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Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study

There are three kinds of lies: lies, damned lies, and statistics. – Mark Twain 1

INTRODUCTION

Statistics represent an essential part of a study because, regardless of the study design, investigators need to summarize the collected information for interpretation and presentation to others. It is therefore important for us to heed Mr Twain’s concern when creating the data analysis plan. In fact, even before data collection begins, we need to have a clear analysis plan that will guide us from the initial stages of summarizing and describing the data through to testing our hypotheses.

The purpose of this article is to help you create a data analysis plan for a quantitative study. For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2 , 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to summarize study data, and a process to help identify relevant statistical tests. My intention here is to introduce the main elements of data analysis and provide a place for you to start when planning this part of your study. Biostatistical experts, textbooks, statistical software packages, and other resources can certainly add more breadth and depth to this topic when you need additional information and advice.

TERMS AND CONCEPTS USED IN DATA ANALYSIS

When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is important to begin with an understanding of the source of the numbers. Let us start with the term variable , which defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values , to help describe the characteristic of an individual study participant. For example, the variable “sex” would have values of “male” and “female”.

Although variables can be defined or grouped in various ways, I will focus on 2 methods at this introductory stage. First, variables can be defined according to the level of measurement. The categories in a nominal variable are names, for example, male and female for the variable “sex”; white, Aboriginal, black, Latin American, South Asian, and East Asian for the variable “ethnicity”; and intervention and control for the variable “treatment group”. Nominal variables with only 2 categories are also referred to as dichotomous variables because the study group can be divided into 2 subgroups based on information in the variable. For example, a study sample can be split into 2 groups (patients receiving the intervention and controls) using the dichotomous variable “treatment group”. An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or always). Nominal-level and ordinal-level variables are also referred to as categorical variables, because each category in the variable can be completely separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive categories also having meaning. Age, weight, and blood glucose can be considered as interval variables, but also as ratio variables, because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories.

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. 4 For example, if age is reduced from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years) we lose the ability to make comparisons across the entire age range and introduce error into the data analysis. 4

A second method of defining variables is to consider them as either dependent or independent. As the terms imply, the value of a dependent variable depends on the value of other variables, whereas the value of an independent variable does not rely on other variables. In addition, an investigator can influence the value of an independent variable, such as treatment-group assignment. Independent variables are also referred to as predictors because we can use information from these variables to predict the value of a dependent variable. Building on the group of variables listed in the first paragraph of this section, blood glucose could be considered a dependent variable, because its value may depend on values of the independent variables age, sex, ethnicity, exercise frequency, weight, and treatment group.

Statistics are mathematical formulae that are used to organize and interpret the information that is collected through variables. There are 2 general categories of statistics, descriptive and inferential. Descriptive statistics are used to describe the collected information, such as the range of values, their average, and the most common category. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. Inferential statistics are used to make comparisons and draw conclusions from the study data. Knowledge gained from inferential statistics allows investigators to make inferences and generalize beyond their study sample to other groups.

Before we move on to specific descriptive and inferential statistics, there are 2 more definitions to review. Parametric statistics are generally used when values in an interval-level or ratio-level variable are normally distributed (i.e., the entire group of values has a bell-shaped curve when plotted by frequency). These statistics are used because we can define parameters of the data, such as the centre and width of the normally distributed curve. In contrast, interval-level and ratio-level variables with values that are not normally distributed, as well as nominal-level and ordinal-level variables, are generally analyzed using nonparametric statistics.

METHODS FOR SUMMARIZING STUDY DATA: DESCRIPTIVE STATISTICS

The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data.

Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable. Data for nominal-level and ordinal-level variables may be interpreted using a pie graph or bar graph . Both options allow us to examine the relative number of participants within each category (by reporting the percentages within each category), whereas a bar graph can also be used to examine absolute numbers. For example, we could create a pie graph to illustrate the proportions of men and women in a study sample and a bar graph to illustrate the number of people who report exercising at each level of frequency (never, sometimes, often, or always).

Interval-level and ratio-level variables may also be interpreted using a pie graph or bar graph; however, these types of variables often have too many categories for such graphs to provide meaningful information. Instead, these variables may be better interpreted using a histogram . Unlike a bar graph, which displays the frequency for each distinct category, a histogram displays the frequency within a range of continuous categories. Information from this type of figure allows us to determine whether the data are normally distributed. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Interested readers can find additional types of figures in the books recommended in the “Further Readings” section.

Figures are also useful for visualizing comparisons between variables or between subgroups within a variable (for example, the distribution of blood glucose according to sex). Box plots are useful for summarizing information for a variable that does not follow a normal distribution. The lower and upper limits of the box identify the interquartile range (or 25th and 75th percentiles), while the midline indicates the median value (or 50th percentile). Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations.

In addition to using figures to present a visual description of the data, investigators can use statistics to provide a numeric description. Regardless of the measurement level, we can find the mode by identifying the most frequent category within a variable. When summarizing nominal-level and ordinal-level variables, the simplest method is to report the proportion of participants within each category.

The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed. If the values are normally distributed, we can summarize the information using the parametric statistics of mean and standard deviation. The mean is the arithmetic average of all values within the variable, and the standard deviation tells us how widely the values are dispersed around the mean. When values of interval-level and ratio-level variables are not normally distributed, or we are summarizing information from an ordinal-level variable, it may be more appropriate to use the nonparametric statistics of median and range. The first step in identifying these descriptive statistics is to arrange study participants according to the variable categories from lowest value to highest value. The range is used to report the lowest and highest values. The median or 50th percentile is located by dividing the number of participants into 2 groups, such that half (50%) of the participants have values above the median and the other half (50%) have values below the median. Similarly, the 25th percentile is the value with 25% of the participants having values below and 75% of the participants having values above, and the 75th percentile is the value with 75% of participants having values below and 25% of participants having values above. Together, the 25th and 75th percentiles define the interquartile range .

PROCESS TO IDENTIFY RELEVANT STATISTICAL TESTS: INFERENTIAL STATISTICS

One caveat about the information provided in this section: selecting the most appropriate inferential statistic for a specific study should be a combination of following these suggestions, seeking advice from experts, and discussing with your co-investigators. My intention here is to give you a place to start a conversation with your colleagues about the options available as you develop your data analysis plan.

