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  • How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

  • Your results should always be written in the past tense.
  • While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
  • Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
  • If you have other results you’d like to include, consider adding them to an appendix or footnotes.
  • Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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data analysis section research paper

If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

  • A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
  • A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
  • A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

  • Tables are used to communicate exact values, giving a concise overview of various results
  • Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Example of using figures in the results section

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

  • Recurring points of agreement or disagreement
  • Patterns and trends
  • Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

  • The type of analysis used
  • Relevant results in the form of descriptive and inferential statistics
  • Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

  • Recurring patterns
  • Significant or representative individual responses
  • Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Research Paper Writing: 6. Results / Analysis

  • 1. Getting Started
  • 2. Abstract
  • 3. Introduction
  • 4. Literature Review
  • 5. Methods / Materials
  • 6. Results / Analysis
  • 7. Discussion
  • 8. Conclusion
  • 9. Reference

Writing about the information

There are two sections of a research paper depending on what style is being written. The sections are usually straightforward commentary of exactly what the writer observed and found during the actual research. It is important to include only the important findings, and avoid too much information that can bury the exact meaning of the context.

The results section should aim to narrate the findings without trying to interpret or evaluate, and also provide a direction to the discussion section of the research paper. The results are reported and reveals the analysis. The analysis section is where the writer describes what was done with the data found.  In order to write the analysis section it is important to know what the analysis consisted of, but does not mean data is needed. The analysis should already be performed to write the results section.

Written explanations

How should the analysis section be written?

  • Should be a paragraph within the research paper
  • Consider all the requirements (spacing, margins, and font)
  • Should be the writer’s own explanation of the chosen problem
  • Thorough evaluation of work
  • Description of the weak and strong points
  • Discussion of the effect and impact
  • Includes criticism

How should the results section be written?

  • Show the most relevant information in graphs, figures, and tables
  • Include data that may be in the form of pictures, artifacts, notes, and interviews
  • Clarify unclear points
  • Present results with a short discussion explaining them at the end
  • Include the negative results
  • Provide stability, accuracy, and value

How the style is presented

Analysis section

  • Includes a justification of the methods used
  • Technical explanation

Results section

  • Purely descriptive
  • Easily explained for the targeted audience
  • Data driven

Example of a Results Section

Publication Manual of the American Psychological Association Sixth Ed. 2010

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How to write statistical analysis section in medical research

Alok kumar dwivedi.

Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA

Associated Data

jim-2022-002479supp001.pdf

Data sharing not applicable as no datasets generated and/or analyzed for this study.

Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.

Introduction

Biostatistics is the overall approach to how we realistically and feasibly execute a research idea to produce meaningful data and translate data to meaningful interpretation and decisions. In this era of evidence-based medicine and practice, basic biostatistical knowledge becomes essential for critically appraising research articles and implementing findings for better patient management, improving healthcare, and research planning. 1 However, it may not be sufficient for the proper execution and reporting of statistical analyses in studies. 2 3 Three things are required for statistical analyses, namely knowledge of the conceptual framework of variables, research design, and evidence-based applications of statistical analysis with statistical software. 4 5 The conceptual framework provides possible biological and clinical pathways between independent variables and outcomes with role specification of variables. The research design provides a protocol of study design and data generation process (DGP), whereas the evidence-based statistical analysis approach provides guidance for selecting and implementing approaches after evaluating data with the research design. 2 5 Ocaña-Riola 6 reported a substantial percentage of articles from high-impact medical journals contained errors in statistical analysis or data interpretation. These errors in statistical analyses and interpretation of results do not only impact the reliability of research findings but also influence the medical decision-making and planning and execution of other related studies. A survey of consulting biostatisticians in the USA reported that researchers frequently request biostatisticians for performing inappropriate statistical analyses and inappropriate reporting of data. 7 This implies that there is a need to enforce standardized reporting of the statistical analysis section in medical research which can also help rreviewers and investigators to improve the methodological standards of the study.

Biostatistical practice in medicine has been improving over the years due to continuous efforts in promoting awareness and involving expert services on biostatistics, epidemiology, and research design in clinical and translational research. 8–11 Despite these efforts, the quality of reporting of statistical analysis in research studies has often been suboptimal. 12 13 We noticed that none of the methods reporting documents were developed using evidence-based biostatistics (EBB) theory and practice. The EBB practice implies that the selection of statistical analysis methods for statistical analyses and the steps of results reporting and interpretation should be grounded based on the evidence generated in the scientific literature and according to the study objective type and design. 5 Previous works have not properly elucidated the importance of understanding EBB concepts and related reporting in the write-up of statistical analyses. As a result, reviewers sometimes ask to present data or execute analyses that do not match the study objective type. 14 We summarize the statistical analysis steps to be reported in the statistical analysis section based on review and thematic frameworks.

We identified articles describing statistical reporting problems in medicine using different search terms ( online supplemental table 1 ). Based on these studies, we prioritized commonly reported statistical errors in analytical strategies and developed essential components to be reported in the statistical analysis section of research grants and studies. We also clarified the purpose and the overall implication of reporting each step in statistical analyses through various examples.

Supplementary data

Although biostatistical inputs are critical for the entire research study ( online supplemental table 2 ), biostatistical consultations were mostly used for statistical analyses only 15 . Even though the conduct of statistical analysis mismatched with the study objective and DGP was identified as the major problem in articles submitted to high-impact medical journals. 16 In addition, multivariable analyses were often inappropriately conducted and reported in published studies. 17 18 In light of these statistical errors, we describe the reporting of the following components in the statistical analysis section of the study.

