Research Paper Structure

A descriptive study is a research design that aims to systematically describe phenomena, characteristics, experiences, or health outcomes in a defined population without attempting to establish causal relationships. Unlike analytical or experimental studies, descriptive studies do not involve manipulation of variables or comparison between different groups. Instead, they provide a detailed portrayal of what exists in a specific context at a particular point in time. Descriptive studies form the foundation of empirical research and are essential for generating baseline data that informs hypothesis development for subsequent analytical studies. They answer the fundamental research questions of "what," "who," "where," "when," and "how often" in relation to health phenomena or social conditions. Main Types of Descriptive Studies Descriptive studies can be classified into several categories based on their design and temporal characteristics: Cross-sectional studies: examining prevalence of a condition at a single point in time Case reports: detailed descriptions of individual cases with unique or instructive features Case series: systematic documentation of multiple cases with similar characteristics Ecological studies: analyzing data aggregated at population or community level Descriptive surveys: collecting standardized information from defined populations Surveillance studies: ongoing systematic monitoring of disease or health indicators When to Choose a Descriptive Design The...

Writing research objectives requires you to express clearly what you are planning to do in your study. What are research objectives? Research objectives are the specific goals that you are going to achieve in your study. Steps to write research objectives Define the research problem Identify a gap in the clinical or scientific literature. Ground the problem in patient outcomes, disease burden, or an unresolved mechanistic question. A well-defined problem naturally constrains your objectives. Check existing literature Conduct a scoping review to understand what is already known. This prevents duplication, reveals methodological limitations in prior work, and highlights where new objectives can make the most contribution. Identify the study population and setting Specify who will be studied: patient demographics, inclusion/exclusion criteria, clinical setting (e.g. ICU, outpatient, community). Objectives must align with what is feasible in your target population. Formulate a primary research question Use a structured framework to build a clear, answerable question. PICO (Population, Intervention, Comparison, Outcome) works well for clinical research. For qualitative or observation research, use the SPIDER framework (Sample, Phenomenon of Interest, Design, Evaluation, Research type) or PEO (Population, Exposure, Outcome). Draft objectives using SMART criteria Write objectives that are Specific, Measurable, Achievable, Relevant, and Time-bound....

To create keywords for a research paper, you need to select 3-8 terms/phrases that Describe the core aspects of your study Are likely to be used by other researchers who are searching for your topic in a database   How do you find the right keywords for your paper? To find the keywords that will work best for your paper, do the following: 1. Analyze your title and abstract Identify the most essential aspects of your study (these will usually be nouns). For example, if your paper is titled “Impact of Nanobiosensor-Derived Lactate Monitoring on Intrapartum Hypoxia Outcomes in High-Risk Pregnancies”, effective keywords could be Intrapartum Hypoxia, High-Risk Pregnancy, Nanobiosensors, Lactate Clearance, Fetal Acidosis, Lactate Monitoring. 2. Brainstorm related terms Think of synonyms that other researchers might use if they are interested in your topic. For example,  if your study is titled “The Relationship between Iron Deficiency Anemia and Dyslipidemia”, your keywords could include iron metabolism, serum ferritin, total cholesterol, atherogenic index, etc. 3. Filter out extra-broad phrases Avoid using very general keywords because they will not actually lead interested researchers to your paper. For example, in a study showing that dietary glucose intake increases inflammation markers in rheumatoid arthritis,...

Writing an operational definition of terms involves defining variables or concepts specifically by the operations, measurements, or procedures used to observe and measure them in your study. Steps to write an operational definition of terms Pick your important words: Look at your research title and questions — what are the big, technical words? Those are the ones you need to define. (Example: "Iron Deficiency Anemia", "Lipid Profile") Think about how it can be measured: Don't just say what the word means — explain how you checked or tested it in your study. Did you use a blood test? A survey? A ruler and scale? Be specific enough that someone else could copy your study: Imagine explaining it to a colleague who wasn't there. Give exact numbers, tools, or steps so they could repeat what you did and get the same results. Use this simple sentence structure: "[Term] in this study refers to [how you measured or defined it]." For example: "Iron Deficiency Anemia in this study refers to a hemoglobin level below 12 g/dL confirmed by a blood test." Why do researchers need to include operational definition of terms? Operational definition of terms is done to standardize the data. Whenever data...

Keywords are important to make your paper discoverable to other researchers because they are used to index your paper in major databases like Web of Science and Scopus and then display your article to researchers who are searching for relevant topics. Without keywords, your paper is not searchable, discoverable, or visible and so won't be cited either. Keywords help other researchers quickly determine what field and topic your paper is covering so that they can decide to view, download, and cite it.  That's why it is important to include the most relevant keywords that will help other authors find your paper.

