analysis vs research

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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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

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

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

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

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

Phase I: Data Validation

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

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

Phase II: Data Editing

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

Phase III: Data Coding

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

LEARN ABOUT: Steps in Qualitative Research

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

Descriptive statistics

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

Measures of Frequency

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

Measures of Central Tendency

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

Measures of Dispersion or Variation

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

Measures of Position

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

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

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

Inferential statistics

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

Here are two significant areas of inferential statistics.

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

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

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

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

LEARN ABOUT: Best Data Collection Tools

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

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

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

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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.

On This Page:

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

• Starting a Business
• Business Ideas
• Business Privacy
• Business Registration
• Limited Liability Companies
• Categories – Manage Business

Market Analysis vs Market Research – Differences & Similarities

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Ever wondered if often interchangeably used terms, market research, and market analysis, don’t mean the same or that your intuitive understanding of them can be wrong? Either way, we will clue you in regarding it. 

Here’s what you need to know about market analysis, market research, and the differences and similarities between them.

Market Analysis vs Market Research – What’s the Difference?

Market research is a subset of market analysis that mainly examines the market potential and gathers feedback for particular business decisions. In comparison, market analysis is an overall market outlook that pursues forecasting and growth options.

What Is Market Analysis?

Market analysis is a detailed assessment that puts together various data, such as market forecasts and conditions. It comprises a SWOT analysis to know the strengths, weaknesses, opportunities, and threats to a business. 

It also includes competitive analysis and industry analysis. All this should be taken into account while considering mistakes to avoid when starting a business . 

Conducting a market analysis is the blueprint for any successful business, and business owners always refer to market analysis while writing a business plan . Mainly, market analysis gathers information from secondary research that scrutinizes the existing information.

Why Conduct Market Analysis?

Market analysis gives a holistic outlook, which is of momentous significance for any business. The reasons for conducting market analysis are listed below.

1. Get a Glimpse of the Competition

Prior to registering a business and launching products and services, it’s essential to know competitors, their market share, and brand value. The market analysis tells you all about it. 

You can price your products and services as well as designate a business location by reviewing the course of competitors. It also provides you with insights into the industry and start-up costs .

2. Proper Interpretation of Market Conditions

With a better understanding of the market conditions, mainly the demand side, market analysis affords more clarity to business decisions with precision. For instance, if the market analysis shows that the demand has decreased drastically, businesses won’t increase supply. 

3. Improved Goal Planning

It helps to set strategic goals. Strategic goals deal with the expansion and retention of the company. Market analysis is the first thing that is taken into account when leaving or entering the market, as it foresees the potential of customers into new markets.

4. Flexibility 

Business success is not all about running headfirst into the business goals as markets change with time. And with real-time knowledge of the ever-changing market, a business can revise its strategies.

5. Take Advantage of SWOT Analysis

Each business within a market has pros and cons. The trick is to use the pros effectively so that it outweighs the cons. You can run a SWOT analysis that will tell you about the strengths, weaknesses, opportunities, and threats your company is exposed to. You can enhance and overcome internal strengths and weaknesses, respectively. 

Location, methods, and costs hint at the opportunities and threats. For instance, if your production cost is relatively lower or your business targets new markets, it’ll count as an opportunity.

6. Run an Industry Analysis

Before starting a business , it’s crucial to have an industry analysis that would include growth statistics and potential in upcoming years. Market analysis touches on industry analysis in quite a detail. 

What Is Market Research?

Market research involves gathering feedback regarding product improvement and consumer perceptions, in which consumers usually fill out questionnaires or give interviews. It serves the purpose of improving customer satisfaction and developing brand loyalty by considering the preferences and needs of the customers. It also includes gathering real-time data on customer purchases, mainly through the internet. 

By and large, market research stands tall on the shoulder of primary research. Primary research comprises information gathered directly by the researchers.

Why Conduct Market Research?

While focusing on particular aspects of the market, market research is unparallel. The reasons for conducting market research are listed below.

1. Discover When Most Sales Take Place

Businesses sometimes research the peak hours and days on which most sales happen for a particular product. For this purpose, companies hire researchers that collect data from different stores.

2. Find Your Audience

It identifies potential customers while addressing questions such as who uses the particular product or service. As consumption is a function of income, it also digs into the potential customers’ income level and wealth status. It also explores factors like age, season, education, and marital status that determine consumption.

3. Conduct Targeted Market Campaigns

With perfect customer profiling, market research paves the way for starting effective market campaigns. A company can find platforms on which most customers exist, like different social media channels, such as Facebook and Instagram. 

Your company can run its marketing campaigns on these platforms to attract potential customers.

4. Collect Data From Current Customers

It gathers more data regarding existing customers. Researchers use questionnaires to reflect on products and services to further delve into consumer preferences. The goal is to improve what you or the competitors are offering to what consumers really want. 

It’s easier to ask existing customers, as they are already buying your product, and it’ll help in customer retention. Honest consumer responses help to understand the problems with your product or service. Putting in some effort would help your business to alleviate the consumers’ concerns and achieve an excellent customer experience.

5. Avoid Making Poor Decisions

Market research helps to mitigate risks related to business decisions by providing and assembling all the relevant information. It’s a top-notch tool to test the viability while launching a new product or service. It helps you to come up with a business idea like no other. 

For instance, if a food company wants to add a burger to its menu, it can check the burger’s viability and demand by offering it for free to its current and potential customers

6. Take Advantage of Unpopular Markets

It helps discover loosely grasped markets by competitors. You can flourish in such markets by only knowing the weak spots of the incumbent firms. You can hire researchers to identify where some customers are unsatisfied and why they are unsatisfied.

How Are Market Research & Analysis Different?

Now that you have some idea of market research and market analysis, we have discussed, in a nutshell, how these two are different.

• Market analysis is general and offers a holistic view of the market. It comprises market research, competitor analysis, industry analysis, and SWOT analysis. On the other hand, market research is more particularized, as it provides a focused market view dealing with resolving questions regarding consumer preference and behavior.
• In general, market research is less costly when compared with market analysis.
• Market analysis assists in strategic decision-making and forecasting market size with more clarity when compared to market research.
• Market analysis provides long-lasting insights, whereas market research usually covers trends that don’t last long.

Similarities Between Market Research and Market Analysis

In the market analysis versus market research debate, it goes without saying that both strengthen each other. Both work hand in hand and serve the same purpose of making a successful venture. 

Researchers conduct both market research and market analysis. In general, there are no specialized researchers for each task.

The approaches to conducting market research and market analysis are the same. Some recognized ways to conduct both tasks are face-to-face interviews, online questionnaires , phone, email, and focus group discussions.

Importance of Knowing the Difference

A proper understanding of the terms market analysis and market research is of paramount importance. For instance, you’ve instructions to hire a team to conduct a market analysis. Now you hire a team and ask them to conduct market research instead of asking them to conduct market analysis. 

The team may ask about the hypothesis around which they have to conduct research. It can probably set you off balance, and you may have to approach your superordinate or colleagues, which is embarrassing in the workplace.

Market Analysis vs Market Research – Bottom Line

Market research is a subset of market analysis that mainly examines the market potential and gathers feedback for particular decisions. On the other hand, market analysis is an overall outlook of a market that pursues forecasting and growth options.

If you are trying to make your business venture successful, consider reading about business incubator programs.

About the author

Dr. Gabriel O'Neill, Esq.

Dr. Gabriel O'Neill, Esq., a distinguished legal scholar with a business law degree and a Doctor of Juridical Science, is a leading expert in business registration and diverse business departments. Renowned for his academic excellence and practical insights, Dr. O'Neill guides businesses through legal complexities, offering invaluable expertise in compliance, corporate governance, and registration processes.

As an accomplished author, his forthcoming book is anticipated to be a comprehensive guide for navigating the dynamic intersection of law and business, providing clarity and practical wisdom for entrepreneurs and legal professionals alike. With a commitment to legal excellence, Dr. Gabriel O'Neill, Esq., is a trusted authority dedicated to empowering businesses within the ever-evolving legal landscape.

What’s the Difference between Analytics and Analysis? [Ask the Expert]

For many of us, the terms analytics and analysis regularly come up in our daily work. But what do these words actually mean, and are we using one when we should be using the other? In this two-part blog series, we’ll first explore the difference between analytics and analysis and how that relates to learning analytics , followed by how analytics and KPIs differ when it comes to learning and development.

What are the differences between analytics and analysis ?

While analytics and analysis are more similar than different, their contrast is in the emphasis of each. They both refer to an examination of information—but while analysis is the broader and more general concept, analytics is a more specific reference to the systematic examination of data.

Think of analysis as what a person is doing when they are interpreting information, gathering it into a coherent understanding, and building a narrative or plan of action in response.

Think of analytics as what a computer is doing when it accepts, stores, calculates, and makes resulting information available for examination.

So, for example, a “business analyst” describes someone who is applying a process of analysis to a body of information for some purpose, while an “analytics platform” describes a system that enables the systematic calculation and analysis of data and statistics. The difference here is in the emphasis analytics places on data and systems.

From a more practical standpoint, we often think of analytics as a thing, and analysis as an action. In that regard, analytics can be thought of as the toolbox, tools, and workbench, while analysis is the process of building or repairing something with those.

Where do people get tripped up when using these terms?

When a person or team is manually bringing together data and other information from various sources, creating presentations and narratives around that information, and then presenting this information to interested parties, this is sometimes incorrectly dubbed an “analytics process,” or the team is incorrectly considered the “analytics team.” Rather, they are analysts in that capacity, bringing analytics to bear on the interpretation and presentation of data.

More often though, the bigger confusion comes in thinking of analytics as analysis. The many heralded successes of machine learning and artificial intelligence typically underemphasize the role of the analyst. As such, many people tend to think analytics are routinely capable of understanding and presenting insights without human intervention. But true value comes from an adept analyst who can work with models and apply them to data correctly using the right set of tools and the right understanding of the data.

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What’s the value of using learning data and learning analytics?

The most fundamental way that organizations compete and deliver value is through the skills and knowledge of their employees. Organizations are made up of people, and what those people learn, how they learn, and how they apply what they learn is paramount to the organization’s success. As such, gaining objective insights into this process and its results represents a key opportunity to maximize the organization’s chances for success.

It’s true that data and analytics aren’t required to disseminate information or deliver learning. But they are absolutely required to understand those processes, not to mention understand the effectiveness of the material, or the resulting impact .

Without insights into where and what people are learning, staggering amounts of investments in learning can go to waste (i.e. course libraries that are never accessed, poorly designed materials and assessments, or even well-designed materials that don’t show any impact on performance). Without an objective view of the data, these types of wasted investments can go on for years.

How can analytics and analysis be used together or in a complementary way?

Indeed they must be used together. Analytics are used for the purpose of analysis. Without analytics, there is little “raw material” for an analyst to use in their understanding, interpretation, and presentation of data. Without analysis, the data and statistics calculated with analytics is just a pile of numbers waiting for a purpose.

Up Next: What’s the difference between analytics and KPIs?

We’ve covered how data is the raw material, analytics is the toolbox, and analysis is the process. Next, we’ll look at key performance indicators (KPIs) —or the blueprint, if you will—that define what success means for both the business and L&D.

About the author

David Ells takes great pride in leading a dynamic team and turning innovative ideas into reality. His passion for technology and development found its roots at Rustici Software. During his tenure, he contributed significantly to the creation of SCORM Cloud, a groundbreaking product in the eLearning industry, and led the development of the world’s first learning record store powered by xAPI.

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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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What is the first step in active reading?

Is a newspaper a primary or secondary source?

Is a letter a primary or a secondary source?

Which of the following should not be included in research and analysis writing?  

What point of view should be avoided in research and analysis writing? 

Researchers need to include _ in their writing to avoid plagiarism . 

Which of the following questions is an analytical question specifically for a secondary source?

When analyzing a primary source, readers should consider the influence of historical, social, and political _.  

Which of the following is an element of active reading?  

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

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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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Which of the following should not be included in research and analysis writing? 

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Analysis vs. Analytics: How Are They Different?

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There is much confusion surrounding the difference between analysis and analytics. Both sound so alike, yet they are separate in terms of definitions. Due to the similarity of the words, however, some people believe they have the same meaning and, thus, use them interchangeably. Technically, this isn’t correct – there is, in fact, a distinct difference between the two.