Step 1: specify study objective type and outcomes (overall approach)

The study objective type provides the role of important variables for a specified outcome in statistical analyses and the overall approach of the model building and model reporting steps in a study. In the statistical framework, the problems are classified into descriptive and inferential/analytical/confirmatory objectives. In the epidemiological framework, the analytical and prognostic problems are broadly classified into association, explanatory, and predictive objectives. 19 These study objectives ( figure 1 ) may be classified into six categories: (1) exploratory, (2) association, (3) causal, (4) intervention, (5) prediction and (6) clinical decision models in medical research. 20

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Object name is jim-2022-002479f01.jpg

Comparative assessments of developing and reporting of study objective types and models. Association measures include odds ratio, risk ratio, or hazard ratio. AUC, area under the curve; C, confounder; CI, confidence interval; E, exposure; HbA1C: hemoglobin A1c; M, mediator; MFT, model fit test; MST, model specification test; PI, predictive interval; R 2 , coefficient of determinant; X, independent variable; Y, outcome.

The exploratory objective type is a specific type of determinant study and is commonly known as risk factors or correlates study in medical research. In an exploratory study, all covariates are considered equally important for the outcome of interest in the study. The goal of the exploratory study is to present the results of a model which gives higher accuracy after satisfying all model-related assumptions. In the association study, the investigator identifies predefined exposures of interest for the outcome, and variables other than exposures are also important for the interpretation and considered as covariates. The goal of an association study is to present the adjusted association of exposure with outcome. 20 In the causal objective study, the investigator is interested in determining the impact of exposure(s) on outcome using the conceptual framework. In this study objective, all variables should have a predefined role (exposures, confounders, mediators, covariates, and predictors) in a conceptual framework. A study with a causal objective is known as an explanatory or a confirmatory study in medical research. The goal is to present the direct or indirect effects of exposure(s) on an outcome after assessing the model’s fitness in the conceptual framework. 19 21 The objective of an interventional study is to determine the effect of an intervention on outcomes and is often known as randomized or non-randomized clinical trials in medical research. In the intervention objective model, all variables other than the intervention are treated as nuisance variables for primary analyses. The goal is to present the direct effect of the intervention on the outcomes by eliminating biases. 22–24 In the predictive study, the goal is to determine an optimum set of variables that can predict the outcome, particularly in external settings. The clinical decision models are a special case of prognostic models in which high dimensional data at various levels are used for risk stratification, classification, and prediction. In this model, all variables are considered input features. The goal is to present a decision tool that has high accuracy in training, testing, and validation data sets. 20 25 Biostatisticians or applied researchers should properly discuss the intention of the study objective type before proceeding with statistical analyses. In addition, it would be a good idea to prepare a conceptual model framework regardless of study objective type to understand study concepts.

A study 26 showed a favorable effect of the beta-blocker intervention on survival outcome in patients with advanced human epidermal growth factor receptor (HER2)-negative breast cancer without adjusting for all the potential confounding effects (age or menopausal status and Eastern Cooperative Oncology Performance Status) in primary analyses or validation analyses or using a propensity score-adjusted analysis, which is an EBB preferred method for analyzing non-randomized studies. 27 Similarly, another study had the goal of developing a predictive model for prediction of Alzheimer’s disease progression. 28 However, this study did not internally or externally validate the performance of the model as per the requirement of a predictive objective study. In another study, 29 investigators were interested in determining an association between metabolic syndrome and hepatitis C virus. However, the authors did not clearly specify the outcome in the analysis and produced conflicting associations with different analyses. 30 Thus, the outcome should be clearly specified as per the study objective type.

Step 2: specify effect size measure according to study design (interpretation and practical value)

The study design provides information on the selection of study participants and the process of data collection conditioned on either exposure or outcome ( figure 2 ). The appropriate use of effect size measure, tabular presentation of results, and the level of evidence are mostly determined by the study design. 31 32 In cohort or clinical trial study designs, the participants are selected based on exposure status and are followed up for the development of the outcome. These study designs can provide multiple outcomes, produce incidence or incidence density, and are preferred to be analyzed with risk ratio (RR) or hazards models. In a case–control study, the selection of participants is conditioned on outcome status. This type of study can have only one outcome and is preferred to be analyzed with an odds ratio (OR) model. In a cross-sectional study design, there is no selection restriction on outcomes or exposures. All data are collected simultaneously and can be analyzed with a prevalence ratio model, which is mathematically equivalent to the RR model. 33 The reporting of effect size measure also depends on the study objective type. For example, predictive models typically require reporting of regression coefficients or weight of variables in the model instead of association measures, which are required in other objective types. There are agreements and disagreements between OR and RR measures. Due to the constancy and symmetricity properties of OR, some researchers prefer to use OR in studies with common events. Similarly, the collapsibility and interpretability properties of RR make it more appealing to use in studies with common events. 34 To avoid variable practice and interpretation issues with OR, it is recommended to use RR models in all studies except for case–control and nested case–control studies, where OR approximates RR and thus OR models should be used. Otherwise, investigators may report sufficient data to compute any ratio measure. Biostatisticians should educate investigators on the proper interpretation of ratio measures in the light of study design and their reporting. 34 35

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Effect size according to study design.

Investigators sometimes either inappropriately label their study design 36 37 or report effect size measures not aligned with the study design, 38 39 leading to difficulty in results interpretation and evaluation of the level of evidence. The proper labeling of study design and the appropriate use of effect size measure have substantial implications for results interpretation, including the conduct of systematic review and meta-analysis. 40 A study 31 reviewed the frequency of reporting OR instead of RR in cohort studies and randomized clinical trials (RCTs) and found that one-third of the cohort studies used an OR model, whereas 5% of RCTs used an OR model. The majority of estimated ORs from these studies had a 20% or higher deviation from the corresponding RR.

