how to write a data analysis section of a research paper

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

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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

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

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.

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.

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

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.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

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. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Writing a Good Data Analysis Report: 7 Steps

As a data analyst, you feel most comfortable when you’re alone with all the numbers and data. You’re able to analyze them with confidence and reach the results you were asked to find. But, this is not the end of the road for you. You still need to write a data analysis report explaining your findings to the laymen - your clients or coworkers.

That means you need to think about your target audience, that is the people who’ll be reading your report.

They don’t have nearly as much knowledge about data analysis as you do. So, your report needs to be straightforward and informative. The article below will help you learn how to do it. Let’s take a look at some practical tips you can apply to your data analysis report writing and the benefits of doing so.

source: Pexels  

Data Analysis Report Writing: 7 Steps

The process of writing a data analysis report is far from simple, but you can master it quickly, with the right guidance and examples of similar reports .

This is why we've prepared a step-by-step guide that will cover everything you need to know about this process, as simply as possible. Let’s get to it.

Consider Your Audience

You are writing your report for a certain target audience, and you need to keep them in mind while writing. Depending on their level of expertise, you’ll need to adjust your report and ensure it speaks to them. So, before you go any further, ask yourself:

Who will be reading this report? How well do they understand the subject?

Let’s say you’re explaining the methodology you used to reach your conclusions and find the data in question. If the reader isn’t familiar with these tools and software, you’ll have to simplify it for them and provide additional explanations.

So, you won't be writing the same type of report for a coworker who's been on your team for years or a client who's seeing data analysis for the first time. Based on this determining factor, you'll think about:

the language and vocabulary you’re using

abbreviations and level of technicality

the depth you’ll go into to explain something

the type of visuals you’ll add

Your readers’ expertise dictates the tone of your report and you need to consider it before writing even a single word.

Draft Out the Sections

The next thing you need to do is create a draft of your data analysis report. This is just a skeleton of what your report will be once you finish. But, you need a starting point.

So, think about the sections you'll include and what each section is going to cover. Typically, your report should be divided into the following sections:

Introduction

Body (Data, Methods, Analysis, Results)

For each section, write down several short bullet points regarding the content to cover. Below, we'll discuss each section more elaborately.

Develop The Body

The body of your report is the most important section. You need to organize it into subsections and present all the information your readers will be interested in.

We suggest the following subsections.

Explain what data you used to conduct your analysis. Be specific and explain how you gathered the data, what your sample was, what tools and resources you’ve used, and how you’ve organized your data. This will give the reader a deeper understanding of your data sample and make your report more solid.

Also, explain why you choose the specific data for your sample. For instance, you may say “ The sample only includes data of the customers acquired during 2021, in the peak of the pandemic.”

Next, you need to explain what methods you’ve used to analyze the data. This simply means you need to explain why and how you choose specific methods. You also need to explain why these methods are the best fit for the goals you’ve set and the results you’re trying to reach.

Back up your methodology section with background information on each method or tool used. Explain how these resources are typically used in data analysis.

After you've explained the data and methods you've used, this next section brings those two together. The analysis section shows how you've analyzed the specific data using the specific methods. 

This means you’ll show your calculations, charts, and analyses, step by step. Add descriptions and explain each of the steps. Try making it as simple as possible so that even the most inexperienced of your readers understand every word.

This final section of the body can be considered the most important section of your report. Most of your clients will skim the rest of the report to reach this section. 

Because it’ll answer the questions you’ve all raised. It shares the results that were reached and gives the reader new findings, facts, and evidence. 

So, explain and describe the results using numbers. Then, add a written description of what each of the numbers stands for and what it means for the entire analysis. Summarize your results and finalize the report on a strong note. 

Write the Introduction

Yes, it may seem strange to write the introduction section at the end, but it’s the smartest way to do it. This section briefly explains what the report will cover. That’s why you should write it after you’ve finished writing the Body.

In your introduction, explain:

the question you’ve raised and answered with the analysis

context of the analysis and background information

short outline of the report

Simply put, you’re telling your audience what to expect.

Add a Short Conclusion

Finally, the last section of your paper is a brief conclusion. It only repeats what you described in the Body, but only points out the most important details.

It should be less than a page long and use straightforward language to deliver the most important findings. It should also include a paragraph about the implications and importance of those findings for the client, customer, business, or company that hired you.

Include Data Visualization Elements

You have all the data and numbers in your mind and find it easy to understand what the data is saying. But, to a layman or someone less experienced than yourself, it can be quite a puzzle. All the information that your data analysis has found can create a mess in the head of your reader.

So, you should simplify it by using data visualization elements.

Firstly, let’s define what are the most common and useful data visualization elements you can use in your report:

There are subcategories to each of the elements and you should explore them all to decide what will do the best job for your specific case. For instance, you'll find different types of charts including, pie charts, bar charts, area charts, or spider charts.

For each data visualization element, add a brief description to tell the readers what information it contains. You can also add a title to each element and create a table of contents for visual elements only.

Proofread & Edit Before Submission

All the hard work you’ve invested in writing a good data analysis report might go to waste if you don’t edit and proofread. Proofreading and editing will help you eliminate potential mistakes, but also take another objective look at your report.

First, do the editing part. It includes:

reading the whole report objectively, like you’re seeing it for the first time

leaving an open mind for changes

adding or removing information

rearranging sections

finding better words to say something

You should repeat the editing phase a couple of times until you're completely happy with the result. Once you're certain the content is all tidied up, you can move on to the proofreading stage. It includes:

finding and removing grammar and spelling mistakes

rethinking vocabulary choices

improving clarity 

improving readability

You can use an online proofreading tool to make things faster. If you really want professional help, Grab My Essay is a great choice. Their professional writers can edit and rewrite your entire report, to make sure it’s impeccable before submission.

Whatever you choose to do, proofread yourself or get some help with it, make sure your report is well-organized and completely error-free.

Benefits of Writing Well-Structured Data Analysis Reports

Yes, writing a good data analysis report is a lot of hard work. But, if you understand the benefits of writing it, you’ll be more motivated and willing to invest the time and effort. After knowing how it can help you in different segments of your professional journey, you’ll be more willing to learn how to do it.

Below are the main benefits a data analysis report brings to the table.

Improved Collaboration

When you’re writing a data analysis report, you need to be aware more than one end user is going to use it. Whether it’s your employer, customer, or coworker - you need to make sure they’re all on the same page. And when you write a data analysis report that is easy to understand and learn from, you’re creating a bridge between all these people.

Simply, all of them are given accurate data they can rely on and you’re thus removing the potential misunderstandings that can happen in communication. This improves the overall collaboration level and makes everyone more open and helpful.

Increased Efficiency

People who are reading your data analysis report need the information it contains for some reason. They might use it to do their part of the job, to make decisions, or report further to someone else. Either way, the better your report, the more efficient it'll be. And, if you rely on those people as well, you'll benefit from this increased productivity as well.

Data tells a story about a business, project, or venture. It's able to show how well you've performed, what turned out to be a great move, and what needs to be reimagined. This means that a data analysis report provides valuable insight and measurable KPIs (key performance indicators) that you’re able to use to grow and develop. 

Clear Communication

Information is key regardless of the industry you're in or the type of business you're doing. Data analysis finds that information and proves its accuracy and importance. But, if those findings and the information itself aren't communicated clearly, it's like you haven't even found them.

This is why a data analysis report is crucial. It will present the information less technically and bring it closer to the readers.

Final Thoughts

As you can see, it takes some skill and a bit more practice to write a good data analysis report. But, all the effort you invest in writing it will be worth it once the results kick in. You’ll improve the communication between you and your clients, employers, or coworkers. People will be able to understand, rely on, and use the analysis you’ve conducted.

So, don’t be afraid and start writing your first data analysis report. Just follow the 7 steps we’ve listed and use a tool such as ProWebScraper to help you with website data analysis. You’ll be surprised when you see the result of your hard work.

Jessica Fender is a business analyst and a blogger. She writes about business and data analysis, networking in this sector, and acquiring new skills. Her goal is to provide fresh and accurate information that readers can apply instantly.

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Data analysis write-ups

What should a data-analysis write-up look like.

Writing up the results of a data analysis is not a skill that anyone is born with. It requires practice and, at least in the beginning, a bit of guidance.

Organization

When writing your report, organization will set you free. A good outline is: 1) overview of the problem, 2) your data and modeling approach, 3) the results of your data analysis (plots, numbers, etc), and 4) your substantive conclusions.

1) Overview Describe the problem. What substantive question are you trying to address? This needn’t be long, but it should be clear.

2) Data and model What data did you use to address the question, and how did you do it? When describing your approach, be specific. For example:

• Don’t say, “I ran a regression” when you instead can say, “I fit a linear regression model to predict price that included a house’s size and neighborhood as predictors.”
• Justify important features of your modeling approach. For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”

Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t. If you feel that a plot helps the reader understand the problem or data set itself—as opposed to your results—then go ahead and include it. A great example here is Tables 1 and 2 in the main paper on the PREDIMED study . These tables help the reader understand some important properties of the data and approach, but not the results of the study itself.

3) Results In your results section, include any figures and tables necessary to make your case. Label them (Figure 1, 2, etc), give them informative captions, and refer to them in the text by their numbered labels where you discuss them. Typical things to include here may include: pictures of the data; pictures and tables that show the fitted model; tables of model coefficients and summaries.

4) Conclusion What did you learn from the analysis? What is the answer, if any, to the question you set out to address?

General advice

Make the sections as short or long as they need to be. For example, a conclusions section is often pretty short, while a results section is usually a bit longer.

It’s OK to use the first person to avoid awkward or bizarre sentence constructions, but try to do so sparingly.

