what is analysis of research paper

Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

• 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

• 📝 Analysis Example
• 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

• Why was the research carried out?
• What gaps does it try to fill, or what controversies to settle?
• How does the study contribute to its field?
• Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

• Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
• Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
• Is the study biased in any way?
• What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

• What are the major findings of the study?
• Did the author present the results clearly and unambiguously?
• Are the findings statistically significant ?
• Does the author provide sufficient information on the validity and reliability of the results?
• Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

• What findings did the authors use to support their conclusions?
• How do the researchers’ conclusions compare to other studies’ findings?
• How does this study contribute to its field?
• What future research directions do the authors suggest?
• What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

• The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
• The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
• Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

• The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
• Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
• The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

• Analyze whether the study’s purpose is significant and relevant.
• Examine whether the chosen methodology allows for answering the research questions.
• Evaluate how the authors presented the results.
• Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

• Gracious Leadership Principles for Nurses: Article Analysis
• Effective Mental Health Interventions: Analysis of an Article
• Nursing Turnover: Article Analysis
• Nursing Practice Issue: Qualitative Research Article Analysis
• Quantitative Article Critique in Nursing
• LIVE Program: Quantitative Article Critique
• Evidence-Based Practice Beliefs and Implementation: Article Critique
• “Differential Effectiveness of Placebo Treatments”: Research Paper Analysis
• “Family-Based Childhood Obesity Prevention Interventions”: Analysis Research Paper Example
• “Childhood Obesity Risk in Overweight Mothers”: Article Analysis
• “Fostering Early Breast Cancer Detection” Article Analysis
• Lesson Planning for Diversity: Analysis of an Article
• Journal Article Review: Correlates of Physical Violence at School
• Space and the Atom: Article Analysis
• “Democracy and Collective Identity in the EU and the USA”: Article Analysis
• China’s Hegemonic Prospects: Article Review
• Article Analysis: Fear of Missing Out
• Article Analysis: “Perceptions of ADHD Among Diagnosed Children and Their Parents”
• Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
• Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

We hope that our article on research paper analysis has been helpful. If you liked it, please share this article with your friends!

• Analyzing Research Articles: A Guide for Readers and Writers | Sam Mathews
• Summary and Analysis of Scientific Research Articles | San José State University Writing Center
• Analyzing Scholarly Articles | Texas A&M University
• Article Analysis Assignment | University of Wisconsin-Madison
• How to Summarize a Research Article | University of Connecticut
• Critique/Review of Research Articles | University of Calgary
• Art of Reading a Journal Article: Methodically and Effectively | PubMed Central
• Write a Critical Review of a Scientific Journal Article | McLaughlin Library
• How to Read and Understand a Scientific Paper: A Guide for Non-scientists | LSE
• How to Analyze Journal Articles | Classroom

How to Write an Animal Testing Essay: Tips for Argumentative & Persuasive Papers

Descriptive essay topics: examples, outline, & more.

• Skip to main content
• Skip to ChatBot Assistant
• Academic Writing
• What is a Research Paper?
• Steps in Writing a Research Paper
• Critical Reading and Writing
• Punctuation
• Writing Exercises
• ELL/ESL Resources

Analysis in Research Papers

To analyze means to break a topic or concept down into its parts in order to inspect and understand it, and to restructure those parts in a way that makes sense to you. In an analytical research paper, you do research to become an expert on a topic so that you can restructure and present the parts of the topic from your own perspective.

For example, you could analyze the role of the mother in the ancient Egyptian family. You could break down that topic into its parts--the mother's duties in the family, social status, and expected role in the larger society--and research those parts in order to present your general perspective and conclusion about the mother's role.

Need Assistance?

If you would like assistance with any type of writing assignment, learning coaches are available to assist you. Please contact Academic Support by emailing [email protected].

Questions or feedback about SUNY Empire's Writing Support?

Contact us at [email protected] .

Smart Cookies

They're not just in our classes – they help power our website. Cookies and similar tools allow us to better understand the experience of our visitors. By continuing to use this website, you consent to SUNY Empire State University's usage of cookies and similar technologies in accordance with the university's Privacy Notice and Cookies Policy .

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues discussed in the overview, as you are not working with people but rather publicly accessible documents. Analysis can be done on new documents or performed on raw data that you yourself have collected.

Here are several examples of analysis:

• Recording commercials on three major television networks and analyzing race and gender within the commercials to discover some conclusion.
• Analyzing the historical trends in public laws by looking at the records at a local courthouse.
• Analyzing topics of discussion in chat rooms for patterns based on gender and age.

Analysis research involves several steps:

• Finding and collecting documents.
• Specifying criteria or patterns that you are looking for.
• Analyzing documents for patterns, noting number of occurrences or other factors.

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

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

Cite this article

You have full access to this open access article

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

143k Accesses

44 Citations

157 Altmetric

Explore all metrics

Avoid common mistakes on your manuscript.

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/ .

Aguinis H, Dalton DR, Bosco FA, Pierce CA, Dalton CM (2011a) Meta-analytic choices and judgment calls: Implications for theory building and testing, obtained effect sizes, and scholarly impact. J Manag 37(1):5–38

Google Scholar  

Aguinis H, Gottfredson RK, Joo H (2013) Best-practice recommendations for defining, identifying, and handling outliers. Organ Res Methods 16(2):270–301

Article   Google Scholar  

Aguinis H, Gottfredson RK, Wright TA (2011b) Best-practice recommendations for estimating interaction effects using meta-analysis. J Organ Behav 32(8):1033–1043

Aguinis H, Pierce CA, Bosco FA, Dalton DR, Dalton CM (2011c) Debunking myths and urban legends about meta-analysis. Organ Res Methods 14(2):306–331

Aloe AM (2015) Inaccuracy of regression results in replacing bivariate correlations. Res Synth Methods 6(1):21–27

Anderson RG, Kichkha A (2017) Replication, meta-analysis, and research synthesis in economics. Am Econ Rev 107(5):56–59

Appelbaum M, Cooper H, Kline RB, Mayo-Wilson E, Nezu AM, Rao SM (2018) Journal article reporting standards for quantitative research in psychology: the APA publications and communications BOARD task force report. Am Psychol 73(1):3–25

Aytug ZG, Rothstein HR, Zhou W, Kern MC (2012) Revealed or concealed? Transparency of procedures, decisions, and judgment calls in meta-analyses. Organ Res Methods 15(1):103–133

Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50(4):1088–1101. https://doi.org/10.2307/2533446

Bergh DD, Aguinis H, Heavey C, Ketchen DJ, Boyd BK, Su P, Lau CLL, Joo H (2016) Using meta-analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership-performance relationship. Strateg Manag J 37(3):477–497

Becker BJ (1992) Using results from replicated studies to estimate linear models. J Educ Stat 17(4):341–362

Becker BJ (1995) Corrections to “Using results from replicated studies to estimate linear models.” J Edu Behav Stat 20(1):100–102

Bergmann C, Tsuji S, Piccinini PE, Lewis ML, Braginsky M, Frank MC, Cristia A (2018) Promoting replicability in developmental research through meta-analyses: Insights from language acquisition research. Child Dev 89(6):1996–2009

Bernerth JB, Aguinis H (2016) A critical review and best-practice recommendations for control variable usage. Pers Psychol 69(1):229–283

Bernerth JB, Cole MS, Taylor EC, Walker HJ (2018) Control variables in leadership research: A qualitative and quantitative review. J Manag 44(1):131–160

Bijmolt TH, Pieters RG (2001) Meta-analysis in marketing when studies contain multiple measurements. Mark Lett 12(2):157–169

Block J, Kuckertz A (2018) Seven principles of effective replication studies: Strengthening the evidence base of management research. Manag Rev Quart 68:355–359

Borenstein M (2009) Effect sizes for continuous data. In: Cooper H, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis. Russell Sage Foundation, pp 221–235

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis. John Wiley, Chichester

Book   Google Scholar  

Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1(2):97–111

Borenstein M, Hedges L, Higgins J, Rothstein H (2013) Comprehensive meta-analysis (version 3). Biostat, Englewood, NJ

Borenstein M, Higgins JP (2013) Meta-analysis and subgroups. Prev Sci 14(2):134–143

Bosco FA, Steel P, Oswald FL, Uggerslev K, Field JG (2015) Cloud-based meta-analysis to bridge science and practice: Welcome to metaBUS. Person Assess Decis 1(1):3–17

Bosco FA, Uggerslev KL, Steel P (2017) MetaBUS as a vehicle for facilitating meta-analysis. Hum Resour Manag Rev 27(1):237–254

Burgard T, Bošnjak M, Studtrucker R (2021) Community-augmented meta-analyses (CAMAs) in psychology: potentials and current systems. Zeitschrift Für Psychologie 229(1):15–23

Cheung MWL (2015a) Meta-analysis: A structural equation modeling approach. John Wiley & Sons, Chichester

Cheung MWL (2015b) metaSEM: An R package for meta-analysis using structural equation modeling. Front Psychol 5:1521

Cheung MWL (2019) A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychol Rev 29(4):387–396

Cheung MWL, Chan W (2005) Meta-analytic structural equation modeling: a two-stage approach. Psychol Methods 10(1):40–64

Cheung MWL, Vijayakumar R (2016) A guide to conducting a meta-analysis. Neuropsychol Rev 26(2):121–128

Combs JG, Crook TR, Rauch A (2019) Meta-analytic research in management: contemporary approaches unresolved controversies and rising standards. J Manag Stud 56(1):1–18. https://doi.org/10.1111/joms.12427

DeSimone JA, Köhler T, Schoen JL (2019) If it were only that easy: the use of meta-analytic research by organizational scholars. Organ Res Methods 22(4):867–891. https://doi.org/10.1177/1094428118756743

DeSimone JA, Brannick MT, O’Boyle EH, Ryu JW (2020) Recommendations for reviewing meta-analyses in organizational research. Organ Res Methods 56:455–463

Duval S, Tweedie R (2000a) Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56(2):455–463

Duval S, Tweedie R (2000b) A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc 95(449):89–98

Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634

Eisend M (2017) Meta-Analysis in advertising research. J Advert 46(1):21–35

Elliott JH, Synnot A, Turner T, Simmons M, Akl EA, McDonald S, Salanti G, Meerpohl J, MacLehose H, Hilton J, Tovey D, Shemilt I, Thomas J (2017) Living systematic review: 1. Introduction—the why, what, when, and how. J Clin Epidemiol 91:2330. https://doi.org/10.1016/j.jclinepi.2017.08.010

Field AP, Gillett R (2010) How to do a meta-analysis. Br J Math Stat Psychol 63(3):665–694

Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Quart 68:103–106

Fortunato S, Bergstrom CT, Börner K, Evans JA, Helbing D, Milojević S, Petersen AM, Radicchi F, Sinatra R, Uzzi B, Vespignani A (2018) Science of science. Science 359(6379). https://doi.org/10.1126/science.aao0185

Geyer-Klingeberg J, Hang M, Rathgeber A (2020) Meta-analysis in finance research: Opportunities, challenges, and contemporary applications. Int Rev Finan Anal 71:101524

Geyskens I, Krishnan R, Steenkamp JBE, Cunha PV (2009) A review and evaluation of meta-analysis practices in management research. J Manag 35(2):393–419

Glass GV (2015) Meta-analysis at middle age: a personal history. Res Synth Methods 6(3):221–231

Gonzalez-Mulé E, Aguinis H (2018) Advancing theory by assessing boundary conditions with metaregression: a critical review and best-practice recommendations. J Manag 44(6):2246–2273

Gooty J, Banks GC, Loignon AC, Tonidandel S, Williams CE (2021) Meta-analyses as a multi-level model. Organ Res Methods 24(2):389–411. https://doi.org/10.1177/1094428119857471

Grewal D, Puccinelli N, Monroe KB (2018) Meta-analysis: integrating accumulated knowledge. J Acad Mark Sci 46(1):9–30

Gurevitch J, Koricheva J, Nakagawa S, Stewart G (2018) Meta-analysis and the science of research synthesis. Nature 555(7695):175–182

Gusenbauer M, Haddaway NR (2020) Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res Synth Methods 11(2):181–217

Habersang S, Küberling-Jost J, Reihlen M, Seckler C (2019) A process perspective on organizational failure: a qualitative meta-analysis. J Manage Stud 56(1):19–56

Harari MB, Parola HR, Hartwell CJ, Riegelman A (2020) Literature searches in systematic reviews and meta-analyses: A review, evaluation, and recommendations. J Vocat Behav 118:103377

Harrison JS, Banks GC, Pollack JM, O’Boyle EH, Short J (2017) Publication bias in strategic management research. J Manag 43(2):400–425

Havránek T, Stanley TD, Doucouliagos H, Bom P, Geyer-Klingeberg J, Iwasaki I, Reed WR, Rost K, Van Aert RCM (2020) Reporting guidelines for meta-analysis in economics. J Econ Surveys 34(3):469–475