Step 3: specify study hypothesis, reporting of p values, and interval estimates (interpretation and decision)

The clinical hypothesis provides information for evaluating formal claims specified in the study objectives, while the statistical hypothesis provides information about the population parameters/statistics being used to test the formal claims. The inference about the study hypothesis is typically measured by p value and confidence interval (CI). A smaller p value indicates that the data support against the null hypothesis. Since the p value is a conditional probability, it can never tell about the acceptance or rejection of the null hypothesis. Therefore, multiple alternative strategies of p values have been proposed to strengthen the credibility of conclusions. 41 42 Adaption of these alternative strategies is only needed in the explanatory objective studies. Although exact p values are recommended to be reported in research studies, p values do not provide any information about the effect size. Compared with p values, the CI provides a confidence range of the effect size that contains the true effect size if the study were repeated and can be used to determine whether the results are statistically significant or not. 43 Both p value and 95% CI provide complementary information and thus need to be specified in the statistical analysis section. 24 44

Researchers often test one or more comparisons or hypotheses. Accordingly, the side and the level of significance for considering results to be statistically significant may change. Furthermore, studies may include more than one primary outcome that requires an adjustment in the level of significance for multiplicity. All studies should provide the interval estimate of the effect size/regression coefficient in the primary analyses. Since the interpretation of data analysis depends on the study hypothesis, researchers are required to specify the level of significance along with the side (one-sided or two-sided) of the p value in the test for considering statistically significant results, adjustment of the level of significance due to multiple comparisons or multiplicity, and reporting of interval estimates of the effect size in the statistical analysis section. 45

A study 46 showed a significant effect of fluoxetine on relapse rates in obsessive-compulsive disorder based on a one-sided p value of 0.04. Clearly, there was no reason for using a one-sided p value as opposed to a two-sided p value. A review of the appropriate use of multiple test correction methods in multiarm clinical trials published in major medical journals in 2012 identified over 50% of the articles did not perform multiple-testing correction. 47 Similar to controlling a familywise error rate due to multiple comparisons, adjustment of the false discovery rate is also critical in studies involving multiple related outcomes. A review of RCTs for depression between 2007 and 2008 from six journals reported that only limited studies (5.8%) accounted for multiplicity in the analyses due to multiple outcomes. 48

Step 4: account for DGP in the statistical analysis (accuracy)

The study design also requires the specification of the selection of participants and outcome measurement processes in different design settings. We referred to this specific design feature as DGP. Understanding DGP helps in determining appropriate modeling of outcome distribution in statistical analyses and setting up model premises and units of analysis. 4 DGP ( figure 3 ) involves information on data generation and data measures, including the number of measurements after random selection, complex selection, consecutive selection, pragmatic selection, or systematic selection. Specifically, DGP depends on a sampling setting (participants are selected using survey sampling methods and one subject may represent multiple participants in the population), clustered setting (participants are clustered through a recruitment setting or hierarchical setting or multiple hospitals), pragmatic setting (participants are selected through mixed approaches), or systematic review setting (participants are selected from published studies). DGP also depends on the measurements of outcomes in an unpaired setting (measured on one occasion only in independent groups), paired setting (measured on more than one occasion or participants are matched on certain subject characteristics), or mixed setting (measured on more than one occasion but interested in comparing independent groups). It also involves information regarding outcomes or exposure generation processes using quantitative or categorical variables, quantitative values using labs or validated instruments, and self-reported or administered tests yielding a variety of data distributions, including individual distribution, mixed-type distribution, mixed distributions, and latent distributions. Due to different DGPs, study data may include messy or missing data, incomplete/partial measurements, time-varying measurements, surrogate measures, latent measures, imbalances, unknown confounders, instrument variables, correlated responses, various levels of clustering, qualitative data, or mixed data outcomes, competing events, individual and higher-level variables, etc. The performance of statistical analysis, appropriate estimation of standard errors of estimates and subsequently computation of p values, the generalizability of findings, and the graphical display of data rely on DGP. Accounting for DGP in the analyses requires proper communication between investigators and biostatisticians about each aspect of participant selection and data collection, including measurements, occasions of measurements, and instruments used in the research study.

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Common features of the data generation process.

A study 49 compared the intake of fresh fruit and komatsuna juice with the intake of commercial vegetable juice on metabolic parameters in middle-aged men using an RCT. The study was criticized for many reasons, but primarily for incorrect statistical methods not aligned with the study DGP. 50 Similarly, another study 51 highlighted that 80% of published studies using the Korean National Health and Nutrition Examination Survey did not incorporate survey sampling structure in statistical analyses, producing biased estimates and inappropriate findings. Likewise, another study 52 highlighted the need for maintaining methodological standards while analyzing data from the National Inpatient Sample. A systematic review 53 identified that over 50% of studies did not specify whether a paired t-test or an unpaired t-test was performed in statistical analysis in the top 25% of physiology journals, indicating poor transparency in reporting of statistical analysis as per the data type. Another study 54 also highlighted the data displaying errors not aligned with DGP. As per DGP, delay in treatment initiation of patients with cancer defined from the onset of symptom to treatment initiation should be analyzed into three components: patient/primary delay, secondary delay, and tertiary delay. 55 Similarly, the number of cancerous nodes should be analyzed with count data models. 56 However, several studies did not analyze such data according to DGP. 57 58

Step 5: apply EBB methods specific to study design features and DGP (efficiency and robustness)

The continuous growth in the development of robust statistical methods for dealing with a specific problem produced various methods to analyze specific data types. Since multiple methods are available for handling a specific problem yet with varying performances, heterogeneous practices among applied researchers have been noticed. Variable practices could also be due to a lack of consensus on statistical methods in literature, unawareness, and the unavailability of standardized statistical guidelines. 2 5 59 However, it becomes sometimes difficult to differentiate whether a specific method was used due to its robustness, lack of awareness, lack of accessibility of statistical software to apply an alternative appropriate method, intention to produce expected results, or ignorance of model diagnostics. To avoid heterogeneous practices, the selection of statistical methodology and their reporting at each stage of data analysis should be conducted using methods according to EBB practice. 5 Since it is hard for applied researchers to optimally select statistical methodology at each step, we encourage investigators to involve biostatisticians at the very early stage in basic, clinical, population, translational, and database research. We also appeal to biostatisticians to develop guidelines, checklists, and educational tools to promote the concept of EBB. As an effort, we developed the statistical analysis and methods in biomedical research (SAMBR) guidelines for applied researchers to use EBB methods for data analysis. 5 The EBB practice is essential for applying recent cutting-edge robust methodologies to yield accurate and unbiased results. The efficiency of statistical methodologies depends on the assumptions and DGP. Therefore, investigators may attempt to specify the choice of specific models in the primary analysis as per the EBB.