In general, there isn’t a consensus about which activities fall solely under the category of analytics and which can’t be defined as analysis. This leads to people incorrectly placing the terms under the same denominator. Additionally, titles like a business analyst and data analyst significantly differ from each other, yet the 2 positions are still confused for each other – if you’re interested in learning what sets them apart, however, check out our article on the differences between the analytics job roles .

In this article, we’ll shed some light on these two very important terms and provide you with clear-cut definitions so you can more clearly distinguish between them.

What Is Analysis?

Analysis is the investigation of why something happened.

When we look at the variance between actual figures and the numbers in the company’s budget, we are implementing an analysis. If we want to understand the underlying business performance of a company, we’d be analyzing the variance of a financial item such as revenue. And the way we do these assessments is by using data we have gathered about our performance so far.

The important thing to remember about analysis, however, is that you seek to interpret events that have already happened in the past. Such as to explain how and why there was a decrease in your company’s sales last summer. Essentially, when you are doing analysis, you look backward in order to understand how your company has performed against the expectations of your stakeholders.

What Is Analytics?

Every stakeholder has expectations that we need to translate into numbers. This is also known as the long-range plan and the annual business plan, or ABP for short.

That means that when we prepare these plans, we need to predict what will happen in the future . Of course, we’d usually have some past business data, however, the past is not always the best outlier for what the future holds. Therefore, we need to have more sophisticated tools that are forward-looking rather than backward.

Here is where analytics comes into play. As you have probably guessed, it generally refers to the future. Instead of explaining past events, it explores potential future ones. Analytics is essentially the application of logical and computational reasoning to the component parts obtained during analysis. And, in doing this, you are looking for patterns in the data and exploring what you could do with them in the future.

This term refers to a model that creates scenarios and predicts performance on the basis of past scenarios. In essence, analytics is a strategic asset that enables top management and the Board of Directors to make better-informed decisions through various techniques, such as customer analytics and time series analysis .

The Two Areas of Analytics

What are the areas of Analytics? If we narrow our focus, we’ll see it branches off into:

Qualitative Analytics

This type of analytics requires using your intuition and experience in conjunction with the analysis to plan your next business move.

Quantitative Analytics

With quantitative analytics, you apply formulas and algorithms to numbers you have gathered from your analysis.

Qualitative and Quantitative Analytics Examples

Say you own an online clothing store. You are also ahead of the competition and have a great understanding of what your customer’s wants and needs are. This is because you’ve performed a very detailed analysis based on women’s clothing articles and feel sure which fashion trends to follow. This intuition helps you decide which styles of clothing to start selling – otherwise known as qualitative analytics.

However, you might wonder when to introduce the new collection. In that case, relying on past sales data and user experience data, you could decide in which month it would be best to do that, based on when your last collection’s sales hit their peak. This is an example of using quantitative analytics in fashion .

The Benefits of Data Analytics

Success in business is crucial – and so is staying on top of all the technological advancements. And, one of the best ways to grasp the numerous benefits of data analytics is to see how hugely successful companies have reaped the rewards from implementing analytics into their ranks.

The streaming service has been dominating the scene for some time now and continues to set precedents of success among its competitors. What lies behind Netflix’s success is their initiative to implement data analytics to predict what their consumer would most like to see. Their analysts spend countless hours poring over not only what the users watch, but also when and for how long. Then, they feed this information onto the company’s higher-ups, assisting them in their decision-making.

Netflix, as of recent years, also plays a large role in producing most of the mainstream original content based on what their viewership responds to best – Backlinko shows that these Netflix Originals actually generate the most traffic for the company.

Furthermore, according to Statista , in 2020, Netflix saw an increase of 36.57 million paying subscribers and reached more than 200 million users worldwide. Clearly, data analytics is working for them.

You are probably familiar with Etsy - the hugely popular online marketplace for bespoke handmade goods. Much like a real-life market, sellers use the platform to set up shops and offer their crafty goods to interested buyers. However, since the products are all so unique, they’re also difficult to categorize.

How has Etsy managed to solve this problem? By taking on a very data-oriented approach in its business. The company has focused on building a product recommendation algorithm that anticipates buyers’ interests in advance, offering them the trinkets they’re most likely to click on. And, what is more, it is clearly working – according to data, the website’s revenue has been steadily growing for the last 5 years , reaching a spectacular surge: 10 billion dollars in gross merchandise revenue in 2020.

But that’s not the only place Etsy’s implemented data analytics. About 80% of the employees access the collected data and work with it on a weekly basis as part of the decision-making process, as well as to prevent payment fraud. In fact, Etsy is so data-driven, they’ve launched an organization where data analysts and machine learning engineers can experiment with data and help improve users’ experience on the website.

Walmart is well-known as the world’s largest retail company. With more than 20,000 stores across 28 countries, the chain offers a variety of goods, ranging from groceries to home renovation tools and tech gadgets.

The company launched a so-called Data Café where teams of analysts go over thousands of datasets, most happening in real time, to locate problem areas – for example, where and why certain products are not selling. This way they fix possible issues and optimize sales for the given location. In addition, Walmart uses real-time analytics to predict customer inflow in order to allocate the most employees at checkout at busier hours .

Of course, they don’t just deal with internal data – analysts pull external data on weather conditions, economics, upcoming local events and more, to ensure that their locations are stocked appropriately and can meet customer demand. One such example is stocking emergency equipment and, surprisingly, Pop-Tarts before a predicted hurricane in the US, based on the data from a previous event.

Analysis vs. Analytics: Next Steps

Analysis and analytics are not exactly homophones but might as well be with how often people get their definitions wrong. The good news is, you’ve now learned that analysis deals with events that have already happened, while analytics steps on past and current data, and is primarily forward-looking. This makes you one step ahead of the game!

Additionally, analytics can completely transform a business. You’ve by now seen the colossal impact it has had on industry giants like Netflix, and how they’ve used it to rise to the top. The way they’ve done this is by implementing data analytics services and hiring expert data scientists that have used their analysis and analytics skills in their favor. Your portfolio and career outlook as an aspiring data science professional will be greatly improved by acquiring these skills as you dive deeper into the data-oriented business world.

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Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

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It was helpful

thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

Waoo! Simplifies language. I have read this several times and had probs. Today it is very clear. Bravo

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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How to write the analysis and discussion chapters in qualitative (SSAH) research

By charlesworth author services.

• Charlesworth Author Services
• 11 November, 2021

While it is more common for Science, Technology, Engineering and Mathematics (STEM) researchers to write separate, distinct chapters for their data/ results and analysis/ discussion , the same sections can feel less clearly defined for a researcher in Social Sciences, Arts and Humanities (SSAH). This article will look specifically at some useful approaches to writing the analysis and discussion chapters in qualitative/SSAH research.

Note : Most of the differences in approaches to research, writing, analysis and discussion come down, ultimately, to differences in epistemology – how we approach, create and work with knowledge in our respective fields. However, this is a vast topic that deserves a separate discussion.

Look for emerging themes and patterns

The ‘results’ of qualitative research can sometimes be harder to pinpoint than in quantitative research. You’re not dealing with definitive numbers and results in the same way as, say, a scientist conducting experiments that produce measurable data. Instead, most qualitative researchers explore prominent, interesting themes and patterns emerging from their data – that could comprise interviews, textual material or participant observation, for example. 

You may find that your data presents a huge number of themes, issues and topics, all of which you might find equally significant and interesting. In fact, you might find yourself overwhelmed by the many directions that your research could take, depending on which themes you choose to study in further depth. You may even discover issues and patterns that you had not expected , that may necessitate having to change or expand the research focus you initially started off with.

It is crucial at this point not to panic. Instead, try to enjoy the many possibilities that your data is offering you. It can be useful to remind yourself at each stage of exactly what you are trying to find out through this research.

What exactly do you want to know? What knowledge do you want to generate and share within your field?

Then, spend some time reflecting upon each of the themes that seem most interesting and significant, and consider whether they are immediately relevant to your main, overarching research objectives and goals.

Suggestion: Don’t worry too much about structure and flow at the early stages of writing your discussion . It would be a more valuable use of your time to fully explore the themes and issues arising from your data first, while also reading widely alongside your writing (more on this below). As you work more intimately with the data and develop your ideas, the overarching narrative and connections between those ideas will begin to emerge. Trust that you’ll be able to draw those links and craft the structure organically as you write.

Let your data guide you

A key characteristic of qualitative research is that the researchers allow their data to ‘speak’ and guide their research and their writing. Instead of insisting too strongly upon the prominence of specific themes and issues and imposing their opinions and beliefs upon the data, a good qualitative researcher ‘listens’ to what the data has to tell them.

Again, you might find yourself having to address unexpected issues or your data may reveal things that seem completely contradictory to the ideas and theories you have worked with so far. Although this might seem worrying, discovering these unexpected new elements can actually make your research much richer and more interesting. 

Suggestion: Allow yourself to follow those leads and ask new questions as you work through your data. These new directions could help you to answer your research questions in more depth and with greater complexity; or they could even open up other avenues for further study, either in this or future research.

Work closely with the literature

As you analyse and discuss the prominent themes, arguments and findings arising from your data, it is very helpful to maintain a regular and consistent reading practice alongside your writing. Return to the literature that you’ve already been reading so far or begin to check out new texts, studies and theories that might be more appropriate for working with any new ideas and themes arising from your data.

Reading and incorporating relevant literature into your writing as you work through your analysis and discussion will help you to consistently contextualise your research within the larger body of knowledge. It will be easier to stay focused on what you are trying to say through your research if you can simultaneously show what has already been said on the subject and how your research and data supports, challenges or extends those debates. By drawing from existing literature , you are setting up a dialogue between your research and prior work, and highlighting what this research has to add to the conversation.

Suggestion : Although it might sometimes feel tedious to have to blend others’ writing in with yours, this is ultimately the best way to showcase the specialness of your own data, findings and research . Remember that it is more difficult to highlight the significance and relevance of your original work without first showing how that work fits into or responds to existing studies. 

In conclusion

The discussion chapters form the heart of your thesis and this is where your unique contribution comes to the forefront. This is where your data takes centre-stage and where you get to showcase your original arguments, perspectives and knowledge. To do this effectively needs you to explore the original themes and issues arising from and within the data, while simultaneously contextualising these findings within the larger, existing body of knowledge of your specialising field. By striking this balance, you prove the two most important qualities of excellent qualitative research : keen awareness of your field and a firm understanding of your place in it.

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• Korean J Anesthesiol
• v.71(2); 2018 Apr

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.

Case Study vs. Research

What's the difference.

Case study and research are both methods used in academic and professional settings to gather information and gain insights. However, they differ in their approach and purpose. A case study is an in-depth analysis of a specific individual, group, or situation, aiming to understand the unique characteristics and dynamics involved. It often involves qualitative data collection methods such as interviews, observations, and document analysis. On the other hand, research is a systematic investigation conducted to generate new knowledge or validate existing theories. It typically involves a larger sample size and employs quantitative data collection methods such as surveys, experiments, or statistical analysis. While case studies provide detailed and context-specific information, research aims to generalize findings to a broader population.

Further Detail

Introduction.

When it comes to conducting studies and gathering information, researchers have various methods at their disposal. Two commonly used approaches are case study and research. While both methods aim to explore and understand a particular subject, they differ in their approach, scope, and the type of data they collect. In this article, we will delve into the attributes of case study and research, highlighting their similarities and differences.

A case study is an in-depth analysis of a specific individual, group, event, or phenomenon. It involves a detailed examination of a particular case to gain insights into its unique characteristics, context, and dynamics. Case studies often employ multiple sources of data, such as interviews, observations, and documents, to provide a comprehensive understanding of the subject under investigation.

One of the key attributes of a case study is its focus on a specific case, which allows researchers to explore complex and nuanced aspects of the subject. By examining a single case in detail, researchers can uncover rich and detailed information that may not be possible with broader research methods. Case studies are particularly useful when studying rare or unique phenomena, as they provide an opportunity to deeply analyze and understand them.

