Reading Scholarly Articles
Read Strategically
Okay, you’ve found an article you want to read, you’ve obtained the full text, you’ve done a cursory evaluation (you’ll do more once you’re reading in-depth, of course). Now it’s time to read! Reading scholarly articles can be a more challenging than reading a book, magazine, news article—or even some textbooks. Theoretical articles are, generally speaking, easier to understand. Empirical articles, because they add new knowledge, must go through great detail to demonstrate that the information they offer is based on solid science. Reading these studies can be intense, so develop your skills in this area to make sure that what you’re doing is the best use of your time and energy.
Note that you may choose to use some AI assistance in your reading. We have previously discussed how AI can find and even summarize articles related to your research, and these tools can be helpful. However, in no uncertain terms, you need to know that you cannot rely solely on these summations. Students need to read the articles AI suggests rather than relying exclusively on the summaries it provides. While AI-generated summaries can be helpful for quickly grasping the main ideas of an article, they are no substitute for a thorough, firsthand understanding of the material. This becomes even more critical when considering the potential for AI to produce hallucinated (that is, made-up) articles, as relying solely on summaries not only risks missing key details but also increases the likelihood of unknowingly incorporating inaccurate or fabricated information into your work.
When you start reading a journal article, you might think you must read it from top-to-bottom, word-for-word in order. However, few professional researchers actually read that way! They read strategically, jumping to certain sections of the article first, second, and so on depending on what information they are most interested in from a given source. To start, we’ll review each section in the order you might expect to see them, but at the end of this page we’ll also discuss three methods we use and that you might consider as you develop your own reading habits for academic articles!
As you will recall from earlier, theoretical articles have no set structure and will look similar to reading a chapter of a book. Empirical articles contain the following sections (although exact section names vary): abstract, introduction, methods, results, and discussion.
Nearly all articles will have an abstract, the short paragraph at the beginning of an article that summarizes the author’s research question, methods used to answer the question, and key findings. The abstract may also give you some idea about the theoretical perspective of the author. In effect, the abstract provides you with a framework to understand the rest of the article and the article’s punch line: what the author(s) found, and whether the article is relevant to your area of inquiry. As we’ve already discussed, reading the abstract as part of the search process is helpful for narrowing down your results and for getting a sense of what the article will be about.
The introduction contains the literature review for the article and is an excellent source of information as you build your own literature review. The methods section reviews how the author gathered their sample, how they measured their variables, and how the data were analyzed. The results section provides an in-depth discussion of the findings of the study. The discussion section reviews the main findings and addresses how those findings fit in with the existing literature. At the end, there will be a list of references (which you should read!) and there may be a few tables, figures, or appendices if applicable.
While you should get into the habit of familiarizing yourself with each part of the articles you wish to cite, there are strategic ways to read journal articles that can make them a little easier to digest. Once you have read the abstract for an article and determined it is one you’d like to read in full, some researchers suggest you read through the introduction and discussion sections next. The introduction section will showcase other articles and findings that are significant in your topic area, so reading through it will be beneficial for your own information-gathering process for your literature review. Reading an article’s discussion section helps you understand what the author views as their study’s major findings and how the author perceives those findings to relate to other research. After having those two sections in your head, reading the methods, so you can see in detail what they actually did, and the results, to see what they actually found, may make a little more sense.
Want to know how we read? Here’s a bit on each of our methods, developed over years of being students of researchers and professional scholars:
The Researcher’s Toolkit: How I Apply What I Teach
Dr. Arocho: It always depends on what my goal is in reading a study, of course, but when I’m reading a study because I might want to use it in my own review of the literature, I’m paying extra attention to what studies this study is in conversation with. To that end, I read the abstract, then the introduction and literature review, then the discussion (and skim the references). This gives me a good sense of what this study is referencing and how it’s building on other studies. Then, I’ll read the methods and results (including tables, figures, and graphs) to see if this study has done good research and what they actually found, and then I’ll usually skim the discussion again to see if knowing details of the methods changes anything I understand about the discussion. At that point, I should have a pretty good idea of what this study means for my own ideas, so I’m ready to write myself some notes and cite this study when I start writing! I like to use Zotero to organize my literature and to keep track of citations and references once I start writing.