Do not include computer code unless explicitly called for. Note: model outputs do not count as computer code. Outputs should be used as evidence in your results section (ideally formatted in a nice way). By code, I mean the sequence of commands you used to process the data and produce the outputs.

When in doubt, use shorter words and sentences.

A very common way for reports to go wrong is when the writer simply narrates the thought process he or she followed: :First I did this, but it didn’t work. Then I did something else, and I found A, B, and C. I wasn’t really sure what to make of B, but C was interesting, so I followed up with D and E. Then having done this…” Do not do this. The desire for specificity is admirable, but the overall effect is one of amateurism. Follow the recommended outline above.

Here’s a good example of a write-up for an analysis of a few relatively simple problems. Because the problems are so straightforward, there’s not much of a need for an outline of the kind described above. Nonetheless, the spirit of these guidelines is clearly in evidence. Notice the clear exposition, the labeled figures and tables that are referred to in the text, and the careful integration of visual and numerical evidence into the overall argument. This is one worth emulating.

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How to Write an Effective Results Section

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• 1 Rothman Orthopaedics Institute, Philadelphia, PA.
• PMID: 31145152
• DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

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• How to write an original radiological research manuscript. Bannas P, Reeder SB. Bannas P, et al. Eur Radiol. 2017 Nov;27(11):4455-4460. doi: 10.1007/s00330-017-4879-8. Epub 2017 Jun 14. Eur Radiol. 2017. PMID: 28616726 Free PMC article. Review.
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How to Write the Results Section: Guide to Structure and Key Points

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The ‘ Results’ section of a research paper, like the ‘Introduction’ and other key parts, attracts significant attention from editors, reviewers, and readers. The reason lies in its critical role — that of revealing the key findings of a study and demonstrating how your research fills a knowledge gap in your field of study. Given its importance, crafting a clear and logically structured results section is essential.   

In this article, we will discuss the key elements of an effective results section and share strategies for making it concise and engaging. We hope this guide will help you quickly grasp ways of writing the results section, avoid common pitfalls, and make your writing process more efficient and effective.  

Structure of the results section  

Briefly restate the research topic in the introduction : Although the main purpose of the  results section  in a research paper is to list the notable findings of a study, it is customary to start with a brief repetition of the research question. This helps refocus the reader, allowing them to better appreciate the relevance of the findings. Additionally, restating the research question establishes a connection to the previous section of the paper, creating a smoother flow of information.  

Systematically present your research findings : Address the primary research question first, followed by the secondary research questions. If your research addresses multiple questions, mention the findings related to each one individually to ensure clarity and coherence.  

Represent your results visually: Graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results. As a rule of thumb, use a visual medium like a graph or a table if you wish to present three or more statistical values simultaneously.  

Graphical or tabular representations of data can also make your results section more visually appealing. Remember, an appealing and well-organized results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.  

Practical guidance for writing an effective ‘Results’ section   

• Always use simple and plain language. Avoid the use of uncertain or unclear expressions.  
• The findings of the study must be expressed in an objective and unbiased manner.  While it is acceptable to correlate certain findings , it is best to avoid over-interpreting the results. In addition, avoid using subjective or emotional words , such as “interestingly” or “unfortunately”, to describe the results as this may cause readers to doubt the objectivity of the paper.  
• The content balances simplicity with comprehensiveness . For statistical data, simply describe the relevant tests and explain their results without mentioning raw data. If the study involves multiple hypotheses, describe the results for each one separately to avoid confusion and aid understanding. To enhance credibility, e nsure that negative results , if any, are included in this section, even if they do not support the research hypothesis.  
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to highlight the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey ¹ .  

Difference between data, results, and discussion sections  

Data ,  results,  and  discussion  sections all communicate the findings of a study, but each serves a distinct purpose with varying levels of interpretation.   

In the  results section , one cannot provide data without interpreting its relevance or make statements without citing data ² . In a sense, the  results section  does not draw connections between different data points. Therefore, there is a certain level of interpretation involved in drawing results out of data.

(The example is intended to showcase how the visual elements and text in the results section complement each other ³ . The academic viewpoints included in the illustrative screenshots should not be used as references.)  

The discussion section allows authors even more interpretive freedom compared to the results section. Here, data and patterns within the data are compared with the findings from other studies to make more generalized points. Unlike the results section , which focuses purely on factual data, the discussion section touches upon hypothetical information, drawing conjectures and suggesting future directions for research.  

The ‘ Results’ section serves as the core of a research paper, capturing readers’ attention and providing insights into the study’s essence. Regardless of the subject of your research paper, a well-written results section can generate interest in your research. By following the tips outlined here, you can create a results section that effectively communicates your finding and invites further exploration. Remember, clarity is the key, and with the right approach, your results section can guide readers through the intricacies of your research.  

Professionals at Elsevier Language Services know the secret to writing a well-balanced results section. With their expert suggestions, you can ensure that your findings come across clearly to the reader. To maximize your chances of publication, reach out to Elsevier Language Services today !  

Type in wordcount for Standard Total: USD EUR JPY Follow this link if your manuscript is longer than 12,000 words. Upload

Reference  

• Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific manuscript. Journal of Surgical Research, 128(2), 165–167. https://doi.org/10.1016/j.jss.2005.07.002  
• Bahadoran, Z., Mirmiran, P., Zadeh-Vakili, A., Hosseinpanah, F., & Ghasemi, A. (2019). The Principles of Biomedical Scientific Writing: Results. International Journal of Endocrinology and Metabolism/International Journal of Endocrinology and Metabolism., In Press (In Press). https://doi.org/10.5812/ijem.92113  
• Guo, J., Wang, J., Zhang, P., Wen, P., Zhang, S., Dong, X., & Dong, J. (2024). TRIM6 promotes glioma malignant progression by enhancing FOXO3A ubiquitination and degradation. Translational Oncology, 46, 101999. https://doi.org/10.1016/j.tranon.2024.101999  

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How to Write Data Analysis Reports in 9 Easy Steps

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To see what Databox can do for you, including how it helps you track and visualize your performance data in real-time, check out our home page. Click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

Data analysis is vital for companies for several reasons.

A reliable source of information

Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.

A better understanding of your business

According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.

An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

• Start with an Outline
• Make a Selection of Vital KPIs
• Pick the Right Charts for Appealing Design
• Use a Narrative
• Organize the Information
• Include a Summary
• Careful with Your Recommendations
• Double-Check Everything
• Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

• Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

• Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

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Academic Paper: Discussion and Analysis

5 min read • june 18, 2024

Dylan Black

Introduction

After presenting your data and results to readers, you have one final step before you can  finally wrap up your paper and write a conclusion: analyzing your data! This is the big part of your paper that finally takes all the stuff you've been talking about - your method, the data you collected, the information presented in your literature review - and uses it to make a point!

The major question to be answered in your analysis section is simply "we have all this data, but  what does it mean?" What questions does this data answer? How does it relate to your research question ? Can this data be explained by, and is it consistent with, other papers? If not, why? These are the types of questions you'll be discussing in this section.

Writing a 🔥 Discussion and Analysis

Explain what your data means.

The primary point of a discussion section is to explain to your readers, through both statistical means and thorough explanation, what your results mean for your project. In doing so, you want to be succinct, clear, and specific about how your data backs up the claims you are making. These claims should be directly tied back to the overall focus of your paper.

What is this overall focus, you may ask? Your research question! This discussion along with your conclusion forms the final analysis of your research - what answers did we find? Was our research successful? How do the results we found tie into and relate to the current consensus by the research community? Were our results expected or unexpected? Why or why not? These are all questions you may consider in writing your discussion section.

Why Did Your Results Happen?

After presenting your results in your results section, you may also want to explain why your results actually occurred. This is integral to gaining a full understanding of your results and the conclusions you can draw from them. For example, if data you found contradicts certain data points found in other studies, one of the most important aspects of your discussion of said data is going to be theorizing as to  why this disparity took place.

Note that making broad, sweeping claims based on your data is not enough!  Everything, and I mean just about   everything you say in your discussions section must be backed up either by your own findings that you showed in your results section   or past research that has been performed in your field.

For many situations, finding these answers is not easy, and a lot of thinking must be done as to why your results actually occurred the way they did. For some fields, specifically STEM-related fields, a discussion might dive into the theoretical foundations of your research, explaining interactions between parts of your study that led to your results. For others, like social sciences and humanities, results may be open to more interpretation.

However, "open to more interpretation" does  not mean you can make claims willy nilly and claim "author's interpretation". In fact, such interpretation may be harder than STEM explanations! You will have to synthesize existing analysis on your topic and incorporate that in your analysis.

Discussion vs. Summary & Repetition

Quite possibly the biggest mistake made within a discussion section is simply restating your data in a different format. The role of the discussion section is to  explain your data and what it means for your project. Many students, thinking they're making discussion and analysis, simply regurgitate their numbers back in full sentences with a surface-level explanation.

Phrases like "this shows" and others similar, while good building blocks and  great planning tools, often lead to a relatively weak discussion that isn't very nuanced and doesn't lead to much new understanding.

Instead, your goal will be to, through this section and your conclusion, establish a  new understanding and in the end, close your gap! To do this effectively, you not only will have to present the numbers and results of your study, but you'll also have to describe how such data forms a new idea that has not been found in prior research.

This, in essence, is the heart of research - finding something new that hasn't been studied before! I don't know if it's just us, but that's pretty darn cool and something that you as the researcher should be incredibly proud of yourself for accomplishing.

Rubric Points

Before we close out this guide, let's take a quick peek at our best friend: the AP Research Rubric for the Discussion and Conclusion sections.