There are 3 key questions to consider when selecting an appropriate inferential statistic for a study: What is the research question? What is the study design? and What is the level of measurement? It is important for investigators to carefully consider these questions when developing the study protocol and creating the analysis plan. The figures that accompany these questions show decision trees that will help you to narrow down the list of inferential statistics that would be relevant to a particular study. Appendix 1 provides brief definitions of the inferential statistics named in these figures. Additional information, such as the formulae for various inferential statistics, can be obtained from textbooks, statistical software packages, and biostatisticians.

What Is the Research Question?

The first step in identifying relevant inferential statistics for a study is to consider the type of research question being asked. You can find more details about the different types of research questions in a previous article in this Research Primer series that covered questions and hypotheses. 5 A relational question seeks information about the relationship among variables; in this situation, investigators will be interested in determining whether there is an association ( Figure 1 ). A causal question seeks information about the effect of an intervention on an outcome; in this situation, the investigator will be interested in determining whether there is a difference ( Figure 2 ).

Decision tree to identify inferential statistics for an association.

Decision tree to identify inferential statistics for measuring a difference.

What Is the Study Design?

When considering a question of association, investigators will be interested in measuring the relationship between variables ( Figure 1 ). A study designed to determine whether there is consensus among different raters will be measuring agreement. For example, an investigator may be interested in determining whether 2 raters, using the same assessment tool, arrive at the same score. Correlation analyses examine the strength of a relationship or connection between 2 variables, like age and blood glucose. Regression analyses also examine the strength of a relationship or connection; however, in this type of analysis, one variable is considered an outcome (or dependent variable) and the other variable is considered a predictor (or independent variable). Regression analyses often consider the influence of multiple predictors on an outcome at the same time. For example, an investigator may be interested in examining the association between a treatment and blood glucose, while also considering other factors, like age, sex, ethnicity, exercise frequency, and weight.

When considering a question of difference, investigators must first determine how many groups they will be comparing. In some cases, investigators may be interested in comparing the characteristic of one group with that of an external reference group. For example, is the mean age of study participants similar to the mean age of all people in the target group? If more than one group is involved, then investigators must also determine whether there is an underlying connection between the sets of values (or samples ) to be compared. Samples are considered independent or unpaired when the information is taken from different groups. For example, we could use an unpaired t test to compare the mean age between 2 independent samples, such as the intervention and control groups in a study. Samples are considered related or paired if the information is taken from the same group of people, for example, measurement of blood glucose at the beginning and end of a study. Because blood glucose is measured in the same people at both time points, we could use a paired t test to determine whether there has been a significant change in blood glucose.

What Is the Level of Measurement?

As described in the first section of this article, variables can be grouped according to the level of measurement (nominal, ordinal, or interval). In most cases, the independent variable in an inferential statistic will be nominal; therefore, investigators need to know the level of measurement for the dependent variable before they can select the relevant inferential statistic. Two exceptions to this consideration are correlation analyses and regression analyses ( Figure 1 ). Because a correlation analysis measures the strength of association between 2 variables, we need to consider the level of measurement for both variables. Regression analyses can consider multiple independent variables, often with a variety of measurement levels. However, for these analyses, investigators still need to consider the level of measurement for the dependent variable.

Selection of inferential statistics to test interval-level variables must include consideration of how the data are distributed. An underlying assumption for parametric tests is that the data approximate a normal distribution. When the data are not normally distributed, information derived from a parametric test may be wrong. 6 When the assumption of normality is violated (for example, when the data are skewed), then investigators should use a nonparametric test. If the data are normally distributed, then investigators can use a parametric test.

ADDITIONAL CONSIDERATIONS

What is the level of significance.

An inferential statistic is used to calculate a p value, the probability of obtaining the observed data by chance. Investigators can then compare this p value against a prespecified level of significance, which is often chosen to be 0.05. This level of significance represents a 1 in 20 chance that the observation is wrong, which is considered an acceptable level of error.

What Are the Most Commonly Used Statistics?

In 1983, Emerson and Colditz 7 reported the first review of statistics used in original research articles published in the New England Journal of Medicine . This review of statistics used in the journal was updated in 1989 and 2005, 8 and this type of analysis has been replicated in many other journals. 9 – 13 Collectively, these reviews have identified 2 important observations. First, the overall sophistication of statistical methodology used and reported in studies has grown over time, with survival analyses and multivariable regression analyses becoming much more common. The second observation is that, despite this trend, 1 in 4 articles describe no statistical methods or report only simple descriptive statistics. When inferential statistics are used, the most common are t tests, contingency table tests (for example, χ 2 test and Fisher exact test), and simple correlation and regression analyses. This information is important for educators, investigators, reviewers, and readers because it suggests that a good foundational knowledge of descriptive statistics and common inferential statistics will enable us to correctly evaluate the majority of research articles. 11 – 13 However, to fully take advantage of all research published in high-impact journals, we need to become acquainted with some of the more complex methods, such as multivariable regression analyses. 8 , 13

What Are Some Additional Resources?

As an investigator and Associate Editor with CJHP , I have often relied on the advice of colleagues to help create my own analysis plans and review the plans of others. Biostatisticians have a wealth of knowledge in the field of statistical analysis and can provide advice on the correct selection, application, and interpretation of these methods. Colleagues who have “been there and done that” with their own data analysis plans are also valuable sources of information. Identify these individuals and consult with them early and often as you develop your analysis plan.

Another important resource to consider when creating your analysis plan is textbooks. Numerous statistical textbooks are available, differing in levels of complexity and scope. The titles listed in the “Further Reading” section are just a few suggestions. I encourage interested readers to look through these and other books to find resources that best fit their needs. However, one crucial book that I highly recommend to anyone wanting to be an investigator or peer reviewer is Lang and Secic’s How to Report Statistics in Medicine (see “Further Reading”). As the title implies, this book covers a wide range of statistics used in medical research and provides numerous examples of how to correctly report the results.

CONCLUSIONS

When it comes to creating an analysis plan for your project, I recommend following the sage advice of Douglas Adams in The Hitchhiker’s Guide to the Galaxy : Don’t panic! 14 Begin with simple methods to summarize and visualize your data, then use the key questions and decision trees provided in this article to identify relevant statistical tests. Information in this article will give you and your co-investigators a place to start discussing the elements necessary for developing an analysis plan. But do not stop there! Use advice from biostatisticians and more experienced colleagues, as well as information in textbooks, to help create your analysis plan and choose the most appropriate statistics for your study. Making careful, informed decisions about the statistics to use in your study should reduce the risk of confirming Mr Twain’s concern.