Although details of evidence-based preferred methods are provided in the SAMBR checklists for each study design/objective, 5 we have presented a simplified version of evidence-based preferred methods for common statistical analysis ( online supplemental table 3 ). Several examples are available in the literature where inefficient methods not according to EBB practice have been used. 31 57 60

Step 6: report variable selection method in the multivariable analysis according to study objective type (unbiased)

Multivariable analysis can be used for association, prediction or classification or risk stratification, adjustment, propensity score development, and effect size estimation. 61 Some biological, clinical, behavioral, and environmental factors may directly associate or influence the relationship between exposure and outcome. Therefore, almost all health studies require multivariable analyses for accurate and unbiased interpretations of findings ( figure 1 ). Analysts should develop an adjusted model if the sample size permits. It is a misconception that the analysis of RCT does not require adjusted analysis. Analysis of RCT may require adjustment for prognostic variables. 23 The foremost step in model building is the entry of variables after finalizing the appropriate parametric or non-parametric regression model. In the exploratory model building process due to no preference of exposures, a backward automated approach after including any variables that are significant at 25% in the unadjusted analysis can be used for variable selection. 62 63 In the association model, a manual selection of covariates based on the relevance of the variables should be included in a fully adjusted model. 63 In a causal model, clinically guided methods should be used for variable selection and their adjustments. 20 In a non-randomized interventional model, efforts should be made to eliminate confounding effects through propensity score methods and the final propensity score-adjusted multivariable model may adjust any prognostic variables, while a randomized study simply should adjust any prognostic variables. 27 Maintaining the event per variable (EVR) is important to avoid overfitting in any type of modeling; therefore, screening of variables may be required in some association and explanatory studies, which may be accomplished using a backward stepwise method that needs to be clarified in the statistical analyses. 10 In a predictive study, a model with an optimum set of variables producing the highest accuracy should be used. The optimum set of variables may be screened with the random forest method or bootstrap or machine learning methods. 64 65 Different methods of variable selection and adjustments may lead to different results. The screening process of variables and their adjustments in the final multivariable model should be clearly mentioned in the statistical analysis section.

A study 66 evaluating the effect of hydroxychloroquine (HDQ) showed unfavorable events (intubation or death) in patients who received HDQ compared with those who did not (hazard ratio (HR): 2.37, 95% CI 1.84 to 3.02) in an unadjusted analysis. However, the propensity score-adjusted analyses as appropriate with the interventional objective model showed no significant association between HDQ use and unfavorable events (HR: 1.04, 95% CI 0.82 to 1.32), which was also confirmed in multivariable and other propensity score-adjusted analyses. This study clearly suggests that results interpretation should be based on a multivariable analysis only in observational studies if feasible. A recent study 10 noted that approximately 6% of multivariable analyses based on either logistic or Cox regression used an inappropriate selection method of variables in medical research. This practice was more commonly noted in studies that did not involve an expert biostatistician. Another review 61 of 316 articles from high-impact Chinese medical journals revealed that 30.7% of articles did not report the selection of variables in multivariable models. Indeed, this inappropriate practice could have been identified more commonly if classified according to the study objective type. 18 In RCTs, it is uncommon to report an adjusted analysis based on prognostic variables, even though an adjusted analysis may produce an efficient estimate compared with an unadjusted analysis. A study assessing the effect of preemptive intervention on development outcomes showed a significant effect of an intervention on reducing autism spectrum disorder symptoms. 67 However, this study was criticized by Ware 68 for not reporting non-significant results in unadjusted analyses. If possible, unadjusted estimates should also be reported in any study, particularly in RCTs. 23 68

Step 7: provide evidence for exploring effect modifiers (applicability)

Any variable that modifies the effect of exposure on the outcome is called an effect modifier or modifier or an interacting variable. Exploring the effect modifiers in multivariable analyses helps in (1) determining the applicability/generalizability of findings in the overall or specific subpopulation, (2) generating ideas for new hypotheses, (3) explaining uninterpretable findings between unadjusted and adjusted analyses, (4) guiding to present combined or separate models for each specific subpopulation, and (5) explaining heterogeneity in treatment effect. Often, investigators present adjusted stratified results according to the presence or absence of an effect modifier. If the exposure interacts with multiple variables statistically or conceptually in the model, then the stratified findings (subgroup) according to each effect modifier may be presented. Otherwise, stratified analysis substantially reduces the power of the study due to the lower sample size in each stratum and may produce significant results by inflating type I error. 69 Therefore, a multivariable analysis involving an interaction term as opposed to a stratified analysis may be presented in the presence of an effect modifier. 70 Sometimes, a quantitative variable may emerge as a potential effect modifier for exposure and an outcome relationship. In such a situation, the quantitative variable should not be categorized unless a clinically meaningful threshold is not available in the study. In fact, the practice of categorizing quantitative variables should be avoided in the analysis unless a clinically meaningful cut-off is available or a hypothesis requires for it. 71 In an exploratory objective type, any possible interaction may be obtained in a study; however, the interpretation should be guided based on clinical implications. Similarly, some objective models may have more than one exposure or intervention and the association of each exposure according to the level of other exposure should be presented through adjusted analyses as suggested in the presence of interaction effects. 70

A review of 428 articles from MEDLINE on the quality of reporting from statistical analyses of three (linear, logistic, and Cox) commonly used regression models reported that only 18.5% of the published articles provided interaction analyses, 17 even though interaction analyses can provide a lot of useful information.