The Researcher’s Toolkit: How I Apply What I Teach
Dr. Knight: I am a big note-taker. I have found that I have to summarize material in my own words if I have any hope of making sense of it. This also applies when I’m reading academic articles. While reading, I take a moment to summarize the key points in my own words. I feel this is key if I want to understand the article. I also have learned to identify key features in each section of the article. In the introduction, I identify the research question and why it is important. When I reach the methods section, I focus on how the study was conducted and what type of data was collected. In the results section, I highlight the key findings, ensuring that I understand how the data support the study’s objectives. The discussion section is where I consider how the results relate to broader knowledge in the field and what implications they have for future research or practice. Finally, in the conclusion, I summarize the study’s main takeaways. Writing these summaries helps me solidify my understanding and improves retention, making it much easier to recall and apply the information later.
I find annotating the articles very useful. On a computer, this can be accomplished using digital annotation tools such as Adobe Acrobat, Microsoft OneNote, and Notion or reference managers like Zotero and Mendeley. I highlight key points selectively, ensuring that I do not overdo it by marking entire paragraphs. Using the comment feature, I write margin notes to summarize important ideas in my own words, ask questions to deepen my understanding and flag any confusing sections I need to revisit. I also use symbols to categorize information, such as ★ for important ideas,? For concepts I need to clarify, and → for key arguments or connections to other studies. This process helps me engage more deeply with the article.
I feel that actively engaging with the research article in this way improves my retention, comprehension, and critical thinking. Instead of struggling through dense material passively, this approach makes reading purposeful and interactive. By skimming strategically, summarizing, and annotating, I can engage in deep learning rather than merely gathering information.
As you progress through your research methods course, you will pick up additional research elements that are important to understand. You will learn how to identify qualitative and quantitative methods, as well as exploratory, explanatory, and descriptive research methods. You will also learn the criteria for establishing causality and the different types of causality. Subsequent chapters of this textbook will address other elements of journal articles, including choices about measurement, sampling, and design. As you learn about these additional items, you will find that the methods and results sections begin to make more sense and you will understand how the authors reached their conclusions.
As you read a research report, there are several questions you can ask yourself about each section, from abstract to conclusion. Those questions are summarized in the table below. Keep in mind that the questions covered here are designed to help you, the reader, to think critically about the research you come across and to get a general understanding of the strengths, weaknesses, and key takeaways from a given study. By considering how you might respond to the following questions while reading research reports, you’ll gain confidence in describing the report to others and discussing its meaning and impact with them.
| Report section | Questions worth asking |
| Abstract | What are the key findings? How were those findings reached? What framework does the researcher employ? |
| Acknowledgments | Who are this study’s major stakeholders? Who provided feedback? Who provided support in the form of funding or other resources? |
| Problem statement (introduction) | How does the author frame their research focus? What other possible ways of framing the problem exist? Why might the author have chosen this particular way of framing the problem? |
| Literature review (introduction) |
How selective does the researcher appear to have been in identifying relevant literature to discuss? Does the review of literature appear appropriately extensive? Does the researcher provide a critical review? |
| Sample (methods) | Where was the data collected? Did the researcher collect their own data or use someone else’s data? What population is the study trying to make claims about, and does the sample represent that population well? What are the sample’s major strengths and major weaknesses? |
| Data collection (methods) | How were the data collected? What do you know about the relative strengths and weaknesses of the method employed? What other methods of data collection might have been employed, and why was this particular method employed? What do you know about the data collection strategy and instruments (e.g., questions asked, locations observed)? What don’t you know about the data collection strategy and instruments? |
| Data analysis (methods) | How were the data analyzed? Is there enough information provided for you to feel confident that the proper analytic procedures were employed accurately? |
| Results | What are the study’s major findings? Are findings linked back to previously described research questions, objectives, hypotheses, and literature? Are sufficient amounts of data (e.g., quotes and observations in qualitative work, statistics in quantitative work) provided in order to support conclusions drawn? Are tables readable? |
| Discussion/conclusion | Does the author generalize to some population beyond her/his/their sample? How are these claims presented? Are claims made supported by data provided in the results section (e.g., supporting quotes, statistical significance)? Have limitations of the study been fully disclosed and adequately addressed? Are implications sufficiently explored? |
Understanding the results section
As mentioned previously, reading the abstract that appears in most reports of scholarly research will provide you with an excellent, easily digestible review of a study’s major findings and of the framework the author is using to position their findings. Abstracts typically contain just a few hundred words, so reading them is a nice way to quickly familiarize yourself with a study. If the study seems relevant to your paper, it’s probably worth reading more. If it’s not, then you have only spent a minute or so reading the abstract. Reading the abstract is not enough, however, if you want to truly understand the article, and if you want to report the results of the study to support your own ideas (cite the work. For example, the abstract may say something like: “we found that poverty is associated with mental health status.” For your literature review, you want the details, not the summary. In the results section of the article, you may find a sentence that states: “for households in poverty, children are three times more likely to have a mental health diagnosis.” This more detailed information provides a stronger basis on which to build a literature review.