Appendix 1. Glossary of statistical terms * (part 1 of 2)

• 1-way ANOVA: Uses 1 variable to define the groups for comparing means. This is similar to the Student t test when comparing the means of 2 groups.
• Kruskall–Wallis 1-way ANOVA: Nonparametric alternative for the 1-way ANOVA. Used to determine the difference in medians between 3 or more groups.
• n -way ANOVA: Uses 2 or more variables to define groups when comparing means. Also called a “between-subjects factorial ANOVA”.
• Repeated-measures ANOVA: A method for analyzing whether the means of 3 or more measures from the same group of participants are different.
• Freidman ANOVA: Nonparametric alternative for the repeated-measures ANOVA. It is often used to compare rankings and preferences that are measured 3 or more times.
• Fisher exact: Variation of chi-square that accounts for cell counts < 5.
• McNemar: Variation of chi-square that tests statistical significance of changes in 2 paired measurements of dichotomous variables.
• Cochran Q: An extension of the McNemar test that provides a method for testing for differences between 3 or more matched sets of frequencies or proportions. Often used as a measure of heterogeneity in meta-analyses.
• 1-sample: Used to determine whether the mean of a sample is significantly different from a known or hypothesized value.
• Independent-samples t test (also referred to as the Student t test): Used when the independent variable is a nominal-level variable that identifies 2 groups and the dependent variable is an interval-level variable.
• Paired: Used to compare 2 pairs of scores between 2 groups (e.g., baseline and follow-up blood pressure in the intervention and control groups).

Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006.

Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003.

Plichta SB, Kelvin E. Munro’s statistical methods for health care research . 6th ed. Philadelphia (PA): Wolters Kluwer Health/ Lippincott, Williams & Wilkins; 2013.

This article is the 12th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

• Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.
• Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.
• Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.
• Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.
• Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.
• Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.
• Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.
• Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.
• Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.
• Sutton J, Austin Z. Qualitative research: data collection, analysis, and management. Can J Hosp Pharm . 2014;68(3):226–31.
• Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2014;68(3):232–7.

Competing interests: None declared.

Further Reading

• Devor J, Peck R. Statistics: the exploration and analysis of data. 7th ed. Boston (MA): Brooks/Cole Cengage Learning; 2012. [ Google Scholar ]
• Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006. [ Google Scholar ]
• Mendenhall W, Beaver RJ, Beaver BM. Introduction to probability and statistics. 13th ed. Belmont (CA): Brooks/Cole Cengage Learning; 2009. [ Google Scholar ]
• Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003. [ Google Scholar ]
• Plichta SB, Kelvin E. Munro’s statistical methods for health care research. 6th ed. Philadelphia (PA): Wolters Kluwer Health/Lippincott, Williams & Wilkins; 2013. [ Google Scholar ]

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

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

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

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Table of contents

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

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

Here are a few best practices:

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

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

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

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

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

A note on tables and figures

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

As a rule of thumb:

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

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

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

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

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

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

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

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

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

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

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

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

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

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I have completed my data collection and analyzed the results.

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

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

I have stated whether each hypothesis was supported or refuted.

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

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

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

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

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

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

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

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

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

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

Don’t interpret or speculate in the results chapter.

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

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

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

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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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Helpful Tips on Composing a Research Paper Data Analysis Section

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

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How to Compose a Data Analysis Section for Your Research Paper

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

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

Where to Find the Necessary Assistance If You Get Stuck

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

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

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

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

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

Results Section in Research

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

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

Structure of Research Results Section

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

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

Outline of Research Results Section

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

I. Introduction

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

II. Descriptive statistics

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

III. Inferential statistics

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

IV. Effect sizes and confidence intervals

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

V. Subgroup analyses

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

VI. Limitations and assumptions

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

VII. Conclusions

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

Example of Research Results Section

An Example of a Research Results Section could be:

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

II. Participants

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

III. Results

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

IV. Discussion

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

V. Conclusion

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

Example of Research Results in Research Paper :

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

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

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

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

Example 3 :

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

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

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

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

Importance of Research Results

Research results are important for several reasons, including:

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

How to Write Results in A Research Paper

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

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

When to Write Research Results

Here are situations When to Write Research Results”

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

Purpose of Research Results

The purposes of Research Results are as follows:

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

Advantages of Research Results

Some Advantages of Research Results are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. 

Data Analysis Checklist

Cleaning  data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

*  Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

• PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

• Qualitative analysis of interview data: A step-by-step guide
• Qualitative Data Analysis - Coding & Developing Themes

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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Research Paper Writing: 6. Results / Analysis

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

Writing about the information

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

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

Written explanations

How should the analysis section be written?

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

How should the results section be written?

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

How the style is presented

Analysis section

• Includes a justification of the methods used
• Technical explanation

Results section

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

Example of a Results Section

Publication Manual of the American Psychological Association Sixth Ed. 2010

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How to Write the Analysis Section of My Research Paper

How to Write a Technical Essay

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

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

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

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

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

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

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

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

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

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

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

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

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

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How to write the results section of a research paper

• 3 minute read
• 70.5K views

Table of Contents

At its core, a research paper aims to fill a gap in the research on a given topic. As a result, the results section of the paper, which describes the key findings of the study, is often considered the core of the paper. This is the section that gets the most attention from reviewers, peers, students, and any news organization reporting on your findings. Writing a clear, concise, and logical results section is, therefore, one of the most important parts of preparing your manuscript.

Difference between results and discussion

Before delving into how to write the results section, it is important to first understand the difference between the results and discussion sections. The results section needs to detail the findings of the study. The aim of this section is not to draw connections between the different findings or to compare it to previous findings in literature—that is the purview of the discussion section. Unlike the discussion section, which can touch upon the hypothetical, the results section needs to focus on the purely factual. In some cases, it may even be preferable to club these two sections together into a single section. For example, while writing  a review article, it can be worthwhile to club these two sections together, as the main results in this case are the conclusions that can be drawn from the literature.

Structure of the results section

Although the main purpose of the results section in a research paper is to report the findings, it is necessary to present an introduction and repeat the research question. This establishes a connection to the previous section of the paper and creates a smooth flow of information.

Next, the results section needs to communicate the findings of your research in a systematic manner. The section needs to be organized such that the primary research question is addressed first, then the secondary research questions. If the research addresses multiple questions, the results section must individually connect with each of the questions. This ensures clarity and minimizes confusion while reading.

Consider representing your results visually. For example, 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.

