone seeing your health care provider about any health problem.
• If you’re considering a probiotic dietary supplement, consult your health care provider first. This is especially important if you have health problems. Anyone with a serious underlying health condition should be monitored closely while taking probiotics.
• Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed decisions.

For More Information

Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Website: https://pubmed.ncbi.nlm.nih.gov/

MedlinePlus

To provide resources that help answer health questions, MedlinePlus (a service of the National Library of Medicine) brings together authoritative information from the National Institutes of Health as well as other Government agencies and health-related organizations.

Website: https://www.medlineplus.gov

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• Bafeta A, Koh M, Riveros C, et al. Harms reporting in randomized controlled trials of interventions aimed at modifying microbiota: a systematic review. Annals of Internal Medicine . 2018;169(4):240-247.
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Acknowledgments

NCCIH thanks Yisong Wang, Ph.D., and David Shurtleff, Ph.D., for their review of the 2019 update of this publication.

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