Furthermore, case studies often employ qualitative research methods, emphasizing the collection of non-numerical data. This qualitative approach allows researchers to capture the subjective experiences, perspectives, and motivations of the individuals or groups involved in the case. By using open-ended interviews and observations, researchers can gather rich and detailed data that provides a holistic view of the subject.

However, it is important to note that case studies have limitations. Due to their focus on a specific case, the findings may not be easily generalized to a larger population or context. The small sample size and unique characteristics of the case may limit the generalizability of the results. Additionally, the subjective nature of qualitative data collection in case studies may introduce bias or interpretation challenges.

Research, on the other hand, is a systematic investigation aimed at discovering new knowledge or validating existing theories. It involves the collection, analysis, and interpretation of data to answer research questions or test hypotheses. Research can be conducted using various methods, including surveys, experiments, and statistical analysis, depending on the nature of the study.

One of the primary attributes of research is its emphasis on generating generalizable knowledge. By using representative samples and statistical techniques, researchers aim to draw conclusions that can be applied to a larger population or context. This allows for the identification of patterns, trends, and relationships that can inform theories, policies, or practices.

Research often employs quantitative methods, focusing on the collection of numerical data that can be analyzed using statistical techniques. Surveys, experiments, and statistical analysis allow researchers to measure variables, establish correlations, and test hypotheses. This objective approach provides a level of objectivity and replicability that is crucial for scientific inquiry.

However, research also has its limitations. The focus on generalizability may sometimes sacrifice the depth and richness of understanding that case studies offer. The reliance on quantitative data may overlook important qualitative aspects of the subject, such as individual experiences or contextual factors. Additionally, the controlled nature of research settings may not fully capture the complexity and dynamics of real-world situations.

Similarities

Despite their differences, case studies and research share some common attributes. Both methods aim to gather information and generate knowledge about a particular subject. They require careful planning, data collection, analysis, and interpretation. Both case studies and research contribute to the advancement of knowledge in their respective fields.

Furthermore, both case studies and research can be used in various disciplines, including social sciences, psychology, business, and healthcare. They provide valuable insights and contribute to evidence-based decision-making. Whether it is understanding the impact of a new treatment, exploring consumer behavior, or investigating social phenomena, both case studies and research play a crucial role in expanding our understanding of the world.

In conclusion, case study and research are two distinct yet valuable approaches to studying and understanding a subject. Case studies offer an in-depth analysis of a specific case, providing rich and detailed information that may not be possible with broader research methods. On the other hand, research aims to generate generalizable knowledge by using representative samples and quantitative methods. While case studies emphasize qualitative data collection, research focuses on quantitative analysis. Both methods have their strengths and limitations, and their choice depends on the research objectives, scope, and context. By utilizing the appropriate method, researchers can gain valuable insights and contribute to the advancement of knowledge in their respective fields.

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Quantitative vs. Qualitative Research in Psychology

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

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Systematic reviews vs meta-analysis: what’s the difference?

Posted on 24th July 2023 by Verónica Tanco Tellechea

You may hear the terms ‘systematic review’ and ‘meta-analysis being used interchangeably’. Although they are related, they are distinctly different. Learn more in this blog for beginners.

What is a systematic review?

According to Cochrane (1), a systematic review attempts to identify, appraise and synthesize all the empirical evidence to answer a specific research question. Thus, a systematic review is where you might find the most relevant, adequate, and current information regarding a specific topic. In the levels of evidence pyramid , systematic reviews are only surpassed by meta-analyses. 

To conduct a systematic review, you will need, among other things: 

• A specific research question, usually in the form of a PICO question.
• Pre-specified eligibility criteria, to decide which articles will be included or discarded from the review. 
• To follow a systematic method that will minimize bias.

You can find protocols that will guide you from both Cochrane and the Equator Network , among other places, and if you are a beginner to the topic then have a read of an overview about systematic reviews.

What is a meta-analysis?

A meta-analysis is a quantitative, epidemiological study design used to systematically assess the results of previous research (2) . Usually, they are based on randomized controlled trials, though not always. This means that a meta-analysis is a mathematical tool that allows researchers to mathematically combine outcomes from multiple studies.

When can a meta-analysis be implemented?

There is always the possibility of conducting a meta-analysis, yet, for it to throw the best possible results it should be performed when the studies included in the systematic review are of good quality, similar designs, and have similar outcome measures.

Why are meta-analyses important?

Outcomes from a meta-analysis may provide more precise information regarding the estimate of the effect of what is being studied because it merges outcomes from multiple studies. In a meta-analysis, data from various trials are combined and generate an average result (1), which is portrayed in a forest plot diagram. Moreover, meta-analysis also include a funnel plot diagram to visually detect publication bias.

Conclusions

A systematic review is an article that synthesizes available evidence on a certain topic utilizing a specific research question, pre-specified eligibility criteria for including articles, and a systematic method for its production. Whereas a meta-analysis is a quantitative, epidemiological study design used to assess the results of articles included in a systematic-review. 

Remember: All meta-analyses involve a systematic review, but not all systematic reviews involve a meta-analysis.

If you would like some further reading on this topic, we suggest the following:

The systematic review – a S4BE blog article

Meta-analysis: what, why, and how – a S4BE blog article

The difference between a systematic review and a meta-analysis – a blog article via Covidence

Systematic review vs meta-analysis: what’s the difference? A 5-minute video from Research Masterminds:

• About Cochrane reviews [Internet]. Cochranelibrary.com. [cited 2023 Apr 30]. Available from: https://www.cochranelibrary.com/about/about-cochrane-reviews
• Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(Suppl 1):29–37.

Verónica Tanco Tellechea

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• Bloomberg, L. D. and Volpe, M. F. (2016) A Complete Dissertation: The Big Picture, in Bloomberg, L.D. and Volpe, M.F. (eds.) Completing Your Qualitative Dissertation: A Road Map From Beginning to End. 3rd ed. California: SAGE Publications, Inc., pp. 3-12.
• Denscombe, M. (2003) The Good Research Guide for Small-Scale Social Research Projects. 2nd ed. Berkshire, England: Open University Press. (See: pp. 314-315 on the structure of research report).
• Rotchie, J. and Lewis, J. (2003) Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: SAGE Publications Ltd.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Research: Consumers Spend Loyalty Points and Cash Differently

• So Yeon Chun,
• Freddy Lim,
• Ville Satopää

Your loyalty strategy needs to consider four ways people value points.

Do consumers treat loyalty points the same way that they treat traditional money? And, how do they choose to spend one versus the other?  The authors of this article present research findings from their analysis of  data describing over 29,000 unique loyalty points earning and spending transactions made during two recent years by 500 airline loyalty program consumers.  They found that points users fell into four distinct categories: 1) Money advocates, who prefer cash over points, even when their value is identical in terms of purchasing power; 2)  Currency impartialists, who regard points and cash interchangeably, valuing them equally based on their financial worth; 3) Point gamers, who actively seek out the most advantageous point redemption opportunities, opting to spend points particularly when their value significantly surpasses that of cash; and 4) Point lovers, who value points more than money even if their purchase power is the same or lower. This article explores the strategic implications of these findings for companies that manage loyalty programs.

In the years since The Economist  spotlighted the astonishing scale of loyalty points — particularly frequent-flyer miles — as a potential global currency rivaling traditional money in 2005, usage has grown rapidly in size and scope. For example, the number of flight redemptions at Southwest Airlines doubled from 5.4 million in 2013 (representing 9.5% of revenue passenger miles) to 10.9 million in 2023 (representing 16.3% of revenue passenger miles).

• SC So Yeon Chun is an Associate Professor of Technology & Operations Management at INSEAD, a  global business school with campuses in Abu Dhabi, France, and Singapore.
• FL Freddy Lim is an Assistant Professor of Information Systems and Analytics at the National University of Singapore, School of Computing in Singapore
• VS Ville Satopää is an Associate Professor of Technology and Operations Management at INSEAD, a  global business school with campuses in Abu Dhabi, France, and Singapore.

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2024 Kamala Harris v. Donald Trump battleground-state estimates

By Kabir Khanna

Updated on: August 18, 2024 / 9:02 PM EDT / CBS News

Here are CBS News' latest estimates of Kamala Harris and Donald Trump's support in the most competitive states in the country leading up to 2024 presidential election. This is where races stand today —  the numbers are updated regularly.

We take a state-by-state approach, because the presidency is determined in the Electoral College, not by national popular vote. We produce estimates of current support using a statistical model  that incorporates all the data we've collected up to this point.

That includes tens of thousands of registered voters who respond to our surveys. We poll voters in every state, but concentrate our efforts in the battlegrounds, which we expect to be more competitive. Our model combines this survey data with voter files and recent election results to anchor estimates.

CBS has a strong track record employing similar models over the past few years . Read more about the Battleground Tracker methodology here .

• Opinion Poll
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Kabir Khanna, Ph.D., is Deputy Director, Elections & Data Analytics at CBS News. He conducts surveys, develops statistical models, and projects races at the network Decision Desk. His scholarly research centers on political behavior and methodology. He holds a Ph.D. in political science from Princeton University.

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Harris Energizes Democrats in Transformed Presidential Race

1. the presidential matchup: harris, trump, kennedy, table of contents.

• Other findings: Both Harris and Trump are viewed more favorably than a few months ago
• Voting preferences among demographic groups
• How have voters shifted their preferences since July?
• Harris’ supporters back her more strongly than Biden’s did last month
• Large gap in motivation to vote emerges between the candidates’ younger supporters
• Harris and Trump have gained ground with their own coalitions
• Share of ‘double negatives’ drops significantly with change in presidential candidates
• Views of Biden have changed little since his withdrawal from the 2024 presidential race
• Acknowledgments
• The American Trends Panel survey methodology

Nationally, Vice President Kamala Harris and former President Donald Trump are essentially tied among registered voters in the current snapshot of the presidential race: 46% prefer Harris, 45% prefer Trump and 7% prefer Robert F. Kennedy Jr.

Following Biden’s exit from the race, Trump’s support among voters has remained largely steady (44% backed him in July against Biden, while 45% back him against Harris today). However, Harris’ support is 6 percentage points higher than Biden’s was in July . In addition to holding on to the support of those who backed Biden in July, Harris’ bump has largely come from those who had previously said they supported or leaned toward Kennedy.

Harris performs best among the same demographic groups as Biden. But this coalition of voters is now much more likely to say they strongly support her: In July, 43% of Biden’s supporters characterized their support as strong – today, 62% of Harris’ do.

Overall, many of the same voting patterns that were evident in the Biden-Trump matchup from July continue to be seen today. Harris fares better than Trump among younger voters, Black voters, Asian voters and voters with college degrees. By comparison, the former president does better among older voters, White voters and voters without a college degree.

But Harris performs better than Biden across many of these groups – making the race tighter than it was just a few weeks ago.

• In July, women’s presidential preferences were split: 40% backed Biden, 40% preferred Trump and 17% favored Kennedy. With Harris at the top of the ticket, 49% of women voters now support her, while 42% favor Trump and 7% back Kennedy.
• Among men, Trump draws a similar level of support as he did in the race against Biden (49% today, compared with 48% in July). But the share of men who now say they support Harris has grown (to 44% today, up from 38% last month). As a result, Trump’s 10-point lead among men has narrowed to a 5-point lead today.

Race and ethnicity

Harris has gained substantial ground over Biden’s position in July among Black, Hispanic and Asian voters. Most of this movement is attributable to declining shares of support for Kennedy. Trump performs similarly among these groups as he did in July.

• 77% of Black voters support or lean toward Harris. This compares with 64% of Black voters who said they backed Biden a few weeks ago. Trump’s support is unchanged (13% then vs. 13% today). And while 21% of Black voters supported Kennedy in July, this has dropped to 7% in the latest survey.
• Hispanic voters now favor Harris over Trump by a 17-point margin (52% to 35%). In July, Biden and Trump were tied among Hispanic voters with 36% each.
• By about two-to-one, Asian voters support Harris (62%) over Trump (28%). Trump’s support among this group is essentially unchanged since July, but the share of Asian voters backing Harris is 15 points higher than the share who backed Biden in July.
• On balance, White voters continue to back Trump (52% Trump, 41% Harris), though that margin is somewhat narrower than it was in the July matchup against Biden (50% Trump, 36% Biden).