Remember, an appealing 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 for a research paper

• Always use simple and clear language. Avoid the use of uncertain or out-of-focus expressions.
• The findings of the study must be expressed in an objective and unbiased manner. While it is acceptable to correlate certain findings in the discussion section, it is best to avoid overinterpreting the results.
• If the research addresses more than one hypothesis, use sub-sections to describe the results. This prevents confusion and promotes understanding.
• Ensure that negative results 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 showcase the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey.
• For statistical data, it is adequate to highlight the tests and explain their results. The initial or raw data should not be mentioned in the results section of a research paper.

The results section of a research paper is usually the most impactful section because it draws the greatest attention. Regardless of the subject of your research paper, a well-written results section is capable of generating interest in your research.

For detailed information and assistance on writing the results of a research paper, refer to Elsevier Author Services.

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Different Types of Data Analysis; Data Analysis Methods and Techniques in Research Projects

International Journal of Academic Research in Management, 9(1):1-9, 2022 http://elvedit.com/journals/IJARM/wp-content/uploads/Different-Types-of-Data-Analysis-Data-Analysis-Methods-and-Tec

9 Pages Posted: 18 Aug 2022

Hamed Taherdoost

Hamta Group

Date Written: August 1, 2022

This article is concentrated to define data analysis and the concept of data preparation. Then, the data analysis methods will be discussed. For doing so, the first six main categories are described briefly. Then, the statistical tools of the most commonly used methods including descriptive, explanatory, and inferential analyses are investigated in detail. Finally, we focus more on qualitative data analysis to get familiar with the data preparation and strategies in this concept.

Keywords: Data Analysis, Data Preparation, Data Analysis Methods, Data Analysis Types, Descriptive Analysis, Explanatory Analysis, Inferential Analysis, Predictive Analysis, Explanatory Analysis, Causal Analysis and Mechanistic Analysis, Statistical Analysis.

Suggested Citation: Suggested Citation

Hamed Taherdoost (Contact Author)

Hamta group ( email ).

Vancouver Canada

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Key Data on Health and Health Care by Race and Ethnicity

Nambi Ndugga , Latoya Hill , and Samantha Artiga Published: June 11, 2024

Executive Summary

Introduction.

Racial and ethnic disparities in health and health care remain a persistent challenge in the United States. The COVID-19 pandemic’s uneven impact on people of color drew increased attention to inequities in health and health care, which have been documented for decades and reflect longstanding structural and systemic inequities rooted in historical and ongoing racism and discrimination. KFF’s 2023 Survey on Racism, Discrimination, and Health documents ongoing experiences with racism and discrimination, including in health care settings. While inequities in access to and use of health care contribute to disparities in health, inequities across broader social and economic factors that drive health, often referred to as social determinants of health , also play a major role. Using data to identify disparities and the factors that drive them is important for developing interventions and directing resources to address them, as well as for assessing progress toward achieving greater equity over time.

This analysis examines how people of color fare compared to White people across 64 measures of health, health care, and social determinants of health using the most recent data available from federal surveys and administrative sets as well as the 2023 KFF Survey on Racism, Discrimination, and Health , which provides unique nationally-representative measures of adults’ experiences with racism and discrimination, including in health care (see About the Data). Where possible, we present data for six groups: White, Asian, Hispanic, Black, American Indian or Alaska Native (AIAN), and Native Hawaiian or Pacific Islander (NHPI). People of Hispanic origin may be of any race, but we classify them as Hispanic for this analysis. We limit other groups to people who identify as non-Hispanic. When the same or similar measures are available in multiple datasets, we use the data that allow us to disaggregate for the largest number of racial and ethnic groups. Future analyses will reflect new federal standards that will utilize a combined race and ethnicity approach for collecting information and include a new category for people who identify as Middle Eastern or North African. Unless otherwise noted, differences described in the text are statistically significant at the p<0.05 level.

We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% — which are outside of what we would typically include in analysis like this — are noted in the figures, and confidence intervals for those measures are included in the figure. Although these small sample sizes may impact the reliability, validity, and reproducibility of data, they are important to include because they point to potential underlying disparities that are hidden without disaggregated data. For some data measures throughout this brief we refer to “women” but recognize that other individuals also give birth, including some transgender men, nonbinary, and gender-nonconforming persons.

Key Takeaways

Black, Hispanic, and AIAN people fare worse than White people across the majority of examined measures of health and health care and social determinants of health (Figure 1). Black people fare better than White people for some cancer screening and incidence measures, although they have higher rates of cancer mortality. Despite worse measures of health coverage and access and social determinants of health, Hispanic people fare better than White people for some health measures, including life expectancy, some chronic diseases, and most measures of cancer incidence and mortality. These findings may, in part, reflect variation in outcomes among subgroups of Hispanic people , with better outcomes for some groups, particularly recent immigrants to the U.S. Examples of some key findings include:

• Nonelderly AIAN (19%) and Hispanic (18%) people were more than twice as likely as their White counterparts (7%) to be uninsured as of 2022.
• Among adults with any mental illness, Hispanic (40%), Black (38%), and Asian (36%) adults were less likely than White adults (56%) to receive mental health services as of 2022.
• Roughly, six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season, compared to less than half of White adults (49%).
• AIAN (67.9 years) and Black (72.8 years) people had a shorter life expectancy compared to White people (77.5 years) as of 2022, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2022; however, all racial and ethnic groups experienced a small increase in life expectancy between 2021 and 2022.
• Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants were at least two times as likely to die as White infants (4.5 per 1,000) as of 2022. Black and AIAN women also had the highest rates of pregnancy-related mortality.
• AIAN (24%) and Black (21%) children were more than three times as likely to have food insecurity as White children (6%), and Hispanic children (15%) were over twice as likely to have food insecurity than White children (6%) as of 2022.

Asian people in the aggregate fare the same or better compared to White people for most examined measures. However, they fare worse for some measures, including receipt of some routine care and screening services, and some social determinants of health, including home ownership, crowded housing, and experiences with racism. They also have higher shares of people who are noncitizens or who have limited English proficiency (LEP), which could contribute to barriers to accessing health coverage and care. Moreover, the aggregate data may mask underlying disparities among subgroups of the Asian population. Asian people also report experiences with discrimination in daily life, which is associated with adverse effects on mental health and well-being.

Data gaps largely prevent the ability to identify and understand health disparities for NHPI people. Data are insufficient or not disaggregated for NHPI people for a number of the examined measures. Among available data, NHPI people fare worse than White people for the majority of measures. There are no significant differences for some measures, but this largely reflects the smaller sample size for NHPI people in many datasets, which limits the power to detect statistically significant differences.