Step 8: assessment of assumptions, specifically the distribution of outcome, linearity, multicollinearity, sparsity, and overfitting (reliability)

The assessment and reporting of model diagnostics are important in assessing the efficiency, validity, and usefulness of the model. Model diagnostics include satisfying model-specific assumptions and the assessment of sparsity, linearity, distribution of outcome, multicollinearity, and overfitting. 61 72 Model-specific assumptions such as normal residuals, heteroscedasticity and independence of errors in linear regression, proportionality in Cox regression, proportionality odds assumption in ordinal logistic regression, and distribution fit in other types of continuous and count models are required. In addition, sparsity should also be examined prior to selecting an appropriate model. Sparsity indicates many zero observations in the data set. 73 In the presence of sparsity, the effect size is difficult to interpret. Except for machine learning models, most of the parametric and semiparametric models require a linear relationship between independent variables and a functional form of an outcome. Linearity should be assessed using a multivariable polynomial in all model objectives. 62 Similarly, the appropriate choice of the distribution of outcome is required for model building in all study objective models. Multicollinearity assessment is also useful in all objective models. Assessment of EVR in multivariable analysis can be used to avoid the overfitting issue of a multivariable model. 18

Some review studies highlighted that 73.8%–92% of the articles published in MEDLINE had not assessed the model diagnostics of the multivariable regression models. 17 61 72 Contrary to the monotonically, linearly increasing relationship between systolic blood pressure (SBP) and mortality established using the Framingham’s study, 74 Port et al 75 reported a non-linear relationship between SBP and all-cause mortality or cardiovascular deaths by reanalysis of the Framingham’s study data set. This study identified a different threshold for treating hypertension, indicating the role of linearity assessment in multivariable models. Although a non-Gaussian distribution model may be required for modeling patient delay outcome data in cancer, 55 a study analyzed patient delay data using an ordinary linear regression model. 57 An investigation of the development of predictive models and their reporting in medical journals identified that 53% of the articles had fewer EVR than the recommended EVR, indicating over half of the published articles may have an overfitting model. 18 Another study 76 attempted to identify the anthropometric variables associated with non-insulin-dependent diabetes and found that none of the anthropometric variables were significant after adjusting for waist circumference, age, and sex, indicating the presence of collinearity. A study reported detailed sparse data problems in published studies and potential solutions. 73

Step 9: report type of primary and sensitivity analyses (consistency)

Numerous considerations and assumptions are made throughout the research processes that require assessment, evaluation, and validation. Some assumptions, executions, and errors made at the beginning of the study data collection may not be fixable 13 ; however, additional information collected during the study and data processing, including data distribution obtained at the end of the study, may facilitate additional considerations that need to be verified in the statistical analyses. Consistencies in the research findings via modifications in the outcome or exposure definition, study population, accounting for missing data, model-related assumptions, variables and their forms, and accounting for adherence to protocol in the models can be evaluated and reported in research studies using sensitivity analyses. 77 The purpose and type of supporting analyses need to be specified clearly in the statistical analyses to differentiate the main findings from the supporting findings. Sensitivity analyses are different from secondary or interim or subgroup analyses. 78 Data analyses for secondary outcomes are often referred to as secondary analyses, while data analyses of an ongoing study are called interim analyses and data analyses according to groups based on patient characteristics are known as subgroup analyses.

Almost all studies require some form of sensitivity analysis to validate the findings under different conditions. However, it is often underutilized in medical journals. Only 18%–20.3% of studies reported some forms of sensitivity analyses. 77 78 A review of nutritional trials from high-quality journals reflected that 17% of the conclusions were reported inappropriately using findings from sensitivity analyses not based on the primary/main analyses. 77

Step 10: provide methods for summarizing, displaying, and interpreting data (transparency and usability)

Data presentation includes data summary, data display, and data from statistical model analyses. The primary purpose of the data summary is to understand the distribution of outcome status and other characteristics in the total sample and by primary exposure status or outcome status. Column-wise data presentation should be preferred according to exposure status in all study designs, while row-wise data presentation for the outcome should be preferred in all study designs except for a case–control study. 24 32 Summary statistics should be used to provide maximum information on data distribution aligned with DGP and variable type. The purpose of results presentation primarily from regression analyses or statistical models is to convey results interpretation and implications of findings. The results should be presented according to the study objective type. Accordingly, the reporting of unadjusted and adjusted associations of each factor with the outcome may be preferred in the determinant objective model, while unadjusted and adjusted effects of primary exposure on the outcome may be preferred in the explanatory objective model. In prognostic models, the final predictive models may be presented in such a way that users can use models to predict an outcome. In the exploratory objective model, a final multivariable model should be reported with R 2 or area under the curve (AUC). In the association and interventional models, the assessment of internal validation is critically important through various sensitivity and validation analyses. A model with better fit indices (in terms of R 2 or AUC, Akaike information criterion, Bayesian information criterion, fit index, root mean square error) should be finalized and reported in the causal model objective study. In the predictive objective type, the model performance in terms of R 2 or AUC in training and validation data sets needs to be reported ( figure 1 ). 20 21 There are multiple purposes of data display, including data distribution using bar diagram or histogram or frequency polygons or box plots, comparisons using cluster bar diagram or scatter dot plot or stacked bar diagram or Kaplan-Meier plot, correlation or model assessment using scatter plot or scatter matrix, clustering or pattern using heatmap or line plots, the effect of predictors with fitted models using marginsplot, and comparative evaluation of effect sizes from regression models using forest plot. Although the key purpose of data display is to highlight critical issues or findings in the study, data display should essentially follow DGP and variable types and should be user-friendly. 54 79 Data interpretation heavily relies on the effect size measure along with study design and specified hypotheses. Sometimes, variables require standardization for descriptive comparison of effect sizes among exposures or interpreting small effect size, or centralization for interpreting intercept or avoiding collinearity due to interaction terms, or transformation for achieving model-related assumptions. 80 Appropriate methods of data reporting and interpretation aligned with study design, study hypothesis, and effect size measure should be specified in the statistical analysis section of research studies.