Scores of One and Two: Nothing New, Your Standard Essay

Responses that earn a score of one or two on this section of the AP Research Academic Paper typically don't find much new and by this point may not have a fully developed method nor well-thought-out results. For the most part, these are more similar to essays you may have written in a prior English class or AP Seminar than a true Research paper. Instead of finding  new ideas, they summarize already existing information about a topic.

Score of Three: New Understanding, Not Enough Support

A score of three is the first row that establishes a new understanding! This is a great step forward from a one or a two. However, what differentiates a three from a four or a five is the explanation and support of such a new understanding. A paper that earns a three lacks in building a line of reasoning and does not present enough evidence, both from their results section and from already published research.

Scores of Four and Five: New Understanding With A Line of Reasoning

We've made it to the best of the best! With scores of four and five, successful papers describe a new understanding with an effective line of reasoning, sufficient evidence, and an all-around great presentation of how their results signify filling a gap and answering a research question.

As far as the discussions section goes, the difference between a four and a five is more on the side of complexity and nuance. Where a four hits all the marks and does it well, a five  exceeds this and writes a truly exceptional analysis. Another area where these two sections differ is in the limitations described, which we discuss in the Conclusion section guide.

You did it!!!! You have, for the most part, finished the brunt of your research paper and are over the hump! All that's left to do is tackle the conclusion, which tends to be for most the easiest section to write because all you do is summarize how your research question was answered and make some final points about how your research impacts your field. Finally, as always...

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Peer Review
• Presentations
• Lab Report Writing Guides on the Web

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

• Factual statements supported by evidence. Short and sweet without excess words
• Present representative data rather than endlessly repetitive data
• Discuss variables only if they had an effect (positive or negative)
• Use meaningful statistics
• Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

• Present the results of the paper, in logical order, using tables and graphs as necessary.
• Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
• Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
• Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
• Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
• It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
• You must refer in the text to each figure or table you include in your paper.
• Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
• Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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Writing the Data Analysis Chapter(s): Results and Evidence

Posted by Rene Tetzner | Oct 19, 2021 | PhD Success | 0 |

4.4 Writing the Data Analysis Chapter(s): Results and Evidence

Unlike the introduction, literature review and methodology chapter(s), your results chapter(s) will need to be written for the first time as you draft your thesis even if you submitted a proposal, though this part of your thesis will certainly build upon the preceding chapters. You should have carefully recorded and collected the data (test results, participant responses, computer print outs, observations, transcriptions, notes of various kinds etc.) from your research as you conducted it, so now is the time to review, organise and analyse the data. If your study is quantitative in nature, make sure that you know what all the numbers mean and that you consider them in direct relation to the topic, problem or phenomenon you are investigating, and especially in relation to your research questions and hypotheses. You may find that you require the services of a statistician to help make sense of the data, in which case, obtaining that help sooner rather than later is advisable, because you need to understand your results thoroughly before you can write about them. If, on the other hand, your study is qualitative, you will need to read through the data you have collected several times to become familiar with them both as a whole and in detail so that you can establish important themes, patterns and categories. Remember that ‘qualitative analysis is a creative process and requires thoughtful judgments about what is significant and meaningful in the data’ (Roberts, 2010, p.174; see also Miles & Huberman, 1994) – judgements that often need to be made before the findings can be effectively analysed and presented. If you are combining methodologies in your research, you will also need to consider relationships between the results obtained from the different methods, integrating all the data you have obtained and discovering how the results of one approach support or correlate with the results of another. Ideally, you will have taken careful notes recording your initial thoughts and analyses about the sources you consulted and the results and evidence provided by particular methods and instruments as you put them into practice (as suggested in Sections 2.1.2 and 2.1.4), as these will prove helpful while you consider how best to present your results in your thesis.

Although the ways in which to present and organise the results of doctoral research differ markedly depending on the nature of the study and its findings, as on author and committee preferences and university and department guidelines, there are several basic principles that apply to virtually all theses. First and foremost is the need to present the results of your research both clearly and concisely, and in as objective and factual a manner as possible. There will be time and space to elaborate and interpret your results and speculate on their significance and implications in the final discussion chapter(s) of your thesis, but, generally speaking, such reflection on the meaning of the results should be entirely separate from the factual report of your research findings. There are exceptions, of course, and some candidates, supervisors and departments may prefer the factual presentation and interpretive discussion of results to be blended, just as some thesis topics may demand such treatment, but this is rare and best avoided unless there are persuasive reasons to avoid separating the facts from your thoughts about them. If you do find that you need to blend facts and interpretation in reporting your results, make sure that your language leaves no doubt about the line between the two: words such as ‘seems,’ ‘appears,’ ‘may,’ ‘might,’ probably’ and the like will effectively distinguish analytical speculation from more factual reporting (see also Section 4.5).

You need not dedicate much space in this part of the thesis to the methods you used to arrive at your results because these have already been described in your methodology chapter(s), but they can certainly be revisited briefly to clarify or lend structure to your report. Results are most often presented in a straightforward narrative form which is often supplemented by tables and perhaps by figures such as graphs, charts and maps. An effective approach is to decide immediately which information would be best included in tables and figures, and then to prepare those tables and figures before you begin writing the text for the chapter (see Section 4.4.1 on designing effective tables and figures). Arranging your data into the visually immediate formats provided by tables and figures can, for one, produce interesting surprises by enabling you to see trends and details that you may not have noticed previously, and writing the report of your results will prove easier when you have the tables and figures to work with just as your readers ultimately will. In addition, while the text of the results chapter(s) should certainly highlight the most notable data included in tables and figures, it is essential not to repeat information unnecessarily, so writing with the tables and figures already constructed will help you keep repetition to a minimum. Finally, writing about the tables and figures you create will help you test their clarity and effectiveness for your readers, and you can make any necessary adjustments to the tables and figures as you work. Be sure to refer to each table and figure by number in your text and to make it absolutely clear what you want your readers to see or understand in the table or figure (e.g., ‘see Table 1 for the scores’ and ‘Figure 2 shows this relationship’).

Beyond combining textual narration with the data presented in tables and figures, you will need to organise your report of the results in a manner best suited to the material. You may choose to arrange the presentation of your results chronologically or in a hierarchical order that represents their importance; you might subdivide your results into sections (or separate chapters if there is a great deal of information to accommodate) focussing on the findings of different kinds of methodology (quantitative versus qualitative, for instance) or of different tests, trials, surveys, reviews, case studies and so on; or you may want to create sections (or chapters) focussing on specific themes, patterns or categories or on your research questions and/or hypotheses. The last approach allows you to cluster results that relate to a particular question or hypothesis into a single section and can be particularly useful because it provides cohesion for the thesis as a whole and forces you to focus closely on the issues central to the topic, problem or phenomenon you are investigating. You will, for instance, be able to refer back to the questions and hypotheses presented in your introduction (see Section 3.1), to answer the questions and confirm or dismiss the hypotheses and to anticipate in relation to those questions and hypotheses the discussion and interpretation of your findings that will appear in the next part of the thesis (see Section 4.5). Less effective is an approach that organises the presentation of results according to the items of a survey or questionnaire, because these lend the structure of the instrument used to the results instead of connecting those results directly to the aims, themes and argument of your thesis, but such an organisation can certainly be an important early step in your analysis of the findings and might even be valid for the final thesis if, for instance, your work focuses on developing the instrument involved.

The results generated by doctoral research are unique, and this book cannot hope to outline all the possible approaches for presenting the data and analyses that constitute research results, but it is essential that you devote considerable thought and special care to the way in which you structure the report of your results (Section 6.1 on headings may prove helpful). Whatever structure you choose should accurately reflect the nature of your results and highlight their most important and interesting trends, and it should also effectively allow you (in the next part of the thesis) to discuss and speculate upon your findings in ways that will test the premises of your study, work well in the overall argument of your thesis and lead to significant implications for your research. Regardless of how you organise the main body of your results chapter(s), however, you should include a final paragraph (or more than one paragraph if necessary) that briefly summarises and explains the key results and also guides the reader on to the discussion and interpretation of those results in the following chapter(s).

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

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Rene Tetzner's blog posts dedicated to academic writing. Although the focus is on How To Write a Doctoral Thesis, many other important aspects of research-based writing, editing and publishing are addressed in helpful detail.

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Structuring a qualitative findings section

Reporting the findings from a qualitative study in a way that is interesting, meaningful, and trustworthy can be a struggle. Those new to qualitative research often find themselves trying to quantify everything to make it seem more “rigorous,” or asking themselves, “Do I really need this much data to support my findings?” Length requirements and word limits imposed by academic journals can also make the process difficult because qualitative data takes up a lot of room! In this post, I’m going to outline a few ways to structure qualitative findings, and a few tips and tricks to develop a strong findings section.

There are A LOT of different ways to structure a qualitative findings section. I’m going to focus on the following:

Tables (but not ONLY tables)

Themes/Findings as Headings

Research Questions as Headings

Anchoring Quotations

Anchoring Excerpts from Field Notes

Before I get into each of those, however, here is a bit of general guidance. First, make sure that you are providing adequate direct evidence for your findings. Second, be sure to integrate that direct evidence into the narrative. In other words, if for example, you were using quotes from a participant to support one of your themes, you should present and explain the theme (akin to a thesis statement), introduce the supporting quote, present it, explain the quote, and connect it to your finding. Below is an example of what I mean from one of my articles on implementation challenges in personalized learning ( Bingham, Pane, Steiner, & Hamilton, 2018 ). The finding supported by this paragraph was: “Inadequate Teacher Preparation, Development, and Support”

To mitigate the difficulties of enacting personalized learning in their classrooms, teachers wanted a model from which they could extrapolate practices that might serve them well in their own classrooms. As one teacher explained, “the ideas and the implementation is what’s lacking I think. I don’t feel like I know what I’m doing. I need to see things modeled and I need to know what it is. I need to be able to touch it. Show me a model, model for me.” Unfortunately, teachers had little to draw on for effective practices. Professional development was not as helpful as teachers had hoped, outside training on using the digital content or learning platforms fell short, and few examples or best practices existed for teachers to use in their own classrooms. As a result, teachers had to work harder to address gaps in their own knowledge. 