These data highlight the importance of continued efforts to address disparities in health and health care and show that it will be key for efforts to address factors both within and beyond the health care system. While these data provide insight into the status of disparities, ongoing data gaps and limitations hamper the ability to get a complete picture, particularly for smaller population groups and among subgroups of the broader racial and ethnic categories. As the share of people who identify as multiracial grows, it will be important to develop improved methods for understanding their experiences. How data are collected and reported by race and ethnicity is important for understanding disparities and efforts to address them. Recent changes to federal standards for collecting and reporting racial and ethnic data are intended to better represent the diversity of the population and will likely support greater disaggregation of data to identify and address disparities.

Racial Diversity Within the U.S. Today

Total population by race and ethnicity.

About four in ten people (42%) in the United States identify as people of color (Figure 2). This group includes 19% who are Hispanic, 12% who are Black, 6% who are Asian, 1% who are AIAN, less than 1% who are NHPI, and 5% who identify as another racial category, including individuals who identify as more than one race. The remaining 58% of the population are White. The share of the population who identify as people of color has been growing over time, with the largest growth occurring among those who identify as Hispanic or Asian. The racial diversity of the population is expected to continue to increase, with people of color projected to account for over half of the population by 2050. Recent changes to how data on race and ethnicity are collected and reported may also influence measures of the diversity of the population.

RACIAL DIVERSITY BY STATE

Certain areas of the country—particularly in the South, Southwest, and parts of the West—are more racially diverse than others (Figure 3). Overall, the share of the population who are people of color ranges from 10% or fewer in Maine, Vermont, and West Virginia to 50% or more of the population in California, District of Columbia, Georgia, Hawaii, Maryland, Nevada, New Mexico, and Texas. Most people of color live in the South and West. More than half (59%) of the Black population resides in the South, and nearly eight in ten Hispanic people live in the West (38%) or South (39%). About three quarters of the NHPI population (75%), almost half (49%) of the AIAN population, and 43% of the Asian population live in the Western region of the country.

TOTAL POPULATION BY AGE, RACE, AND ETHNICITY

People of color are younger compared to White people. Hispanic people are the youngest racial and ethnic group, with 31% ages 18 or younger and 56% below age 35 (Figure 4). Roughly half of Black (48%), AIAN (50%), and NHPI (51%) people are below age 35, compared to 42% of Asian people and 38% of White people.

Health Coverage, Access to and Use of Care

Racial disparities in health coverage, access, and use.

Overall, Hispanic and AIAN people fare worse compared to White people across most examined measures of health coverage, and access to and use of care (Figure 5). Black people fare worse than White people across half of these measures, and experiences for Asian people are mostly similar to or better than White people across these examined measures. NHPI people fare worse than White people across some measures, but several measures lacked sufficient data for a reliable estimate for NHPI people.

HEALTH COVERAGE

Despite gains in health coverage across racial and ethnic groups over time, nonelderly AIAN, Hispanic, NHPI, and Black people remain more likely to be uninsured compared to their White counterparts. After the Affordable Care Act (ACA), Medicaid, and Marketplace coverage expansions took effect in 2014, all racial and ethnic groups experienced large increases in coverage . Beginning in 2017, coverage gains began reversing and the number of uninsured people increased for three consecutive years. However, between 2019 and 2022, there were small gains in coverage across most racial and ethnic groups, with pandemic enrollment protections in Medicaid and enhanced ACA premium subsidies. Despite these gains over time, disparities in health coverage persist as of 2022. Nonelderly AIAN (19%) and Hispanic (18%) people have the highest uninsured rates (Figure 6). Uninsured rates for nonelderly NHPI (13%) and Black (10%) people are also higher than the rate for their White counterparts (7%). Nonelderly White (7%) and Asian (6%) people have the lowest uninsured rates.

ACCESS TO AND USE OF CARE

Most groups of nonelderly adults of color are more likely than nonelderly White adults to report not having a usual doctor or provider and going without care. Roughly one third (36%) of Hispanic adults, one quarter of AIAN (25%) and NHPI (24%) adults, and about one in five (21%) Asian adults report not having a personal health care provider compared to 17% of White adults (Figure 7). The share of Black adults who report not having a personal health care provider is the same as their White counterparts (17% for both). In addition, Hispanic (21%), NHPI (18%), AIAN (16%), and Black (14%) adults are more likely than White adults (11%) to report not seeing a doctor in the past 12 months because of cost, while Asian adults (8%) are less likely than White adults to say they went without a doctor visit due to cost. Hispanic (32%) and AIAN (31%) adults are more likely than White adults (28%) to say they went without a routine checkup in the past year, while Asian (26%), NHPI (24%), and Black (20%) adults are less likely to report going without a checkup. Hispanic and AIAN (both 45%) and Black (40%) adults are more likely than White adults (34%) to report going without a visit to a dentist or dental clinic in the past year.

In contrast to the patterns among adults, racial and ethnic differences in access to and use of care are more mixed for children. Nearly one in ten (9%) Hispanic children lack a usual source of care when sick compared to 5% of White children, but there are no significant differences for other groups for which data are available (Figure 8). Similar shares of Hispanic (7%), Asian (7%), and Black (4%) children went without a health care visit in the past year as White children (6%). However, higher shares of Asian (23%) and Black (21%) children went without a dental visit in the past year compared to White children (17%). Data are not available for NHPI children for these measures, and data for AIAN children should be interpreted with caution due to small sample sizes and large standard errors.

Among adults with any mental illness, Black, Hispanic, and Asian adults are less likely than White adults to report receiving mental health services. Roughly half (56%) of White adults with any mental illness report receiving mental health services in the past year. (Figure 9). In contrast, about four in ten (40%) Hispanic adults and just over a third of Black (38%) and Asian (36%) adults with any mental illness report receiving mental health care in the past year. Data are not available for AIAN and NHPI adults.

Experiences across racial and ethnic groups are mixed regarding receipt of recommended cancer screenings (Figure 10). Among women ages 50-74 (the age group recommended for screening prior to updates in 2024, which lowered the starting age to 40), Black people (24%) are less likely than White people (29%) to go without a recent mammogram. In contrast, AIAN (41%) and Hispanic (35%) people are more likely than White people (29%) to go without a mammogram. Among those recommended for colorectal cancer screening, Hispanic, Asian, AIAN, NHPI, and Black people are more likely than White people to not be up to date on their screening. Increases in cancer screenings, particularly for breast, colorectal, and prostate cancers, have been identified as one of the drivers of the decline in cancer mortality over the past few decades.