While the age patterns present in the Harris-Trump matchup remain broadly the same as those in the Biden-Trump matchup in July, Harris performs better across age groups than Biden did last month. That improvement is somewhat more pronounced among voters under 50 than among older voters.

• Today, 57% of voters under 30 say they support Harris, while 29% support Trump and 12% prefer Kennedy. In July, 48% of these voters said they backed Biden. Trump’s support among this group is essentially unchanged. And 12% now back Kennedy, down from 22% in July.
• Voters ages 30 to 49 are now about evenly split (45% Harris, 43% Trump). This is a shift from a narrow Trump lead among this group in July.
• Voters ages 50 and older continue to tilt toward Trump (50% Trump vs. 44% Harris).

With Harris now at the top of the Democratic ticket, the race has become tighter.

Much of this is the result of shifting preferences among registered voters who, in July, said they favored Kennedy over Trump or Biden.

Among the same group of voters surveyed in July and early August, 97% of those who backed Biden a few weeks ago say they support or lean toward Harris today. Similarly, Trump holds on to 95% of those who supported him a few weeks ago.

But there has been far more movement among voters who previously expressed support for Kennedy. While Kennedy holds on to 39% of those who backed him in July, the majority of these supporters now prefer one of the two major party candidates: By about two-to-one, those voters are more likely to have moved to Harris (39%) than Trump (20%). This pattern is evident across most voting subgroups.

In July, Trump’s voters were far more likely than Biden’s voters to characterize their support for their candidate as “strong” (63% vs. 43%). But that gap is no longer present in the Harris-Trump matchup.

Today, 62% of Harris voters say they strongly support her, while about a third (32%) say they moderately support her. Trump’s voters are just about as likely to say they strongly back him today as they were in July (64% today, 63% then).

Kennedy’s voters make up a smaller share of voters today than a month ago – and just 18% of his voters say they strongly support him, similar to the 15% who said the same in July.

Across demographic groups, strong support for Harris is higher than it was for Biden

Among women voters who supported Biden in July, 45% said they did so strongly. That has grown to 65% today among women voters who support Harris.

Increased intensity of support is similar among men voters who back the Democratic candidate: In July, 42% of men voters who supported Biden said they did so strongly. This has since grown to 59% of Harris’ voters who are men.

Across racial and ethnic groups, Harris’ supporters are more likely than Biden’s were to say they back their candidates strongly.

Among White voters, 43% who supported Biden in July did so strongly. Today, Harris’ strong support among White voters sits at 64%.

A near identical share of Harris’ Black supporters (65%) characterize their support for her as strong today. This is up from the 52% of Biden’s Black supporters who strongly backed him in July. Among Harris’ Hispanic supporters, 56% support her strongly, while 45% of Asian Harris voters feel the same. Strong support for Harris among these voters is also higher than it was for Biden in July.

Across all age groups, Harris’ strength of support is higher than Biden’s was. But the shift from Biden is less pronounced among older Democratic supporters than among younger groups.

Still, older Harris voters are more likely than younger Harris voters to describe their support as strong. For instance, 51% of Harris’ voters under 50 say they strongly support her, while 71% of Harris supporters ages 50 and older characterize their support as strong.

Today, about seven-in-ten of both Trump supporters (72%) and Harris supporters (70%) say they are extremely motivated to vote.

Motivation to vote is higher in both the Democratic and Republican coalitions than it was in July .

These shifts have occurred across groups but are more pronounced among younger voters.

Today, half of voters under 30 say they are extremely motivated to vote, up 16 points since July. Motivation is up 11 points among voters ages 30 to 49 and 50 to 64, and up 6 points among those ages 65 and older.

Among the youngest voters, the increased motivation to vote is nearly all driven by shifts among Democratic supporters.

• In July, 38% of 18- to 29-year-old Trump voters said they were extremely motivated to vote. Today, a similar share of his voters (42%) report that level of motivation.
• But 18- to 29-year-old Harris supporters are far more likely to say they are extremely motivated to vote than Biden’s supporters in this age group were about a month ago. Today, 61% of Harris’ voters under 30 say this. In July, 42% of voters under 30 who supported Biden said they were extremely motivated to vote.

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

Published on 26.8.2024 in Vol 26 (2024)

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

Authors of this article:

Original Paper

• Azade Tabaie 1, 2 , PhD   ; 
• Alberta Tran 3 , RN, CCRN, PhD   ; 
• Tony Calabria 3 , MA, CPHQ, CSSBB   ; 
• Sonita S Bennett 1 , MSc   ; 
• Arianna Milicia 4 , BSc   ; 
• William Weintraub 5, 6 , MACC, MD   ; 
• William James Gallagher 6, 7 , MD   ; 
• John Yosaitis 6, 8 , MD   ; 
• Laura C Schubel 4 , MPH   ; 
• Mary A Hill 9, 10 , MS   ; 
• Kelly Michelle Smith 9, 10 , PhD   ; 
• Kristen Miller 4, 6 , MSPH, MSL, CPPS, DrPH  

1 Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, DC, United States

2 Department of Emergency Medicine, Georgetown University School of Medicine, Washington, DC, United States

3 Department of Quality and Safety, MedStar Health Research Institute, Washington, DC, United States

4 National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC, United States

5 Population Health, MedStar Health Research Institute, Washington, DC, United States

6 Georgetown University School of Medicine, Washington, DC, United States

7 Family Medicine Residency Program, MedStar Health Georgetown-Washington Hospital Center, Washington, DC, United States

8 MedStar Simulation Training & Education Lab (SiTEL), MedStar Institute for Innovation, Washington, DC, United States

9 Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, ON, Canada

10 Michael Garron Hospital, Toronto, ON, Canada

Corresponding Author:

Azade Tabaie, PhD

Center for Biostatistics, Informatics, and Data Science

MedStar Health Research Institute

3007 Tilden Street NW

Washington, DC, 20008

United States

Phone: 1 202 244 9810

Email: [email protected]

Background: Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay.

Objective: This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data.

Methods: Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors.

Results: In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P =.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P =.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P =.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P =.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F 1 -score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients.

Conclusions: Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

Introduction

Diagnostic errors are an underappreciated cause of preventable mortality in hospitals, estimated to affect a quarter million hospital inpatients, and account for an estimated 40,000-80,000 deaths annually in the United States [ 1 ]. These errors pose a risk for severe patient harm [ 2 , 3 ], increase hospital length of stay [ 4 ], and made up 22% and accounted for US $5.7 billion of paid malpractice claims in hospitalized patients throughout a nearly 13-year period [ 5 ]. In their analysis of malpractice claims occurring in the US National Practitioner Database from 1999 to 2011, Gupta et al [ 5 ] found that diagnosis-related paid claims were most likely to be associated with death and cost (following surgery); among diagnosis-related paid claims, failure to diagnose was the most common subtype and was more likely than other types to be associated with mortality. Several factors have been proposed as contributors to inpatient diagnostic errors including time constraints related to the concurrent care of multiple patients, unpredictable workflows, distractions, and competing priorities for trainees. From their systematic review and meta-analysis, Gunderson et al [ 2 ] estimate that 250,000 diagnostic adverse events occur annually among hospitalized patients in the United States, and this is likely an underestimation of the problem due to several challenges in diagnostic error measurement [ 6 ].

Challenges in identifying and measuring diagnostic errors occur due to the evolving and iterative nature of the diagnostic process, making it difficult to determine when, if at all, a correct or more specific diagnosis could have been established by clinicians to start the appropriate treatment [ 6 ]. Since its landmark report, Improving Diagnosis in Health Care , the National Academies of Science, Engineering, and Medicine (NASEM) has produced a common understanding of diagnostic error that includes accuracy, timeliness, and communication of the explanation to the patient or patient’s family member [ 3 ]. Diagnostic errors often involve missed opportunities related to various aspects of the diagnostic process [ 7 - 9 ] and diagnostic adverse events resulting in harm [ 10 ]. However, many hospitals currently do not capture or include surveillance for diagnostic errors, despite having robust systems in place to report and analyze patient safety issues [ 6 , 11 , 12 ].

A crucial first step to improving diagnosis in hospitals is the creation of programs to identify, analyze, and learn from diagnostic errors. Ongoing efforts by the Agency for Health Care Research and Quality have supported pragmatic measurement approaches for health organizations to build a diagnostic safety program and identify and learn from diagnostic errors such as those described in the Measure Dx resource [ 9 ]. One proposed and promising solution for hospitals to improve diagnostic surveillance is to build on existing efforts to collect patient safety data, root cause analyses, or other forms of case reviews for quality improvement purposes. Cases that have already been reviewed or investigated in the organization for general patient safety and quality purposes may be able to inform or be rereviewed for information and learning opportunities specific to diagnostic safety. Widely used case-based learning methodologies in particular, such as the “Learning From Every Death” initiative developed at Mayo Clinic [ 13 ] used both nationally and worldwide, offer an excellent opportunity for hospitals to augment their existing quality and safety efforts and support diagnostic safety.

Clinical notes in electronic health records (EHRs) written by health providers in free-text format are rich sources of a patient’s diagnoses and care trajectory through hospitalization time. Approaches to processing free text, such as through natural language processing (NLP) and machine learning (ML), have demonstrated significant opportunities to improve quality and safety within health care organizations in diverse applications [ 14 - 16 ] such as cancer research [ 17 , 18 ] and infection prediction [ 19 ] to sleep issues [ 20 ] and neurological outcome prediction [ 21 ]. Besides its use in the diagnostic process, ML models proved to have added benefits when used in diagnostic error identification [ 22 , 23 ]. However, despite significant progress and evidence about the use of these ML and NLP approaches to improve patient safety, the use of ML and NLP approaches to diagnostic safety and surveillance has largely remained untapped. A 2022 study demonstrates how an academic medical center’s implementation of an NLP-based algorithm to flag safety event reports for manual review enabled early detection of emerging diagnostic risks from large volumes of safety reports, and was among the first to apply an NLP approach to safety event reports to facilitate identification of COVID-19 related diagnostic errors [ 24 ]. Meanwhile, progress in the use of data mining approaches to develop electronic trigger tools offers promising methods to detect potential diagnostic events, promote organizational learning, and support the monitoring of data prospectively to identify patients at high risk for future adverse events [ 25 ]. To our knowledge, however, NLP has not yet been applied to case review data to facilitate the identification of diagnostic errors and understand its features and sources.

While free-text formatted clinical notes provide unique opportunities to incorporate ML models, the lack of reliable labels to represent diagnostic errors often limits the use of clinical notes for diagnostic safety surveillance efforts. The opportunity to train ML and NLP algorithms to identify diagnostic errors and opportunities depends on the collation of EHR data with existing efforts to identify diagnostic errors such as through case review findings from the Safety Learning System (SLS). To further explore the potential for this approach to be used to improve diagnostic safety surveillance, a rigorous evaluation of the feasibility and potential of using EHR and existing case review data is needed.

We hypothesized that ML and NLP methods can be applied to train models based on available case review data to examine content potentially related to diagnostic errors within EHR clinical notes. These approaches automatically identify features or information from free text using controlled vocabularies, rule sets, reference dictionaries, or lexicons.

Data Sets and Case Review Approach

We analyzed SLS data from 1 large health system comprised of 10 hospitals in the mid-Atlantic region of the United States. The SLS is one example of a holistic case review methodology delivered by health care organizations in the United States and globally. Established in 2015, the SLS builds upon the Mayo Clinic Mortality Review System of Huddleston et al [ 13 ] to review and analyze EHR data from patient mortality cases to find safety issues that could be found and mitigated. This approach was designed to enhance current quality improvement projects done within health organizations, providing a perspective and strategy based on the Safety II lens and rooted in the belief that every death provides an opportunity to improve care. With a Safety II lens, participating organizations use a holistic case review methodology designed to identify vulnerabilities in systems and processes of care delivery. Reviewers identify and translate these into different categories and labels to (1) define and quantify types of process of care and system failures contributing to adverse outcomes (errors) and (2) identify the components of the process of care and system failures that when fixed will improve performance (opportunities for improvement [OFIs]).