Finally, you should not leave quotations to speak for themselves and you should not have quotations as standalone paragraphs or sentences, with no introduction or explanation. Don’t make the reader do the analytic work for you.

Now, on to some specific ways to structure your findings section.

Tables can be used to give an overview of what you’re about to present in your findings, including the themes, some supporting evidence, and the meaning/explanation of the theme. Tables can be a useful way to give readers a quick reference for what your findings are. However, tables should not be used as your ONLY means of presenting those findings.

If you are choosing to use a table to present qualitative findings, you must also describe the findings in context, and provide supporting evidence in a narrative format (as in the paragraph outlined in the previous section).

2). Themes/Findings as Headings

Another option is to present your themes/findings as general or specific headings in your findings section. Here are some examples of findings as general headings:

Importance of Data Utilization and Analysis in the Classroom  The Role of Student Discipline and Accountability Differences in the Experiences of Teachers 

As you can see these headings do not describe precisely what the finding is, but they give the general idea/subject of the finding. You can have sub-headings within these findings that are more specific if you would like.

Another way to do this would be to be a bit more specific. For example:

School Infrastructure and Available Technology Do Not yet Fully Align with Teachers’ Needs 

Structural support for high levels of technology use is not fully developed 

Using multiple sources of digital content led to alignment issues 

Measures of School and Student Success are Misaligned

Traditional methods of measuring student progress conflict with personalized learning

Difficulties communicating new measures of student success to colleges and universities.

As you can see, here the findings are shown as headings, but are structured as specific sentences, with sub-themes included as well.

3). Research Questions as Headings

You can also present your findings using your research questions as the headings in the findings section. This is a useful strategy that ensures you’re answering your research questions and also allows the reader to quickly ascertain where the answers to your research questions are. Often, you will also need to present themes within each research question to keep yourself organized and to adequately flesh out your findings. The example below presents a research question from my study of blended learning at a charter high school (Bingham, 2016) , and an excerpt from my findings that answered that research question. I have also included the associated theme.

Research Question 1: What challenges, if any, do teachers face in implementing a blended model in a school’s first year? Theme: TROUBLESHOOTING AND TASK-MANAGING: TECHNOLOGY USE IN THE CLASSROOM In the original vision for instruction at Blended Academy, technology was to be an integral part of students’ learning, meant to allow students to find their own answers to their questions, to explore their personal interests, and to provide multiple opportunities for learning. The use of iPods in the classroom was partially intended to serve the social-emotional component of the model, allowing students to enjoy music and to “tune out” from other classroom activities when working on Digital X. Further, the iPods would allow stu- dents to listen to podcasts or teacher-created content at any time, in any location. However, prior to the school’s opening, little attention was paid to the management of these devices, and their potential for misuse. As a result, teachers spent much of their time managing students’ technology use, troubleshooting, and developing classroom procedures to ensure that technology use was relevant to learning. For example, in Ms. L’s classroom, she attempted to ensure learning was happening by instituting “Technology-Free” periods in the classroom. When students had to be working on their laptops in order to complete lessons or quizzes, the majority of her time was spent walking from student to student, watching for off-task behavior, and calling out students for how long they were “logged in” to the digital curriculum. In one typical interaction, Ms. L admonished one student, saying “It says you only logged in for one minute . . . when are you going to finish your English if you only logged in one minute today?” The difficulties around ensuring students were using technology productively resulted in teachers “hovering” over students, making it difficult to provide targeted instructional help. Teachers often responded to off-task behavior/ technology use by confiscating computers and devices or restricting their use, in order to ensure that students were working. However, because the majority of tasks were meant to be delivered online or through technological devices, this was not a productive or effective solution.

4). Vignettes

Vignettes can be a strategy to spark interest in your study, add narrative context, and provide a descriptive overview of your study/site/participants. They can also be used as a strategy to introduce themes. You can place them at the beginning of a paper, or at the start of the findings section, or in your discussion of each theme. They wouldn’t typically be the only representation of your findings that you present, but you can use them to hook the reader and provide a story that exemplifies findings, themes, contexts, participants, etc. Below is an example from one of my recent studies.

The Role of Pilot Teachers in Schoolwide Technology Integration Blended High School is a lot like many other charter schools. Students wear uniforms, and as you walk through the halls, there is almost always a teacher issuing a demerit to a student who is not wearing the right shoes, or who hasn’t tucked in their shirt. In this school, however, teachers use technology in almost every facet of their instruction, operating in a school model that blends face-to-face and online learning in the classroom in order to personalize students’ learning experiences. It has, however, been a long road to this level of technology use. BHS’s first year of operation was, arguably, disastrous. Teachers were overwhelmed and students didn’t progress as expected. In one staff meeting toward the end of the schools’ first year, teachers and administrators expressed frustration with each other and with the school model, with several teachers arguing that technology was hurting, not helping. The atmosphere was tense, with one teacher finally shrugging anxiously and saying “Maybe need to ask ourselves, ‘Is this the best model to use with some of our kids?’” Ultimately, by the end of the first year, technology was not a regular classroom practice. In BHS’s second year, the administration again pushed for full technology integration, but they wanted to start slow. In a fall semester staff meeting, the principal and the assistant principal ran what the principal referred to as a “technology therapy session,” where teachers could share their struggles with using technology to engage in PL. During the session, one of the new teachers mentions that she is having a difficult time letting go – changing her focus from lecturing to computer-based work. Another teacher worries about finding good online resources. Most of the teachers, new and veteran, are alarmed by the time it is taking for them design lessons that integrate technology. Some admit only engaging in technology use in a shallow way – uploading worksheets to Google Docs, recording Powerpoints, etc.  A few months after the discussion in which teachers aired their fears and struggles, the principal leads the teachers in analyzing student data from that week and spends a bit of time highlighting the work of a few teachers whose students are doing particularly well and who have been able to use technology in everyday classroom practice. Those teachers are part of a small group of “pilot teachers,” each of whom have been experimenting with various technology-based practices, including testing new learning management systems, designing their own online modules with personalized student objectives, providing students with technology-facilitated immediate feedback, and using up-to-the-minute data to develop technology-guided small-group instruction.  Over the course of the next several months, administrators encouraged teachers to continue to be transparent about their concerns and share those concerns in regular staff meetings. Administrators conferred with the pilot teachers and administrators and teachers together set incremental goals based on the pilot teachers’ recommendations. In weekly staff meetings, the pilot teachers shared their progress, including concerns and challenges. They collaborated with the other teachers to find solutions and worked with the administration to get what they needed to enact those solutions. For example, after a push from the pilot teachers, administration increased funding for technology purchases and introduced shifts in the school schedule to allow for planning in order to help teachers manage the demands of a high-tech classroom. Because the pilot teachers emphasized how much time meaningful technology integration took, and knew what worked and what didn’t, they were able to train other teachers in high-tech practices and to make the case to administration for needed changes.  By BHS’s third year, teachers schoolwide were able to fully integrate technology in their classrooms. All teachers were using the same learning management system, which had been initially chosen and tested by a pilot teacher. In every classroom, teachers were also engaging online modules, technology-facilitated breakout groups, and real time technology-based data analysis – all of which were practices the pilot teachers had tested and shared in the second year. The consistent collaboration between administration and pilot teachers and pilot teachers and other teachers helped calibrate classroom changes to manage the conflict between existing practices and new high-tech practices. By focusing on student learning data, creating the room for experimentation, collaborating consistently, and distributing the leadership for technology integration, teachers and administrators felt comfortable with the increasing reliance on tech-heavy practices.

I developed this vignette as a composite from my field notes and interviews and used it to set the stage for the rest of the findings section.

4). Anchoring Quotes

Using exemplar quotes from your participants is another way to structure your findings. In the following, which also comes from Bingham et al. (2018) , the finding itself is used as the heading, and the anchoring quotes come directly after the heading, prior to the rest of the narrative discussion of the finding. These quotations help provide some initial evidence and set the stage for what’s to come.

School Infrastructure and Available Technology Do Not Yet Fully Align With Teachers’ Needs  “I know that computer problems are an issue almost daily.” (Middle school personalized learning teacher)  “If the data was exactly what we needed, it would be easier. I think a lot of times we’re not using it enough because the way we’re using the data is not as effective as it should be.” (High school personalized learning teacher) 

You can note the source next to or after the quote. This can be done with your chosen pseudonyms, or with a general description, as I've done above.

5). Anchoring Excerpts from Field Notes

Similarly, excerpts from field notes can be used to start your discussion of a finding. Again, the finding itself is used as the heading, and the excerpt from field notes supporting that finding comes directly after the heading, prior to the rest of the narrative discussion of the finding. The example below comes from a study in which I explored how a personalized learning model evolved over the course of three years (Bingham, 2017) . I used excerpts from my field notes to open the discussion of each year.

Year 1: Navigating the disconnect between vision and practice  Walking into the large classroom space shared by Ms. Z and Ms. H, it is not immediately evident that these are high-tech PL classrooms. At first, there are no laptops out in either class. Both Ms. Z’s and Ms. H’s students are completing warm-up activities that are projected on each teacher’s white board. After a few minutes, Ms. Z’s students get up and get laptops. Ms. Z walks around to students and asks them what lesson from the digital curriculum they will be working on today. As Ms. Z speaks to a table of students, other students in the room listen to their iPods, sometimes singing loudly. Some students are on YouTube, watching music videos; others are messaging friends on GChat or Facebook. As Ms. Z makes her way around, students toggle back to the screen devoted to the digital curriculum. Sometimes, Ms. Z notices that students are off-task and she redirects them. Other times, she is too busy unlocking an online quiz for a student, or confiscating a student’s iPod. 