Racial and ethnic differences persist in flu and childhood vaccinations (Figure 11). Roughly six in ten Hispanic (63%), AIAN (63%), and Black (58%) adults went without a flu vaccine in the 2022-2023 season compared to about half (49%) of White adults. However, among children, White children (44%) are more likely than Asian (28%) and Hispanic (39%) children to go without the flu vaccine; data are not available to assess flu vaccinations among NHPI adults and children. In 2019-2020, AIAN (42%), Black (37%), and Hispanic (33%) children were more likely than White children (28%) to have not received all recommended childhood immunizations.

Health Status and Outcomes

Racial disparities in health status and outcomes.

Black and AIAN people fare worse than White people across the majority of examined measures of health status and outcomes (Figure 12). In contrast, Asian and Hispanic people fare better than White people for a majority of examined health measures. Nearly half of the examined measures did not have data available for NHPI people, limiting the ability to understand their experiences. Among available data, NHPI people fare worse than White people for more than half of the examined measures.   

LIFE EXPECTANCY

AIAN and Black people have a shorter life expectancy at birth compared to White people, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2021. Life expectancy at birth represents the average number of years a group of infants would live if they were to experience the age-specific death rates prevailing during a specified period. Life expectancy declined by 2.7 years between 2019 and 2021, largely reflecting an increase in excess deaths due to COVID-19, which disproportionately impacted Black, Hispanic, and AIAN people. AIAN people experienced the largest life expectancy decline of 6.6 years, followed by Hispanic (4.2 years) and Black people (4.0 years), and a smaller decline of 2.4 years for White people. Asian people had the smallest decline in life expectancy of 2.1 years between 2019 and 2021. Provisional data from 2022 show that overall life expectancy increased across all racial and ethnic groups between 2021 and 2022, but racial disparities persist (Figure 13). Life expectancy is lowest for AIAN people at 67.9 years, followed by Black people at 72.8 years, while White and Hispanic people have higher life expectancies of 77.5 and 80 years, respectively, and Asian people have the highest life expectancy at 84.5 years. Life expectancies are even lower for AIAN and Black males, at 64.6 and 69.1 years, respectively. Data are not available for NHPI people.

SELF-REPORTED HEALTH STATUS

Black, Hispanic, and AIAN adults are more likely to report fair or poor health status than their White counterparts, while Asian adults are less likely to indicate fair or poor health. Nearly three in ten (29%) AIAN adults and roughly two in ten Hispanic (23%) and Black (21%) adults report fair or poor health status compared to 16% of White adults (Figure 14). One in ten Asian adults report fair or poor health status.

BIRTH RISKS AND OUTCOMES

NHPI (62.8 per 100,000), Black (39.9 per 100,000), and AIAN (32 per 100,000) women have the highest rates of pregnancy-related mortality (deaths within one year of pregnancy) between 2017-2019, while Hispanic women (11.6 per 100,000) have the lowest rate (Figure 15). More recent data for maternal mortality, which measures deaths that occur during pregnancy or within 42 days of pregnancy, shows that Black women (49.5 per 100,000) have the highest maternal mortality rate across racial and ethnic groups in 2022 (Figure 16). However, maternal mortality rates decreased significantly across most racial and ethnic groups between 2021 and 2022. Experts suggest the decline may reflect a return to pre-pandemic levels following the large increase in maternal death rates due to COVID-19 related deaths. The Dobbs decision eliminating the constitutional right to abortion could widen the already large disparities in maternal health as people of color may face disproportionate challenges accessing abortions due to state restrictions.

Black, AIAN, and NHPI women have higher shares of preterm births, low birthweight births, or births for which they received late or no prenatal care compared to White women (Figure 17). Additionally, Asian women are more likely to have low birthweight births than White women. Notably, NHPI women (22%) are four times more likely than White women (5%) to begin receiving prenatal care in the third trimester or to receive no prenatal care at all.

Teen birth rates have declined over time, but the birth rates among Black, Hispanic, AIAN, and NHPI teens are over two times higher than the rate among White teens (Figure 18). In contrast, the birth rate for Asian teens is more than four times lower than the rate for White teens.

Infants born to women of color are at higher risk for mortality compared to those born to White women. Infant mortality rates have declined over time although provisional 2022 data suggest a slight increase relative to 2021. As of 2022, Black (10.9 per 1,000) and AIAN (9.1 per 1,000) infants are at least two times as likely to die as White infants (4.5 per 1,000) (Figure 19). NHPI infants (8.5 per 1,000) are nearly twice as likely to die as White infants (4.5 per 1,000). Asian infants have the lowest mortality rate at 3.5 per 1,000 live births.

HIV AND AIDS DIAGNOSIS INDICATORS

Black, Hispanic, NHPI, and AIAN people are more likely than White people to be diagnosed with HIV or AIDS, the most advanced stage of HIV infection. In 2021, the HIV diagnosis rate for Black people is roughly eight times higher than the rate for White people, and the rate for Hispanic people is about four times higher than the rate for White people (Figure 20). AIAN and NHPI people also have higher HIV diagnosis rates compared to White people. Similar patterns are present in AIDS diagnosis rates, the most advanced stage of HIV, reflecting barriers to treatment. Black people have a roughly nine times higher rate of AIDS diagnosis compared to White people, and Hispanic, AIAN, and NHPI people also have higher rates of AIDS diagnoses. Most groups have seen decreases in HIV and AIDS diagnosis rates since 2013, although the HIV diagnosis rate has remained stable for Hispanic people and increased for AIAN and NHPI people.

Among people ages 13 and older living with diagnosed HIV infection, viral suppression rates are lower among AIAN (64%), Hispanic (64%), NHPI (63%), and Black (62%) people compared with White (72%) and Asian (70%) people (Figure 21) . Viral suppression refers to having less than 200 copies of HIV per milliliter of blood. Increasing the viral suppression rate among people with HIV is one of the key strategies of the Ending the HIV Epidemic in the U.S. initiative. Viral suppression promotes optimal health outcomes for people with HIV and also offers a preventive benefit as when someone is virally suppressed, they cannot sexually transmit HIV.

