analysis format research

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:

• Nursing Practice Issue: Qualitative Research Article Analysis
• Quantitative Article Critique in Nursing
• 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
• Space and the Atom: Article Analysis
• China’s Hegemonic Prospects: Article Review
• Article Analysis: Fear of Missing Out
• 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

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Data Analysis in Research: Types & Methods

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.

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.”

Why analyze data in research?

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

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

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Types of data in research

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 .

Finding patterns in the qualitative data

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.

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.

Methods used for data analysis in qualitative research

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.

Choosing the right software can be tough. Whether you’re a researcher, business leader, or marketer, check out the top 10  qualitative data analysis software  for analyzing qualitative data.

Data analysis in quantitative research

Preparing data for analysis.

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

Methods used for data analysis in quantitative 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.

Considerations in research data analysis

• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

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

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

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