Hedges LV, Olkin I (1985) Statistical methods for meta-analysis. Academic Press, Orlando

Hedges LV, Vevea JL (2005) Selection methods approaches. In: Rothstein HR, Sutton A, Borenstein M (eds) Publication bias in meta-analysis: prevention, assessment, and adjustments. Wiley, Chichester, pp 145–174

Hoon C (2013) Meta-synthesis of qualitative case studies: an approach to theory building. Organ Res Methods 16(4):522–556

Hunter JE, Schmidt FL (1990) Methods of meta-analysis: correcting error and bias in research findings. Sage, Newbury Park

Hunter JE, Schmidt FL (2004) Methods of meta-analysis: correcting error and bias in research findings, 2nd edn. Sage, Thousand Oaks

Hunter JE, Schmidt FL, Jackson GB (1982) Meta-analysis: cumulating research findings across studies. Sage Publications, Beverly Hills

Jak S (2015) Meta-analytic structural equation modelling. Springer, New York, NY

Kepes S, Banks GC, McDaniel M, Whetzel DL (2012) Publication bias in the organizational sciences. Organ Res Methods 15(4):624–662

Kepes S, McDaniel MA, Brannick MT, Banks GC (2013) Meta-analytic reviews in the organizational sciences: Two meta-analytic schools on the way to MARS (the Meta-Analytic Reporting Standards). J Bus Psychol 28(2):123–143

Kraus S, Breier M, Dasí-Rodríguez S (2020) The art of crafting a systematic literature review in entrepreneurship research. Int Entrepreneur Manag J 16(3):1023–1042

Levitt HM (2018) How to conduct a qualitative meta-analysis: tailoring methods to enhance methodological integrity. Psychother Res 28(3):367–378

Levitt HM, Bamberg M, Creswell JW, Frost DM, Josselson R, Suárez-Orozco C (2018) Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: the APA publications and communications board task force report. Am Psychol 73(1):26

Lipsey MW, Wilson DB (2001) Practical meta-analysis. Sage Publications, Inc.

López-López JA, Page MJ, Lipsey MW, Higgins JP (2018) Dealing with effect size multiplicity in systematic reviews and meta-analyses. Res Synth Methods 9(3):336–351

Martín-Martín A, Thelwall M, Orduna-Malea E, López-Cózar ED (2021) Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics 126(1):871–906

Merton RK (1968) The Matthew effect in science: the reward and communication systems of science are considered. Science 159(3810):56–63

Moeyaert M, Ugille M, Natasha Beretvas S, Ferron J, Bunuan R, Van den Noortgate W (2017) Methods for dealing with multiple outcomes in meta-analysis: a comparison between averaging effect sizes, robust variance estimation and multilevel meta-analysis. Int J Soc Res Methodol 20(6):559–572

Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine. 6(7):e1000097

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106(1):213–228

Moreau D, Gamble B (2020) Conducting a meta-analysis in the age of open science: Tools, tips, and practical recommendations. Psychol Methods. https://doi.org/10.1037/met0000351

O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S (2015) Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev 4(1):1–22

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan—a web and mobile app for systematic reviews. Syst Rev 5(1):1–10

Owen E, Li Q (2021) The conditional nature of publication bias: a meta-regression analysis. Polit Sci Res Methods 9(4):867–877

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E,McDonald S,McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372. https://doi.org/10.1136/bmj.n71

Palmer TM, Sterne JAC (eds) (2016) Meta-analysis in stata: an updated collection from the stata journal, 2nd edn. Stata Press, College Station, TX

Pigott TD, Polanin JR (2020) Methodological guidance paper: High-quality meta-analysis in a systematic review. Rev Educ Res 90(1):24–46

Polanin JR, Tanner-Smith EE, Hennessy EA (2016) Estimating the difference between published and unpublished effect sizes: a meta-review. Rev Educ Res 86(1):207–236

Polanin JR, Hennessy EA, Tanner-Smith EE (2017) A review of meta-analysis packages in R. J Edu Behav Stat 42(2):206–242

Polanin JR, Hennessy EA, Tsuji S (2020) Transparency and reproducibility of meta-analyses in psychology: a meta-review. Perspect Psychol Sci 15(4):1026–1041. https://doi.org/10.1177/17456916209064

R Core Team (2021). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .

Rauch A (2020) Opportunities and threats in reviewing entrepreneurship theory and practice. Entrep Theory Pract 44(5):847–860

Rauch A, van Doorn R, Hulsink W (2014) A qualitative approach to evidence–based entrepreneurship: theoretical considerations and an example involving business clusters. Entrep Theory Pract 38(2):333–368

Raudenbush SW (2009) Analyzing effect sizes: Random-effects models. In: Cooper H, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis, 2nd edn. Russell Sage Foundation, New York, NY, pp 295–315

Rosenthal R (1979) The file drawer problem and tolerance for null results. Psychol Bull 86(3):638

Rothstein HR, Sutton AJ, Borenstein M (2005) Publication bias in meta-analysis: prevention, assessment and adjustments. Wiley, Chichester

Roth PL, Le H, Oh I-S, Van Iddekinge CH, Bobko P (2018) Using beta coefficients to impute missing correlations in meta-analysis research: Reasons for caution. J Appl Psychol 103(6):644–658. https://doi.org/10.1037/apl0000293

Rudolph CW, Chang CK, Rauvola RS, Zacher H (2020) Meta-analysis in vocational behavior: a systematic review and recommendations for best practices. J Vocat Behav 118:103397

Schmidt FL (2017) Statistical and measurement pitfalls in the use of meta-regression in meta-analysis. Career Dev Int 22(5):469–476

Schmidt FL, Hunter JE (2015) Methods of meta-analysis: correcting error and bias in research findings. Sage, Thousand Oaks

Schwab A (2015) Why all researchers should report effect sizes and their confidence intervals: Paving the way for meta–analysis and evidence–based management practices. Entrepreneurship Theory Pract 39(4):719–725. https://doi.org/10.1111/etap.12158

Shaw JD, Ertug G (2017) The suitability of simulations and meta-analyses for submissions to Academy of Management Journal. Acad Manag J 60(6):2045–2049

Soderberg CK (2018) Using OSF to share data: A step-by-step guide. Adv Methods Pract Psychol Sci 1(1):115–120

Stanley TD, Doucouliagos H (2010) Picture this: a simple graph that reveals much ado about research. J Econ Surveys 24(1):170–191

Stanley TD, Doucouliagos H (2012) Meta-regression analysis in economics and business. Routledge, London

Stanley TD, Jarrell SB (1989) Meta-regression analysis: a quantitative method of literature surveys. J Econ Surveys 3:54–67

Steel P, Beugelsdijk S, Aguinis H (2021) The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews. J Int Bus Stud 52(1):23–44

Suurmond R, van Rhee H, Hak T (2017) Introduction, comparison, and validation of Meta-Essentials: a free and simple tool for meta-analysis. Res Synth Methods 8(4):537–553

The Cochrane Collaboration (2020). Review Manager (RevMan) [Computer program] (Version 5.4).

Thomas J, Noel-Storr A, Marshall I, Wallace B, McDonald S, Mavergames C, Glasziou P, Shemilt I, Synnot A, Turner T, Elliot J (2017) Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol 91:31–37

Thompson SG, Higgins JP (2002) How should meta-regression analyses be undertaken and interpreted? Stat Med 21(11):1559–1573

Tipton E, Pustejovsky JE, Ahmadi H (2019) A history of meta-regression: technical, conceptual, and practical developments between 1974 and 2018. Res Synth Methods 10(2):161–179

Vevea JL, Woods CM (2005) Publication bias in research synthesis: Sensitivity analysis using a priori weight functions. Psychol Methods 10(4):428–443

Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36(3):1–48

Viechtbauer W, Cheung MWL (2010) Outlier and influence diagnostics for meta-analysis. Res Synth Methods 1(2):112–125

Viswesvaran C, Ones DS (1995) Theory testing: combining psychometric meta-analysis and structural equations modeling. Pers Psychol 48(4):865–885

Wilson SJ, Polanin JR, Lipsey MW (2016) Fitting meta-analytic structural equation models with complex datasets. Res Synth Methods 7(2):121–139. https://doi.org/10.1002/jrsm.1199

Wood JA (2008) Methodology for dealing with duplicate study effects in a meta-analysis. Organ Res Methods 11(1):79–95

Download references

Open Access funding enabled and organized by Projekt DEAL. No funding was received to assist with the preparation of this manuscript.

Author information

Authors and affiliations.

University of Luxembourg, Luxembourg, Luxembourg

Christopher Hansen

Leibniz Institute for Psychology (ZPID), Trier, Germany

Holger Steinmetz

Trier University, Trier, Germany

Erasmus University Rotterdam, Rotterdam, The Netherlands

Wittener Institut Für Familienunternehmen, Universität Witten/Herdecke, Witten, Germany

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jörn Block .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table 1 .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

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

Download citation

Published : 30 November 2021

Issue Date : February 2022

DOI : https://doi.org/10.1007/s11301-021-00247-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

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

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

Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

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

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

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

LEARN ABOUT: Research Process Steps

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

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

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

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

Create a Free Account

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.

LEARN ABOUT: Average Order Value

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

MORE LIKE THIS

21 Best Customer Advocacy Software for Customers in 2024

Apr 19, 2024

10 Quantitative Data Analysis Software for Every Data Scientist

Apr 18, 2024

11 Best Enterprise Feedback Management Software in 2024

17 Best Online Reputation Management Software in 2024

Apr 17, 2024

Other categories

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

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Korean J Anesthesiol
• v.71(2); 2018 Apr

Introduction to systematic review and meta-analysis

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

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

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

Introduction

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

Levels of evidence.

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

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

Study Planning

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

Flowchart illustrating a systematic review.

Formulating research questions

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

Protocols and registration

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

Defining inclusion and exclusion criteria

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

Literature search and study selection

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

Quality of evidence

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

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

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

Data extraction

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

Data Analysis

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

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

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

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

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

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

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

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

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

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

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

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

Heterogeneity

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

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

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

Publication bias

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

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

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

Result Presentation

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

The GRADE Evidence Quality for Each Outcome

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research Analysis Paper: How to Analyze a Research Article [2024]

Do you need to write a research analysis paper but have no idea how to do that? Then you’re in the right place.

Our specialists will write a custom essay specially for you!

While completing this type of assignment, your key aim is to critically analyze a research article. An article from a serious scientific journal would be a good choice. You can analyze and interpret either quantitative or qualitative research.

Below, you’ll find a how-to guide on research analysis paper writing prepared by our experts. It contains outlining and formatting tips, topics, and examples of research articles analysis.

• Scan the Paper
• Examine the Content
• Check the Format
• Critique & Evaluate
• ✅ Key Questions

🔗 References

🔎 how to analyze a research article.

This analysis will be beneficial for you since it develops your critical thinking and research skills. So, let us present the main steps that should be undertaken to read and evaluate the paper correctly.

Now, let’s figure out what an analysis paper should include. There are several essential elements the reader should identify:

• logical reasons for conducting the study;
• the description of the methodology applied in the research;
• concise and clear report of the findings;
• a logical conclusion based on the results.

You can use free paper samples for college students before you work with your own writing to get a feel of how the analyzing process goes.

Just in 1 hour! We will write you a plagiarism-free paper in hardly more than 1 hour

Step 1: Scan the Paper

First, briefly look through the found paper and evaluate whether it’s appropriate for your research. Scanning helps you to start the content analysis and get the general idea of the study.

To scan the paper effectively, follow these simple steps:

• Get familiar with the title, abstract , and introduction . Carefully read these parts and make sure you got the author’s point.
• Read the headings of each section and sub-section. But don’t spend time to get familiar with the content.
• Look through the conclusions. Check the overall one and the last sentence of each section.
• Scan the references. Have you read any of these sources before? Highlight them and decide whether they are appropriate for your research or not.

Have you completed these steps of your research paper’s critical analysis? Now, you should be able to answer these questions:

• What kind of a paper is it (qualitative research, quantitative research, a case study, etc.)?
• What is the research paper topic? How is it connected to your subject of study?
• Do you feel like the findings and the conclusions are valid?
• How can the source contribute to your study?
• Is the paper clear and well-written?

After completing this step, you should have a clear image of the text’s general idea. Also, here you can decide whether the given paper is worth further examination.

Step 2: Examine the Content

The next step leads to a deeper understanding of the topic. Here, again, you can try the following course of action to take the maximum benefit from the evaluation of the source.

Receive a plagiarism-free paper tailored to your instructions. Cut 20% off your first order!