To ensure a sufficient cross-sampling of patients across different specialties and areas, patients are selected for case reviews at this health system based on their primary provider service line category (eg, medicine, surgery, etc) and hospital length of stay; patients in primary and ambulatory care settings are not included for case review selection. The case review process occurs according to the standardized SLS methodology and recommendations [ 13 , 26 ], and between at least 1 physician and 1 nurse within the health system who have both received training in the SLS approach. The case review outcome and identification of OFIs, including diagnostic OFIs, relies on the reviewer’s consensus of any findings and through multiple multidisciplinary and multispecialty meetings that may involve a committee Chair member, clinical department leader, or escalation to other leadership.

We obtained SLS data from February 2016 to September 2021; data in later years were available but not included because of key changes to the case selection process made during and in response to the COVID-19 pandemic. All hospitalized adult patients older than 18 years were included for analysis, regardless of their hospitalization outcome (eg, mortality or discharge location). Pediatric and neonatal patients were excluded.

Ethical Considerations

The original data collection and study protocol was approved by the institutional review board (00001245) at MedStar Health Research Institute on August 26, 2019.

Data Extraction

Medical record number, encounter number, length of stay, age, date of birth, sex, diagnosis at the time of admission (ie, ICD-10 [ International Statistical Classification of Diseases, Tenth Revision ] diagnosis codes), mortality, OFI categories (eg, delayed or missed diagnosis and diagnostic opportunities), number of identified OFIs and diagnosis issues (eg, the accuracy of diagnosis and confirmation or fixation bias) were the features and patient identifiers which were extracted from SLS data [ 13 , 26 ].

Because chart reviews generally occur at a single point in time within the patient’s care trajectory, they often do not contain information or details of the patient’s full hospital course. However, clinical notes written by health care providers are rich sources of patient’s health status throughout their hospitalization period [ 27 - 29 ]. Therefore, to supplement these chart review data, we additionally extracted and included all clinical notes from the EHR for patients who could be matched by patient identifiers (eg, encounter number and date of birth).

Coding Diagnostic Errors

Case reviewers can select any number of labels to describe a diagnosis issue or an OFI identified and agreed upon by consensus. For this study, diagnostic errors were defined by the available features from chart review pertaining to diagnosis and impacting the timeliness, accuracy, or communication of a diagnosis. Our definition of diagnostic errors was limited to the categories identified during chart reviews and recorded within the SLS data set; therefore, our diagnostic error definition does not include all aspects of the definition developed by the NASEM report [ 3 ]. Table 1 describes the SLS categories and values that were labeled as diagnostic errors and used to train our classification models. Patients were coded as having experienced a diagnostic error if one or more of the conditions listed in Table 1 were identified in their SLS case review.

a OFI: opportunity for improvement.

NLP Approach

We used an NLP approach on critical incident reporting system data to explore the features and risk of diagnostic error among hospitalized patients.

Features From Free-Text Data

Descriptive statistical analyses were performed to identify any differences among age, length of stay, and mortality between the female and male patients who had experienced diagnostic errors.

All EHR clinical notes were transformed to lowercase. Extra white spaces, numbers, punctuations, and stop words were removed and words were stemmed. The term frequency-inverse document frequency (TF-IDF) matrix was calculated for each clinical note using the bag-of-words from the preprocessed EHR clinical notes [ 30 ]. TF-IDF is a statistical measure that evaluates how relevant a word is to a document in a collection of documents and is a popular method to translate free text to numerical features in training ML models. The TF-IDF of a word in a document is calculated by multiplying 2 metrics: the number of times a word appeared in a document and the inverse document frequency of the word across a set of documents. TF-IDF is computationally efficient and easy to interpret. We excluded the most frequent words that had appeared in more than 95% of the EHR clinical notes, as these frequent words do not provide information to help with the classification. Moreover, we excluded the rare words that appeared in less than 5% of the EHR clinical notes [ 31 ].

In a TF-IDF matrix, the number of rows corresponds to the unique patients, and the number of columns represents the unique words found in EHR clinical notes. There are numerous unique words used in EHR clinical notes; therefore, the TF-IDF approach provides a high-dimensional input matrix for the classification task. The high-dimensional input matrix can lead to training inaccurate classifiers. To overcome that issue, we used the chi-square statistical test to select the most relevant words to identify diagnostic errors; therefore, if P values associated with a word (also called a feature) are less than .05, that word is selected and included in the feature matrix to train ML classification models.

Classification Models

In lieu of an existing model with the same objective in the literature, a simple logistic regression model was trained as the baseline classifier to identify patients within SLS data who were at higher risk of diagnostic error. Moreover, 3 forms of logistic regression models with regularization functions were trained on this data to compare classification performances and identify the best-performing model [ 32 ]: Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Elastic Net.

• LASSO: for a more accurate prediction, LASSO regularization is used with a logistic regression model. The LASSO procedure encourages simple, sparse models which has fewer parameters in a way that the estimated coefficient of features with less effect will be set to zero. This characteristic makes LASSO well-suited for models showing high levels of multicollinearity or variable selection and parameter elimination is needed. LASSO is also called L1 regularization.
• Ridge: also called L2 regularization, Ridge is a regularization method used for models suffering from multicollinearity or high-dimensional feature space. Ridge regularization keeps all the features regardless of their effect on the model. However, it pushes the estimated coefficient of features with less effect toward zero to minimize their effect on the classification outcome. This characteristic of Ridge makes it well-suited when most features impact the outcome variable.
• Elastic Net: a logistic regression model with Elastic Net regularization is a weighted combination of LASSO (L1) and Ridge (L2) regularizations [ 33 ]. Elastic Net can remove the effect of the insignificant features by setting their estimated coefficient to zero and lower the effect of the less significant features by pushing their estimated coefficient toward zero while adding more weights to the more important features. From implementation and interpretation aspects, the Elastic Net model is simple to use. Such characteristics make this model an accepted baseline in ML-based studies [ 34 ].

The hyperparameters of the 3 classification models were optimized through cross-validation. All the analyses were conducted using Python 3 (Python Software Foundation).

Classification Performance Metrics

We calculated 7 common performance metrics reported for binary classifiers to compare the performance of the 4 classification models: area under receiver operating characteristics curve (AUROC), sensitivity or recall or true positive rate, specificity or true negative rate, positive predictive value (PPV) or precision, negative predictive value (NPV), F 1 -score, and area under precision-recall curve (AUPRC). The 7 metrics take values between 0 and 1. Values closer to 1 indicate a well-performing classifier. Multimedia Appendix 1 presents the definition of the performance metrics used in this study. Figure 1 presents the summary of the methods used in this analysis.

Descriptive Summary

In total, there were 2184 unique patient records within SLS data from February 2016 to September 2021. EHR clinical notes were cross-matched, extracted, and included in analyses for 1704 (78%) of these SLS patient records. Of those patients with cross-matched EHR data, 126 (7.4%) patients had been identified by case reviewers as having experienced at least 1 diagnostic error. A total number of 20,848 EHR clinical notes associated with the 1704 unique patients were used in this study.

Patients who had experienced diagnostic errors were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men in the larger cross-matched sample had been found to have a diagnostic error. Table 2 presents the descriptive statistics between female and male patient groups. We applied the Wilcoxon rank sum test for numerical features (ie, age and length of stay), and the chi-square test for mortality rate, admission diagnosis, and admission department or specialty. Patients in the female group were older than the male group by a median of 72 (IQR 66-80) versus a median of 67 (IQR 57-76; P =.02). Compared to the male group, female patients who experienced diagnostic error had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P =.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P =.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P =.04). We observed no differences between groups in mortality rates and length of stay.

Classification Models’ Performance

Clinical notes were preprocessed for TF-IDF feature calculation. The bag-of-words included 2227 words, and each word was considered a feature (see Table S1 in Multimedia Appendix 2 for the top 100 words). We found that abscess, ascend, abnormality, scant, pair, and prefer were the top 5 features with the highest positive estimated coefficient (0.42 to 0.28); post, select, gave, muscl, hours, and unrespons were the top 5 features with the highest negative coefficients (–0.35 to –0.26). After applying the chi-square test, 250 features with a P value less than .05 were selected for the modeling process. All 4 ML classifiers were trained using the 250 selected features.

Table 3 presents the performances of the simple logistic regression and 3 regularized logistic regression models (LASSO, Ridge, and Elastic Net). The Ridge model achieved the highest AUROC (0.885), specificity (0.797), PPV (0.24), NPV (0.981), and F 1 -score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients in SLS system. The simple logistic regression model obtained the highest AUPRC (0.537). The simple logistic regression model classified all patients as the ones with diagnostic errors; therefore, it achieved a sensitivity of 1, and specificity and NPV of 0.

Figures 2 and 3 present the receiver operating characteristics curves and precision-recall curves for the 4 classifiers in this study. Models that give ROC curves closer to the top-left corner indicate a better performance. The AUROC values represent the probability that a patient who experienced a diagnostic error, chosen at random, is ranked higher by the Ridge model than a randomly chosen patient who did not experience a diagnostic error. The higher value of AUPRC indicates that the Ridge model can identify patients who experienced diagnostic errors more precisely with fewer false positives compared to LASSO and Elastic Net models.

a LASSO: Least Absolute Shrinkage and Selection Operator.

b AUROC: area under receiver operating characteristics curve.

c AUPRC: area under precision-recall curve.

Principal Findings

Our contribution is 2-fold; first, we integrated 2 data sources that are currently used by and available to many organizations across the United States, SLS and EHR data, to explore the use of ML and NLP algorithms to help identify diagnostic errors among hospitalized patients. Although case review methodologies offer rich insights into systems errors and OFIs, the predefined pull-down menus and structured data labels typically do not capture all the necessary clinical and contextual details that are considered by reviewers. Therefore, a large portion of these case review data are stored in free-text narratives that typically record key information and judgments decided upon by the multidisciplinary reviewers. However, given persistent issues of staff shortage and lack of time in health care settings, it is becoming increasingly important to lower the burden of systematic EHR data reviews for health care providers while maintaining the review systems in place. Second, any developed ML and NLP approaches can potentially be incorporated to generate a diagnostic error risk score for each patient. The predicted risk score can be used in identifying and prioritizing patients for focused chart reviews, thus lowering the burden of systematic EHR data reviews for health care providers while maintaining the review systems in place.

To our knowledge, this study is the first attempt to apply and test several different ML classification models to identify diagnostic errors within routinely collected organizational case review data. Despite a substantial body of literature about the prevalence of diagnostic errors in hospital settings, current efforts to identify diagnostic errors generally rely on reviews of patient cases and data by clinical or quality teams that often are resource-intensive. ML classification models and NLP techniques offer an opportunity to generate diagnostic error risk scores to sort through large data sets and identify signals of potential diagnostic errors that can be flagged for further review. However, these classification models require a high number of observations (and identified diagnostic errors) to perform well, which might not be feasible for health organizations that are just beginning to identify diagnostic errors or may have limited personnel and efforts to perform high numbers of case reviews. In this study, we accessed nearly 2000 patient records (and of those, only 126 cases of diagnostic errors), which is considered to be a limited data sample size in the field of ML. However, techniques, such as feature selection and n-fold cross-validations, can potentially be approaches to address small sample size challenges [ 35 ].

Using the results of the simple logistic regression model as the baseline performance, we found that 3 regularization functions, namely LASSO, Ridge, and Elastic Net, boosted the performance of the baseline model. The Ridge model outperformed the rest of the models in terms of multiple performance metrics: AUROC of 0.885, specificity of 0.797, PPV of 0.24, NPV of 0.981, and F 1 -score of 0.369. The Ridge algorithm tries to keep all features in the model even the features with a slight effect on the classification outcome. Since the patterns pointing at a diagnostic error were subtle in the clinical notes, even a small effect of a feature on the model’s classification outcome could be important for the classification model to learn. On the other hand, the LASSO algorithm rigorously removes features that have a small effect on the classification outcome. The Elastic Net model is a weighted combination of LASSO and Ridge. The performance results presented in Table 3 show that the values achieved by the Elastic Net model lie between those of the LASSO and Ridge models.