CHRONIC DISEASE AND CANCER

The prevalence of chronic disease varies across racial and ethnic groups and by type of disease. Diabetes rates for AIAN (18%), Black (16%), and Hispanic (13%) adults are all higher than the rate for White adults (11%). AIAN people (11%) are more likely to have had a heart attack or heart disease than White people (8%), while rates for Black (6%), NHPI (6%), Hispanic (4%) and Asian (3%) people are lower than White people. Black (12%) and AIAN (13%) adults have higher rates of asthma compared to their White counterparts (10%), while rates for Hispanic (8%) and Asian (5%) adults are lower, and the rate for NHPI is the same (10%). Among children, Black children (16%) are nearly twice as likely to have asthma compared to White children (9%), while Asian children (6%) have a lower asthma rate (Figure 22). Differences are not significant for other racial and ethnic groups, and data are not available for NHPI children.

AIAN, NHPI, and Black people are roughly twice as likely as White people to die from diabetes, and Black people are more likely than White people to die from heart disease (Figure 23). Hispanic people (28.3 per 100,000) also have a higher diabetes death rate compared to White people (21.3 per 100,000). In contrast, Asian people (17.2 per 100,000) are less likely than White people (21.3 per 100,000) to die from diabetes, and AIAN, Hispanic, and Asian people have lower heart disease death rates than their White counterparts.

People of color generally have lower rates of new cancer cases compared to White people, but Black people have higher incidence rates for some cancer types (Figure 24). Black people have lower rates of cancer incidence compared to White people for cancer overall, and most of the leading types of cancer examined. However, they have higher rates of new colon, and rectum, and prostate cancer. AIAN people have a higher rate of colon and rectum cancer than White people. Other groups have lower cancer incidence rates than White people across all examined cancer types.

Although Black people do not have higher cancer incidence rates than White people overall and across most types of cancer, they are more likely to die from cancer. Black people have a higher cancer death rate than White people for cancer overall and for most of the leading cancer types (Figure 25). In contrast, Hispanic, Asian and Pacific Islander, and AIAN people have lower cancer mortality rates across most cancer types compared to White people. The higher mortality rate among Black people despite similar or lower rates of incidence compared to White people could reflect a combination of factors , including more limited access to care, later stage of diagnosis, more comorbidities, and lower receipt of guideline-concordant care, which are driven by broader social and economic inequities.

COVID-19 DEATHS

AIAN, Hispanic, NHPI, and Black people have higher rates of COVID-19 deaths compared to White people. As of March 2024, provisional age-adjusted data from the Centers for Disease Control and Prevention (CDC) show that between 2020 and 2023, AIAN people are roughly two times as likely as White people to die from COVID-19, and Hispanic, NHPI and Black people are about 1.5 times as likely to die from COVID-19 (Figure 26). Asian people have lower COVID-19 death rates during this period compared to all other race and ethnicity groups.

Obesity rates vary across race and ethnicity groups. As of 2022, Black (43%), AIAN (39%), and Hispanic (37%) adults all have higher obesity rates than White adults (32%), while Asian adults (13%) have a lower obesity rate (Figure 27).

Mental Health and Drug Overdose Deaths

Overall rates of mental illness are lower for people of color compared to White people but could be underdiagnosed among people of color. About one in five Hispanic and Black (21% and 20%, respectively) adults and 17% of Asian adults report having a mental illness compared to 25% of White adults (Figure 28). Among  adolescents , the share with symptoms of a past year major depressive episode were not significantly different across racial and ethnic groups, with roughly one in five White (21%) and Hispanic (20%) adolescents, 17% of Black, and about one in seven Asian (15%), and AIAN (14%) adolescents reporting symptoms. Data are not available for NHPI people. Research suggests that a lack of  culturally sensitive  screening  tools  that detect mental illness, coupled with  structural barriers could contribute to  underdiagnosis  of mental illness among people of color.

AIAN and White people have the highest rates of deaths by suicide as of 2022. People of color have been disproportionately affected by recent increases in deaths by suicide compared with their White counterparts. As of 2022, AIAN (27.1 per 100,000) and White (17.6 per 100,000) people have the highest rates of deaths by suicide compared to all other racial and ethnic groups (Figure 29). Rates of deaths by suicide are also over three times higher among AIAN adolescents (32.9 per 100,000) than White adolescents (10.6 per 100,000). In contrast, Black, Hispanic, and Asian adolescents have lower rates of suicide deaths compared to their White peers.

Drug overdose death rates increased among AIAN, Black, Hispanic, and Asian people between 2021 and 2022. As of 2022, AIAN people continue to have the highest rates of drug overdose deaths (65.2 per 100,000 in 2022) compared with all other racial and ethnic groups. Drug overdose death rates among Black people (47.5 per 100,000) exceed rates for White people (35.6 per 100,000), reflecting larger increases among Black people in recent years (Figure 30). Hispanic (22.7 per 100,000), NHPI (18.8 per 100,000), and Asian (5.3 per 100,000) people have lower rates of drug overdose deaths than White people (35.6 per 100,000). Data on drug overdose deaths among adolescents show that while White adolescents account for the largest share of drug overdose deaths, Black and Hispanic adolescents have experienced the fastest increase in these deaths in recent years.

Social Determinants of Health

Racial disparities in social and economic factors.

Social determinants of health are the conditions in which people are born, grow, live, work, and age. They include factors like socioeconomic status, education, immigration status, language, neighborhood and physical environment, employment, and social support networks, as well as access to health care. There has been extensive research and recognition that addressing social, economic, and environmental factors that influence health is important for advancing health equity. Research also shows how racism and discrimination drive inequities across these factors and impact health and well-being.  

Black, Hispanic, AIAN, and NHPI people fare worse compared to White people across most examined measures of social determinants of health (Figure 31). Experiences for Asian people are more mixed relative to White people across these examined measures. Reliable or disaggregated data for NHPI people are missing for a number of measures.

WORK STATUS, FAMILY INCOME, AND EDUCATION

Across racial and ethnic groups, most nonelderly people live in a family with a full-time worker, but Black, Hispanic, AIAN, and NHPI nonelderly people are more likely than White people to be in a family with income below poverty (Figure 32). While most people across racial and ethnic groups live in a family with a full-time worker, disparities persist. AIAN (68%), Black (73%), NHPI (77%), and Hispanic (81%) people are less likely than White people (83%) to have a full-time worker in the family. In contrast, Asian people (86%) are more likely than their White counterparts (83%) to have a full-time worker in the family. Despite the majority of people living in a family with a full-time worker, over one in five AIAN (25%) and Black (22%) people have family incomes below the federal poverty level, over twice the share as White people (10%), and rates of poverty were also higher among Hispanic (17%) and NHPI (16%) people.