This excerpt from my field notes provided an overview of what teacher practice looked like in the first year of the school, so that I could then discuss several themes that were representative of how practice evolved over that first year.

The key takeaway here is that there are many ways to structure your findings section. You have to choose the method that best supports your study, and best represents your data and participants. No matter what you choose, the findings section itself should be constructed to answer your research questions, while also providing context and thick description, and, of course, telling a story.

Writing a discussion section

Some tips for academic writing.

Research Voyage

Research Tips and Infromation

07 Easy Steps for Writing Discussion Section of a Research Paper

In my role as a journal reviewer, I’ve had the privilege (and sometimes the frustration) of reviewing numerous research papers.

I’ve encountered papers where authors neglect to discuss how their results benefit the problem at hand, the domain they are working in, and society at large. This oversight often fails to make a lasting impression on reviewers

One recurring issue that I’ve noticed, time and again, is the challenge of properly delineating the boundaries of the discussion section in research papers. It’s not uncommon to come across papers where authors blur the lines between various crucial sections in a research paper.

Some authors mistakenly present their results within the discussion section, failing to provide a clear delineation between the findings of their study and their interpretation. Even if the journal allows to combine the results and discussion section, adopting a structured approach, such as dedicating a paragraph for results and a paragraph for discussing the results, can enhance the flow and readability of the paper, ensuring that readers can easily follow the progression of the study and its implications.

I vividly recall one instance where an author proceeded to rehash the entire methodology, complete with block diagrams, within the discussion section—without ever drawing any substantive conclusions. It felt like wading through familiar territory, only to find myself back at square one.

And then there are those authors who seem more interested in speculating about future directions than analyzing the outcomes of their current work in the discussion section. While it’s important to consider the implications of one’s research, it shouldn’t overshadow the critical analysis of the results at hand.

In another instance, a researcher concealed all failures or limitations in their work, presenting only the best-case scenarios, which created doubts about the validity of their findings. As a reviewer, I promptly sent it back for a relook and suggested adding various scenarios to reflect the true behaviour of the experiment.

In this post, I’ll delve into practical strategies for crafting the discussion section of a research paper. Drawing from my experiences as a reviewer and researcher, I’ll explore the nuances of the discussion section and provide insights into how to engage in critical discussion.

Introduction

I. focus on the relevance.

• II. Highlight  the Limitations 
• III. Introduce  New Discoveries

IV. Highlight the Observations

V. compare and relate with other research works.

• VI. Provide  Alternate View Points

A. Future Directions

B. conclusion, how to validate the claims i made in the discussion section of my research paper, phrases that can be used in the discussion section of a research paper, phrases that can be used in the analysis part of the discussion section of a research paper.

• Your Next Move...

Whether charts and graphs are allowed in discussion section of my Research Paper?

Can i add citations in discussion section of my research paper, can i combine results and discussion section in my research paper, what is the weightage of discussion section in a research paper in terms of selection to a journal, whether literature survey paper has a discussion section.

The Discussion section of a research paper is where authors interpret their findings, contextualize their research, and propose future directions. It is a crucial section that provides the reader with insights into the significance and implications of the study.

Writing an effective discussion section is a crucial aspect of any research paper, as it allows researchers to delve into the significance of their findings and explore their implications. A well-crafted discussion section not only summarizes the key observations and limitations of the study but also establishes connections with existing research and opens avenues for future exploration. In this article, we will present a comprehensive guide to help you structure your discussion section in seven simple steps.

By following these steps, you’ll be able to write a compelling Discussion section that enhances the reader’s understanding of your research and contributes to the broader scientific community.

Please note, the discussion section usually follows after the Results Section. I have written a comprehensive article on ” How to Write Results Section of your Research Paper “. Please visit the article to enhance your write-up on the results section.

Which are these 07 steps for writing an Effective Discussion Section of a Research Paper?

Step 1: Focus on the Relevance : In the first step, we will discuss the importance of emphasizing the relevance of your research findings to the broader scientific context. By clearly articulating the significance of your study, you can help readers understand how your work contributes to the existing body of knowledge and why it matters.

Step 2: Highlight the Limitations : Every research study has its limitations, and it is essential to address them honestly and transparently. We will explore how to identify and describe the limitations of your study, demonstrating a thorough understanding of potential weaknesses and areas for improvement.

Step 3: Highlight the Observations : In this step, we will delve into the core findings of your study. We will discuss the key observations and results, focusing on their relevance to your research objectives. By providing a concise summary of your findings, you can guide readers through the main outcomes of your study.

Step 4: Compare and Relate with Other Research Works : Research is a collaborative and cumulative process, and it is vital to establish connections between your study and previous research. We will explore strategies to compare and relate your findings to existing literature, highlighting similarities, differences, and gaps in knowledge.

Step 5: Provide Alternate Viewpoints: Science thrives on the diversity of perspectives. Acknowledging different viewpoints and interpretations of your results fosters a more comprehensive understanding of the research topic. We will discuss how to incorporate alternative viewpoints into your discussion, encouraging a balanced and nuanced analysis.

Step 6: Show Future Directions : A well-crafted discussion section not only summarizes the present but also points towards the future. We will explore techniques to suggest future research directions based on the implications of your study, providing a roadmap for further investigations in the field.

Step 7: Concluding Thoughts : In the final step, we will wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers.

By following these seven steps, you can craft a comprehensive and insightful discussion section that not only synthesizes your findings but also engages readers in a thought-provoking dialogue about the broader implications and future directions of your research. Let’s delve into each step in detail to enhance the quality and impact of your discussion section.

The purpose of every research is to implement the results for the positive development of the relevant subject. In research, it is crucial to emphasize the relevance of your study to the field and its potential impact. Before delving into the details of how the research was conceived and the sequence of developments that took place, consider highlighting the following factors to establish the relevance of your work:

• Identifying a pressing problem or research gap: Example: “This research addresses the critical problem of network security in wireless communication systems. With the widespread adoption of wireless networks, the vulnerability to security threats has increased significantly. Existing security mechanisms have limitations in effectively mitigating these threats. Therefore, there is a pressing need to develop novel approaches that enhance the security of wireless communication systems.”
• Explaining the significance and potential impact of the research: Example: “By developing an intelligent intrusion detection system using machine learning algorithms, this research aims to significantly enhance the security of wireless networks. The successful implementation of such a system would not only protect sensitive data and communication but also ensure the reliability and integrity of wireless networks in various applications, including Internet of Things (IoT), smart cities, and critical infrastructure.”
• Establishing connections with previous research and advancements in the field: Example: “This study builds upon previous research on intrusion detection systems and machine learning techniques. By leveraging recent advancements in deep learning algorithms and anomaly detection methods, we aim to overcome the limitations of traditional rule-based intrusion detection systems and achieve higher detection accuracy and efficiency.”

By emphasizing the relevance of your research and articulating its potential impact, you set the stage for readers to understand the significance of your work in the broader context. This approach ensures that readers grasp the motivations behind your research and the need for further exploration in the field.

II. Highlight  the Limitations 

Many times the research is on a subject that might have legal limitations or restrictions. This limitation might have caused certain imperfections in carrying out research or in results. This issue should be acknowledged by the researcher before the work is criticized by others later in his/her discussion section.

In computer science research, it is important to identify and openly acknowledge the limitations of your study. By doing so, you demonstrate transparency and a thorough understanding of potential weaknesses, allowing readers to interpret the findings in a more informed manner. Here’s an example:

Example: “It is crucial to acknowledge certain limitations and constraints that have affected the outcomes of this research. In the context of privacy-sensitive applications such as facial recognition systems, there are legal limitations and ethical concerns that can impact the accuracy and performance of the developed algorithm. These limitations stem from regulations and policies that impose restrictions on data collection, access, and usage to protect individuals’ privacy rights. As a result, the algorithm developed in this study operates under these legal constraints, which may have introduced certain imperfections.”

In this example, the researcher is working on a facial recognition system and acknowledges the legal limitations and ethical concerns associated with privacy-sensitive applications. By openly addressing these limitations, the researcher demonstrates an understanding of the challenges imposed by regulations and policies. This acknowledgement sets the stage for a more nuanced discussion and prevents others from solely criticizing the work based on these limitations without considering the broader legal context.

By highlighting the limitations, researchers can also offer potential solutions or future directions to mitigate the impact of these constraints. For instance, the researcher may suggest exploring advanced privacy-preserving techniques or collaborating with legal experts to find a balance between privacy protection and system performance.

By acknowledging and addressing the limitations, researchers demonstrate their awareness of potential weaknesses in their study, maintaining credibility, and fostering a more constructive discussion of their findings within the context of legal and ethical considerations.

III. Introduce  New Discoveries

Begin the discussion section by stating all the major findings in the course of the research. The first paragraph should have the findings mentioned, which is expected to be synoptic, naming and briefly describing the analysis of results.

Example: “In this study, several significant discoveries emerged from the analysis of the collected data. The findings revealed compelling insights into the performance of parallel computing architectures for large-scale data processing. Through comprehensive experimentation and analysis, the following key discoveries were made:

• Discovery 1: The proposed parallel computing architecture demonstrated a 30% improvement in processing speed compared to traditional sequential computing methods. This finding highlights the potential of parallel computing for accelerating data-intensive tasks.
• Discovery 2: A direct relationship between the number of processing cores and the overall system throughput was observed. As the number of cores increased, the system exhibited a near-linear scalability, enabling efficient utilization of available computational resources.
• Discovery 3: The analysis revealed a trade-off between processing speed and energy consumption. While parallel computing achieved faster processing times, it also resulted in higher energy consumption. This finding emphasizes the importance of optimizing energy efficiency in parallel computing systems.

