the analysis of research

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

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About Research Methods

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

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

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

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

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

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

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Library Data Services Program and Digital Scholarship Services

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

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

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

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• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

Keep reading

Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

How to conduct a meta-analysis in eight steps: a practical guide

• Open access
• Published: 30 November 2021
• Volume 72 , pages 1–19, ( 2022 )

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• Christopher Hansen 1 ,
• Holger Steinmetz 2 &
• Jörn Block 3 , 4 , 5  

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1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php .

The macros of David B. Wilson can be downloaded from: http://mason.gmu.edu/~dwilsonb/ .

The macros of Field and Gillet ( 2010 ) can be downloaded from: https://www.discoveringstatistics.com/repository/fieldgillett/how_to_do_a_meta_analysis.html .

The tutorials can be found via: https://www.metafor-project.org/doku.php .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: https://www.erim.eur.nl/research-support/meta-essentials/ .

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Hansen, C., Steinmetz, H. & Block, J. How to conduct a meta-analysis in eight steps: a practical guide. Manag Rev Q 72 , 1–19 (2022). https://doi.org/10.1007/s11301-021-00247-4

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What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base.

This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Conflicts of interest.

There are no conflicts of interest.

Research Writing and Analysis

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• Step 1: Seek Out Evidence
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Designing the Theoretical Framework

What is it.

• A foundational review of existing theories. 
• Serves as a roadmap or blueprint for developing arguments and supporting research.
• Overview of the theory that the research is based on.
• Can be made up of theories, principles, and concepts.

What does it do?

• Explains the why and how of a particular phenomenon within a particular body of literature.
• Connects the research subject with the theory.
• Specifies the study’s scope; makes it more valuable and generalizable.
• Guides further actions like framing the research questions, developing the literature review, and data collection and analyses.

What should be in it?

• Theory or theories that the researcher considers relevant for their research, principles, and concepts.

Theoretical Framework Guide

• Theoretical Framework Guide Use this guide to determine the guiding framework for your theoretical dissertation research.

Making a Theoretical Framework

How to make a theoretical framework.

• Specify research objectives.
• Note the prominent variables under the study.
• Explore and review the literature through keywords identified as prominent variables.
• Note the theories that contain these variables or the keywords.
• Review all selected theories again in the light of the study’s objectives, and the key variables identified.
• Search for alternative theoretical propositions in the literature that may challenge the ones already selected.
• Ensure that the framework aligns with the study’s objectives, problem statement, the main research question, methodology, data analysis, and the expected conclusion.
• Decide on the final framework and begin developing.

Example Framework

• Theoretical Framework Example for a Thesis or Dissertation This link offers an example theoretical framework.

Additional Framework Resources

Some additional helpful resources in constructing a theoretical framework for study:.

• https://www.scribbr.com/dissertation/theoretical-framework/
• https://www.scribbr.com/dissertation/theoretical-framework-example/
• https://www.projectguru.in/how-to-write-the-theoretical-framework-of-research/

Theoretical Framework Research

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). A theoretical framework provides the theoretical assumptions for the larger context of a study, and is the foundation or ‘lens’ by which a study is developed. This framework helps to ground the research focus understudy within theoretical underpinnings and to frame the inquiry for data analysis and interpretation.  The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013).

Casanave, C.P.,& Li,Y.(2015). Novices’ struggles with conceptual and theoretical framing in writing  dissertations and papers for publication. Publications,3 (2),104-119.doi:10.3390/publications3020104

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

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China’s Christian population appears to have stopped growing after rising rapidly in the 1980s and ’90s

The Chinese government banned religion during the Cultural Revolution of the 1960s and ’70s. And although Christianity and other religions bounced back in the 1980s and ’90s as restrictions were lifted, the size of China’s Christian population now appears to have leveled off, according to a Pew Research Center analysis of the latest available survey data collected by academic organizations in China.

This analysis of China’s Christian population is based on Pew Research Center’s August 2023 report Measuring Religion in China . Since the Center, like other non-Chinese organizations, is not allowed to conduct surveys in China, this post draws on data collected between 2010 and 2021 in the Chinese General Social Survey (CGSS), a nationally representative survey conducted by the National Survey Research Center at Renmin University of China. Most CGSS waves include about 12,000 respondents. Other surveys by academic organizations in China find similar results about the size of China’s Christian population. The report chapter on Christianity has more detail.