Published articles from reputed journals inappropriately summarized a categorized variable with mean and range, 81 summarized a highly skewed variable with mean and standard deviation, 57 and treated a categorized variable as a continuous variable in regression analyses. 82 Similarly, numerous examples from published studies reporting inappropriate graphical display or inappropriate interpretation of data not aligned with DGP or variable types are illustrated in a book published by Bland and Peacock. 83 84 A study used qualitative data on MRI but inappropriately presented with a Box-Whisker plot. 81 Another study reported unusually high OR for an association between high breast parenchymal enhancement and breast cancer in both premenopausal and postmenopausal women. 85 This reporting makes suspicious findings and may include sparse data bias. 86 A poor tabular presentation without proper scaling or standardization of a variable, missing CI for some variables, missing unit and sample size, and inconsistent reporting of decimal places could be easily noticed in table 4 of a published study. 29 Some published predictive models 87 do not report intercept or baseline survival estimates to use their predictive models in clinical use. Although a direct comparison of effect sizes obtained from the same model may be avoided if the units are different among variables, 35 a study had an objective to compare effect sizes across variables but the authors performed comparisons without standardization of variables or using statistical tests. 88

A sample for writing statistical analysis section in medical journals/research studies

Our primary study objective type was to develop a (select from figure 1 ) model to assess the relationship of risk factors (list critical variables or exposures) with outcomes (specify type from continuous/discrete/count/binary/polytomous/time-to-event). To address this objective, we conducted a (select from figure 2 or any other) study design to test the hypotheses of (equality or superiority or non-inferiority or equivalence or futility) or develop prediction. Accordingly, the other variables were adjusted or considered as (specify role of variables from confounders, covariates, or predictors or independent variables) as reflected in the conceptual framework. In the unadjusted or preliminary analyses as per the (select from figure 3 or any other design features) DGP, (specify EBB preferred tests from online supplemental table 3 or any other appropriate tests) were used for (specify variables and types) in unadjusted analyses. According to the EBB practice for the outcome (specify type) and DGP of (select from figure 3 or any other), we used (select from online supplemental table 1 or specify a multivariable approach) as the primary model in the multivariable analysis. We used (select from figure 1 ) variable selection method in the multivariable analysis and explored the interaction effects between (specify variables). The model diagnostics including (list all applicable, including model-related assumptions, linearity, or multicollinearity or overfitting or distribution of outcome or sparsity) were also assessed using (specify appropriate methods) respectively. In such exploration, we identified (specify diagnostic issues if any) and therefore the multivariable models were developed using (specify potential methods used to handle diagnostic issues). The other outcomes were analyzed with (list names of multivariable approaches with respective outcomes). All the models used the same procedure (or specify from figure 1 ) for variable selection, exploration of interaction effects, and model diagnostics using (specify statistical approaches) depending on the statistical models. As per the study design, hypothesis, and multivariable analysis, the results were summarized with effect size (select as appropriate or from figure 2 ) along with (specify 95% CI or other interval estimates) and considered statistically significant using (specify the side of p value or alternatives) at (specify the level of significance) due to (provide reasons for choosing a significance level). We presented unadjusted and/or adjusted estimates of primary outcome according to (list primary exposures or variables). Additional analyses were conducted for (specific reasons from step 9) using (specify methods) to validate findings obtained in the primary analyses. The data were summarized with (list summary measures and appropriate graphs from step 10), whereas the final multivariable model performance was summarized with (fit indices if applicable from step 10). We also used (list graphs) as appropriate with DGP (specify from figure 3 ) to present the critical findings or highlight (specify data issues) using (list graphs/methods) in the study. The exposures or variables were used in (specify the form of the variables) and therefore the effect or association of (list exposures or variables) on outcome should be interpreted in terms of changes in (specify interpretation unit) exposures/variables. List all other additional analyses if performed (with full details of all models in a supplementary file along with statistical codes if possible).

Concluding remarks

We highlighted 10 essential steps to be reported in the statistical analysis section of any analytical study ( figure 4 ). Adherence to minimum reporting of the steps specified in this report may enforce investigators to understand concepts and approach biostatisticians timely to apply these concepts in their study to improve the overall quality of methodological standards in grant proposals and research studies. The order of reporting information in statistical analyses specified in this report is not mandatory; however, clear reporting of analytical steps applicable to the specific study type should be mentioned somewhere in the manuscript. Since the entire approach of statistical analyses is dependent on the study objective type and EBB practice, proper execution and reporting of statistical models can be taught to the next generation of statisticians by the study objective type in statistical education courses. In fact, some disciplines ( figure 5 ) are strictly aligned with specific study objective types. Bioinformaticians are oriented in studying determinant and prognostic models toward precision medicine, while epidemiologists are oriented in studying association and causal models, particularly in population-based observational and pragmatic settings. Data scientists are heavily involved in prediction and classification models in personalized medicine. A common thing across disciplines is using biostatistical principles and computation tools to address any research question. Sometimes, one discipline expert does the part of others. 89 We strongly recommend using a team science approach that includes an epidemiologist, biostatistician, data scientist, and bioinformatician depending on the study objectives and needs. Clear reporting of data analyses as per the study objective type should be encouraged among all researchers to minimize heterogeneous practices and improve scientific quality and outcomes. In addition, we also encourage investigators to strictly follow transparent reporting and quality assessment guidelines according to the study design ( https://www.equator-network.org/ ) to improve the overall quality of the study, accordingly STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) for observational studies, CONSORT (Consolidated Standards of Reporting Trials) for clinical trials, STARD (Standards for Reporting Diagnostic Accuracy Studies) for diagnostic studies, TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis OR Diagnosis) for prediction modeling, and ARRIVE (Animal Research: Reporting of In Vivo Experiments) for preclinical studies. The steps provided in this document for writing the statistical analysis section is essentially different from other guidance documents, including SAMBR. 5 SAMBR provides a guidance document for selecting evidence-based preferred methods of statistical analysis according to different study designs, while this report suggests the global reporting of essential information in the statistical analysis section according to study objective type. In this guidance report, our suggestion strictly pertains to the reporting of methods in the statistical analysis section and their implications on the interpretation of results. Our document does not provide guidance on the reporting of sample size or results or statistical analysis section for meta-analysis. The examples and reviews reported in this study may be used to emphasize the concepts and related implications in medical research.