These discoveries shed light on the performance characteristics and trade-offs associated with parallel computing architectures for large-scale data processing tasks. The following sections will delve into the implications of these findings, discussing their significance, limitations, and potential applications.”

In this example, the researcher presents a concise overview of the major discoveries made during the research. Each discovery is briefly described, highlighting the key insights obtained from the analysis. By summarizing the findings in a synoptic manner, the reader gains an immediate understanding of the notable contributions and can anticipate the subsequent detailed discussion.

This approach allows the discussion section to begin with a clear and impactful introduction of the major discoveries, capturing the reader’s interest and setting the stage for a comprehensive exploration of each finding in subsequent paragraphs.

Coming to the major part of the findings, the discussion section should interpret the key observations, the analysis of charts, and the analysis of tables. In the field of computer science, presenting and explaining the results in a clear and accessible manner is essential for readers to grasp the significance of the findings. Here are some examples of how to effectively highlight observations in computer science research:

Begin with explaining the objective of the research, followed by what inspired you as a researcher to study the subject:

In a study on machine learning algorithms for sentiment analysis, start by stating the goal of developing an accurate and efficient sentiment analysis model. Share your motivation for choosing this research topic, such as the increasing importance of sentiment analysis in various domains like social media, customer feedback analysis, and market research.

Example: The objective of this research was to develop a sentiment analysis model using machine learning algorithms. As sentiment analysis plays a vital role in understanding public opinion and customer feedback, we were motivated by the need for an accurate and efficient model that could be applied in various domains such as social media analysis, customer reviews, and market research.

Explain the meaning of the findings, as every reader might not understand the analysis of graphs and charts as easily as people who are in the same field as you:

If your research involves analyzing performance metrics of different algorithms, consider presenting the results in a visually intuitive manner, such as line graphs or bar charts. In the discussion section, explain the significance of the trends observed in the graphs. For instance, if a particular algorithm consistently outperforms others in terms of accuracy, explain why this finding is noteworthy and how it aligns with existing knowledge in the field.

Example: To present the performance evaluation of the algorithms, we analyzed multiple metrics, including precision, recall, and F1 score. The line graph in Figure 1 demonstrates the trends observed. It is noteworthy that Algorithm A consistently outperformed the other algorithms across all metrics. This finding indicates that Algorithm A has a higher ability to accurately classify sentiment in comparison to its counterparts. This aligns with previous studies that have also highlighted the robustness of Algorithm A in sentiment analysis tasks.

Ensure the reader can understand the key observations without being forced to go through the whole paper:

In computer science research, it is crucial to present concise summaries of your key observations to facilitate understanding for readers who may not have the time or expertise to go through the entire paper. For example, if your study compares the runtime performance of two programming languages for a specific task, clearly state the observed differences and their implications. Highlight any unexpected or notable findings that may challenge conventional wisdom or open up new avenues for future exploration.

Example: In this study comparing the runtime performance of Python and Java for a specific computational task, we observed notable differences. Python consistently showed faster execution times, averaging 20% less time than Java across varying input sizes. These results challenge the common perception that Java is the superior choice for computationally intensive tasks. The observed performance advantage of Python in this context suggests the need for further investigation into the underlying factors contributing to this discrepancy, such as differences in language design and optimization strategies.

By employing these strategies, researchers can effectively highlight their observations in the discussion section. This enables readers to gain a clear understanding of the significance of the findings and their implications without having to delve into complex technical details.

No one is ever the only person researching a particular subject. A researcher always has companions and competitors. The discussion section should have a detailed comparison of the research. It should present the facts that relate the research to studies done on the same subject.

Example: The table below compares some of the well-known prediction techniques with our fuzzy predictor with MOM defuzzification for response time, relative error and Environmental constraints. Based on the results obtained it can be concluded that the Fuzzy predictor with MOM defuzzification has a less relative error and quick response time as compared to other prediction techniques.  The proposed predictor is more flexible, simple to implement and deals with noisy and uncertain data from real-life situations. The relative error of 5-10% is acceptable for our system as the predicted fuzzy region and the fuzzy region of the actual position remains the same.

Table 1 : Comparison of well-known Robot Motion prediction Techniques

VI. Provide  Alternate View Points

Almost every time, it has been noticed that analysis of charts and graphs shows results that tend to have more than one explanation. The researcher must consider every possible explanation and potential enhancement of the study from alternative viewpoints. It is critically important that this is clearly put out to the readers in the discussion section.

In the discussion section of a research paper, it is important to acknowledge that data analysis often yields results that can be interpreted in multiple ways. By considering different viewpoints and potential enhancements, researchers can provide a more comprehensive and nuanced analysis of their findings. Here are some examples:

Example 1: “The analysis of our experimental data showed a decrease in system performance following the implementation of the proposed optimization technique. While our initial interpretation suggested that the optimization failed to achieve the desired outcome, an alternate viewpoint could be that the decrease in performance was influenced by an external factor, such as the configuration of the hardware setup. Further investigation into the hardware settings and benchmarking protocols is necessary to fully understand the observed results and identify potential enhancements.”

Example 2: “The analysis of user feedback revealed a mixed response to the redesigned user interface. While some participants reported improved usability and satisfaction, others expressed confusion and dissatisfaction. An alternate viewpoint could be that the diverse range of user backgrounds and preferences might have influenced these varied responses. Further research should focus on conducting user studies with a larger and more diverse sample to gain a deeper understanding of the underlying factors contributing to the contrasting user experiences.”

Example 3: “Our study found a positive correlation between the implementation of agile methodologies and project success rates. However, an alternate viewpoint suggests that other factors, such as team dynamics and project complexity, could have influenced the observed correlation. Future research should explore the interactions between agile methodologies and these potential confounding factors to gain a more comprehensive understanding of their impact on project success.”

In these examples, researchers present alternative viewpoints that offer different interpretations or explanations for the observed results. By acknowledging these alternate viewpoints, researchers demonstrate a balanced and comprehensive analysis of their findings. It is crucial to clearly communicate these alternative perspectives to readers in the discussion section, as it encourages critical thinking and highlights the complexity and potential limitations of the research.

By presenting alternate viewpoints, researchers invite further exploration and discussion, fostering a more comprehensive understanding of the research topic. This approach enriches the scientific discourse and promotes a deeper analysis of the findings, contributing to the overall advancement of knowledge in the field.

VII. Future Directions and Conclusion

The section must have suggestions for research that should be done to unanswered questions. These should be suggested at the beginning of the discussion section to avoid questions being asked by critics. Emphasizing the importance of following future directions can lead to new research as well.

Example: ” While this study provides valuable insights into the performance of the proposed algorithm, there are several unanswered questions and avenues for future research that merit attention. By identifying these areas, we aim to stimulate further exploration and contribute to the continuous advancement of the field. The following future directions are suggested:

• Future Direction 1: Investigating the algorithm’s performance under different dataset characteristics and distributions. The current study focused on a specific dataset, but it would be valuable to evaluate the algorithm’s robustness and generalizability across a broader range of datasets, including real-world scenarios and diverse data sources.
• Future Direction 2: Exploring the potential integration of additional machine learning techniques or ensemble methods to further enhance the algorithm’s accuracy and reliability. By combining the strengths of multiple models, it is possible to achieve better performance and handle complex patterns and outliers more effectively.
• Future Direction 3: Extending the evaluation to consider the algorithm’s scalability in large-scale deployment scenarios. As the volume of data continues to grow exponentially, it is crucial to assess the algorithm’s efficiency and scalability in handling big data processing requirements.

By suggesting these future directions, we hope to inspire researchers to explore new avenues and build upon the foundation laid by this study. Addressing these unanswered questions will contribute to a more comprehensive understanding of the algorithm’s capabilities and limitations, paving the way for further advancements in the field.”

In this example, the researcher presents specific future directions that can guide further research. Each future direction is described concisely, highlighting the specific area of investigation and the potential benefits of pursuing those directions. By suggesting these future directions early in the discussion section, the researcher proactively addresses potential questions or criticisms and demonstrates a proactive approach to knowledge expansion.

By emphasizing the importance of following future directions, researchers not only inspire others to continue the research trajectory but also contribute to the collective growth of the field. This approach encourages ongoing exploration, innovation, and collaboration, ensuring the continuous development and improvement of computer science research.

In the final step, wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers. This section serves as a crucial opportunity to reinforce the main findings and highlight the broader impact of your work. Here are some examples:

Example 1: “In conclusion, this research has made significant contributions to the field of natural language processing. By proposing a novel neural network architecture for language generation, we have demonstrated the effectiveness and versatility of the model in generating coherent and contextually relevant sentences. The experimental results indicate a significant improvement in language generation quality compared to existing approaches. The implications of this research extend beyond traditional applications, opening up new possibilities for automated content creation, chatbot systems, and dialogue generation in artificial intelligence.”

Example 2: “In summary, this study has provided valuable insights into the optimization of network routing protocols for wireless sensor networks. By proposing a novel hybrid routing algorithm that combines the advantages of both reactive and proactive protocols, we have demonstrated enhanced network performance in terms of latency, energy efficiency, and scalability. The experimental results validate the effectiveness of the proposed algorithm in dynamic and resource-constrained environments. These findings have implications for various applications, including environmental monitoring, industrial automation, and smart city infrastructure.”