Insights From Diagnostic Errors Within Free-Text Clinical Notes

We did not find the free text formatted clinical notes in the EHR to reflect any sort of direct language around diagnostic errors. Our analysis identified no use of the terms misdiagnosis, missed diagnosis, or diagnostic error within clinical notes, finding instead more subtle signals pointing at diagnostic errors such as “there may be a chance of misreading the test,” or “insufficient data to make a diagnosis.” Our findings demonstrate that NLP algorithms can be used to identify such patterns and find the associations between diagnostic errors and the subtle signals in the clinical notes. A natural extension of this work can focus on using other feature extraction methods, such as Bidirectional Encoder Representations from Transformers contextualized word embeddings, and explore the use of the pretrained language models for this objective.

We found that the presence of terms, such as abscess, abnormality, “cp” (chest pain) , and dialysis in a patient’s EHR clinical note were associated with reviewer-identified diagnostic errors ( Multimedia Appendix 2 ). Misinterpretation of chest pain, specifically among female patients, has the potential to cause a cardiovascular-related diagnosis error [ 36 ]. Patients with chronic kidney disease are at higher risk of cardiovascular complications [ 37 ]. Missing such risk for a patient who is on dialysis, adds to the risk of diagnostic error.

Clinical and System Implications Around Diagnostic Inequity

Diagnostic inequity is defined as “the presence of preventable unwarranted variations in diagnostic process among population groups that are socially, economically, demographically, or geographically disadvantaged” [ 38 ]. Despite persistent and well-documented disparities in health care access and outcomes across different population groups, few studies have examined the association between diagnostic errors and health care disparities [ 39 ]. Recent evidence supports the notion that variation in diagnostic error rates across demographic groups may exist, particularly across sex. A systematic review of diagnostic errors in the emergency department, for example, found that female sex and non-White race were often associated with increased risk for diagnostic errors across several clinical conditions in emergency settings [ 40 ]. In cardiovascular medicine, a national cohort study of acute myocardial infarctions found that women were nearly twice as likely as men to receive the wrong initial diagnosis following signs of a heart attack [ 41 ]. Despite efforts to understand and reduce disparities in diagnosis and treatment, women not only continue to be understudied, underdiagnosed, and undertreated in cardiovascular medicine [ 42 ] but also may experience longer lengths of time to diagnosis than men in most patterns of disease diagnosis [ 43 ].

The analysis of case review data and other system-based data (eg, patient safety events or incident reporting) by subsets offer an opportunity to identify events in vulnerable patient populations and help sensitize clinicians to potential biases within the diagnostic process. To explore sex differences in diagnostic errors within our case review data, we statistically compared demographic and clinical differences between female and male patients who had been identified in case reviews as having experienced diagnostic error or errors. We found that of those patients who had experienced diagnostic error or errors, the female group of patients were older, had higher rates of being admitted through general or internal medicine or hospitalist (vs specialty) departments, and had lower rates of having a cardiovascular diagnosis on admission. These preliminary results of this study revealed unexpected differences between male and female diagnostic error groups, offering novel insights that warrant further investigation to fully understand the mechanisms underlying these relationships and their implications for clinical decision-making and practice. Future uses of NLP can potentially support clinical and system-based approaches to capture and increase the evidence around structural biases or disparities in diagnoses. Individual cases from these types of data sources could be used as example narratives to engage clinicians and improve clinician learning, contributing to the development of tailored clinician and systemic interventions that can improve quality and equity throughout the diagnostic process.

Limitations

This study has several limitations. Our definition of diagnostic errors was limited to the categories and labels used within the SLS data set, reviewer interpretations of cases (subject to reviewer bias), and does not include all aspects of the definition developed by the NASEM report [ 3 ]. Despite several continued differences in definitions of diagnostic error in the peer-reviewed literature [ 8 ], we recommend that quality and safety teams within health systems use the NASEM definition for diagnostic error—including errors in communicating the diagnosis to the patient—to develop any definitions, categories, or labels used in their case review and surveillance initiatives. Although a time-consuming task, future studies could consider EHR data chart reviews to have the ground truth for the diagnostic error cases and add to the accuracy of the data set used for training the ML classifiers. Additionally, due to staffing challenges and shifting organizational priorities, case review selection varies by hospital and has changed over time, resulting in a relatively small sample size and also introducing the potential for bias. Our data came from a single health system and may reflect the specific language, culture, and practices occurring within the system and therefore may not be similar to that of other health systems. To enhance the external validity and generalizability of results, future efforts and research studies should consider the random selection of cases to evaluate both diagnostic and general quality issues within the organization; studies with larger sample sizes can build on our preliminary findings and test differences between clinical subgroups. Finally, our classification models were developed and evaluated based on a retrospective cohort from EHR; therefore, the performance may deteriorate when the method is applied to real-time data. Further work or future studies should be conducted to prospectively validate the models.

Conclusions

We performed an NLP approach and compared 4 techniques to classify patients who were at a higher risk of experiencing diagnostic error during hospitalization. Our findings demonstrate that NLP can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review, and therefore, reducing the case review burden.

Acknowledgments

This work was supported by the Agency for Health Care Research and Quality (grant 5R18HS027280-02).

Conflicts of Interest

None declared.

Binary classification performance metrics.

The Estimated Coefficient from the Ridge Model.

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Abbreviations

Edited by S Ma, T Leung; submitted 17.07.23; peer-reviewed by D Chrimes, M Elbattah; comments to author 18.01.24; revised version received 21.03.24; accepted 20.06.24; published 26.08.24.

©Azade Tabaie, Alberta Tran, Tony Calabria, Sonita S Bennett, Arianna Milicia, William Weintraub, William James Gallagher, John Yosaitis, Laura C Schubel, Mary A Hill, Kelly Michelle Smith, Kristen Miller. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

• Open access
• Published: 17 August 2024

Epidemiology, ventilation management and outcomes of COVID–19 ARDS patients versus patients with ARDS due to pneumonia in the Pre–COVID era

• Fleur–Stefanie L. I. M. van der Ven 1 , 2   na1 ,
• Siebe G. Blok 1   na1 ,
• Luciano C. Azevedo 3 , 4 ,
• Giacomo Bellani 5 , 6 ,
• Michela Botta 1 ,
• Elisa Estenssoro 7 ,
• Eddy Fan 8 ,
• Juliana Carvalho Ferreira 9 , 10 , 11 ,
• John G. Laffey 12 , 13 ,
• Ignacio Martin–Loeches 14 , 15 ,
• Ana Motos 16 , 17 , 28 ,
• Tai Pham 18 , 19 ,
• Oscar Peñuelas 17 , 20 ,
• Antonio Pesenti 21 ,
• Luigi Pisani 1 , 22 , 24 ,
• Ary Serpa Neto 4 , 23 ,
• Marcus J. Schultz 1 , 24 , 25 , 26 , 27 ,
• Antoni Torres 16 , 17 , 28 , 29 ,
• Anissa M. Tsonas 1 ,
• Frederique Paulus 1 , 30 &
• David M. P. van Meenen 1 , 31

for the ERICC–, LUNG SAFE–, PRoVENT–COVID–, EPICCoV–, CIBERESUCICOVID–, SATI–COVID–19–investigators

Respiratory Research volume  25 , Article number:  312 ( 2024 ) Cite this article

639 Accesses

Metrics details

Ventilation management may differ between COVID–19 ARDS (COVID–ARDS) patients and patients with pre–COVID ARDS (CLASSIC–ARDS); it is uncertain whether associations of ventilation management with outcomes for CLASSIC–ARDS also exist in COVID–ARDS.

Individual patient data analysis of COVID–ARDS and CLASSIC–ARDS patients in six observational studies of ventilation, four in the COVID–19 pandemic and two pre–pandemic. Descriptive statistics were used to compare epidemiology and ventilation characteristics. The primary endpoint were key ventilation parameters; other outcomes included mortality and ventilator–free days and alive (VFD–60) at day 60.

This analysis included 6702 COVID–ARDS patients and 1415 CLASSIC–ARDS patients. COVID–ARDS patients received lower median V T (6.6 [6.0 to 7.4] vs 7.3 [6.4 to 8.5] ml/kg PBW; p  < 0.001) and higher median PEEP (12.0 [10.0 to 14.0] vs 8.0 [6.0 to 10.0] cm H 2 O; p  < 0.001), at lower median ΔP (13.0 [10.0 to 15.0] vs 16.0 [IQR 12.0 to 20.0] cm H 2 O; p  < 0.001) and higher median Crs (33.5 [26.6 to 42.1] vs 28.1 [21.6 to 38.4] mL/cm H 2 O; p  < 0.001). Following multivariable adjustment, higher ΔP had an independent association with higher 60–day mortality and less VFD–60 in both groups. Higher PEEP had an association with less VFD–60, but only in COVID–ARDS patients.

Conclusions

Our findings show important differences in key ventilation parameters and associations thereof with outcomes between COVID–ARDS and CLASSIC–ARDS.

Trial registration

Clinicaltrials.gov (identifier NCT05650957), December 14, 2022.

The high numbers of patients who needed invasive ventilation early in the unprecedented pandemic of coronavirus disease 2019 (COVID–19) has led to numerous studies of epidemiology, ventilation management and outcomes in patients with acute respiratory distress syndrome (ARDS) related to an infection with SARS–CoV–2. COVID–19 ARDS would differ from ARDS before the pandemic (CLASSIC–ARDS) in several aspects [ 1 , 2 ], and different phenotypes have even been suggested [ 3 , 4 ].

The number of studies that directly compared ventilation management of COVID–ARDS with CLASSIC–ARDS is limited [ 5 , 6 ]. It remains uncertain whether practice of invasive ventilation in COVID–ARDS patients really differed from that in CLASSIC–ARDS patients. It is also unknown whether associations of certain aspects of ventilation with outcomes found in CLASSIC–ARDS also exist in COVID–ARDS. This would have serious implications on how to set the ventilator in the two patient groups, as then certain recommendations in guidelines for ventilation in CLASSIC–ARDS may not apply in COVID–ARDS [ 7 ].

We performed an analysis of a conveniently–sized database that pooled the data of individual patients of six observational ventilation studies, four of which were conducted in the COVID–19 pandemic and two pre–pandemic, to compare epidemiology, ventilator management and associations of ventilation characteristics and outcome between COVID–ARDS and CLASSIC–ARDS patients. To have comparable patient groups, we only selected patients with ARDS from a respiratory infection from the two pre–pandemic studies. We hypothesized that key ventilator parameters would be different between the two groups, and used multivariable analyses to determine associations with outcomes.

Study design and participants

This is a meta–analysis using the individual patient data of patients in six preselected large observational studies focusing on a diverse representation of epidemiological features and ventilation management in both COVID–19 and pre–pandemic ARDS. The six studies were selected because they all contained detailed data on epidemiological features, ventilation data, and outcomes, originating from various regions worldwide, both in resource–limited and resource–rich settings.

The corresponding authors of the original studies accepted the invitation, after which the data dictionaries of the studies were compared to check whether the data could be harmonized. Then, the databases were transferred after local approval and agreement on the analysis plan of the current investigation.

The two pre–pandemic studies were the national ‘Epidemiology of Respiratory Insufficiency in Critical Care’ study (ERICC) conducted in 2011 in Brazil [ 8 ], and the international ‘Large Observational Study to UNderstand the Global Impact of Severe Acute Respiratory FailurE’ study (LUNG SAFE) conducted in 2014 in 50 countries worldwide [ 9 ]. All four studies were conducted during the COVID–19 pandemic, ranging from March 2020 to 2021 and included: the national ‘Practice of Ventilation in COVID–19 patients’ study (PRoVENT–COVID) from The Netherlands [ 10 ], the national ‘EPIdemiology of Critical COVID–19’ study (EPICCoV) from Brazil [ 11 , 12 ], the national ‘Centro de Investigación Biomédica en Red Enfermedades Respiratorias COVID–19 study’ (CIBERESUCICOVID) from Spain [ 13 ], and the national ‘Sociedad Argentina de Terapia Intensiva–COVID–19 study’ (SATI–COVID–19) from Argentina [ 14 ].

The study protocols of the original studies were approved by Institutional Review Boards if applicable, and need for individual patient informed consent was waived for all studies due to their observational designs. Details of all studies can be found in the original publications [ 8 , 9 , 10 , 12 , 13 , 14 ]. We invited the corresponding investigators of the original studies to provide us the case report forms and data dictionaries, and the data of all patients. The creation of the pooled database did not require additional ethical approval. The databases of the original studies were harmonised using the case report forms and data dictionaries, and finally merged. This current analysis is registered at clinicaltrials.gov (study identifier NCT05650957), and its statistical analysis plan was finalized before cleaning and closing of the database.