This post also discusses Chinese government data on religion, which is primarily released by China’s State Council and the National Religious Affairs Administration – formerly known as the State Administration for Religious Affairs – and data from state-run religious associations, such as the China Christian Council and the Three-Self Patriotic Movement (CCC and TSPM).

This research is part of the Pew-Templeton Global Religious Futures project , which analyzes religious change and its impact on societies around the world.

No survey assessed the Chinese religious landscape in the years immediately following the Cultural Revolution, but government figures suggest that the number of Christians worshipping in registered churches in China more than doubled, from 6 million in 1982 to 14 million in 1997. By comparison, the country’s overall population increased by a more modest 22% during that period.

There was also reported growth in the number of Christians worshipping in “underground” or “house” churches, which are not registered with the state-sanctioned Catholic and Protestant associations (the Catholic Patriotic Association and the Three-Self Patriotic Movement).

Some journalists, scholars and Christian advocacy groups have suggested that Christianity in China continues to grow rapidly in the 21st century and that Christians are on track to make up a majority of the population there by 2050 .

But survey data from the Chinese General Social Survey (CGSS) does not show ongoing growth. Between 2010 and 2018, the share of Chinese adults who formally identify with Christianity remained stable at about 2%, according to the CGSS. Roughly nine-in-ten Christians in China are Protestant.

Some scholars have suggested that the coronavirus pandemic prompted increased religiosity in China . But the most recent wave of the CGSS – conducted in 2021, during the pandemic – provides no hint of a revival of Christian identity. Only 1% of respondents formally identified with Christianity that year.

It’s important to note that the 2021 survey data is not directly comparable to earlier waves because COVID-19 outbreaks in some regions of China made it impossible to achieve the same coverage as in pre-pandemic waves.

Survey-based estimates of China’s Christian population may be conservative

In China, religious identity can be measured through data points from many surveys. But religious identity alone may be a conservative measure of religion’s influence in China.

More Chinese adults share some Christian beliefs than identify with the faith in surveys. In the 2018 China Family Panel Studies survey, for instance, 3% of respondents reported exclusively believing in the Christian God (the survey measured belief in Jesus Christ and in Tianzhu, the Chinese word for God used by Catholics). An additional 4% said they believe in the Christian God and at least one other non-Christian deity.

More broadly, all survey-based estimates of China’s Christian population could be conservative. Some people may choose not to reveal Christian identity because they fear negative social or financial consequences should their identity become known, especially if they belong to an unregistered church.

Christianity in China has faced increased restrictions since Xi Jinping became president in 2013. We cannot be certain how survey patterns are affected by political circumstances . Hypothetically, there could be a real increase in the share of Chinese adults who identify with Christianity that is hidden from survey measurement. This could be the case if respondents are increasingly reluctant to reveal their Christian identity due to the government’s intensifying scrutiny of religious activity .

Why China’s Christian population may be leveling off

Although we can’t be certain how precisely surveys capture the reality of China’s religious landscape, there are several reasons why it’s plausible that the country’s Christian population is plateauing.

Government policies that monitor and discourage religious activity may have prevented some Chinese people from becoming or remaining Christian. An official ban on religious education and activity for children, for example, may be inhibiting the transmission of Christian identity to the next generation. Demographics also pose obstacles to Christian growth in China. The CGSS indicates that Christians are concentrated among older adults and relatively scarce among younger Chinese.

Among remaining Christians, the government’s recent program of absorbing previously unregistered churches into the official system may have removed one reason for people to conceal their Christian identity.

Other estimates of the Christian count in China vary widely

The Chinese government has occasionally published statistics estimating how many people associate with Christianity and other religions. However, it’s often unclear how these estimates were derived and whether they are comparable to earlier government figures. Government statistics also vary in the extent to which they include members of unregistered churches, and it is often unclear whether they include children or not.

In 2010, the Chinese Academy of Social Sciences (CASS) Blue Book on Religion estimated there were 23 million Protestants in China. This government estimate, based for the first time on a nationally representative household survey, included members of the official state-sanctioned Three-Self Patriotic Movement, as well as house church members.