Example 3: “In closing, this research sheds light on the security vulnerabilities of blockchain-based smart contracts. By conducting an extensive analysis of existing smart contract platforms and identifying potential attack vectors, we have highlighted the need for robust security measures to mitigate risks and protect user assets. The insights gained from this study can guide the development of more secure and reliable smart contract frameworks, ensuring the integrity and trustworthiness of blockchain-based applications across industries such as finance, supply chain, and decentralized applications.”

In these examples, the concluding thoughts summarize the main contributions and findings of the research. They emphasize the significance of the study’s implications and highlight the potential impact on various domains within computer science. By providing a succinct and impactful summary, the researcher leaves a lasting impression on readers, reinforcing the value and relevance of the research in the field.

Validating claims in the discussion section of a research paper is essential to ensure the credibility and reliability of your findings. Here are some strategies to validate the claims made in the discussion section:

• Referencing supporting evidence: Cite relevant sources from the existing literature that provide evidence or support for your claims. These sources can include peer-reviewed studies, research articles, and authoritative sources in your field. By referencing credible and reputable sources, you establish the validity of your claims and demonstrate that your interpretations are grounded in existing knowledge.
• Relating to the results: Connect your claims to the results presented in the earlier sections of your research paper. Clearly demonstrate how the findings support your claims and provide evidence for your interpretations. Refer to specific data, measurements, statistical analyses, or other evidence from your results section to substantiate your claims.
• Comparing with previous research: Discuss how your findings align with or diverge from previous research in the field. Reference relevant studies and explain how your results compare to or build upon existing knowledge. By contextualizing your claims within the broader research landscape, you provide further validation for your interpretations.
• Addressing limitations and alternative explanations: Acknowledge the limitations of your study and consider alternative explanations for your findings. By addressing potential counterarguments and alternative viewpoints, you demonstrate a thorough evaluation of your claims and increase the robustness of your conclusions.
• Seeking peer feedback: Prior to submitting your research paper, consider seeking feedback from colleagues or experts in your field. They can provide valuable insights and suggestions for further validating your claims or improving the clarity of your arguments.
• Inviting replication and further research: Encourage other researchers to replicate your study or conduct further investigations. By promoting replication and future research, you contribute to the ongoing validation and refinement of your claims.

Remember, the validation of claims in the discussion section is a critical aspect of scientific research. By employing rigorous methods and logical reasoning, you can strengthen the credibility and impact of your findings and contribute to the advancement of knowledge in your field.

Here are some common phrases that can be used in the discussion section of a paper or research article. I’ve included a table with examples to illustrate how these phrases might be used:

Here are some common academic phrases that can be used in the analysis section of a paper or research article. I have included a table with examples to illustrate how these phrases might be used:

Your Next Move…

I believe you will proceed to write conclusion section of your research paper. Conclusion section is the most neglected part of the research paper as many authors feel it is unnecessary but write in a hurry to submit the article to some reputed journal.

Please note, once your paper gets published , the readers decide to read your full paper based only on abstract and conclusion. They decide the relevance of the paper based on only these two sections. If they don’t read then they don’t cite and this in turn affects your citation score. So my sincere advice to you is not to neglect this section.

Visit my article on “How to Write Conclusion Section of Research Paper” for further details.

Please visit my article on “ Importance and Improving of Citation Score for Your Research Paper ” for increasing your visibility in research community and on Google Scholar Citation Score.

The Discussion section of a research paper is an essential part of any study, as it allows the author to interpret their results and contextualize their findings. To write an effective Discussion section, authors should focus on the relevance of their research, highlight the limitations, introduce new discoveries, highlight their observations, compare and relate their findings to other research works, provide alternate viewpoints, and show future directions.

By following these 7 steps, authors can ensure that their Discussion section is comprehensive, informative, and thought-provoking. A well-written Discussion section not only helps the author interpret their results but also provides insights into the implications and applications of their research.

In conclusion, the Discussion section is an integral part of any research paper, and by following these 7 steps, authors can write a compelling and informative discussion section that contributes to the broader scientific community.

Frequently Asked Questions

Yes, charts and graphs are generally allowed in the discussion section of a research paper. While the discussion section is primarily focused on interpreting and discussing the findings, incorporating visual aids such as charts and graphs can be helpful in presenting and supporting the analysis.

Yes, you can add citations in the discussion section of your research paper. In fact, it is highly recommended to support your statements, interpretations, and claims with relevant and credible sources. Citations in the discussion section help to strengthen the validity and reliability of your arguments and demonstrate that your findings are grounded in existing literature.

Combining the results and discussion sections in a research paper is a common practice in certain disciplines, particularly in shorter research papers or those with specific formatting requirements. This approach can help streamline the presentation of your findings and provide a more cohesive narrative. However, it is important to note that the decision to combine these sections should be based on the guidelines of the target journal or publication and the specific requirements of your field.

The weightage of the discussion section in terms of the selection of a research paper for publication in a journal can vary depending on the specific requirements and criteria of the journal. However, it is important to note that the discussion section is a critical component of a research paper as it allows researchers to interpret their findings, contextualize them within the existing literature, and discuss their implications.

In general, literature survey papers typically do not have a separate section explicitly labeled as “Discussion.” However, the content of a literature survey paper often incorporates elements of discussion throughout the paper. The focus of a literature survey paper is to review and summarize existing literature on a specific topic or research question, rather than presenting original research findings.

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How to Conduct Qualitative Data Analysis? (+The Best Tool to Use)

12 min read

Let’s face it: qualitative data analysis is vital to understanding why users act in a particular way and how they feel about your product in a way that quantitative product analytics can’t.

This article will teach you how to analyze qualitative data to inform product development and improve the product experience.

You will discover:

• Five qualitative data analysis methods.
• A six-step analysis process and how to streamline it with Userpilot.
• How to act on your research findings.

What is qualitative data analysis?

Qualitative data analysis (QDA) involves organizing, examining, and interpreting non-numerical data collected from customers . For example, their survey responses or session recordings.

The purpose?

To understand their interactions with the product and gain in-depth insights into user pain points , unmet needs, motivations, and expectations.

Quantitative data analysis vs. qualitative data analysis

Quantitative and qualitative data analyses have different objectives and use different data types, methods, and tools to achieve them.

• The aim of quantitative research is to illustrate objectively what happens inside the product, while qualitative research focuses on the why . For example, quantitative data can reveal the percentage of users who drop off at a particular touchpoint, whereas qualitative data explains the reasons for that.
• Quantitative data analysis uses numerical and measurable data, such as event occurrences. In contrast, qualitative data analysis relies on non-numerical and descriptive data, like open-ended survey responses .
• To collect quantitative data, you use closed-ended survey questions and analytics tools to track user actions . Qualitative data, on the other hand, comes from open-ended survey questions , interviews, focus groups, phone calls and chats, and session recordings.
• Analyzing quantitative data involves visualizing it in charts and graphs and conducting statistical operations. Qualitative analysis is about finding themes and patterns in data, often manually.

Benefits of qualitative data analysis

Here are the key advantages of qualitative data analysis that underscore its importance in customer and market research :

• Deep insights : Qualitative analysis helps understand complex patterns by looking at reasons and perspectives behind the data.
• Flexibility : It allows researchers to adjust their approach as they continuously discover new information or themes.
• Contextual understanding : It explores contextual factors, which adds depth to number-based findings and reveals hidden connections.
• Participant voice : It highlights what participants actually say, experience, and feel and uses their input to shape the analysis and the results.

Challenges of qualitative data analysis

Let’s get it straight: qualitative data analysis comes with its challenges. The key ones include:

• Data overload and management : Qualitative data analysis involves tons of text or multimedia, which is challenging to organize, manage, and analyze.
• Reliability and validity : Ensuring the reliability and validity of qualitative findings is difficult because the process isn’t as standardized as quantitative analysis. Personal biases can skew the results.
• Time-intensive nature : Qualitative data analysis is resource-intensive and time-consuming. It involves iterative processes of coding, categorizing, and synthesizing data.

What are the 5 qualitative data analysis methods?

There are 5 main methods of qualitative research studies:

• Content analysis.
• Narrative analysis.
• Discourse analysis.
• Thematic analysis.
• Grounded theory analysis.

Mind you, the differences between them aren’t always clear-cut, and there is always some overlap.

1. Content analysis

Content analysis is a qualitative data analysis method that systematically evaluates a text to identify specific features or patterns. This could be anything from a customer interview transcript to survey responses, social media posts, or customer success calls.

The first step in the content analysis research method is data coding, or labeling and categorizing.

For example, if you are looking at customer feedback , you might code all mentions of “price” as “P,” all mentions of “quality” as “Q,” and so on.

Once manual coding is done, start looking for patterns and trends in the codes.

While it’s a qualitative approach, it often aims to quantify the content, for example, by counting how many times a word or theme appears.

One of the main advantages of content analysis is its relative simplicity – anyone with a good understanding of the data can do it.

The advantages of the content analysis process

• Content analysis can provide rich insights into how customers feel about your product, what their unmet needs are, and their motives.
• Once you have developed a coding system, content analysis is relatively quick and easy because it’s a systematic process.
• Content analysis requires very little investment since all you need is a good understanding of the data, and it doesn’t require any specialist qualitative research data analysis software .

The disadvantages of the content analysis process

• Coding data takes time, particularly if you have large amounts to analyze.
• Content analysis can ignore the context in which the data was collected. This may lead to misinterpretations.
• Some people argue that content analysis is a reductive approach to qualitative data because it involves breaking the data down into smaller pieces and quantifying, which can lead to oversimplifications.

2. Narrative analysis

Narrative analysis involves identifying, analyzing, and interpreting customer stories, for example, in the form of customer interviews or testimonials.

This kind of analysis helps product managers understand customers’ feelings toward the product, identify trends in customer behavior, and personalize their in-app experiences .