Using the summarized results in an abstract is an understandable mistake to make. The results section often contains terminology, diagrams, and symbols that may be hard to understand without having completed advanced coursework on statistical or qualitative analysis. To that end, the purpose of this section is to improve reading comprehension by providing an introduction to the basic components of a results section.
Journal articles often contain tables, and scanning them is a good way to begin reading an article. A table provides a quick, condensed summary of the report’s key findings. The use of tables is not limited to one form or type of data, though they are used most commonly in quantitative research. Tables are a concise way to report large amounts of data. Some tables present descriptive information about a researcher’s sample, which is often the first table in a results section. These tables will likely contain frequencies (n) and percentages (%). For example, if gender happened to be an important variable for the researcher’s analysis, a descriptive table would show how many and what percent of all study participants are women, men, or other genders. Frequencies or counts will probably be listed as n, while the percent symbol (%) might be used to indicate percentages.
In a table presenting a causal relationship, two sets of variables are represented. The independent variable, or cause, and the dependent variable, the effect. We’ll go into more detail on variables later. The independent variable attributes are typically presented in the table’s columns, while dependent variable attributes are presented in rows. This allows the reader to scan across a table’s rows to see how values on the dependent variable attributes change as the independent variable attribute values change. Tables displaying results of quantitative analysis will also likely include some information about the strength and statistical significance of the relationships presented in the table. These details tell the reader how likely it is that the relationships presented will have occurred simply by chance. These statistics represent what the researchers found in their sample, and they are using their sample to make conclusions about the true population of all employees in the real world. Because the methods we use in social science are never perfect, there is some amount of error in that value. Researchers will thus often provide a confidence interval, or a range of values in which the true value is likely to be, to provide a more accurate description of their data. Confidence intervals are often listed with a percentage, like 90% or 95%, and a range of values; if the range of values includes 0, then that might mean there’s a good chance there’s no association, so most studies reporting an association will have these ranges to help you see just how close it gets to 0 (but if the results are considered significant – more on that in a moment – then the range will probably not cross 0).
Of course, we cannot assume that these patterns didn’t simply occur by chance. How confident can we be that the findings presented in the table did not occur by chance? This is where tests of statistical significance come in handy. Statistical significance tells us the likelihood that the relationships we observe could be caused by something other than chance. While your statistics class will give you more specific details on tests of statistical significance and reading quantitative tables, the important thing to be aware of as a non-expert reader of tables is that some of the relationships presented will be statistically significant and others may not be. Tables should provide information about the statistical significance of the relationships presented. When reading a researcher’s conclusions, pay attention to which relationships are statistically significant and which are not, both by examining the confidence interval or the p-value. A p-value is a statistical measure of the probability that there is no relationship between the variables under study. Another way of putting this is that the p value provides guidance on whether or not we should reject the null hypothesis. The null hypothesis is simply the assumption that no relationship exists between the variables in question. If a p-value is below a given number (traditionally, 0.05) then it is considered statistically significant because there is a low chance (5% in the case of p = 0.05) that the null hypothesis is correct. Social science often uses 0.05, but other values are used occasionally. Studies using 0.1 are using a more forgiving standard of significance, and therefore, have a higher likelihood of error (10%). Studies using 0.01 are using a more stringent standard of significance, and therefore, have a lower likelihood of error (1%). You might even see p-values reported as <0.001, meaning that the results were so strong that there is only a very, very small chance that the null hypothesis was correct (though we can never say it’s 0%!).