Black, Hispanic, AIAN, and NHPI people have lower levels of educational attainment compared to their White counterparts. Among people ages 25 and older, over two thirds (69%) of White people have completed some post-secondary education, compared to less than half (45%) of Hispanic people, just over half of AIAN and NHPI people (both at 52%), and about six in ten Black people (58%) (Figure 33). Asian people are more likely than White people to have completed at least some post-secondary education, with 74% completing at least some college.

NET WORTH AND HOME OWNERSHIP

Black and Hispanic families have less wealth than White families. Wealth can be defined using net worth, a measure of the difference between a family’s assets and liabilities. The median net worth for White households is $285,000 compared to $44,900 for Black households and $61,600 for Hispanic households (Figure 34). Asian households have the highest median net worth of $536,000. Data are not available for AIAN and NHPI people.

People of color are less likely to own a home than White people (Figure 35). Nearly eight in ten (77%) White people own a home compared to 70% of Asian people, 62% of AIAN people, 55% of Hispanic people, and about half of Black (49%) and NHPI (48%) people.

FOOD SECURITY, HOUSING QUALITY, AND INTERNET ACCESS

Black and Hispanic adults and children are more likely to experience food insecurity compared to their White counterparts. Among adults, AIAN (18%), Black (14%), and Hispanic (12%) adults report low or very low food security compared to White adults (6%) (Figure 36). Among children, AIAN (24%), Black (21%) and Hispanic (15%) children are over twice as likely to be food insecure than White children (6%). Data are not available for NHPI adults and children.

People of color are more likely to live in crowded housing than their White counterparts (Figure 37). Among White people, 3% report living in a crowded housing arrangement, that is having more than one person per room, as defined by the American Community Survey. In contrast, almost three in ten (28%) NHPI people, roughly one in five (18%) Hispanic people, 16% AIAN people, and about one in ten Asian (12%) and Black (8%) people report living in crowded housing.

AIAN, NHPI, and Black people are less likely to have internet access than White people (Figure 38). Higher shares of AIAN (12%), and Black and NHPI people (both at 6%) say they have no internet access compared to their White counterparts (4%). In contrast, Asian people (2%) are less likely to report no internet access than White people (4%).

TRANSPORTATION

People of color are more likely to live in a household without access to a vehicle than White people (Figure 39) . About one in eight Black people (12%) and about one in ten AIAN (9%) and Asian (8%) people live in a household without a vehicle available followed by 7% of Hispanic and NHPI people. White people are the least likely to report not having access to a vehicle in the household (4%).

CITIZENSHIP AND ENGLISH PROFICIENCY

Asian, Hispanic, NHPI, and Black people include higher shares of noncitizen immigrants compared to White people. Asian and Hispanic people have the highest shares of noncitizen immigrants at 25% and 19%, respectively (Figure 40). Asian people are projected to become the largest immigrant group in the United States by 2055. Immigrants are more likely to be uninsured than citizens and face increased barriers to accessing health care.

Hispanic and Asian people are more likely to have LEP compared to White people. Almost one in three Asian (31%) and Hispanic (28%) people report speaking English less than very well compared to White people (1%)(Figure 41). Adults with LEP are more likely to report worse health status and increased barriers in accessing health care compared to English proficient adults.

EXPERIENCES WITH RACISM, DISCRIMINATION, AND UNFAIR TREATMENT

Racism is an underlying driver of health disparities, and repeated and ongoing exposure to perceived experiences of racism and discrimination can increase risks for poor health outcomes. Research has shown that exposure to racism and discrimination can lead to  negative  mental health  outcomes  and certain negative impacts on physical health, including depression, anxiety, and hypertension.

Black, AIAN, Hispanic, and Asian adults are more likely to report certain experiences with discrimination in daily life compared with their White counterparts, with the greatest frequency reported among Black and AIAN adults.  A 2023 KFF survey shows that at least half of AIAN (58%), Black (54%), and Hispanic (50%) adults and about four in ten (42%) Asian adults say they experienced at least one type of discrimination in daily life in the past year (Figure 42). These experiences include receiving poorer service than others at restaurants or stores; people acting as if they are afraid of them or as if they aren’t smart; being threatened or harassed; or being criticized for speaking a language other than English. Data are not available for NHPI adults.

About one in five (18%) Black adults and roughly one in eight AIAN (12%) adults, followed by roughly one in ten Hispanic (11%), and Asian (10%) adults who received health care in the past three years report being treated unfairly or with disrespect by a health care provider because of their racial or ethnic background.  These shares are higher than the 3% of White adults who report this (Figure 43). Overall, roughly three in ten (29%) AIAN adults and one in four (24%) Black adults say they were treated unfairly or with disrespect by a health care provider in the past three years for any reason compared with 14% of White adults.

About the Data

Data sources.

This chart pack is based on the KFF Survey on Racism, Discrimination, and Health and KFF analysis of a wide range of health datasets, including the 2022 American Community Survey, the 2022 Behavioral Risk Factor Surveillance System, the 2022 National Health Interview Survey, the 2022 National Survey on Drug Use and Health, and the 2022 Survey of Consumer Finances as well as from several online reports and databases including the Centers for Disease Control and Prevention (CDC) Morbidity and Mortality Weekly Report (MMWR) on vaccination coverage, the National Center for Health Statistics (NCHS) National Vital Statistics Reports, the CDC Influenza Vaccination Dashboard Flu Vaccination Coverage Webpage Report, the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP) Atlas, the United States Cancer Statistics Incidence and Mortality Web-based Report, the 2022 CDC Natality Public Use File, CDC Web-based Injury Statistics Query and Reporting System (WISQARS) database, and the CDC WONDER online database.

Methodology

Unless otherwise noted, race/ethnicity was categorized by non-Hispanic White (White), non-Hispanic Black (Black), Hispanic, non-Hispanic American Indian and Alaska Native (AIAN), non-Hispanic Asian (Asian), and non-Hispanic Native Hawaiian or Pacific Islander (NHPI). Some datasets combine Asian and NHPI race categories limiting the ability to disaggregate data for these groups. Non-Hispanic White persons were the reference group for all significance testing. All noted differences were statistically significant differences at the p<0.05. We include data for smaller population groups wherever available. Instances in which the unweighted sample size for a subgroup is less than 50 or the relative standard error is greater than 30% are noted in the figures, and confidence intervals for those measures are included in the figure.