• Find the author’s thesis. A thesis statement is usually the last sentence of the introduction (or several sentences). It is an essential part of the paper since it reflects the author’s main point. Make sure you determined the thesis statement and understood it.
• Consider the author’s arguments. How does the author support his position? What are the key arguments they present in their research paper? Are they logical? Evaluate whether the points are clear and concise enough for any reader to get. Do they support the author’s thesis?
• Check the evidence. Try to find all the proof provided by the writer. A successful research paper should have valid evidence for every argument. These can be statistics, diagrams, facts taken from documentaries or books, experiments hold by researchers, etc.
• Determine the limits of the study. An author is supposed to set limits to avoid making their research too broad. Find out what are the variables the writer relied on while determining the exact field of study. Keep them in mind when you decide whether the paper accomplished its goals within limits.
• Establish the author’s perspective. What position does the author take? What methods are applied to prove the correctness of the writer’s point? Does it match with your opinion? Why/ why not?

Sometimes, even after the second step of evaluation, the writer’s perspective is not evident. What to do in this case? There are three scenarios:

• Stop investigating the paper and hope that you will not need it for your research.
• Read some background information on the given topic. Then, reread the paper. This might help you to comprehend the general idea.
• Don’t give up and move on to the next step of the evaluation.

Step 3: Check the Format and Presentation

At this stage, analyze the research paper format and the general presentation of the arguments and facts. Start with the evaluation of the sentence levels. In the research paper, there should be a hierarchy of sentences. To trace the research paper structure, take a look at the tips:

• First-level sentences. They include only general statements and present the ideas that will be explored further in the paper.
• Middle-level sentences. These sentences summarize, give a narrower idea, and present specific arguments.
• Deep-level sentences. They contain specific facts and evidence that correspond to the arguments stated in middle-level sentences.

Your research paper analysis should also include format evaluation. This task might be challenging unless you have the formatting style manual open in front of your eyes.

Figure out what citation style the author applied and check whether all the requirements are met. Here is a mini checklist you have to follow:

• in-text citations
• reference list
• font style and size, spacing
• abstract (if needed)
• appendix (if needed)

Step 4: Critique & Evaluate

This step requires attention to every detail in the paper. Identify each of the author’s assumptions and question them. Do you agree with the author’s evidence? How would you support the arguments? What are your opinions regarding the author’s ideas?

Get an originally-written paper according to your instructions!

For starters:

Try to re-implement the entire paper from your perspective and see how your version differs from the initial work. This trick will help you to determine the strong and weak sides of the work.

Then, move on to criticism. An effective way to evaluate a research paper consists of asking the right questions and assessing the crucial aspects, like:

• The author’s objective and whether it was reached. Did you get the author’s main idea? Did the writer reach their aim and explain the arguments in great detail? Remember that even if the reader is not majoring in the study field, they should understand the objective. Is there something that remained unclear for you? In your opinion, what is the cause of your inability to comprehend the material?
• The role in the broader context. Make sure the author’s arguments and evidence sound adequately in the larger context. Do the writer’s ideas contradict social norms. If so, why? Also, check the sources the author uses for their research. Make sure they are reliable and not outdated.
• Grammar and organization. A professional research paper should not contain any mistakes. Make sure the text is flawless regarding grammar and structure. The ideas have to follow the logical flow; the tone should be academic; the paper should include transitions, summaries should be on point (which is easier to achieve with the help of a paper summarizer ) and so on.
• What the reader learns. The primary aim of an author is to deliver useful information to the reader. Did you, as a reader, find some new insights? Were they relevant and valuable? Consider whether you’ve read something similar before and how the data fit within limits set by the author.

✅ Research Analysis Paper: Key Questions

As you can see, the task requires a lot of time and effort. That is why we’ve prepared a list of questions you should ask while analyzing a research paper. Use them as a ground for critical reading and evaluation.

Research Article Analysis Topics

• Research article analysis: Using Evidence-Based Practice to Prevent Ventilator-Associated Pneumonia .
• Critical analysis of Seligman’s research article on post-traumatic stress disorder.
• Analyze the article on the role of interprofessional communication in healthcare.
• Examine the articles on the controversy of stem cell research.
• Write a critical analysis of a research article on abortion .
• Discuss a research article on nursing and proactive care program.
• Analyze a quantitative research article on the efficiency of methods used in nursing education .
• Critical analysis of the research article on the role of environmental biology.
• Analysis of the articles about primary quantitative and qualitative research .
• Evaluate Goeders and Guerin’s research on the connection between stress and drug use.
• Study Angela F. Clark’s research article on the efficacy of a nursing education program.
• Analyze the research article by Park, Nisch, and Baptiste examining the connection between immigrants’ mental health and the length of stay in the United States.
• Discuss the scholarly articles researching the connection between obesity and depression.
• Analysis of nursing research article on level of education .
• Write a critical analysis of the scholarly article The Effect of Nurse Staffing on Patient Safety Outcomes .
• Examine a recent research article on spinal cord injuries.
• Analyze Ronald F. Wright’s research article examining the specifics of jury selection.
• Study the article by McConnell et al. on the impact of domestic animals on human well-being.
• Critical evaluation and analysis of the article on ethics and informed consent in research.
• Analysis of a research article on preventing hospital falls .
• Write an analysis of the research article studying the challenges of implementing research findings into practice in nursing.
• Examine the article on the thrombosis process by Bruce Furie and Barbara C. Furie.
• Analyze Mendenhall and Doherty’s research on a new diabetes management approach.
• Qualitative research article critique.
• Critical analysis of a research article on the effectiveness of drug round tabards .
• Discuss quantitative research about the barriers to electronic commerce implementation.
• Study the article Health Information Source Use by Jessica Gall Myrick and Michael Hendryx.
• Analyze a research article by Lengyel et al. That studies the amount of sugar in school breakfast .
• Write a critical analysis of the research studying the quality of pain management .
• Examine the research article The Mental Health of Indigenous Peoples in Canada by Sarah E Nelson and Kathi Wilson.
• Analysis of the article Development of a Proactive Care Program .
• Study the article on nursing REST: Break Through to Resilience by Rajamohan et al.
• Critically analyze the research article Quality Management in Healthcare: The Pivotal Desideratum .
• Examine and interpret the academic article In Defense of the Randomized Controlled Trial by Rosen et al.
• Write an analysis of a research article Cardiovascular Changes Resulting from Sexual Activity by Bispo, De Lima Lopes, and De Barros.
• Study the topicality and consistency of Dillner’s article Obstetrician Suspended After Research Inquiry .
• Critical analysis of research article on nosocomial pneumonia .
• Discuss the methods used by Johanna Brenner in her research on intersections and class relations.
• Analyze the research article by Ansari et al. examining the connection between type 2 diabetes and environmental factors.
• Analysis of research article Nurses’ Perceptions of Research Utilization in a Corporate Health Care System .
• Examine the importance of the research Effectiveness of Hand Hygiene Interventions in Reducing Illness Absence .
• Analyze and interpret the article on the toolkit for postgraduate research supervisors by E. Blass & S. Bertone.
• Discuss the utility and credibility of K. Than’s article A Brief History of Twin Studies .
• Write a critical analysis of the article researching the current US gun policy and its effect on the rates of gun violence cases.
• Analysis of articles on evidence-based prevention of surgical site infections.
• Examine the research article Nurses’ Knowledge about Palliative Care by Etafa et al.
• Analyze the research conducted by Sandelowski et al. on the stigmatization of HIV-positive women .
• Discuss the theoretical framework and methodology of a research article on psychological studies .
• Analysis of a research article about sports and creatine .
• Study the presentation of research findings in the scholarly article Leadership Characteristics and Digital Transformation .

Congrats! Now you know how to write a research paper analysis. You are welcome to check out our writing tips available on the website and save a ton of time on your academic papers. Share the link with your peers who may need our advice as well.

• An Introduction to Critical Analysis of Publications in Experimental Biomedical Science, the Research Paper in Basic Medical Sciences: K. Rangachari, modified by D.J. Crankshaw, McMaster University Honours Biology & Pharmacology Program
• Critical Analysis Template: Keiran Rankin and Sara Wolfe, the Writing Centre, Thompson Rivers University
• How to Read a Paper: S. Keshav, David R. Cheriton, School of Computer Science, the University of Waterloo
• How to Read a Research Paper: School of Engineering and Applied Sciences, Harvard University
• Reading Research Effectively, Organizing Your Social Sciences Research Paper: Research Guides at the University of Southern California
• Share to Facebook
• Share to Twitter
• Share to LinkedIn
• Share to email

You might be wondering about how to write a conclusion paragraph for a research paper. It may seem like your readers should understand your main arguments by the end, so there is no need for it. However, there are several aspects that prove the importance of a conclusion section in...

American Antiquity is a professional quarterly journal, which contains various papers on the American archeology. It is incredibly popular among archeologists and the students majoring in history. The organization adopted the rules of The Society for American Archaeology (SAA) citation style. As a result: The journal includes numerous references that...

The most tedious and time-consuming part of any school or college written assignment is the bibliography. Sometimes, it can even be challenging! For example, if you’re confused by the variety of citation styles. This is probably when the most students wonder “Is there someone who could complete my assignment?” That...

An appendix is the part of the paper that contains supplementary material. The information from an appendix in paper writing is not essential. If the readers ignore this part, they still have to get the paper’s idea. Appendices help the readers to understand the research better. They might be useful...

Writing an abstract is one of the skills you need to master to succeed in your studies. An abstract is a summary of an academic text. It contains information about the aims and the outcomes of the research. The primary purpose of an abstract is to help readers understand what...

So you have to write a literature review. You find your favorite novel and then start analyzing it. This is how it’s usually done, right? It’s not. You have to learn the elements of literature review and how to deal with them.

Looking for relevant sources of information for your research is already a tiring process, but when you also need to pay attention to their credibility, it becomes almost impossible! However, it’s still a quite critical aspect to pay attention to. Using unreliable sites like Wikipedia, even if it’s a two-page...

Only two words, but you already feel a chill down your spine. A research paper is no joke. It’s a super detailed piece of academic writing where you analyze a chosen issue in-depth. The main aim of such torture is to show how knowledgeable you are and that your opinion...

A research proposal is a text that suggests a topic or research problem, justifies the need to study it, and describes the ways and methods of conducting the study. Scholars usually write proposals to get funding for their research. In their turn, students might have to do that to get...

It’s longer than your Bachelor’s thesis. It’s more stressful. It’s more important. And you have no clue how to write it. We understand that a lot comes with the responsibility of creating a Master’s thesis from scratch. But no need to stress out; you can get all the help you...

Students struggle with academic writing rules. Mastering them takes longer than writing the paper. But when the conventions become a normal state of things, the process becomes faster and easier than before. The less you think about the form, the more time you have to think about the contents.

Few students fully get the meaning and the importance of a research proposal. If you have a good research proposal, it means that you are going to carry out adequate research. A low-quality research proposal may be the reason your research will never start.

I would be grateful if you kindly upload a sample of research papers analysis in order to make the points mentioned tangible.

Dear Mustafa, There might be one available on our free essays page – you are welcome to check it out and find out more about the available sample papers that we have! Have a great day!

P.S. Link to the free essays database

Thanks so much for compiling and sharing this great information on research papers analysis! This is what I need to complete my paper fast and effectively! Thanks again!

When I read your post on research papers analysis, it seems that this is the simplest thing in the world, but I’m afraid of failing. As you were able to write this excellent post, you’ll be able to write a research papers analysis for me too!

• Privacy Policy

Buy Me a Coffee

Home » Critical Analysis – Types, Examples and Writing Guide

Critical Analysis – Types, Examples and Writing Guide

Table of Contents

Critical Analysis

Definition:

Critical analysis is a process of examining a piece of work or an idea in a systematic, objective, and analytical way. It involves breaking down complex ideas, concepts, or arguments into smaller, more manageable parts to understand them better.

Types of Critical Analysis

Types of Critical Analysis are as follows:

Literary Analysis

This type of analysis focuses on analyzing and interpreting works of literature , such as novels, poetry, plays, etc. The analysis involves examining the literary devices used in the work, such as symbolism, imagery, and metaphor, and how they contribute to the overall meaning of the work.

Film Analysis

This type of analysis involves examining and interpreting films, including their themes, cinematography, editing, and sound. Film analysis can also include evaluating the director’s style and how it contributes to the overall message of the film.

Art Analysis

This type of analysis involves examining and interpreting works of art , such as paintings, sculptures, and installations. The analysis involves examining the elements of the artwork, such as color, composition, and technique, and how they contribute to the overall meaning of the work.

Cultural Analysis

This type of analysis involves examining and interpreting cultural artifacts , such as advertisements, popular music, and social media posts. The analysis involves examining the cultural context of the artifact and how it reflects and shapes cultural values, beliefs, and norms.

Historical Analysis

This type of analysis involves examining and interpreting historical documents , such as diaries, letters, and government records. The analysis involves examining the historical context of the document and how it reflects the social, political, and cultural attitudes of the time.