Thank you for approaching us with your question. We can understand how worried this must be making you. First of all, don’t panic. This was an oversight on your part, but journals understand that these mistakes can happen and generally try to accommodate corrections when possible (see also similar questions answered here and here).   It appears that your manuscript is not at a revision stage (which would have made the process a bit easier) but has already been published. Here are some steps to consider in your case:  1. Follow up promptly with the journal editorial office. Explain the situation clearly—that you inadvertently left out your grant number in the acknowledgments section and need to get it added. Most journals have a process for making post-publication corrections.  2. Get the exact grant number/funding source details you need to add. Double check with your supervisor or institutional grants office if needed.  3. Offer to provide the updated acknowledgments text exactly as it should be published in the correction. This makes it easy for the journal production team. Plus, you do not want to request another round of changes!  4. Thank them for their understanding and willingness to publish the correction. Most...

Most journals run a “technical check” to see if a manuscript is formatted correctly and complies with journal policies. This initial screening might be to check if the manuscript is written in an acceptable language; contains the required elements such as author details, abstract, and declarations; uses the required citation style; and is within the journal’s length limits.   Some journals may even run a plagiarism check. These checks happen before the manuscript reaches the editor or reviewers. If a manuscript passes these checks, the journal editor or managing editor will consider if the manuscript is worth moving ahead. If the editor is satisfied, they will initiate the peer review process by choosing and contacting appropriate peer reviewers to evaluate the work.  In this case, however, it appears that peer review has taken place, and the editor is yet to reach a decision. A technical check at this stage does seem unusual, but different journals have different workflows and assign different terms for the various stages in the workflow. Perhaps the editorial board is reviewing the decision and the manuscript, and a technical editor needs to check the manuscript and the reviewer comments to ensure that journal standards have been followed.  ...

Most journals run a “technical check” to see if a manuscript is formatted correctly and complies with journal policies. This initial screening might be to check if the manuscript is written in an acceptable language; contains the required elements such as author details, abstract, and declarations; uses the required citation style; and is within the journal’s length limits.   Some journals may even run a plagiarism check. These checks happen before the manuscript reaches the editor or reviewers. If a manuscript passes these checks, the journal editor or managing editor will consider if the manuscript is worth moving ahead. If the editor is satisfied, they will initiate the peer review process by choosing and contacting appropriate peer reviewers to evaluate the work.  In this case, however, it appears that peer review has taken place, and the editor is yet to reach a decision. A technical check at this stage does seem unusual, but different journals have different workflows and assign different terms for the various stages in the workflow. Perhaps the editorial board is reviewing the decision and the manuscript, and a technical editor needs to check the manuscript and the reviewer comments to ensure that journal standards have been followed.  ...

SD reflects sample variability, while SE is an estimate of how close your mean is to the true population mean. Both are useful measures, but your readers will need to know how much your data is dispersed from the mean. High variability suggests that individual data points deviate widely from the mean, indicating potential heterogeneity in the sample. This information helps readers gauge the reliability and generalizability of study findings. If a sample has low variability, the mean is a more robust descriptive statistic of the dataset. On the other hand, high variability may signal that the mean might not be as reliable, and that tests using means (e.g., t-tests) may not be appropriate. Hence, it is necessary to report SD along with means in your paper.Barde and Barde (2012) have explained the use of SD vs SE in detail. The SAMPL (Statistical Analyses and Methods in the Published Literature) guidelines explicitly recommend against using SE to indicate variability in a dataset. Would you like personalized guidance when using descriptive or inferential statistics? Talk to an experienced biostatistician under Editage’s Statistical Analysis & Review Services.

SD reflects sample variability, while SE is an estimate of how close your mean is to the true population mean. Both are useful measures, but your readers will need to know how much your data is dispersed from the mean. High variability suggests that individual data points deviate widely from the mean, indicating potential heterogeneity in the sample. This information helps readers gauge the reliability and generalizability of study findings. If a sample has low variability, the mean is a more robust descriptive statistic of the dataset. On the other hand, high variability may signal that the mean might not be as reliable, and that tests using means (e.g., t-tests) may not be appropriate. Hence, it is necessary to report SD along with means in your paper.Barde and Barde (2012) have explained the use of SD vs SE in detail. The SAMPL (Statistical Analyses and Methods in the Published Literature) guidelines explicitly recommend against using SE to indicate variability in a dataset. Would you like personalized guidance when using descriptive or inferential statistics? Talk to an experienced biostatistician under Editage’s Statistical Analysis & Review Services.