The advantages of narrative analysis

• The stories people tell give a deep understanding of customers’ needs and pain points.
• It collects unique, in-depth data based on customer stories.

The disadvantages of narrative analysis

• Hard to implement in large-scale studies.
• Transcribing customer interviews or testimonials is labor-intensive.
• Impossible to replicate as it relies on unique customer stories and your ability to interpret them.

3. Discourse analysis

Discourse analysis is about understanding how people communicate with each other. You can use it to analyze written or spoken language.

For instance, product teams can use discourse analysis to understand how customers talk about their products on the web.

The advantages of discourse analysis

• Uncovers emotions behind customers’ words.
• Gives insights into customer data.

The disadvantages of disclosure analysis

• Takes a lot of time and effort as the process is highly specialized and requires training and practice. There’s no “right” way to do it.
• Focuses solely on language.

4. Thematic analysis

Thematic analysis is similar to content analysis.

It also looks for patterns and themes in qualitative data and involves labeling and categorizing the data.

The difference?

It focuses on the meaning of the data and how the themes relate to the research questions .

You can pair it with sentiment analysis to determine whether a piece of writing is positive, negative, or neutral. This is done using a lexicon (i.e., a list of words and their associated sentiment scores).

SaaS companies use thematic analysis to analyze qualitative NPS survey responses to identify patterns among their customer base.

The advantages of thematic analysis

• Anyone with little training on how to label the data can perform thematic analysis.
• Survey or customer interview raw data can be easily converted into insights and quantitative data with the help of labeling.
• If done automatically, it’s an effective way to process large amounts of data (you need AI tools for this).

The disadvantages of thematic analysis

• If the data isn’t coded correctly, it can be difficult to identify themes since it’s a phrase-based method.
• It’s difficult to implement from scratch because balancing the themes and categories is tricky. They shouldn’t be too generic or too large.

5. Grounded theory analysis

Grounded theory analysis is a method used by qualitative researchers when there is little existing theory on the topic. Instead of starting with hypotheses and testing them through research, it involves generating them from the data on the fly as it emerges.

The grounded theory approach is useful for product managers who want to understand how customers interact with their products. You can also use it to generate hypotheses about how customers will behave in the future.

Suppose product teams want to understand the reasons behind the high churn rate . They can use customer surveys and grounded theory to analyze responses and develop hypotheses about why users churn and how to reengage inactive ones .

The advantages of grounded theory analysis

• It reduces bias because it doesn’t rely on assumptions.
• It’s great for analyzing poorly researched topics.

The disadvantages of grounded theory analysis

• It can come across as overly theoretical.
• It requires a lot of objectivity, creativity, and critical thinking.

Which qualitative data analysis method should you choose?

The choice of the appropriate qualitative data analysis method depends on various factors, including:

• Research question : Different qualitative methods are suitable for different research questions. For example, you can use content analysis to categorize and quantify customer feedback from support tickets to prioritize areas for improvement. Contrastingly, discourse analysis will show you how users feel about your product.
• Nature of data : Choose qualitative data analysis techniques that align with the data’s characteristics. For instance, content analysis is versatile and can be applied to various types of qualitative data, while narrative analysis focuses specifically on stories and narratives.
• Researcher expertise : Consider your own skills and expertise in qualitative analysis techniques. Some methods may require specialized training or familiarity with specific software tools . Choose a method that you feel comfortable with and confident in applying effectively.
• Research goals and resources : Evaluate your research goals, timeline, and resources available for analysis. Consider the balance between the depth of analysis and practical constraints. For example, narrative analysis is more time-consuming or resource-intensive than others.

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How to perform qualitative data analysis? A step-by-step process

With all that qualitative research methods covered, it’s time for our step-by-step guide to qualitative data analysis.

Step 1: Define your qualitative research questions

It’s important to be as specific as possible with the questions, as this will guide how you collect qualitative research data and the rest of your analysis.

Examples are:

• What are the primary reasons for customer dissatisfaction with our product?
• How does X group of users feel about our new feature?
• What are our customers’ needs, and how do they vary by segment?
• How do our products fit into our customers’ lives?
• What factors influence the low usage rate of the new feature ?

Step 2: Gather your qualitative customer data

Now, you decide what type of data collection to use based on previously defined goals.

Here are 5 methods to collect qualitative data for product companies:

• In-app surveys : triggered at a specific time or contextually, when the user completes an action, they allow you to reach product users (not account managers) and to collect qualitative data at scale. They should include both closed-ended and open-ended follow-up questions.
• Email surveys : delivered to users’ mailboxes, these surveys tend to have lower response rates than in-app surveys, but they’re sometimes the only way to reach inactive users .

• Review sites : Customer reviews on sites like G2, Capterra, or Clutch are often unfiltered and give you honest insights into customer sentiment and how to improve the product .

• User interviews : one-to-one conversations with customers give you the flexibility that no survey offers. A skilled interviewer can follow up on responses to help users get to the root cause of an issue, even if they’re not very good at articulating their thoughts or reflecting on their behaviors.

• Focus groups : Just like interviews, these moderated group discussions can offer valuable insights about your product and customer preferences . They aren’t very scalable, though.

Pro tip : Use a mix of in-app surveys and in-person interviews to collect qualitative data. For example, send regular NPS surveys to customers to track their sentiment over time. Segment your detractors and invite them to interviews to better understand why they’re dissatisfied with the product.

Step 3: Organize and categorize collected data

Before analyzing customer feedback and assigning any value, organize the unstructured feedback data in a single place. This is to easily detect patterns and similar themes.

One way to do this is to create a spreadsheet with all the data organized by research questions. Then, arrange the data by theme or category within each research question.

Or use Userpilot’s NPS response tagging to group similar responses. For example, you can tag all feedback about technical issues with “bug.”

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Step 4: use qualitative data coding to identify themes and patterns.

Themes in data analysis help you organize and make sense of your information.

In SaaS products, common themes from customer feedback might include:

• Product issues ( bugs or defects).
• Pricing concerns.
• Customer service experiences.
• Usability problems.
• User interface (UI) design.
• User experience (UX) issues.
• Missing features.

Identifying specific themes is just a start. You also need to identify their patterns: how often they occur, when, and who is affected. For example, if users complain about a missing feature, find out how many and what their JTBDs are.

You can detect those patterns using survey analytics .

Step 5: Act on the insights to improve product metrics

Once you understand the nature of the problem, look for ways to resolve it.

How you act on feedback depends on the issues.

Imagine your users request a feature . But it turns out you have a similar feature that allows them to complete the same task – they just haven’t found it. So, the solution is redesigning your onboarding process to improve feature discovery .

And if you have to build the feature, don’t just blindly follow customer requests or what competitors are doing – look for innovative solutions to get the edge.

Step 6: Continuously analyze qualitative data to stay ahead of changing customer needs

Qualitative data analysis isn’t a one-off exercise. As customer needs constantly evolve, you must always keep your hand on the pulse.

Collect customer feedback in-app at regular intervals and make a habit of interviewing customers all the time, even if you’re not planning to launch new features or products.

Qualitative data analysis example: How Unolo reduced customer churn with Userpilot NPS survey

One of our customers, Unolo, faced a challenge: a high month-on-month churn rate of around 3%.

They implemented in-app NPS surveys to analyze qualitative and quantitative responses about why their customers were leaving.

In addition to collecting the feedback in-app, their customer success team also reached out to the dissatisfied customers directly.

What was the outcome?

UX and product experience improvements that reduced the churn by up to 1%!

How to perform qualitative data analysis with Userpilot?

Userpilot is a product growth platform with advanced feedback, analytics, and engagement features. So you can use it to collect qualitative and quantitative data and act on the insights to improve user experience .

Collect qualitative feedback from users with in-app surveys

You can collect qualitative user data in Userpilot through in-app surveys .

Thanks to the template library and visual editor, creating customized surveys takes minutes.

Once ready, there are two ways to trigger them.

One is by setting the date, time, and page where the user should see it. Great for regular surveys, like CSAT or NPS .

The other is through event-based triggering. That’s when you link the survey to another user action in-app, for example, using a feature. That’s how you can measure their satisfaction with specific product aspects and collect ideas on how to improve them.

In both instances, you can choose to send the survey to specific user segments .

Finally, Userpilot offers localization functionality, so you can automatically translate the survey into multiple languages for users around the globe.

Analyze data no-code using survey analytics

Once you collect the data, you can easily analyze it without any coding.

Userpilot offers you survey analytics where you can quickly view the results and analyze user engagement with the surveys.

And let’s not forget about the NPS dashboard , which provides a comprehensive overview of your NPS scores over time, allowing you to track changes and trends in user loyalty and advocacy.

And don’t worry, Userpilot does all the NPS analysis for you: calculates the overall score and segments users into detractors, passives , and promoters.

You can also tag the NPS responses to identify common themes.

Gather customer insights via chatbots

With Userpilot, you can embed a chatbot in the resource center .

This serves two purposes:

• It allows you to support your users in-app and help them overcome issues that could lower their satisfaction and potentially lead to churn .
• You can also use it to collect qualitative customer feedback .

Build different in-app experiences based on the insights from qualitative data analysis

By analyzing qualitative feedback collected through in-app surveys , you can segment users based on these insights and create targeted in-app experiences designed to address specific user concerns or enhance key workflows.

For example, you can personalize the onboarding experiences for different user personas so that they discover the most relevant features as quickly as possible. Or use interactive walkthroughs to guide them through challenging processes.

The qualitative data analysis process is iterative and should be revisited as new data is collected. The goal is to constantly refine your understanding of your customer base and how they interact with your product.

Want to get started with qualitative analysis? Get a Userpilot demo and automate the qualitative data collection process. Save time on mundane work and understand your customers better!

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