Philosophical Analysis

This type of analysis involves examining and interpreting philosophical texts and ideas, such as the works of philosophers and their arguments. The analysis involves evaluating the logical consistency of the arguments and assessing the validity and soundness of the conclusions.

Scientific Analysis

This type of analysis involves examining and interpreting scientific research studies and their findings. The analysis involves evaluating the methods used in the study, the data collected, and the conclusions drawn, and assessing their reliability and validity.

Critical Discourse Analysis

This type of analysis involves examining and interpreting language use in social and political contexts. The analysis involves evaluating the power dynamics and social relationships conveyed through language use and how they shape discourse and social reality.

Comparative Analysis

This type of analysis involves examining and interpreting multiple texts or works of art and comparing them to each other. The analysis involves evaluating the similarities and differences between the texts and how they contribute to understanding the themes and meanings conveyed.

Critical Analysis Format

Critical Analysis Format is as follows:

I. Introduction

• Provide a brief overview of the text, object, or event being analyzed
• Explain the purpose of the analysis and its significance
• Provide background information on the context and relevant historical or cultural factors

II. Description

• Provide a detailed description of the text, object, or event being analyzed
• Identify key themes, ideas, and arguments presented
• Describe the author or creator’s style, tone, and use of language or visual elements

III. Analysis

• Analyze the text, object, or event using critical thinking skills
• Identify the main strengths and weaknesses of the argument or presentation
• Evaluate the reliability and validity of the evidence presented
• Assess any assumptions or biases that may be present in the text, object, or event
• Consider the implications of the argument or presentation for different audiences and contexts

IV. Evaluation

• Provide an overall evaluation of the text, object, or event based on the analysis
• Assess the effectiveness of the argument or presentation in achieving its intended purpose
• Identify any limitations or gaps in the argument or presentation
• Consider any alternative viewpoints or interpretations that could be presented
• Summarize the main points of the analysis and evaluation
• Reiterate the significance of the text, object, or event and its relevance to broader issues or debates
• Provide any recommendations for further research or future developments in the field.

VI. Example

• Provide an example or two to support your analysis and evaluation
• Use quotes or specific details from the text, object, or event to support your claims
• Analyze the example(s) using critical thinking skills and explain how they relate to your overall argument

VII. Conclusion

• Reiterate your thesis statement and summarize your main points
• Provide a final evaluation of the text, object, or event based on your analysis
• Offer recommendations for future research or further developments in the field
• End with a thought-provoking statement or question that encourages the reader to think more deeply about the topic

How to Write Critical Analysis

Writing a critical analysis involves evaluating and interpreting a text, such as a book, article, or film, and expressing your opinion about its quality and significance. Here are some steps you can follow to write a critical analysis:

• Read and re-read the text: Before you begin writing, make sure you have a good understanding of the text. Read it several times and take notes on the key points, themes, and arguments.
• Identify the author’s purpose and audience: Consider why the author wrote the text and who the intended audience is. This can help you evaluate whether the author achieved their goals and whether the text is effective in reaching its audience.
• Analyze the structure and style: Look at the organization of the text and the author’s writing style. Consider how these elements contribute to the overall meaning of the text.
• Evaluate the content : Analyze the author’s arguments, evidence, and conclusions. Consider whether they are logical, convincing, and supported by the evidence presented in the text.
• Consider the context: Think about the historical, cultural, and social context in which the text was written. This can help you understand the author’s perspective and the significance of the text.
• Develop your thesis statement : Based on your analysis, develop a clear and concise thesis statement that summarizes your overall evaluation of the text.
• Support your thesis: Use evidence from the text to support your thesis statement. This can include direct quotes, paraphrases, and examples from the text.
• Write the introduction, body, and conclusion : Organize your analysis into an introduction that provides context and presents your thesis, a body that presents your evidence and analysis, and a conclusion that summarizes your main points and restates your thesis.
• Revise and edit: After you have written your analysis, revise and edit it to ensure that your writing is clear, concise, and well-organized. Check for spelling and grammar errors, and make sure that your analysis is logically sound and supported by evidence.

When to Write Critical Analysis

You may want to write a critical analysis in the following situations:

• Academic Assignments: If you are a student, you may be assigned to write a critical analysis as a part of your coursework. This could include analyzing a piece of literature, a historical event, or a scientific paper.
• Journalism and Media: As a journalist or media person, you may need to write a critical analysis of current events, political speeches, or media coverage.
• Personal Interest: If you are interested in a particular topic, you may want to write a critical analysis to gain a deeper understanding of it. For example, you may want to analyze the themes and motifs in a novel or film that you enjoyed.
• Professional Development : Professionals such as writers, scholars, and researchers often write critical analyses to gain insights into their field of study or work.

Critical Analysis Example

An Example of Critical Analysis Could be as follow:

Research Topic:

The Impact of Online Learning on Student Performance

Introduction:

The introduction of the research topic is clear and provides an overview of the issue. However, it could benefit from providing more background information on the prevalence of online learning and its potential impact on student performance.

Literature Review:

The literature review is comprehensive and well-structured. It covers a broad range of studies that have examined the relationship between online learning and student performance. However, it could benefit from including more recent studies and providing a more critical analysis of the existing literature.

Research Methods:

The research methods are clearly described and appropriate for the research question. The study uses a quasi-experimental design to compare the performance of students who took an online course with those who took the same course in a traditional classroom setting. However, the study may benefit from using a randomized controlled trial design to reduce potential confounding factors.

The results are presented in a clear and concise manner. The study finds that students who took the online course performed similarly to those who took the traditional course. However, the study only measures performance on one course and may not be generalizable to other courses or contexts.

Discussion :

The discussion section provides a thorough analysis of the study’s findings. The authors acknowledge the limitations of the study and provide suggestions for future research. However, they could benefit from discussing potential mechanisms underlying the relationship between online learning and student performance.

Conclusion :

The conclusion summarizes the main findings of the study and provides some implications for future research and practice. However, it could benefit from providing more specific recommendations for implementing online learning programs in educational settings.

Purpose of Critical Analysis

There are several purposes of critical analysis, including:

• To identify and evaluate arguments : Critical analysis helps to identify the main arguments in a piece of writing or speech and evaluate their strengths and weaknesses. This enables the reader to form their own opinion and make informed decisions.
• To assess evidence : Critical analysis involves examining the evidence presented in a text or speech and evaluating its quality and relevance to the argument. This helps to determine the credibility of the claims being made.
• To recognize biases and assumptions : Critical analysis helps to identify any biases or assumptions that may be present in the argument, and evaluate how these affect the credibility of the argument.
• To develop critical thinking skills: Critical analysis helps to develop the ability to think critically, evaluate information objectively, and make reasoned judgments based on evidence.
• To improve communication skills: Critical analysis involves carefully reading and listening to information, evaluating it, and expressing one’s own opinion in a clear and concise manner. This helps to improve communication skills and the ability to express ideas effectively.

Importance of Critical Analysis

Here are some specific reasons why critical analysis is important:

• Helps to identify biases: Critical analysis helps individuals to recognize their own biases and assumptions, as well as the biases of others. By being aware of biases, individuals can better evaluate the credibility and reliability of information.
• Enhances problem-solving skills : Critical analysis encourages individuals to question assumptions and consider multiple perspectives, which can lead to creative problem-solving and innovation.
• Promotes better decision-making: By carefully evaluating evidence and arguments, critical analysis can help individuals make more informed and effective decisions.
• Facilitates understanding: Critical analysis helps individuals to understand complex issues and ideas by breaking them down into smaller parts and evaluating them separately.
• Fosters intellectual growth : Engaging in critical analysis challenges individuals to think deeply and critically, which can lead to intellectual growth and development.

Advantages of Critical Analysis

Some advantages of critical analysis include:

• Improved decision-making: Critical analysis helps individuals make informed decisions by evaluating all available information and considering various perspectives.
• Enhanced problem-solving skills : Critical analysis requires individuals to identify and analyze the root cause of a problem, which can help develop effective solutions.
• Increased creativity : Critical analysis encourages individuals to think outside the box and consider alternative solutions to problems, which can lead to more creative and innovative ideas.
• Improved communication : Critical analysis helps individuals communicate their ideas and opinions more effectively by providing logical and coherent arguments.
• Reduced bias: Critical analysis requires individuals to evaluate information objectively, which can help reduce personal biases and subjective opinions.
• Better understanding of complex issues : Critical analysis helps individuals to understand complex issues by breaking them down into smaller parts, examining each part and understanding how they fit together.
• Greater self-awareness: Critical analysis helps individuals to recognize their own biases, assumptions, and limitations, which can lead to personal growth and development.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Cluster Analysis – Types, Methods and Examples

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Discriminant Analysis – Methods, Types and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Reference management. Clean and simple.

What is a research paper?

A research paper is a paper that makes an argument about a topic based on research and analysis.

Any paper requiring the writer to research a particular topic is a research paper. Unlike essays, which are often based largely on opinion and are written from the author's point of view, research papers are based in fact.

A research paper requires you to form an opinion on a topic, research and gain expert knowledge on that topic, and then back up your own opinions and assertions with facts found through your thorough research.

➡️ Read more about  different types of research papers .

What is the difference between a research paper and a thesis?

A thesis is a large paper, or multi-chapter work, based on a topic relating to your field of study.

A thesis is a document students of higher education write to obtain an academic degree or qualification. Usually, it is longer than a research paper and takes multiple years to complete.

Generally associated with graduate/postgraduate studies, it is carried out under the supervision of a professor or other academic of the university.

A major difference between a research paper and a thesis is that:

• a research paper presents certain facts that have already been researched and explained by others
• a thesis starts with a certain scholarly question or statement, which then leads to further research and new findings

This means that a thesis requires the author to input original work and their own findings in a certain field, whereas the research paper can be completed with extensive research only.

➡️ Getting ready to start a research paper or thesis? Take a look at our guides on how to start a research paper or how to come up with a topic for your thesis .

Frequently Asked Questions about research papers

Take a look at this list of the top 21 Free Online Journal and Research Databases , such as ScienceOpen , Directory of Open Access Journals , ERIC , and many more.

Mason Porter, Professor at UCLA, explains in this forum post the main reasons to write a research paper:

• To create new knowledge and disseminate it.
• To teach science and how to write about it in an academic style.
• Some practical benefits: prestige, establishing credentials, requirements for grants or to help one get a future grant proposal, and so on.

Generally, people involved in the academia. Research papers are mostly written by higher education students and professional researchers.

Yes, a research paper is the same as a scientific paper. Both papers have the same purpose and format.

A major difference between a research paper and a thesis is that the former presents certain facts that have already been researched and explained by others, whereas the latter starts with a certain scholarly question or statement, which then leads to further research and new findings.

Related Articles

• How it works

Meta-Analysis – Guide with Definition, Steps & Examples

Published by Owen Ingram at April 26th, 2023 , Revised On April 26, 2023

“A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies. “

Meta-analysis and systematic review are the two most authentic strategies in research. When researchers start looking for the best available evidence concerning their research work, they are advised to begin from the top of the evidence pyramid. The evidence available in the form of meta-analysis or systematic reviews addressing important questions is significant in academics because it informs decision-making.

What is Meta-Analysis  

Meta-analysis estimates the absolute effect of individual independent research studies by systematically synthesising or merging the results. Meta-analysis isn’t only about achieving a wider population by combining several smaller studies. It involves systematic methods to evaluate the inconsistencies in participants, variability (also known as heterogeneity), and findings to check how sensitive their findings are to the selected systematic review protocol.   

When Should you Conduct a Meta-Analysis?

Meta-analysis has become a widely-used research method in medical sciences and other fields of work for several reasons. The technique involves summarising the results of independent systematic review studies. 

The Cochrane Handbook explains that “an important step in a systematic review is the thoughtful consideration of whether it is appropriate to combine the numerical results of all, or perhaps some, of the studies. Such a meta-analysis yields an overall statistic (together with its confidence interval) that summarizes the effectiveness of an experimental intervention compared with a comparator intervention” (section 10.2).

A researcher or a practitioner should choose meta-analysis when the following outcomes are desirable. 

For generating new hypotheses or ending controversies resulting from different research studies. Quantifying and evaluating the variable results and identifying the extent of conflict in literature through meta-analysis is possible. 

To find research gaps left unfilled and address questions not posed by individual studies. Primary research studies involve specific types of participants and interventions. A review of these studies with variable characteristics and methodologies can allow the researcher to gauge the consistency of findings across a wider range of participants and interventions. With the help of meta-analysis, the reasons for differences in the effect can also be explored. 

To provide convincing evidence. Estimating the effects with a larger sample size and interventions can provide convincing evidence. Many academic studies are based on a very small dataset, so the estimated intervention effects in isolation are not fully reliable.