Notice the language being used here: researchers hedge their bets here by using words like somewhat and may be. When testing hypotheses, social scientists generally phrase their findings in terms of rejecting the null hypothesis rather than making bold statements about the relationships observed in their tables. This is important because no one study can “prove” anything, and even with a body of evidence, there’s always room for additional data that might introduce new information. You can learn more about creating tables, reading tables, and tests of statistical significance in a class focused exclusively on statistical analysis. For now, hopefully this brief introduction will improve your confidence in reading and understanding the quantitative tables you encounter while reading reports of social science research.
A final caveat is worth noting here. The discussion of tables and reading the results section is applicable to quantitative articles, as these studies will contain a lot of numbers and the results of statistical tests demonstrating association between those numbers. Qualitative articles, on the other hand, will consist mostly of quotations from participants and themes from these quotes or other non-statistical data. For most qualitative articles, the authors want to put their results in the words of their participants, as they are the experts. The results section may be organized by theme, with each paragraph or subsection illustrating through quotes how the authors interpret what people in their study said.
Can I use AI for this?
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Using AI to Understand Research Results: Opportunities and Responsibilities
Can AI help you understand the results section of an academic journal article?
Understanding the results section of an academic journal article can be challenging, especially when complex data and statistical analyses are involved. AI can be a valuable tool in breaking down these findings, simplifying technical language, and providing context for statistical tests. By guiding readers through tables, figures, and key significance measures, AI helps interpret research outcomes. The following are ways you might find AI useful in understanding the results section of an academic journal article.
- Summarizing Complex Data
- Breaks down statistical analyses and findings into plain language.
- Highlights key takeaways without technical jargon.
- Explaining Statistical Tests
- Clarifies statistical analyses such as ANOVA, regression, t-tests, and chi-square.
- Provides context for what these tests measure and what the results indicate.
- Interpreting Tables and Figures
- Guides in reading tables and understanding p-values, confidence intervals, and effect sizes.
- Explains trends in graphs and charts.
- Assessing Significance & Practical Meaning
- Identifies whether the results are statistically significant.
- Evaluates the practical implications of the findings.
- Comparing Results to the Research Question
- Assesses whether the findings address the research question.
- Explores how the results fit within the study’s framework.
As always, there are precautions students need to take when employing AI. If you chose to use AI to interpret the results section of an article, please keep the following in mind:
- It is your responsibility to verify AI-generated interpretations
- Cross-check AI-generated summaries with the original text to ensure accuracy.
- Compare AI explanations with course materials, textbooks, or reputable academic sources.
- Seek clarification from professors or experts if interpretations seem unclear or inconsistent.
- It is your responsibility to understand the limits of AI
- AI may oversimplify or misinterpret complex statistical analyses.
- AI cannot evaluate the study’s methodological rigor or potential biases as critically as a human expert.
- AI can provide a misrepresentation of findings
- Ensure that AI-generated explanations do not distort or misstate the study’s actual results.
- Be cautious of AI-generated interpretations that introduce assumptions not present in the original research.
- It is your responsibility to maintain academic integrity
- AI should be used as a tool for comprehension, not for direct copying into assignments without proper disclosure.
- Always follow institutional guidelines regarding AI use in academic work.
- Take care to develop critical thinking skills
- Use AI to support learning rather than relying entirely on it to understand research findings.
- Practice analyzing results independently before consulting AI to strengthen statistical literacy.
- You must consider ethical and privacy concerns
- Avoid sharing proprietary or unpublished research with AI tools unless permitted by the research team.
- Be mindful of data privacy when using AI platforms, especially if results contain sensitive information.
By taking these precautions, you can use AI to enhance your understanding of research results while maintaining academic integrity and critical thinking.