When this estimate was challenged by Christian leaders, CASS fellows Duan Qi and Huang Haibo responded that the survey showed the number of Protestants was at least 23 million, but that because of possible underreporting, the total count could be up to 40 million. The next year’s edition of the Blue Book, in 2011, said the Protestant population was in the 23 million to 40 million range (2% to 3% of Chinese people of all ages).

In 2018, the State Council of the People’s Republic of China White Paper on Religious Freedom reported there were 38 million Protestants, but the paper did not detail how this estimate was made. Around this time, a Peking University study estimated that the total number of adults who either formally identify as Christian, profess Christian beliefs or regularly attend Christian worship services, including Protestants and Catholics, was about 40 million, based on analysis of measures from China Family Panel Studies surveys .

Several Christian organizations claim that the count of Christians in China has continued to grow steadily and that it is much higher than survey data or government statistics indicate . For example, the Center for the Study of Global Christianity estimates that the Christian share of China’s population of adults and children increased from 6% in 2000 to 7% in 2020. And Asia Harvest , a Christian ministry that works to expand the number of churches in Asia, estimates that Christians grew from 8% of the country’s total population in 2010 to 9% in 2020.

Related:  8 key findings about Christians in India and   How many Christians are there in Egypt?

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Conrad Hackett is a senior demographer and associate director of research at Pew Research Center

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A New Use for Wegovy Opens the Door to Medicare Coverage for Millions of People with Obesity

Juliette Cubanski , Tricia Neuman , Nolan Sroczynski , and Anthony Damico Published: Apr 24, 2024

The FDA recently approved a new use for Wegovy (semaglutide), the blockbuster anti-obesity drug, to reduce the risk of heart attacks and stroke in people with cardiovascular disease who are overweight or obese. Wegovy belongs to a class of medications called GLP-1 (glucagon-like peptide-1) agonists that were initially approved to treat type 2 diabetes but are also highly effective anti-obesity drugs. The new FDA-approved indication for Wegovy paves the way for Medicare coverage of this drug and broader coverage by other insurers. Medicare is currently prohibited by law from covering Wegovy and other medications when used specifically for obesity. However, semaglutide is covered by Medicare as a treatment for diabetes, branded as Ozempic.

What does the FDA’s decision mean for Medicare coverage of Wegovy?

The FDA’s decision opens the door to Medicare coverage of Wegovy, which was first approved by the FDA as an anti-obesity medication. Soon after the FDA’s approval of the new use for Wegovy, the Centers for Medicare & Medicaid Services (CMS) issued a memo indicating that Medicare Part D plans can add Wegovy to their formularies now that it has a medically-accepted indication that is not specifically excluded from Medicare coverage . Because Wegovy is a self-administered injectable drug, coverage will be provided under Part D , Medicare’s outpatient drug benefit offered by private stand-alone drug plans and Medicare Advantage plans, not Part B, which covers physician-administered drugs.

How many Medicare beneficiaries could be eligible for coverage of Wegovy for its new use?

Of these 3.6 million beneficiaries, 1.9 million also had diabetes (other than Type 1) and may already have been eligible for Medicare coverage of GLP-1s as diabetes treatments prior to the FDA’s approval of the new use of Wegovy.

Not all people who are eligible based on the new indication are likely to take Wegovy, however. Some might be dissuaded by the potential side effects and adverse reactions . Out-of-pocket costs could also be a barrier. Based on the list price of $1,300 per month (not including rebates or other discounts negotiated by pharmacy benefit managers), Wegovy could be covered as a specialty tier drug, where Part D plans are allowed to charge coinsurance of 25% to 33%. Because coinsurance amounts are pegged to the list price, Medicare beneficiaries required to pay coinsurance could face monthly costs of $325 to $430 before they reach the new cap on annual out-of-pocket drug spending established by the Inflation Reduction Act – around $3,300 in 2024, based on brand drugs only, and $2,000 in 2025. But even paying $2,000 out of pocket would still be beyond the reach of many people with Medicare who live on modest incomes . Ultimately, how much beneficiaries pay out of pocket will depend on Part D plan coverage and formulary tier placement of Wegovy.

Further, some people may have difficulty accessing Wegovy if Part D plans apply prior authorization and step therapy tools to manage costs and ensure appropriate use. These factors could have a dampening effect on use by Medicare beneficiaries, even among the target population.

When will Medicare Part D plans begin covering Wegovy?