Elements of a Meta-Analysis

Deeks et al. (2019), Haidilch (2010), and Grant & Booth (2009) explored the characteristics, strengths, and weaknesses of conducting the meta-analysis. They are briefly explained below. 

Characteristics: 

• A systematic review must be completed before conducting the meta-analysis because it provides a summary of the findings of the individual studies synthesised. 
• You can only conduct a meta-analysis by synthesising studies in a systematic review. 
• The studies selected for statistical analysis for the purpose of meta-analysis should be similar in terms of comparison, intervention, and population. 

Strengths: 

• A meta-analysis takes place after the systematic review. The end product is a comprehensive quantitative analysis that is complicated but reliable. 
• It gives more value and weightage to existing studies that do not hold practical value on their own. 
• Policy-makers and academicians cannot base their decisions on individual research studies. Meta-analysis provides them with a complex and solid analysis of evidence to make informed decisions. 

Criticisms: 

• The meta-analysis uses studies exploring similar topics. Finding similar studies for the meta-analysis can be challenging.
• When and if biases in the individual studies or those related to reporting and specific research methodologies are involved, the meta-analysis results could be misleading.

Steps of Conducting the Meta-Analysis 

The process of conducting the meta-analysis has remained a topic of debate among researchers and scientists. However, the following 5-step process is widely accepted. 

Step 1: Research Question

The first step in conducting clinical research involves identifying a research question and proposing a hypothesis . The potential clinical significance of the research question is then explained, and the study design and analytical plan are justified.

Step 2: Systematic Review 

The purpose of a systematic review (SR) is to address a research question by identifying all relevant studies that meet the required quality standards for inclusion. While established journals typically serve as the primary source for identified studies, it is important to also consider unpublished data to avoid publication bias or the exclusion of studies with negative results.

While some meta-analyses may limit their focus to randomized controlled trials (RCTs) for the sake of obtaining the highest quality evidence, other experimental and quasi-experimental studies may be included if they meet the specific inclusion/exclusion criteria established for the review.

Step 3: Data Extraction

After selecting studies for the meta-analysis, researchers extract summary data or outcomes, as well as sample sizes and measures of data variability for both intervention and control groups. The choice of outcome measures depends on the research question and the type of study, and may include numerical or categorical measures.

For instance, numerical means may be used to report differences in scores on a questionnaire or changes in a measurement, such as blood pressure. In contrast, risk measures like odds ratios (OR) or relative risks (RR) are typically used to report differences in the probability of belonging to one category or another, such as vaginal birth versus cesarean birth.

Step 4: Standardisation and Weighting Studies

After gathering all the required data, the fourth step involves computing suitable summary measures from each study for further examination. These measures are typically referred to as Effect Sizes and indicate the difference in average scores between the control and intervention groups. For instance, it could be the variation in blood pressure changes between study participants who used drug X and those who used a placebo.

Since the units of measurement often differ across the included studies, standardization is necessary to create comparable effect size estimates. Standardization is accomplished by determining, for each study, the average score for the intervention group, subtracting the average score for the control group, and dividing the result by the relevant measure of variability in that dataset.

In some cases, the results of certain studies must carry more significance than others. Larger studies, as measured by their sample sizes, are deemed to produce more precise estimates of effect size than smaller studies. Additionally, studies with less variability in data, such as smaller standard deviation or narrower confidence intervals, are typically regarded as higher quality in study design. A weighting statistic that aims to incorporate both of these factors, known as inverse variance, is commonly employed.

Step 5: Absolute Effect Estimation

The ultimate step in conducting a meta-analysis is to choose and utilize an appropriate model for comparing Effect Sizes among diverse studies. Two popular models for this purpose are the Fixed Effects and Random Effects models. The Fixed Effects model relies on the premise that each study is evaluating a common treatment effect, implying that all studies would have estimated the same Effect Size if sample variability were equal across all studies.

Conversely, the Random Effects model posits that the true treatment effects in individual studies may vary from each other, and endeavors to consider this additional source of interstudy variation in Effect Sizes. The existence and magnitude of this latter variability is usually evaluated within the meta-analysis through a test for ‘heterogeneity.’

Forest Plot

The results of a meta-analysis are often visually presented using a “Forest Plot”. This type of plot displays, for each study, included in the analysis, a horizontal line that indicates the standardized Effect Size estimate and 95% confidence interval for the risk ratio used. Figure A provides an example of a hypothetical Forest Plot in which drug X reduces the risk of death in all three studies.

However, the first study was larger than the other two, and as a result, the estimates for the smaller studies were not statistically significant. This is indicated by the lines emanating from their boxes, including the value of 1. The size of the boxes represents the relative weights assigned to each study by the meta-analysis. The combined estimate of the drug’s effect, represented by the diamond, provides a more precise estimate of the drug’s effect, with the diamond indicating both the combined risk ratio estimate and the 95% confidence interval limits.

Figure-A: Hypothetical Forest Plot

Relevance to Practice and Research 

  Evidence Based Nursing commentaries often include recently published systematic reviews and meta-analyses, as they can provide new insights and strengthen recommendations for effective healthcare practices. Additionally, they can identify gaps or limitations in current evidence and guide future research directions.

The quality of the data available for synthesis is a critical factor in the strength of conclusions drawn from meta-analyses, and this is influenced by the quality of individual studies and the systematic review itself. However, meta-analysis cannot overcome issues related to underpowered or poorly designed studies.

Therefore, clinicians may still encounter situations where the evidence is weak or uncertain, and where higher-quality research is required to improve clinical decision-making. While such findings can be frustrating, they remain important for informing practice and highlighting the need for further research to fill gaps in the evidence base.

Methods and Assumptions in Meta-Analysis 

Ensuring the credibility of findings is imperative in all types of research, including meta-analyses. To validate the outcomes of a meta-analysis, the researcher must confirm that the research techniques used were accurate in measuring the intended variables. Typically, researchers establish the validity of a meta-analysis by testing the outcomes for homogeneity or the degree of similarity between the results of the combined studies.

Homogeneity is preferred in meta-analyses as it allows the data to be combined without needing adjustments to suit the study’s requirements. To determine homogeneity, researchers assess heterogeneity, the opposite of homogeneity. Two widely used statistical methods for evaluating heterogeneity in research results are Cochran’s-Q and I-Square, also known as I-2 Index.

Difference Between Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are both research methods used to synthesise evidence from multiple studies on a particular topic. However, there are some key differences between the two.

Systematic reviews involve a comprehensive and structured approach to identifying, selecting, and critically appraising all available evidence relevant to a specific research question. This process involves searching multiple databases, screening the identified studies for relevance and quality, and summarizing the findings in a narrative report.

Meta-analysis, on the other hand, involves using statistical methods to combine and analyze the data from multiple studies, with the aim of producing a quantitative summary of the overall effect size. Meta-analysis requires the studies to be similar enough in terms of their design, methodology, and outcome measures to allow for meaningful comparison and analysis.

Therefore, systematic reviews are broader in scope and summarize the findings of all studies on a topic, while meta-analyses are more focused on producing a quantitative estimate of the effect size of an intervention across multiple studies that meet certain criteria. In some cases, a systematic review may be conducted without a meta-analysis if the studies are too diverse or the quality of the data is not sufficient to allow for statistical pooling.

Software Packages For Meta-Analysis

Meta-analysis can be done through software packages, including free and paid options. One of the most commonly used software packages for meta-analysis is RevMan by the Cochrane Collaboration.

Assessing the Quality of Meta-Analysis 

Assessing the quality of a meta-analysis involves evaluating the methods used to conduct the analysis and the quality of the studies included. Here are some key factors to consider:

• Study selection: The studies included in the meta-analysis should be relevant to the research question and meet predetermined criteria for quality.
• Search strategy: The search strategy should be comprehensive and transparent, including databases and search terms used to identify relevant studies.
• Study quality assessment: The quality of included studies should be assessed using appropriate tools, and this assessment should be reported in the meta-analysis.
• Data extraction: The data extraction process should be systematic and clearly reported, including any discrepancies that arose.
• Analysis methods: The meta-analysis should use appropriate statistical methods to combine the results of the included studies, and these methods should be transparently reported.
• Publication bias: The potential for publication bias should be assessed and reported in the meta-analysis, including any efforts to identify and include unpublished studies.
• Interpretation of results: The results should be interpreted in the context of the study limitations and the overall quality of the evidence.
• Sensitivity analysis: Sensitivity analysis should be conducted to evaluate the impact of study quality, inclusion criteria, and other factors on the overall results.

Overall, a high-quality meta-analysis should be transparent in its methods and clearly report the included studies’ limitations and the evidence’s overall quality.

Hire an Expert Writer

Orders completed by our expert writers are

• Formally drafted in an academic style
• Free Amendments and 100% Plagiarism Free – or your money back!
• 100% Confidential and Timely Delivery!
• Free anti-plagiarism report
• Appreciated by thousands of clients. Check client reviews

Examples of Meta-Analysis

• STANLEY T.D. et JARRELL S.B. (1989), « Meta-regression analysis : a quantitative method of literature surveys », Journal of Economics Surveys, vol. 3, n°2, pp. 161-170.
• DATTA D.K., PINCHES G.E. et NARAYANAN V.K. (1992), « Factors influencing wealth creation from mergers and acquisitions : a meta-analysis », Strategic Management Journal, Vol. 13, pp. 67-84.
• GLASS G. (1983), « Synthesising empirical research : Meta-analysis » in S.A. Ward and L.J. Reed (Eds), Knowledge structure and use : Implications for synthesis and interpretation, Philadelphia : Temple University Press.
• WOLF F.M. (1986), Meta-analysis : Quantitative methods for research synthesis, Sage University Paper n°59.
• HUNTER J.E., SCHMIDT F.L. et JACKSON G.B. (1982), « Meta-analysis : cumulating research findings across studies », Beverly Hills, CA : Sage.

Frequently Asked Questions

What is a meta-analysis in research.

Meta-analysis is a statistical method used to combine results from multiple studies on a specific topic. By pooling data from various sources, meta-analysis can provide a more precise estimate of the effect size of a treatment or intervention and identify areas for future research.

Why is meta-analysis important?

Meta-analysis is important because it combines and summarizes results from multiple studies to provide a more precise and reliable estimate of the effect of a treatment or intervention. This helps clinicians and policymakers make evidence-based decisions and identify areas for further research.

What is an example of a meta-analysis?

A meta-analysis of studies evaluating physical exercise’s effect on depression in adults is an example. Researchers gathered data from 49 studies involving a total of 2669 participants. The studies used different types of exercise and measures of depression, which made it difficult to compare the results.

Through meta-analysis, the researchers calculated an overall effect size and determined that exercise was associated with a statistically significant reduction in depression symptoms. The study also identified that moderate-intensity aerobic exercise, performed three to five times per week, was the most effective. The meta-analysis provided a more comprehensive understanding of the impact of exercise on depression than any single study could provide.

What is the definition of meta-analysis in clinical research?

Meta-analysis in clinical research is a statistical technique that combines data from multiple independent studies on a particular topic to generate a summary or “meta” estimate of the effect of a particular intervention or exposure.

This type of analysis allows researchers to synthesise the results of multiple studies, potentially increasing the statistical power and providing more precise estimates of treatment effects. Meta-analyses are commonly used in clinical research to evaluate the effectiveness and safety of medical interventions and to inform clinical practice guidelines.

Is meta-analysis qualitative or quantitative?

Meta-analysis is a quantitative method used to combine and analyze data from multiple studies. It involves the statistical synthesis of results from individual studies to obtain a pooled estimate of the effect size of a particular intervention or treatment. Therefore, meta-analysis is considered a quantitative approach to research synthesis.

You May Also Like

Action research for my dissertation?, A brief overview of action research as a responsive, action-oriented, participative and reflective research technique.

Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.

Discourse analysis is an essential aspect of studying a language. It is used in various disciplines of social science and humanities such as linguistic, sociolinguistics, and psycholinguistic.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

• Master Your Homework
• Do My Homework

Analyzing the Differences: Research Paper vs. Analysis Paper

This article seeks to analyze the differences between two types of writing – research papers and analysis papers. While both require a similar level of thought, each type requires a different approach when it comes to researching and presenting information. Through an examination of the respective characteristics that distinguish these forms of writing from one another, we can gain valuable insight into what factors make for effective written communication in either format. Furthermore, by considering how best to utilize these features within our own work, we can enhance its overall quality and effectiveness in conveying our ideas and messages accurately.

I. Introduction: Exploring the Distinction between a Research Paper and an Analysis Paper

Ii. understanding the purpose of a research paper, iii. defining elements of analysing in an analysis paper, iv. identifying common formats for writing each type of paper.