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Summary of reporting steps, purpose, and evaluation measures in the statistical analysis section.

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Object name is jim-2022-002479f05.jpg

Role of interrelated disciplines according to study objective type.

Acknowledgments

The author would like to thank the reviewers for their careful review and insightful suggestions.

Contributors: AKD developed the concept and design and wrote the manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: AKD is a Journal of Investigative Medicine Editorial Board member. No other competing interests declared.

Provenance and peer review: Commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not required.

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How to Write the Results/Findings Section in Research

data analysis section research paper

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

  • Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
  • A contextual analysis of this data explaining its meaning in sentence form
  • All data that corresponds to the central research question(s)
  • All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

results section of a research paper, figures

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

results section of a research paper, figures

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

results section of a research paper, figures

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

  • “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

  • The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
  • Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
  • Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

  • Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
  • Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
  • Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

  • Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
  • Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
  • Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

  • The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
  • In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
  • Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
  • Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

  • Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
  • Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
  • Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
  • Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

Wordvice Resources

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How to Write the Analysis Section of My Research Paper

How to Write a Technical Essay

How to Write a Technical Essay

Data collection is only the beginning of the research paper writing process. Writing up the analysis is the bulk of the project. As Purdue University’s Online Writing Lab notes, analysis is a useful tool for investigating content you find in various print and other sources, like journals and video media.

Locate and collect documents. Make multiple photocopies of all relevant print materials. Label and store these in a way that provides easy access. Conduct your analysis.

Create a heading for the analysis section of your paper. Specify the criteria you looked for in the data. For instance, a research paper analyzing the possibility of life on other planets may look for the weight of evidence supporting a particular theory, or the scientific validity of particular publications.

Write about the patterns you found, and note the number of instances a particular idea emerged during analysis. For example, an analysis of Native American cultures may look for similarities between spiritual beliefs, gender roles or agricultural techniques. Researchers frequently repeat the process to find patterns that were missed during the first analysis. You can also write about your comparative analysis, if you did one. It is common to ask a colleague to perform the process and compare their findings with yours.

Summarize your analysis in a paragraph or two. Write the transition for the conclusions section of your paper.

  • Use compare and contrast language. Indicate where there are similarities and differences in the data through the use of phrases like ''in contrast'' and ''similarly.''

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Adam Simpson is an author and blogger who started writing professionally in 2006 and has written for OneStopEnglish and other Web sites. He has chapters in the volumes "Teaching and learning vocabulary in another language" and "Educational technology in the Arabian gulf," among others. Simpson attended the University of Central Lancashire where he earned a B.A. in international management.

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Research Method

Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

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Research Results

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

  • Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
  • Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
  • Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
  • Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
  • Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
  • Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
  • Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

  • A brief overview of the research objectives and hypotheses
  • A statement of the research question

II. Descriptive statistics

  • Summary statistics (e.g., mean, standard deviation) for each variable analyzed
  • Frequencies and percentages for categorical variables

III. Inferential statistics

  • Results of statistical analyses, including tests of hypotheses
  • Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

  • Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
  • Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

  • Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

  • Discussion of any limitations of the study and potential sources of bias
  • Assumptions made in the statistical analyses

VII. Conclusions

  • A summary of the key findings and their implications
  • A statement of whether the hypotheses were supported or not
  • Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

  • This study sought to examine the relationship between sleep quality and academic performance in college students.
  • Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
  • Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

  • Participants were college students (N=200) from a mid-sized public university in the United States.
  • The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
  • Participants were recruited through flyers and online advertisements.

III. Results

  • Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
  • See Table 1 for a summary of the results.
  • Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

  • The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
  • These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
  • Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

  • College students who prioritize sleep may see a positive impact on their academic performance.
  • These findings highlight the importance of sleep in academic success.
  • Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

  • Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
  • Developing theories: Research results can help to develop or modify existing theories and create new ones.
  • Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
  • Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
  • Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
  • Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

  • Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
  • Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
  • Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
  • Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
  • Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
  • Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
  • Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
  • Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

  • After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
  • Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
  • Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
  • Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
  • Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
  • The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
  • The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
  • Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
  • Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
  • Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

  • Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
  • Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
  • Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
  • Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
  • Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
  • Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
  • Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
  • Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
  • Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

  • Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
  • Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
  • Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
  • Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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Helpful Tips on Composing a Research Paper Data Analysis Section

If you are given a research paper assignment, you should create a list of tasks to be done and try to stick to your working schedule. It is recommended that you complete your research and then start writing your work. One of the important steps is to prepare your data analysis section. However, that step is vital as it aims to explain how the data will be described in the results section. Use the following helpful tips to complete that section without a hitch.

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How to Compose a Data Analysis Section for Your Research Paper

Usually, a data analysis section is provided right after the methods and approaches used. There, you should explain how you organized your data, what statistical tests were applied, and how you evaluated the obtained results. Follow these simple tips to compose a strong piece of writing:

  • Avoid analyzing your results in the data analysis section.
  • Indicate whether your research is quantitative or qualitative.
  • Provide your main research questions and the analysis methods that were applied to answer them.
  • Report what software you used to gather and analyze your data.
  • List the data sources, including electronic archives and online reports of different institutions.
  • Explain how the data were summarized and what measures of variability you have used.
  • Remember to mention the data transformations if any, including data normalizing.
  • Make sure that you included the full name of statistical tests used.
  • Describe graphical techniques used to analyze the raw data and the results.