Some Part D plans have already announced that they will begin covering Wegovy this year, although it is not yet clear how widespread coverage will be in 2024. While Medicare drug plans can add new drugs to their formularies during the year to reflect new approvals and expanded indications, plans are not required to cover every new drug that comes to market. Part D plans are required to cover at least two drugs in each category or class and all or substantially all drugs in six protected classes . However, facing a relatively high price and potentially large patient population for Wegovy, many Part D plans might be reluctant to expand coverage now, since they can’t adjust their premiums mid-year to account for higher costs associated with use of this drug. So, broader coverage in 2025 could be more likely.

How might expanded coverage of Wegovy affect Medicare spending?

The impact on Medicare spending associated with expanded coverage of Wegovy will depend in part on how many Part D plans add coverage for it and the extent to which plans apply restrictions on use like prior authorization; how many people who qualify to take the drug use it; and negotiated prices paid by plans. For example, if plans receive a 50% rebate on the list price of $1,300 per month (or $15,600 per year), that could mean annual net costs per person around $7,800. If 10% of the target population (an estimated 360,000 people) uses Wegovy for a full year, that would amount to additional net Medicare Part D spending of $2.8 billion for one year for this one drug alone.

It’s possible that Medicare could select semaglutide for drug price negotiation as early as 2025, based on the earliest FDA approval of Ozempic in late 2017 . For small-molecule drugs like semaglutide, at least seven years must have passed from its FDA approval date to be eligible for selection, and for drugs with multiple FDA approvals, CMS will use the earliest approval date to make this determination. If semaglutide is selected for negotiation next year, a negotiated price would be available beginning in 2027. This could help to lower Medicare and out-of-pocket spending on semaglutide products, including Wegovy as well as Ozempic and Rybelsus, the oral formulation approved for type 2 diabetes. As of 2022, gross Medicare spending on Ozempic alone placed it sixth among the 10 top-selling drugs in Medicare Part D, with annual gross spending of $4.6 billion, based on KFF analysis . This estimate does not include rebates, which Medicare’s actuaries estimated to be  31.5% overall in 2022  but could be as high as  69%  for Ozempic, according to one estimate.

What does this mean for Medicare coverage of anti-obesity drugs?

For now, use of GLP-1s specifically for obesity continues to be excluded from Medicare coverage by law. But the FDA’s decision signals a turning point for broader Medicare coverage of GLP-1s since Wegovy can now be used to reduce the risk of heart attack and stroke by people with cardiovascular disease and obesity or overweight, and not only as an anti-obesity drug. And more pathways to Medicare coverage could open up if these drugs gain FDA approval for other uses . For example, Eli Lilly has just reported clinical trial results showing the benefits of its GLP-1, Zepbound (tirzepatide), in reducing the occurrence of sleep apnea events among people with obesity or overweight. Lilly reportedly plans to seek FDA approval for this use and if approved, the drug would be the first pharmaceutical treatment on the market for sleep apnea.

If more Medicare beneficiaries with obesity or overweight gain access to GLP-1s based on other approved uses for these medications, that could reduce the cost of proposed legislation to lift the statutory prohibition on Medicare coverage of anti-obesity drugs. This is because the Congressional Budget Office (CBO), Congress’s official scorekeeper for proposed legislation, would incorporate the cost of coverage for these other uses into its baseline estimates for Medicare spending, which means that the incremental cost of changing the law to allow Medicare coverage for anti-obesity drugs would be lower than it would be without FDA’s approval of these drugs for other uses. Ultimately how widely Medicare Part D coverage of GLP-1s expands could have far-reaching effects on people with obesity and on Medicare spending.

• Medicare Part D
• Chronic Diseases
• Heart Disease
• Medicare Advantage

news release

• An Estimated 1 in 4 Medicare Beneficiaries With Obesity or Overweight Could Be Eligible for Medicare Coverage of Wegovy, an Anti-Obesity Drug, to Reduce Heart Risk

Also of Interest

• An Overview of the Medicare Part D Prescription Drug Benefit
• FAQs about the Inflation Reduction Act’s Medicare Drug Price Negotiation Program
• What Could New Anti-Obesity Drugs Mean for Medicare?
• Medicare Spending on Ozempic and Other GLP-1s Is Skyrocketing