• V. Assessing Sources Appropriate to Use for each Kind of Assignment
• VI. Examining Strategies Used by Writers When Composing either Type of Document

VII. Conclusion: Analyzing the Key Differences Between A Research and An Analysis Paper

Understanding the Variance in Research and Analysis Papers

It is essential to understand how research papers and analysis papers differ, as many of their features can be easily confused. They are both academic documents used for assessment or scholarly communication, but they present information differently. The most notable distinction between them lies in the presentation of evidence: while a research paper relies on facts gathered from an extensive background search, an analysis paper takes this data further by exploring deeper implications that provide greater insight into the topic at hand.

The first step when writing either type of document is proper organization; structure is key to getting your point across accurately and effectively. When constructing a research paper you must maintain objectivity with clear explanations supported by accurate sources; conversely, an analysis involves interpretation rather than straightforward facts – so strong reasoning skills should take precedence here as well. In addition to providing reliable arguments based upon sound logic throughout your composition, there are other areas where these two forms vary substantially including content length and depth of discussion required around each issue addressed within them respectively.

• Research Paper:
• >May be longer (5-10 pages)

Research Papers vs Analysis Papers

At first glance, the terms research paper and analysis paper may appear interchangeable. However, these two types of writing projects have distinct purposes that must be understood before starting any project. A research paper involves a deep dive into a particular subject to uncover new facts or data while an analysis paper uses those facts and data in order to form an argument.

When conducting research for a research paper, it is important to source information from reliable sources such as academic journals and books written by professionals on the topic at hand. With this knowledge, authors are then able to generate their own original ideas regarding the researched material which can further inform their findings in additional ways than what was originally found through researching existing literature on said topics. This newfound understanding can provide insight into different interpretations of similar material which adds depth and understanding beyond simply recounting someone else’s work; it provides readers with various perspectives based off objective fact-finding methods rather than personal opinion or bias towards one side over another.

In contrast, when writing an analysis essay all of this prior contextual information serves only as evidence that informs your conclusion – not necessarily as primary content within your argument itself; meaning instead you should focus on organizing these pieces of evidence provided alongside relevant examples/data (elements like logos & ethos) with well structured statements designed around persuasively conveying your perspective(s). Additionally depending upon who you’re attempting to reach via said piece you should also seek out counterarguments along with rebuttals so that any audience reading feels both informed and engaged throughout each part of its composition without feeling bias coming through too strongly either way at times too – resulting in effective arguments more akin most closely resembling judicial decisions rather than complex philosophical musings about life!

Exploring the Different Types of Analysis Papers

When writing an analysis paper, it’s important to understand that there are two primary types: research papers and analytical papers. Research papers present information about a specific topic through investigation, while analytical papers focus more on exploring and breaking down a concept or idea into its components in order to explain how they work together. Each type serves different purposes depending upon the scope of the assignment; however, both share some common elements.

The defining elements for analyzing in an analysis paper include gathering relevant data related to the topic at hand, evaluating this data objectively with logical reasoning processes such as deductive thinking methods, researching evidence-based sources for further clarification and validation of points being made within the paper itself. Further understanding can be gained by constructing strong arguments based on supportive evidence that has been collected from reliable source material. Ultimately any conclusions should be drawn from these objective evaluations and supported with thorough research so as not to bias opinion when forming argumentative claims throughout one’s essay.

When writing papers, the formatting and content of each document may vary based on its purpose. To ensure your paper is correctly formatted, it’s important to consider which type you are creating. Here are two popular formats for different types of documents:

• Research Papers:
• Analysis Papers:

In conclusion, there are a number of key differences between research and analysis papers. Research focuses on investigating existing knowledge from primary and secondary sources while analysis centers around interpretation of the collected information to generate new ideas or draw specific conclusions. A research paper involves extensive literature review which helps build an understanding for further investigation into a topic, whereas an analysis paper requires one to delve deeper into data in order to dissect patterns that may exist within it.

When creating either type of document, researchers should be sure they approach the task with the right mindset: when researching ask “what has been said”; when analyzing ask “how does this change what we know?” To truly understand both concepts fully is paramount for successful outcomes – whether it is uncovering trends through statistical methods or writing compelling essays based on evidence found from credible sources.

The analysis of the differences between research papers and analysis papers has been explored in great detail, providing useful insights for readers. From outlining the characteristics of each type to highlighting the appropriate purpose for each paper, this article has provided a comprehensive look at how these two types of writing differ from one another. Furthermore, it is important that students recognize when an assignment calls for a research paper or an analysis paper so they can successfully meet their academic requirements. Ultimately, with all this information now available to them regarding analyzing the differences between research papers and analytical papers, students should be well-equipped to tackle any task ahead of them!

AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

Our Supporting Partners

Analytics & Research Partners

Stay up to date on the AI Index by subscribing to the  Stanford HAI newsletter.

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Gender pay gap in U.S. hasn’t changed much in two decades

The gender gap in pay has remained relatively stable in the United States over the past 20 years or so. In 2022, women earned an average of 82% of what men earned, according to a new Pew Research Center analysis of median hourly earnings of both full- and part-time workers. These results are similar to where the pay gap stood in 2002, when women earned 80% as much as men.

As has long been the case, the wage gap is smaller for workers ages 25 to 34 than for all workers 16 and older. In 2022, women ages 25 to 34 earned an average of 92 cents for every dollar earned by a man in the same age group – an 8-cent gap. By comparison, the gender pay gap among workers of all ages that year was 18 cents.

While the gender pay gap has not changed much in the last two decades, it has narrowed considerably when looking at the longer term, both among all workers ages 16 and older and among those ages 25 to 34. The estimated 18-cent gender pay gap among all workers in 2022 was down from 35 cents in 1982. And the 8-cent gap among workers ages 25 to 34 in 2022 was down from a 26-cent gap four decades earlier.

The gender pay gap measures the difference in median hourly earnings between men and women who work full or part time in the United States. Pew Research Center’s estimate of the pay gap is based on an analysis of Current Population Survey (CPS) monthly outgoing rotation group files ( IPUMS ) from January 1982 to December 2022, combined to create annual files. To understand how we calculate the gender pay gap, read our 2013 post, “How Pew Research Center measured the gender pay gap.”

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting response rates. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

In addition to findings about the gender wage gap, this analysis includes information from a Pew Research Center survey about the perceived reasons for the pay gap, as well as the pressures and career goals of U.S. men and women. The survey was conducted among 5,098 adults and includes a subset of questions asked only for 2,048 adults who are employed part time or full time, from Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used in this analysis, along with responses, and its methodology .

The  U.S. Census Bureau has also analyzed the gender pay gap, though its analysis looks only at full-time workers (as opposed to full- and part-time workers). In 2021, full-time, year-round working women earned 84% of what their male counterparts earned, on average, according to the Census Bureau’s most recent analysis.

Much of the gender pay gap has been explained by measurable factors such as educational attainment, occupational segregation and work experience. The narrowing of the gap over the long term is attributable in large part to gains women have made in each of these dimensions.

Related: The Enduring Grip of the Gender Pay Gap

Even though women have increased their presence in higher-paying jobs traditionally dominated by men, such as professional and managerial positions, women as a whole continue to be overrepresented in lower-paying occupations relative to their share of the workforce. This may contribute to gender differences in pay.

Other factors that are difficult to measure, including gender discrimination, may also contribute to the ongoing wage discrepancy.

Perceived reasons for the gender wage gap

When asked about the factors that may play a role in the gender wage gap, half of U.S. adults point to women being treated differently by employers as a major reason, according to a Pew Research Center survey conducted in October 2022. Smaller shares point to women making different choices about how to balance work and family (42%) and working in jobs that pay less (34%).

There are some notable differences between men and women in views of what’s behind the gender wage gap. Women are much more likely than men (61% vs. 37%) to say a major reason for the gap is that employers treat women differently. And while 45% of women say a major factor is that women make different choices about how to balance work and family, men are slightly less likely to hold that view (40% say this).

Parents with children younger than 18 in the household are more likely than those who don’t have young kids at home (48% vs. 40%) to say a major reason for the pay gap is the choices that women make about how to balance family and work. On this question, differences by parental status are evident among both men and women.

Views about reasons for the gender wage gap also differ by party. About two-thirds of Democrats and Democratic-leaning independents (68%) say a major factor behind wage differences is that employers treat women differently, but far fewer Republicans and Republican leaners (30%) say the same. Conversely, Republicans are more likely than Democrats to say women’s choices about how to balance family and work (50% vs. 36%) and their tendency to work in jobs that pay less (39% vs. 30%) are major reasons why women earn less than men.

Democratic and Republican women are more likely than their male counterparts in the same party to say a major reason for the gender wage gap is that employers treat women differently. About three-quarters of Democratic women (76%) say this, compared with 59% of Democratic men. And while 43% of Republican women say unequal treatment by employers is a major reason for the gender wage gap, just 18% of GOP men share that view.

Pressures facing working women and men

Family caregiving responsibilities bring different pressures for working women and men, and research has shown that being a mother can reduce women’s earnings , while fatherhood can increase men’s earnings .

Employed women and men are about equally likely to say they feel a great deal of pressure to support their family financially and to be successful in their jobs and careers, according to the Center’s October survey. But women, and particularly working mothers, are more likely than men to say they feel a great deal of pressure to focus on responsibilities at home.

About half of employed women (48%) report feeling a great deal of pressure to focus on their responsibilities at home, compared with 35% of employed men. Among working mothers with children younger than 18 in the household, two-thirds (67%) say the same, compared with 45% of working dads.

When it comes to supporting their family financially, similar shares of working moms and dads (57% vs. 62%) report they feel a great deal of pressure, but this is driven mainly by the large share of unmarried working mothers who say they feel a great deal of pressure in this regard (77%). Among those who are married, working dads are far more likely than working moms (60% vs. 43%) to say they feel a great deal of pressure to support their family financially. (There were not enough unmarried working fathers in the sample to analyze separately.)

About four-in-ten working parents say they feel a great deal of pressure to be successful at their job or career. These findings don’t differ by gender.

Gender differences in job roles, aspirations

Overall, a quarter of employed U.S. adults say they are currently the boss or one of the top managers where they work, according to the Center’s survey. Another 33% say they are not currently the boss but would like to be in the future, while 41% are not and do not aspire to be the boss or one of the top managers.

Men are more likely than women to be a boss or a top manager where they work (28% vs. 21%). This is especially the case among employed fathers, 35% of whom say they are the boss or one of the top managers where they work. (The varying attitudes between fathers and men without children at least partly reflect differences in marital status and educational attainment between the two groups.)

In addition to being less likely than men to say they are currently the boss or a top manager at work, women are also more likely to say they wouldn’t want to be in this type of position in the future. More than four-in-ten employed women (46%) say this, compared with 37% of men. Similar shares of men (35%) and women (31%) say they are not currently the boss but would like to be one day. These patterns are similar among parents.

Note: This is an update of a post originally published on March 22, 2019. Anna Brown and former Pew Research Center writer/editor Amanda Barroso contributed to an earlier version of this analysis. Here are the questions used in this analysis, along with responses, and its methodology .

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

• Gender & Work
• Gender Equality & Discrimination
• Gender Pay Gap
• Gender Roles

Women have gained ground in the nation’s highest-paying occupations, but still lag behind men

Diversity, equity and inclusion in the workplace, the enduring grip of the gender pay gap, more than twice as many americans support than oppose the #metoo movement, women now outnumber men in the u.s. college-educated labor force, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy

Ryan Garcia defeats Devin Haney by majority decision: Round-by-round fight analysis

Ryan Garcia knocked down Devin Haney three times and won their much-anticipated fight by majority decision Saturday night at Barclays Center in Brooklyn, New York.

Garcia, a heavy underdog, rocked Haney with his patented left hook on each of the knockdowns in their 12-round super lightweight fight.

"He caught me early but I was sleeping on it. Caught me by surprise," said Haney, who suffered his first defeat.

Haney seemed in control for a portion of the fight. But when Garcia went on the attack, he unloaded punches that caught Haney ill-prepared or Haney was simply ill-equipped.

And by the end, Garcia had the crowd chanting his name.

“Ry-an! Ry-an!’’

Garcia’s stunning victory came after months of erratic behavior and outlandish claims, such as he’d conjured up demons.

“Come on guys, you really thought I was crazy?’’ Garcia bellowed during his in-ring interview after the fight. "You guys lost your own mind," he told the cheering crowd.

Garcia scored knockdowns in the seventh, 10th and 11th rounds with his left hook. He landed that vicious punch in the first round and rocked Haney.

“I just knew I had control after that,’’ Garcia said. “It’s hard to recover from big shots. You know, maybe my conditioning wasn’t my best, but at the end of the day I got the job done.’’

Garcia improved to 25-1 and Haney fell to 31-1.

Why was Ryan Garcia turning his back?

Garcia repeatedly turned his back to Haney, especially in the corners, in what looked like a defensive posture.