Patients in the merged database were eligible for participation in this current analysis if: (1) aged 18 years or higher; (2) having received invasive ventilation within the first 48 h of ICU admission, regardless of its duration; and (3) fulfilling the Berlin definition of ARDS. We excluded CLASSIC–ARDS patients when ARDS was reported not to be caused by a respiratory infection.

Data available for merging

The following baseline and demographic variables were available for merging into the new database—sex, age, body weight and height, comorbidities including hypertension and cardiac failure, chronic obstructive pulmonary disease (COPD), diabetes mellitus, kidney failure, liver failure, and cancer, date of hospital and intensive care unit (ICU) admission, and disease severity scores, including the Simplified Acute Physiology Score (SAPS) II at ICU admission and a daily Sequential Organ Failure Assessment (SOFA) scores.

Collected ventilation variables were––mode of ventilation, tidal volume (V T ), positive end–expiratory pressure (PEEP), fraction of inspired oxygen (FiO 2 ), respiratory rate (RR), peak pressure (Ppeak) in volume–controlled ventilation and plateau pressure (Pplat) in pressure–controlled ventilation, blood gas analyses results, and adjunctive therapies to improve oxygenation in case of refractory hypoxaemia. The first available measurement of the day was used. If multiple measurements were taken on the same day, we selected earliest one.

The dynamic driving pressure (ΔP) was calculated by subtracting PEEP from the maximum airway pressure [ 15 , 16 ]. Respiratory system compliance (Crs) was calculated by dividing V T by ΔP. MP was calculated using the power Eq. (17), wherein MP (J/min) = 0.098 * V T * RR * (Ppeak − 0.5 * ΔP) [ 17 ]; a modified power equation was used if no Ppeak was available 0.098 * V T * RR * (Pplat − 0.5 * ΔP) [ 16 ]. The ventilatory ratio was calculated as (minute ventilation * PaCO 2 )/(predicted bodyweight * 100 * 37.5) [ 18 ]. The number of ventilator–free days at day 60 (VFD–60) was calculated by subtracting the number of calendar days a patient received invasive ventilation up to the day of successful extubation from 60, similar to the method used for calculating VFD–28. Patients that died before or at day 60 received zero VFD–60 [ 19 , 20 ].

The following follow–up data were available for merging—last day of ventilation, tracheostomy use, last day in ICU and hospital, and life status at day 60.

The primary endpoint of this analysis was a combination of the following key ventilation characteristics as done before [ 10 ]—V T , PEEP, ΔP, and Crs. Secondary outcomes were other ventilator parameters, the use of prone positioning, muscle paralysis or extracorporeal membrane oxygenation, and 60–day mortality and the number of VFD–60.

Power analysis

We did not perform a formal power analysis; instead, the number of available patients served as the sample size.

Statistical analysis

Baseline demographics were compared using Fisher’s exact tests for categorical variables and Wilcoxon rank–sum tests for continuous variables. Continuous distributed variables are presented as medians and interquartile ranges, categorical variables are presented as frequencies and proportions.

The first day a patient received invasive ventilation and the first full calendar day were combined into ‘day 1’, the next day was designated as ‘day 2’. Information on missing values for each ventilation parameters and other variable can be found in the Supplementary Material (eTable 1). Only SOFA scores were available for all patients, therefore, we chose to only report these instead of other severity scores.

To compare ventilation characteristics between COVID–ARDS and CLASSIC–ARDS patients, a Wilcoxon rank–sum test was used. Cumulative distribution plots were constructed to visualize cumulative distribution frequencies of each ventilation variable or parameter, wherein vertical dotted lines represent broadly accepted safety cutoffs for each variable, and horizontal dotted lines show the respective proportion of patients reaching that cutoff.

As a post–hoc analysis to identify whether V T , PEEP and ΔP have independent associations with 60–day mortality and the number of VFD–60, a multivariable mixed–effects model with centre as random effect was performed. A linear mixed–effects model was used for the number of VFD–60 and a logistic mixed–effects model for 60–day mortality.

The following covariates, with a known or suspected association with these two outcomes were included in the model, based on clinical relevance: (1) PaO 2 /FiO 2 ; and (2) demographic variables, including sex, age, BMI, history of heart failure, COPD, diabetes mellitus, kidney failure, liver failure and cancer.

In this mixed model analysis, when a covariate exhibited more than 10% missing data, we utilized multiple imputation techniques implemented through the MICE package in R. The model was checked for collinearity using variance–inflation factors, wherein a variance–inflation factor < 5 was deemed acceptable. The variance–inflation factor was < 2 for all included variables in our model.

The estimate refers to the average effect of the ventilation parameter, i.e., V T , PEEP or ΔP on the outcome of interest, i.e., 60–day mortality and VFD–60 while controlling for the other variables in the model. A positive estimate indicates that an increase in the predictor variable tends to lead to a corresponding increase in the response variable, indicating a proportional relationship between them. Conversely, a negative estimate suggests that an increase in the predictor variable tends to result in a decrease in the response variable, indicating an inverse proportional relationship between them.

All analyses were conducted in R v.4.0.3 (R Foundation for Statistical Computing, Vienna, Austria). A p value < 0.05 was considered statistically significant.

We received the individual data of a total of 8374 COVID–ARDS patients and 3795 CLASSIC–ARDS patients (Fig.  1 ). After exclusion of patients that did not fulfil the Berlin definition of ARDS, patients that did not receive invasive ventilation on the first and second day in the study, and patients included in the two pre–pandemic studies who did not have a respiratory infection as the cause for ARDS, we had 6702 fully–analysable COVID–ARDS patients and 1415 fully–analysable CLASSIC–ARDS. COVID–ARDS patients were more often male, had higher median BMI, a history of diabetes more often, and a history of COPD or chronic kidney disease less often (Table  1 ). COVID–ARDS patients had lower median SOFA scores, and ARDS severity was more often classified as moderate or severe.

Flowchart of included studies. Abbreviations: ARDS = acute respiratory distress syndrome; COVID–19 = coronavirus disease 2019

COVID–ARDS patients were ventilated with volume–controlled ventilation more often than CLASSIC–ARDS patients (Table  2 ) and received ventilation with lower V T (6.6 [6.0 to 7.4] vs 7.3 [6.4 to 8.5] ml/kg PBW; p  < 0.001), higher PEEP (12.0 [10.0 to 14.0] vs 8.0 [6.0 to 10.0] cm H 2 O; p  < 0.001), at lower ΔP (13.0 [10.0 to 15.0] vs 16.0 [IQR 12.0 to 20.0] cm H 2 O; p  < 0.001) and higher Crs (33.5 [26.6 to 42.1] vs 28.1 [21.6 to 38.4] mL/cm H 2 O; p  < 0.001) (Fig.  2 ) COVID–ARDS patients received higher PEEP than CLASSIC–ARDS patients at any FiO 2 level (eFigure 2). Within each group, the ventilation characteristics were not different between day 1 and 2 (eTable 2 and eFigure 1 and 2).

Key ventilation parameters. Cumulative frequency distribution of V T , PEEP, ΔP, and respiratory system compliance on the first calendar day for each variable. Vertical dotted lines represent broadly accepted safety cutoffs for each variable, and horizontal dotted lines show the respective proportion of patients reaching that cutoff. Abbreviations: V T  = tidal volume; PBW = predicted bodyweight; PEEP = positive end–expiratory pressure; ΔP = driving pressure; C RS  = respiratory system compliance

Prone positioning and neuromuscular blocking agents were more often used in COVID–ARDS patients than in CLASSIC–ARDS patients (Table  2 ). COVID–ARDS patients received a tracheostomy more often than CLASSIC–ARDS patients.

Mortality at day 60 was higher in COVID–ARDS patients compared to CLASSIC–ARDS patients (Table  2 and Fig.  3 ), and COVID–ARDS patients had significantly less VFD–60. Following multivariable adjustment, higher ΔP had an association with higher 60–day mortality and less VFD–60 in both groups. Higher PEEP also had an association with less VFD–60, but only in COVID–ARDS patients and not in CLASSIC–ARDS patients. In both groups, V T neither had an association with 60–day mortality nor with VFD–60 (eFigure 3 and eFigure 4).

Mortality and ventilator–free days and Alive at day–60, and associations with ventilator parameters. The estimate is the average effect of the predictor variable on the response variable, while controlling for the other variables in the model. A positive estimate suggests a proportional effect, whereas a negative estimate suggests an inversely proportional effect. Abbreviations: ARDS = acute respiratory distress syndrome; VFD = ventilator–free days and alive; IQR = interquartile range; N = number; CI = confidence interval; V T  = tidal volume; PBW = predicted bodyweight; PEEP = positive end–expiratory pressure; ΔP = driving pressure

We pooled the individual data of patients from six observational studies of ventilation and compared ventilation characteristics and associations with outcomes between COVID–ARDS with CLASSIC–ARDS. The main findings were: (1) compared to CLASSIC–ARDS patients, COVID–ARDS patients were ventilated with lower V T and higher PEEP, at lower ΔP and higher Crs, however with a higher MP; (2) 60–day mortality was not different between COVID–ARDS and CLASSIC–ARDS, but COVID–ARDS patients had less VFD–60; (3) higher ΔP had an association with higher 60–day mortality and less VFD–60 in COVID–ARDS and CLASSIC–ARDS; and (4) higher PEEP also had an association with less VFD–60, but only in COVID–ARDS.

Our findings add to the current understanding of differences and similarities between COVID–19 ARDS patients and pre–COVID ARDS patients. The international design of our study increases the generalizability of the findings across diverse healthcare systems, both in ARDS patients caused by COVID–19 and in patients with ARDS due to pneumonia from before the pandemic. The large sample size and high quality of the collected data allowed for sophisticated analyses of epidemiology, respiratory support strategies, and outcomes. Additionally, we found associations between key ventilator settings and patient outcomes.

Several studies have compared COVID–19 ARDS with pre–COVID ARDS. The epidemiological differences between COVID–19 ARDS and pre–COVID ARDS patients in our study align with previous findings [ 21 ]. As with other studies [ 22 , 23 ], we also found significant differences in ventilator variables like V T , PEEP, and ΔP, and in the use of adjunctive therapies. Our study contributes by demonstrating these differences specifically among ARDS patients and comparing COVID–19 ARDS to pre–COVID ARDS due to respiratory infections. Differences in outcomes found in our study are, at least in part, in line with prior research findings [ 21 , 23 ]. Our findings confirm that there are differences in mortality and the number of VFD–60 between COVID–19 ARDS and pre–COVID ARDS patients. However, these difference disappeared after propensity matching. This is important as it shows that, at least when comparing outcomes in ARDS patients from an infectious cause, outcomes are not different, opposite to what was thought at the start of the pandemic.

We observed more frequent use of lower V T in COVID–ARDS compared to CLASSIC–ARDS. Indeed, proportions of COVID–ARDS patients that received ventilation with a V T  < 6 or between 6 and 8 ml/kg PBW was higher than in CLASSIC–ARDS patients. This finding can be explained in several ways––e.g., it could be that the use of lung–protective ventilation with a lower V T has improved in the last decade [ 15 ]. It is also conceivable that, at least early in the pandemic care for COVID–ARDS patients was provided by inexperienced ICU staff which could have been more adherent to existing guidelines for management of patients with ARDS [ 10 , 24 ]. It is also possible that use of low V T in COVID–ARDS is easier to control––these patients were often deeply sedated and paralyzed allowing a stricter adherence to lower V T . Of note, especially in those patients, ventilation with a lower V T might be more beneficial than in spontaneous breathing patients [ 25 ].