Where to Find the Necessary Assistance If You Get Stuck

Research paper writing is hard, so if you get stuck, do not wait for enlightenment and start searching for some assistance. It is a good idea to consult a statistics expert if you have a large amount of data and have no idea on how to summarize it. Your academic advisor may suggest you where to find a statistician to ask your questions.

Another great help option is getting a sample of a data analysis section. At the school’s library, you can find sample research papers written by your fellow students, get a few works, and study how the students analyzed data. Pay special attention to the word choices and the structure of the writing.

If you decide to follow a section template, you should be careful and keep your professor’s instructions in mind. For example, you may be asked to place all the page-long data tables in the appendices or build graphs instead of providing tables.

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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.

This image shows the main sections of a research article.

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?

This image shows how to analyze a research article.

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.

This image shows how to write a research analysis.

📝 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:

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  4. 😀 Data analysis research paper. How to Write a Data Analysis. 2019-02-15

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  5. FREE 42+ Research Paper Examples in PDF

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VIDEO

  1. Qualitative Research Data Analysis

  2. Data Analysis

  3. Economics- Data Analysis Question Paper BA PROG 4th Semester DU SOL| Data Analysis Exam Pattern 2023

  4. Research Methodology and Data Analysis-Refresher Course

  5. What to avoid in writing the methodology section of your research

  6. Research Methodology and Data Analysis-Refresher Course

COMMENTS

  1. How to Write an APA Methods Section

    Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods. In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample, measures, and procedures used.

  2. PDF Structure of a Data Analysis Report

    A typical psych/social science paper orgainzed around "intro/methods/analysis/results/discussion" sections. A research article in an academic journal. An essay. A lab report in a science class. The overall structure of a data analysis report is simple: Introduction Body Conclusion(s)/Discussion Appendix/Appendices

  3. PDF How to Write the Methods Section of a Research Paper

    The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen.

  4. Reporting Research Results in APA Style

    Revised on January 17, 2024. The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses. The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.

  5. How to Write a Results Section

    A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation. You should report all relevant results concisely and objectively, in a logical order.

  6. Creating a Data Analysis Plan: What to Consider When Choosing

    For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2, 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to summar...

  7. PDF Methodology Section for Research Papers

    The methodology section of your paper describes how your research was conducted. This information allows readers to check whether your approach is accurate and dependable. A good methodology can help increase the reader's trust in your findings. First, we will define and differentiate quantitative and qualitative research.

  8. Learning to Do Qualitative Data Analysis: A Starting Point

    In this article, we take up this open question as a point of departure and offer thematic analysis, an analytic method commonly used to identify patterns across language-based data ( Braun & Clarke, 2006 ), as a useful starting point for learning about the qualitative analysis process.

  9. PDF Method Sections for Empirical Research Papers

    An annotated Method section and other empirical research paper resources are available here. What is the purpose of the Method section in an empirical research paper? The Method section (also sometimes called Methods, Materials and Methods, or Research Design and Methods) describes the data collection and analysis procedures for a research project.

  10. PDF Results/Findings Sections for Empirical Research Papers

    The Results (also sometimes called Findings) section in an empirical research paper describes what the researcher(s) found when they analyzed their data. Its primary purpose is to use the data collected to answer the research question(s) posed in the introduction, even if the findings challenge the hypothesis.

  11. PDF Results Section for Research Papers

    The results (or findings) section is one of the most important parts of a research paper, in which an author reports the findings of their study in connection to their research question(s). The results section should not attempt to interpret or analyze the findings, only state the facts.

  12. Library Guides: Research Paper Writing: 6. Results / Analysis

    The analysis section is where the writer describes what was done with the data found. In order to write the analysis section it is important to know what the analysis consisted of, but does not mean data is needed. The analysis should already be performed to write the results section. Written explanations How should the analysis section be written?

  13. (PDF) Basic Approach to Data Analysis and Writing of Results and

    (PDF) Basic Approach to Data Analysis and Writing of Results and Discussion Sections Home Statistics as Topic Basic Approach to Data Analysis and Writing of Results and Discussion Sections...

  14. Qualitative Research: Data Collection, Analysis, and Management

    INTRODUCTION. In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area.

  15. How to clearly articulate results and construct tables and figures in a

    As an example elucidating the abovementioned issues, graphics, and flow diagram in the 'Results' section of a research paper written by the authors of this review article, and published in the World Journal of Urology in the year 2010 (World J Urol 2010;28:17-22.) are shown in Figures 1, and and2 2.

  16. How to write statistical analysis section in medical research

    The research design provides a protocol of study design and data generation process (DGP), whereas the evidence-based statistical analysis approach provides guidance for selecting and implementing approaches after evaluating data with the research design. 2 5 Ocaña-Riola 6 reported a substantial percentage of articles from high-impact medical jo...

  17. Research Paper

    The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. ... Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic ...

  18. How to Write the Results/Findings Section in Research

    What is the research paper Results section and what does it do? The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information.

  19. How to Write the Analysis Section of My Research Paper

    How to Write the Analysis Section of My Research Paper - The Classroom How to Write the Analysis Section of My Research Paper Adam Simpson - Updated May 17, 2019 Data collection is only the beginning of the research paper writing process. Writing up the analysis is the bulk of the project.

  20. Research Results Section

    The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

  21. How To Write The Research Paper Data Analysis Section

    How to Compose a Data Analysis Section for Your Research Paper Usually, a data analysis section is provided right after the methods and approaches used. There, you should explain how you organized your data, what statistical tests were applied, and how you evaluated the obtained results.

  22. data analysis Latest Research Papers

    pp. 567-590 Author (s): Kiomars Motarjem Keyword (s): Data Analysis Random Effects Survival Model Download Full-text Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN International Journal of Business Analytics 10.4018/ijban.292055 2022 Vol 9 (3) pp. 0-0 Keyword (s): Data Analysis Missing Data

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

    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.