“I don’t know,’’ Garcia said when asked about the move. “I have ADHD, so I just start doing random things."

Initially, it seemed to confuse Haney. But he just pounded away on Garcia’s unprotected back. But Garcia was protecting his face and the front of his body.

“I thought the ref let him turn his back … a little too much,’’ Haney said.

Ryan Garcia lashes out at media

During the in-ring interview, Garcia grew angry and appeared to target the media.

“You guys hate on me because I’m pretty and (expletive),’’ he said. “At the end of the day, I’ve been boxing all of my life and all I do is love God and try to help the children and you guys straight hated on me. You guys do not love the truth.’’

But rage did not rule Garcia after the fight.

“I need a shot of beer, or like alcohol or something to get my mind going,’’ he said at one point. “You feel me? I’m just kidding. Man, can’t you guys take a joke, man?’’

Ryan Garcia vs. Devin Haney result: Ryan Garcia defeats Devin Haney by decision

Ryan Garcia knocked down Devin Haney three times and won their much-anticipated super lightweight fight by majority decision Saturday night at Barclays Center in Brooklyn, New York.

The judges scored it 112-112, 114-110 and 115-109.

Ryan Garcia vs. Devin Haney round-by-round fight analysis

Round 1: Garcia strikes with a couple of blows. A big left hand by Garcia. Haney rocked! Another left and combo from Garcia. Haney fires back with a right. More punches from Garcia. Garcia unloading. Haney steadies self as the round closes. Garcia 10, Haney 9.

Round 2: Haney strikes early. A couple of wild lefts from Garcia. Haney methodically using his left. Garcia no longer freewheeling. Looks measured now. Surely there’s more power to come. Haney lands a big right. Garcia strangely inactive. Now delivering again. Haney strikes. Big hook from Garcia misses. Garcia 19, Haney 19. 

Round 3: Garcia with an overhand right. Glancing blow. Garcia unloading again. Nothing lands. Yet. Garcia lets the left fly again. Haney connects. Tangled up. Haney using his jab, and looks cautious of Garcia’s power. Haney tags Garcia. And again. Garcia looks angry. Haney looking in more control. Haney 29, Garcia 28.

Round 4: Garcia now more aggressive. Opening up. But the fighters are getting tangled up again. Haney now stalking. Garcia trying to hide behind his left shoulder. There’s no hiding from Haney. Haney lands a sharp left. Now Garcia connects with a right. An inactive Garcia backpedaling. Haney 39, Garcia 37.

Round 5: Haney firing jabs. Garcia unloads punches, none of which land. Haney appears to be in control. Another missed right hand from Garcia and a headlock. That first round seems light years ago. Crowd booing. Now Garcia lands a right and grabs Haney around the neck. Fighters separated. Haney showing some apprehension. Haney 49, Garcia 46. 

Round 6: Garcia looking more aggressive. A big right. Lands a left. Haney could be in trouble. Garcia clearly has the superior power. Haney quickly reasserts control. Haney pounding on Garcia in the corner. Warned for hitting Garcia in the back, but it’s Garcia who’s ducking as Haney approaches. Crowd booing again as fighters grow inactive. Haney 58, Garcia 56.

Round 7: Haney bulls Garcia into a corner. But back out they come. Garcia lands a left. Down goes Haney! Looks wobbly. Haney’s back on his feet and Garcia is on the attack. Referee deducts a point from Garcia for a late hit coming out of a clinch. Haney down again, ruled a slip. Craziness. One knockdown. Haney on the canvas twice more and ruled slips. Haney wobbling, exhausted. Haney 66, Garcia 65.

Round 8: Garcia looking for the knockout blow. Has Haney in the corner, but referee sets him free. Haney surprisingly lively. Pace slowing. Lots of energy expended. Haney throwing rights that lack zip. But he’s more active than Garcia. Haney 76, Garcia 74.

Round 9: Garcia doing lots of turning his back as Haney approaches and absorbing left hands from Haney. The duck-and-back turn is Garcia’s signature move. Garcia back on the attack, landing big shots. But just a burst. Haney 86, Garcia 83. 

Round 10: Garcia lands with a right, then a left. And down goes Haney again! He makes the count. Garcia on the attack again. Connecting, but both fighters looked tired as they clinch. Garcia still using this unconventional strategy – turning his back on Haney. Another Garcia left. Staggered. A barrage of punches. Haney trying to hang on. Haney 94, Garcia 93.

Round 11: Haney opens with two lefts. Neither land, but he looks alert. Garcia measuring Haney, looking to load up. Now Haney just holding off after Garcia lands a glancing blow. Tangled up again. Big right from Garcia. Lands left. Down goes Haney again! For the third time! Makes the count. Garcia pushes Haney through the ropes. Back in the ring. “Ry-an, Ry-an,’’ chants come from the crowd. Garcia 103, Haney 102.

Round 12: Garcia and Haney willing to exchange punches. Know it’s a critical round. Garcia turning his back again. Now at center of ring. Haney working on wobbly legs, but lands a jab. Garcia looking for the KO punch, or cruising? Garcia sticking his tongue out at Haney and throwing jabs. Garcia 113, Haney 111.

Ryan Garcia's ominous words

During a quick interview before heading to the ring, Garcia said on the DAZN broadcast of Haney, “I’m willing to kill this man if I have to, with all due respect. ... I just don’t leave this ring without his head.”

And, yes, Garcia wore a crown as he walked from his locker room to the ring.

Ryan Garcia vs. Devin Haney: all about the music?

Ryan Garcia and Devin Haney working with different vibes as the fight approaches.

What time is the Ryan Garcia-Devin Haney fight?

The main card starts at 8 p.m. ET. Ring walks for Ryan Garcia and Devin Haney are estimated to start at 11 p.m. ET.

How to watch Ryan Garcia vs. Devin Haney

The boxing match can be viewed on DAZN ($69.99 with a subscription) and PPV.com ($79.99, no subscription required).

Mike Tyson in house for Ryan Garcia vs. Devin Haney

Tyson drew loud cheers as he headed to his seat. Scheduled to fight Jake Paul July 20, he’ll be watching Ryan Garcia and Devin Haney not quite from ringside.

Dressed in all black, Tyson settled into a seat a row behind at least one other row of apparent VIPs. He didn’t look particularly bothered either.

Ryan Garcia has guests in locker room: A string quartet

What does the fighter who may have guzzled a beer at weigh-in do for an encore?

Ryan Garcia has brought a string quartet to the locker room.

Arnold Barboza Jr. def. Sean McComb

Arnold Barboza Jr. won a split decision over Sean McComb, and the judges’ decision drew boos after the 10-round super lightweight fight.

“Shout out to Sean McComb,’’ Barboza said. “Tough guy.’’

McComb kept Barboza off balance with his long arms and footwork. But Barboza did land punches in close quarters and stayed undefeated at 30-0.

One judge scored it 98-92 in favor of McComb, the 31-year-old Irishman. A second judge scored it 96-94 and the third judge scored it 97-93, both in favor of Barboza.

Round 1: Lots of jabs, and McComb seems to have landed the most. But Barboza responds with two straight rights, but here’s McComb finishing up the round with more jabs. McComb 10, Barboza 9. 

Round 2: Barboza more aggressive, and McComb countering with the jab. Barboza’s throwing with more power, he’ll have to land more cleanly to score. Barboza looking for room to strike. But McComb mostly fending him off with the jab. McComb 20, Barboza 18.

Round 3: McComb keeping Barboza off balance. And he’s left an abrasion under Barboza’s left eye. How long can he do it? Barboza now connecting, landing to McComb’s body. But McComb stays slippery. McComb 30, Barboza 27.

Round 4: McComb comes out bouncing and bobbing and moving and … Barboza looks befuddled. Barboza and McComb exchange solid punches. Pace slowing, possibly in favor of Barboza. McComb 40, Barboza 36. 

Round 5: McComb sticking with game plan – jab, jab, jab. Firing a left too. Barboza closes the distance but looks wary of McComb’s jab. Now it looks like Barboza has an abrasion under his left eye, but landed heavier punches. McComb 49, Barboza 46.

Round 6: McComb’s got more than a jab. Now he’s unloading combinations. McComb’s height advantage has emerged as a serious issue for Barboza, who has no answers at this point. McComb 59, Barboza 55.

Round 7: McComb wielding those long arms with expertise. Has been able to land with either hand. Barboza lands a heavy right, then more. But nothing’s coming easy for Barboza. McComb 68, Barboza 65.

Round 8: McComb making good use of his jab again, and Barboza’s looking for openings. Crowd booing. Maybe getting as frustrated with McComb as Barboza is. But McComb fighting effectively. McComb 78, Barboza 74. 

Round 9: Barboza looks tentative even though he’s clearly trailing on the cards. No urgency. It’s McComb who’s initiating much of the action, with both hands. Feinting, juking, bobbing, weaving, McComb in control. McComb 88, Barboza 83.

Round 10: Sounds of singing Irish fans. McComb, the Irishman, has given them reason to celebrate. Now McComb bleeding over the right eye. Barboza picking up the pace, but way too late. McComb 97, Barboza 93.

Bektemir Melikuziev def. Pierre Dibombe

Two accidental headbutts turned the ring into a gruesome sight, with Bektemir Melikuziev of Uzbekistan and Pierre Dibombe of France each bleeding from cuts above an eye.

A ringside doctor halted the super middleweight fight shortly after the eighth round began, and Melikuziev beat the previously unbeaten Dibombe by unanimous decision.

The judges scored the fight 79-73, 79-73, 78-74 in favor of Melikuziev.

Melikuziev improved to 14-1 and Dibombe fell to 22-1-1.

Round 1: Melikuziev threw plenty of punches but it was hard to tell what landed. Dibombe was bleeding badly from his right eye as the round came to an end. It was ruled an accidental headbutt, according to the DAZN broadcast. Melikuziev 10, Dimbobe 9.

Round 2: With less than a minute left in the round, it’s stopped so a doctor can inspect the cut over Dibombe’s eye. Looks awful, but he wants to continue and so he does – landing punches too. Melikuziev 19, Dibombe 19.

Round 3: Essentially fighting with one eye, Dibombe is a warrior, throwing punches and landing a few. But Melikuziev looks in control, and he’s not going to help Dibombe protect that right eye. Melikuziev 29, Dibombe 28.

Round 4: Dibombe lands a big right. But Melikuziev isn’t shaken. He stays on the attack. Some good exchanges here, and now Dibombe draws blood from Melikuziev’s nose and left eye. May have been another accidental headbutt. Melikuziev 38, Dibombe 38.

Round 5: Melikuziev scores a knockdown even as Dibombe protests. But he's quickly up and ready to go as the scene turns gruesome. Now both fighters are bleeding profusely from an eye. A ringside doctor inspects Melikuziev’s cut left eye and lets the fight continue. Melikuziev closes out with a flourish. Melikuziev 48 Dibombe 46.

Round 6: Action is slowing. Melikuziev has not been able to maintain his momentum from Round 5. But Melikuziev the aggressor. Melikuziev 58, Dibombe 55.

Round 7: In close range, but nothing to write home about. Or to write to you about, dear reader. A solid combo from Dibombe. But now Melikuziev bullies Dibombe into the corner. Melikuziev unloaded punches, but Dibombe’s landed with more force. Melikuziev 67, Dibombe 65. 

Doctor halts the fight after the seventh round because of the cut under Melikuziev's eye opened by an accidental headbutt.

David Jimenez def. John Ramirez

David Jimenez of Costa Rica bloodied and battered American John Ramirez and won their 12-round super flyweight fight by a unanimous decision.

Jimenez routinely charged out of the corner and took the fight to Ramirez. He absorbed shots, but dished out more, leaving Ramirez bleeding profusely from the right eye.

The judges scored it 117-111, 117-111, 116-112. 

Round 1: Ramirez falls to the canvas after Jimenez bulls into him. Might have been worth something in the Octagon. Back on his feet, Ramirez looks unshaken. Ramirez 10, Jimenez 9.

Round 2: Jimenez more active now and landing punches. Fighters look content at center of ring, trading punches. Ramirez looks like stronger fighter, but Jimenez not backing away. Ramirez 19, Jimenez 19.

Round 3: Jimenez lands a solid left – and throwing it again. Ramirez steadying himself behind his jab. Jimenez outworking Ramirez. Now Jimenez showboating. Ramirez catches him late. Jimenez 29, Ramirez 28.

Round 4: Punches flying. Both fighters still willing to trade at the center of the ring. Ramirez might be getting the better of these exchanges. Jimenez has Ramirez on the ropes, digging in. Ramirez turns the tables and begins landing his own punches. Jimenez 39, Ramirez 37.