Higher PEEP was more often used in COVID–ARDS patients than in CLASSIC–ARDS patients, at any FiO 2 level. Indeed, proportions of COVID–ARDS patients that received ventilation with a PEEP between 8 and 12 cmH 2 O and even between 12 and 16 cmH 2 O was higher than in CLASSIC–ARDS patients. This finding can also be explained in several ways––e.g., a preference for use of higher PEEP in COVID–ARDS patients may have been triggered by the severity of ARDS, as COVID–ARDS was more often classified as moderate or severe, and more severe hypoxaemia naturally triggers the use of higher PEEP if PEEP/FiO 2 tables are used. It is also possible that higher PEEP was used in the assumption that lung lesions with COVID–ARDS are more recruitable than in CLASSIC–ARDS. This may at least explain the lower ΔP and higher Crs in COVID–ARDS patients.

In COVID–ARDS patients, mechanical power exceeded that of CLASSIC–ARDS, even though the driving pressure was lower. This observation marks the significance of considering factors beyond driving pressure, such as respiratory rate and PEEP, when evaluating the protective nature of invasive ventilation. These findings emphasize the complexity of respiratory management in COVID–ARDS and the need for a comprehensive approach to optimize lungprotective ventilation strategies.

COVID–ARDS patients received prone positioning more often than CLASSIC–ARDS patients. Before the pandemic, prone positioning remained underused, probably because it was more considered a rescue therapy for refractory hypoxaemia [ 26 ]. While we cannot rule out that use of prone positioning increased already before the pandemic, we favour the idea that the higher use of prone positioning in COVID–ARDS patients was triggered by the more severe hypoxaemia in COVID–ARDS patients.

Our analysis found several associations between ventilation parameters and outcome. The association of higher ΔP with higher 60–day mortality and less VFD–60 is in line with previous studies [ 27 , 28 , 29 ]. The association of higher PEEP with worse outcome confirms the findings of earlier studies [ 30 , 31 ]. Of note, this association was only found for COVID–ARDS. This may have been caused by the more frequent use of higher PEEP in COVID–ARDS than in CLASSIC–ARDS. One reason for the association between higher PEEP and worse outcome may be that sicker patients, with a higher chance of dying and prolonged ventilation, received higher PEEP than patients that were less sick. Nonetheless, a high PEEP is suggested to have detrimental effects [ 32 ], emphasizing the need to determine the optimal PEEP level based on lung recruitability rather than hypoxemia alone. Actually, one analysis of PRoVENT–COVID suggested worse outcomes if patients received ventilation according to a higher PEEP/lower FiO 2 table as compared to ventilation according to a lower PEEP/higher FiO 2 [ 30 ]. A post–hoc Bayesian analysis of a randomised clinical study, named the ‘Alveolar Recruitment for ARDS Trial’ (ART), wherein patients were randomized to receive ventilation with PEEP titrated to the best Crs and aggressive recruitment manoeuvres versus ventilation with a low PEEP strategy, suggested that higher PEEP with recruitment manoeuvres worsens the outcome of ARDS from pneumonia, while it may be beneficial in ARDS from another cause [ 33 ]. A posthoc analysis of a randomised clinical study named ‘Lung Imaging for Ventilator Setting in ARDS trial’ (LIFE), suggest that higher PEEP worsens outcomes in patients with ARDS with lesions that may not be recruitable with higher PEEP [ 34 ].

The findings of this pooled analysis extend the existing knowledge of the epidemiology, management of invasive ventilation and outcomes in COVID–ARDS. Our study shows that lung–protective ventilation was applied well in COVID–ARDS, and was comparable to best practice used in management for patients with CLASSIC–ARDS. Additionally, the effect of PEEP on major outcomes may have implications for care. At least it should trigger new studies that directly compare different PEEP strategies. Meanwhile, it could be more attractive to not use higher PEEP by default.

Our study has several strengths. We managed to receive and merge the datasets of four large observational studies of ventilation conducted in the COVID–19 pandemic with two well–performed pre–pandemic observational studies of ventilation––these six studies all focused on ventilation management and reported outcomes of invasively ventilated ARDS patients, allowing a robust analysis of ventilation management and the impact of certain ventilation parameters on outcome. While the COVID–19 studies were all national investigations, they are from different regions worldwide and were conducted in different types of hospitals, which increases the generalizability of our findings. The datasets from the original studies were rich and comprehensive, encompassing baseline and demographic data, granular ventilator settings and ventilation variables, and key clinical outcomes. All data could be harmonized and merged into one database.

We had an analysis plan in place before cleaning and closing of the new database, and this plan was strictly followed. The large numbers of patients allowed us to perform sophisticated statistical analyses of associations with outcomes.

This study has limitations. First, individual data was obtained from observational studies, which limits the ability to establish causality. Additionally, the willingness of data sharing could have led to selection bias towards the inclusion of ICUs with an interest in invasive ventilation and management of ARDS in the original studies. Second, studies in COVID–ARDS were conducted early in the COVID–19 pandemic, during which inexperienced staff and resource limitations could have influenced clinical decision making. Third, data was collected early in the pandemic when patient care took priority over data collection, resulting in more missing data than in previous studies. This affects the completeness and may impact the accuracy of our analysis. Fourth, we only reported on ventilation characteristics on day 1 and 2, because not all studies collected ventilation data beyond this timepoint. Therefore we were not able to compare ventilation management beyond day 2. Nevertheless, previous studies have shown ventilation characteristics don’t significantly change in the first four days after initiation of invasive ventilation [ 10 ]. Fifth, it is imperative to acknowledge the temporal distance between comparator cohorts. For the pre–COVID ARDS group we used patients of which data was collected between seven to nine years before the pandemic. We cannot exclude temporal differences, for instance due to studies that showed the importance of limiting liberal use of oxygen, and reducing the intensity of ventilation, e.g., by targeting a low driving pressure or a low mechanical power of ventilation, as well as the importance of early use of prone positioning. Sixth, is the lack of detailed subgroup analyses, particularly in patients with chronic respiratory comorbidities such as COPD. Although recent findings from a post–hoc analysis of the PRoVENT–COVID study by Tripipitsiriwat et al. [ 35 ] indicated that ventilation parameters did not show significant differences between COPD and non–COPD patients, it could be interesting to explore these subgroups. However, it was beyond the scope of our primary endpoint. Conducting such detailed subgroup investigations would require careful consideration to ensure the data from all included studies are appropriate for this type of analysis.

Finally, all COVID–19 ARDS patients, by definition, had a viral pneumonia, while patients in the classic ARDS group had respiratory infections of which the pathogen was not collected. This is an important limitation, as ARDS from a viral respiratory infection may differ from ARDS due to bacterial pneumonia. Consistent with other studies comparing COVID–19 ARDS to ARDS caused by other viruses, we found that the duration of ventilation was longer, and mortality was higher [ 21 , 36 , 37 ].

Epidemiology and key ventilation characteristics were different in patients with COVID–ARDS compared to CLASSIC–ARDS, also ΔP was lower in COVID––ARDS patients. ΔP had an independent association with outcome in both groups, whereas PEEP had an independent association with outcome only in COVID–ARDS patients.

Availability of data and materials

Data sharing: A de–identified dataset can be made available upon request to the corresponding authors one year after publication of this study, but only after permission of the principal investigators of all original studies. The request must include a statistical analysis plan.

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Acknowledgements

for the ERICC a –,LUNG SAFE b –, PRoVENT–COVID c –, EPICCoV d –, CIBERESUCICOVID e – and SATI–COVID–19 f –investigators

a ERICC, ‘Epidemiology of Respiratory Insufficiency in Critical Care’

b LUNG SAFE, ‘Large Observational Study to UNderstand the Global Impact of Severe Acute Respiratory FailurE’

c PRoVENT–COVID, ‘Practice of Ventilation in COVID–19 patients’

d EPICCoV, EPIdemiology of Critical COVID–19

e CIBERESUCICOVID, ‘Centro de Investigación Biomédica en Red Enfermedades Respiratorias COVID–19’

f SATI–COVID–19, ‘Sociedad Argentina de Terapia Intensiva–COVID–19’

No additional funding was received for this analysis.

Author information

Fleur–Stefanie L. I. M. van der Ven and Siebe G. Blok contributed equally to this work.

Authors and Affiliations

Department of Intensive Care, Amsterdam University Medical Centers, Location ‘AMC’, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands

Fleur–Stefanie L. I. M. van der Ven, Siebe G. Blok, Michela Botta, Luigi Pisani, Marcus J. Schultz, Anissa M. Tsonas, Frederique Paulus & David M. P. van Meenen

Department of Intensive Care, Rode Kruis Ziekenhuis, Beverwijk, The Netherlands

Fleur–Stefanie L. I. M. van der Ven

Department of Emergency Medicine, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil

Luciano C. Azevedo

Department of Intensive Care, Hospital Israelita Albert Einstein, São Paulo, Brazil

Luciano C. Azevedo & Ary Serpa Neto

Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy

Giacomo Bellani

Department of Anesthesia and Intensive Care, Santa Chiara Hospital, APSS Trento, Trento, Italy

Department of Intensive Care, Hospital Interzonal de Agudos General San Martin La Plata, Buenos Aires, Argentina

Elisa Estenssoro

Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada

Department of Pulmonology, Instituto Do Coracao (InCor), Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil

Juliana Carvalho Ferreira

Department of Intensive Care, AC Camargo Cancer Center, São Paulo, Brazil

Brazilian Research in Intensive Care Network (BRICNet), São Paulo, Brazil

Department of Anaesthesiology and Intensive Care, Galway University Hospital, Saolta Hospital Group, Galway, Ireland

John G. Laffey

School of Medicine, University of Galway, Galway, Ireland

Department of Intensive Care, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland

Ignacio Martin–Loeches

Department of Intensive Care, Hospital Clínic de Barcelona, Barcelona, Spain

Departement of Pulmonology, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Barcelona, Spain

Ana Motos & Antoni Torres

Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Institute of Health Carlos III, Madrid, Spain

Ana Motos, Oscar Peñuelas & Antoni Torres

Equipe d’Epidémiologie Respiratoire Integrative, Université Paris–Saclay, Paris, France

Service de Médecine Intensive-Réanimation, DMU CORREVE, FHU SEPSIS, Groupe de Recherche Clinique CARMAS, Hôpital de Bicêtre, Paris, France

Department of Intensive Care, Hospital Universitario de Getafe, Getafe, Spain

Oscar Peñuelas

Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

Antonio Pesenti

Department of Anesthesia and Intensive Care, Miulli Regional Hospital, Acquaviva Delle Fonti, Italy

Luigi Pisani

Australian and New Zealand Intensive Care Research Centre (ANZIC–RC), Monash University, Melbourne, Australia

Ary Serpa Neto

Mahidol–Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand

Luigi Pisani & Marcus J. Schultz

Nuffield Department of Medicine, University of Oxford, Oxford, UK

Marcus J. Schultz

Department of Anesthesia, General Intensive Care and Pain Management, Division of Cardiothoracic and Vascular Anesthesia & Critical Care Medicine, Medical University of Vienna, Vienna, Austria

Laboratory of Experimental Intensive Care & Anaesthesiology (L·E·I·C·A), Amsterdam UMC, Location AMC, Amsterdam, The Netherlands

University of Barcelona, Barcelona, Spain

Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

Antoni Torres

Center of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

Frederique Paulus

Department of Anaesthesiology, Amsterdam UMC, Location AMC, Amsterdam, The Netherlands

David M. P. van Meenen

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Contributions

Author contribution: FV, SB, MS, FP and DM had full access to all the data and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: All authors Acquisition, analysis, or interpretation of data: FV, SB, MS, FP and DM Drafting of the manuscript: FV, SB, MS, FP and DM Critical revision of the manuscript for important intellectual content: All authors Statistical analysis and data verification: FV, SB, and DM Obtained funding: Not applicable; the original studies were performed with funding as stated in the original reports. Administrative, technical, or material support: LA, GB, MB, EE, JF, JL, TP, AT Supervision: MS, FP and DM.

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Correspondence to Fleur–Stefanie L. I. M. van der Ven .

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van der Ven, F.L.I.M., Blok, S.G., Azevedo, L.C. et al. Epidemiology, ventilation management and outcomes of COVID–19 ARDS patients versus patients with ARDS due to pneumonia in the Pre–COVID era. Respir Res 25 , 312 (2024). https://doi.org/10.1186/s12931-024-02910-2

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Received : 24 December 2023

Accepted : 07 July 2024

Published : 17 August 2024

DOI : https://doi.org/10.1186/s12931-024-02910-2

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