Round 5: Jimenez looks like a marionette. Trying to keep Ramirez off balance with the awkward movement. But now it’s back to crawling. Jimenez delivering combos, striking the body and head of Ramirez, who responds with effective counterpunching. Jimenez 48, Ramirez 47. 

Round 6: Jimenez charges out of his corner, setting the tone. Initiating the action, and there’s lots of it. Ramirez in control for a stretch. But Jimenez works Ramirez back onto the ropes again. Whenever they go, it’s Jimenez who’s the more active fighter – and he finishes with a flurry. Jimenez 58, Ramirez 56.

Round 7: Might as well move these guys inside a phone booth. Jimenez wants to close the distance and Ramirez obliges. Maybe take it to a back alley. Both have landed their fair share of punches. Jimenez 67, Ramirez 66.

Round 8: Ramirez to self: “Will this guy ever run out of energy?’’ You gotta wonder, as Jimenez is forcing the action yet again. Jimenez showboating again, a sign he’s got plenty of fuel in the tank after getting the best of Ramirez in that round. Jimenez 77, Ramirez 75.

Round 9: Surprise, surprise. Here they are trading blows again. When does exhaustion set in? Jimenez just pounding on Ramirez during the final 15 seconds of that round. Jimenez 87, Ramirez 84.

Round 10: Jimenez has blood on his back. It belongs to Ramirez. Jimenez the cleaner fighter and the fresher fighter. Ramirez trying to bull his way into Jimenez, who’s all too glad to tangle in close quarters. Jimenez 96, Ramirez 94.

Round 11: Lots of grappling. Things devolving here. But Jimenez finding space to throw punches. Stalking now. Has Ramirez in the corner. Jimenez 106, Ramirez 103.

Round 12: Jimenez has Ramirez in the corner again. Largely been the aggressor for 12 rounds. Ramirez landing, but Jimenez fires right back. Now Ramirez bleeding profusely from his right eye. Face covered with blood. Jimenez wearing Ramirez’s blood and finishing strong. Jimenez 116, Ramirez 112.

Main card begins: Charles Conwell def. Nathaniel Gallimore by TKO

Charles Conwell hadn’t fought in more than a year.

There was no sign of ring rest Saturday, when he stopped Nathaniel Gallimore by TKO in the sixth round of their super welterweight bout.

Gallimore failed to capitalize on his reach and instead ate a barrage of punches from Conwell. The referee waved off the fight with 2:08 left in the sixth round.

Conwell landed 136 punches compared to 39 punches for Gallimore, according to the DAZN broadcast.

Conwell, who’s signed with Oscar De La Hoya’s Golden Boy Promotions, improved to 19-0 with 14 knockouts and Gallimore fell to 22-8-1.

Round 1: Fighters measuring each other, trading jabs in the center of the ring. Conwell stalking now. Conwell lands a right to Gallimore’s ear. Lands two hard body shots. Conwell 10, Gallimore 9.  

Round 2: Conwell on the offensive again, but nothing big landing yet. An uppercut from Conwell. Conwell unloading now, but only for a burst. Gallimore trying to use his jab and reach advantage to keep Conwell at bay. Not working. Another barrage from Conwell to close out the round. Conwell 20, Gallimore 18. 

Round 3: Gallimore firing an effective jab, but probably won’t be enough to slow Conwell. In fact, it’s not. Conwell unleashing a furious flurry. Gallimore bleeding from right eye, mouth and nose. Conwell just pounding away. Conwell 30, Gallimore 27.

Round 4: Gallimore has taken a beating, but he’s at the center of the ring, firing jabs. Good stretch here for Gallimore, cornering Conwell and landing body shots. Hard right from Conwell. Conwell 40, Gallimore 36.

Round 5: The pace is slowing down now. Well, wrote that too soon. Conwell pounding away. Gallimore absorbing the punches and bleeding. Conwell is relentless. Conwell 50, Gallimore 45. 

Round 6: Conwell firing from the outset. Gallimore lands a good left – a pebble amidst Conwell’s boulders. It’s over. Referee stops the fight with 2:08 left in the sixth round. Chares Conwell is the winner by TKO.

Claressa Shields on weight advantage for Ryan Garcia

Ryan Garcia came in three pounds over the contracted weight of 140 pounds. Will that make a difference in his bout against Devin Haney? Claressa Shields doesn't think so.

"I don’t think that weight makes a difference. It may give him a little more energy or what not. Or not being drained. But he’s still going to fight the same. So I don’t think it’s changing nothing," Shields said.

Devin Haney vs. Ryan Garcia predictions 

Predictions from select boxing writers and fighters:  

Robert Segal, Boxing News 24:  “Even if Haney is planning to fight in a toe-to-toe fight, once he tastes Ryan’s power, he’ll change his mind about that and revert to the safety-first style we saw him use in his last four fights.’’ 

• Prediction:  Haney by a one-sided decision or late knockout 

Lucky Ngamwajasat, Bleacher Report:  “One punch can turn any fight and 'King Ry' definitely has the arsenal to catch Haney if he isn't careful. However, the smart money here says Haney keeps Garcia at arm's length and cruises to victory. 

• Prediction:  Haney by unanimous decision 

Josh Peter, USA TODAY:  "With Ryan Garcia’s erratic behavior, I’m hoping for the best but expecting the worst." 

• Prediction:  Haney wins after Garcia is DQ’d in the 8 th  round. 

Claressa Shields  told Fight Hub TV, “It’ll be entertaining. Like you’ll see Ryan go out there and be flashy and throw his combinations and be sharp and long and stuff. But I think that Devin is just the better boxer and he’ll be able to make Ryan run into some things and I think it’ll be a unanimous decision for Devin.”

Canelo Alvarez  told Fight Hub TV he thinks Garcia will win “if he’s 100 percent.’’ But Alvarez also said, “The people around him need to help him. So, I feel a little sad for him because he needs to have a good person, good people around him.” 

Shane Mosley  told Fight Hype, “I would say Devin has the bigger advantage, meaning he’ll be able to outbox, move around and understand Ryan. But at the same time … Ryan does have a big left hook and a big punch.” 

Shawn Porter  said of Garcia on his podcast, “I think he’s going to come to the ring comfortable against Devin, confident against Devin. You got (trainer) Derrick James in the corner now, so they’re working fundamentals and basics.” 

Adrien Broner  said, “My brother say he got a $1 million that Devin win this fight tonight. And if I had money like I had back in the day, I’d probably put my $1 million with H too. But taking that I only got $13, I like Ryan for the knockout. I like my odds. I got to triple this (expletive) up."

Devin Haney's mom responds to Ryan Garcia 

During a promotional event at the Empire State Building, Garcia repeatedly barked in Haney’s face, “Where’s you mom, (expletive)?’’ 

Haney violently shoved Garcia in the face and neck. 

But it didn’t end there. 

Later, during a conversation that aired live on X, formerly Twitter, Garcia said he’d heard that Haney’s father, Bill, had “pimped out’’ Haney’s mother. 

Then on Thursday, with the two boxers on the stage for the final press conference, Garcia asked, “Where’s your mama? Oh, I’m going to go flirt with your mama. …I want some of your mama.’’ 

Haney’s mother was in the audience. 

 And when someone approached her after the press conference, she said of Garcia, “I’ll bite the (expletive). I’ll bite him.’’ 

Sergiy Derevyanchenko def. Vaughn Alexander by unanimous decision

In a sterling display of accuracy and skill, Derevyanchenko outclassed Alexander with endless combinations of punches in the 10-round super middleweight fight. The Ukrainian fighter dropped Alexander to the canvas with a left hook to the body in the eighth round.

In the 2023 Fight of the Year, Derevyanchenko lost to Jaime Munguia by unaminous decision in a 10-round battle. But his bout against Alexander was a 10-round highlight reel.

The Ukrainian improved to 15-5. Alexander fell to 18-11-1.

Ryan Garcia and Devin Haney fought as amateurs 

The fight on Saturday will not be the first meeting between Ryan Garcia and Devin Haney. 

They faced off four times as amateurs and each won twice, according to BoxRec. (Oscar De La Hoya, the co-promoter of the Garcia-Haney fight, and others have said they’ve fought six times as amateurs and that each fighter won three times. De La Hoya touts the eminent bout Game 7.) 

Well, it was game on at the 2014 USA National Junior Championships when Garcia and Haney fought as 14-year-olds in the final at 125 pounds. 

Here’s footage of that fight:

Darius Fulghum def. Cristian Olivas by TKO

Fulghum, who has a nursing degree from Prairie View A&M, is focusing on his boxing career and it’s paying off.

He pounded Olivas with a barrage of punches and a flurry in the fourth round led the referee to wave off the super middleweight fight.

Fulghum, 27, improved to 11-0. Olivas, 32, fell to 22-11.

Jonathan Canas def. Markus Bowes by unanimous decision

Canas, a 6-foot righty, capitalized on his reach advantage against the 5-6 Bowes. Not only did Canas, 22, use his jab effectively, but he also landed plenty of strong rights in their four-round super lightweight bout.

Canas improved to 4-0 and Bowes fell to 2-6.

Amari Jones def. Armel Mbumba-Yassa by TKO

Jones, part of Devin Haney’s stable, knocked Mbumba-Yassa in the sixth round. Soon after the referee later waved off the Super Middleweight  fight that Jones dominated from the outset.

Jones, 31, improved to 12-0. Mbumba-Yassa, a native of The Republic of the Congo who had fought only 13 days earlier, fell to 10-2-1.

Who decides if Ryan Garcia is mentally fit to fight?

Whether Ryan Garcia is mentally fit to fight Haney ultimately is a matter for the New York State Athletic Commission to decide. And this week, as Garcia’s erratic behavior has continued, well regarded boxing analyst Teddy Atlas said he was “disappointed” with the state athletic commission. 

“There’s only one reason we have commissions, to ensure the welfare and health of a fighter going into a ring, to make sure that mentally and physically, they are of good, sound mind and body,” Atlas said on his podcast, “The Fight with Teddy Atlas.” “I don’t know that you can say that about Garcia if you didn’t evaluate him with the right people.” 

Garcia has said he underwent psychological evaluation and passed. 

In a statement provided to USA TODAY Sports, the commission said it will not comment on “the specific medical testing and evaluations of any particular person.” 

The commission also said it “has broad authority to assess the medical fitness of professional athletes (mental and physical health included), and engages in a thorough case-by-case due diligence process with every professional athlete based on their personal medical history and circumstances prior to their participation in competition. No match is held until the professional athletes are found medically fit for competition.” 

Kevin Newman II def. Eric Robles by TKO

With Roy Jones Jr. in his corner, Newman scored a TKO in the fourth round of their cruiserweight bout.

Newman dropped Robles with a flurry of punches earlier in the fourth round, then landed a hard right that left Robles on his knees. The referee promptly waved off the fight.

Newman, 32, improved to 15-3-1 and Robles, 32, of Mexico fell to 9-4.

How much does Ryan Garcia weigh? 

At the weigh-in on Friday, as Ryan Garcia stepped on the scale , he guzzled what appeared to be a beer. On a video he shared on X, formerly Twitter, Garcia wrote that it was "100 percent worth it." But he later posted a message that said, "Apple Juice and sparkling water HEHE." 

What was indisputable: Garcia weighed in at 143.2 pounds. 

There was no such drama with Devin Haney, who weighed in at 140 pounds. 

And because the two boxers made a pre-fight bet – Each would owe the other $500,000 for every pound they were over the 140-pound contracted weight – Haney found himself $1.5 million richer. 

Shamar Canal def. Pedro Borgaro by unanimous decision

Trained by Devin Haney’s father, Canal knocked down Borgaro in the third round and was in control throughout the six-round, lightweight fight.

Canal, 21 and signed to Devin Haney Promotions, improved to 8-0. Borgaro, an 18-year-old from Mexico, fell to 7-2.

Devin Haney vs. Ryan Garcia full card 

Here are the bouts on the main card:  

≻ Arnold Barboza Jr. vs. Sean McComb, co-main, WBO Inter-Continental Super Lightweight, 10 rounds 

≻ Bektemir Melikuziev vs. Pierre Dibombe, WBA Inter-Continental Super Middleweight, 10 rounds 

≻ John Ramirez vs. David Jimenez, WBA Interim World Super Flyweight, 12 rounds 

≻ Charles Conwell vs. Nathaniel Gallimore, Super Welterweight, 12 rounds 

What are the win-loss records for Ryan Garcia and Devin Haney?

≻ Ryan Garcia: 24-1 with 20 KO’s 

≻ Devin Haney: 31-0 with 15 KO’s 

Tickets for Garcia vs. Haney fight 

As of Friday night, tickets for the fight at Barclays Center were available for $112 and up on Stubhub before fees. 

We occasionally recommend interesting products and services. If you make a purchase by clicking one of the links, we may earn an affiliate fee. USA TODAY Network newsrooms operate independently, and this doesn’t influence our coverage.