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Textual Presentation of Data: Meaning, Suitability, and Drawbacks

Presentation of Data refers to the exhibition of data in such a clear and attractive way that it is easily understood and analysed. Data can be presented in different forms, including Textual or Descriptive Presentation, Tabular Presentation, and Diagrammatic Presentation.

Textual Presentation

Textual or Descriptive Presentation of Data is one of the most common forms of data presentation. In this, data is a part of the text of the study or a part of the description of the subject matter of the study. It is usually preferred when the quantity of data is not very large. For example, there are 50 students in a class, among them 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Suitability

Textual Presentation of Data is suitable when the quantity of data is not large. It means that a small portion of data that is presented as a part of the subject matter of study can become useful supportive evidence to the given text. Therefore, instead of saying that the price of petrol is skyrocketing, it can be said that the price of petrol has increased by 20% in the last 2 years, and this statement will be more meaningful and precise. Under textual presentation of data, an individual does not have to support the text with the help of a diagram or table as the text in itself is very small and has few observations. 

Advantages of Textual Presentation of Data

Textual Presentation of Data has the following benefits:

1. It allows the researcher to make an elaborate interpretation of data during the presentation. 

2. A researcher can easily present qualitative data that cannot be presented in tabular or graphical form using the textual presentation of data. 

3. If the data is present in small sets, a textual presentation can be easily used. For example, there are 50 students in a class, among them, 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same. 

Disadvantages of Textual Presentation of Data

Textual Presentation of Data has the following drawbacks:

1. One of the major drawbacks of the textual presentation of data is that it provides extensive data in the form of text and paragraphs which makes it difficult for the user of data to draw a proper conclusion at a glance. This facility is provided in tabular or diagrammatic presentation of data.

2. This method of presenting data is not suitable for large sets of data as these sets contain too many details. 

3. Besides, one has to read through the whole text in order to understand and comprehend the main point of the data.

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How To Present Research Data?

Tong seng fah.

MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia

Aznida Firzah Abdul Aziz

Introduction.

The result section of an original research paper provides answer to this question “What was found?” The amount of findings generated in a typical research project is often much more than what medical journal can accommodate in one article. So, the first thing the author needs to do is to make a selection of what is worth presenting. Having decided that, he/she will need to convey the message effectively using a mixture of text, tables and graphics. The level of details required depends a great deal on the target audience of the paper. Hence it is important to check the requirement of journal we intend to send the paper to (e.g. the Uniform Requirements for Manuscripts Submitted to Medical Journals 1 ). This article condenses some common general rules on the presentation of research data that we find useful.

SOME GENERAL RULES

• Keep it simple. This golden rule seems obvious but authors who have immersed in their data sometime fail to realise that readers are lost in the mass of data they are a little too keen to present. Present too much information tends to cloud the most pertinent facts that we wish to convey.
• First general, then specific. Start with response rate and description of research participants (these information give the readers an idea of the representativeness of the research data), then the key findings and relevant statistical analyses.
• Data should answer the research questions identified earlier.
• Leave the process of data collection to the methods section. Do not include any discussion. These errors are surprising quite common.
• Always use past tense in describing results.
• Text, tables or graphics? These complement each other in providing clear reporting of research findings. Do not repeat the same information in more than one format. Select the best method to convey the message.

Consider these two lines:

• Mean baseline HbA 1c of 73 diabetic patients before intervention was 8.9% and mean HbA 1c after intervention was 7.8%.
• Mean HbA 1c of 73 of diabetic patients decreased from 8.9% to 7.8% after an intervention.

In line 1, the author presents only the data (i.e. what exactly was found in a study) but the reader is forced to analyse and draw their own conclusion (“mean HbA 1c decreased”) thus making the result more difficult to read. In line 2, the preferred way of writing, the data was presented together with its interpretation.

• Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values of HbA 1c in the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple sentence including its interpretation. For example, the majority of diabetic patients enrolled in the study were male (80%) compare to female (20%).
• Using qualitative words to attract the readers’ attention is not helpful. Such words like “remarkably” decreased, “extremely” different and “obviously” higher are redundant. The exact values in the data will show just how remarkable, how extreme and how obvious the findings are.

“It is clearly evident from Figure 1B that there was significant different (p=0.001) in HbA 1c level at 6, 12 and 18 months after diabetic self-management program between 96 patients in intervention group and 101 patients in control group, but no difference seen from 24 months onwards.” [Too wordy]

Changes of HbA 1c level after diabetic self-management program.

The above can be rewritten as:

“Statistical significant difference was only observed at 6, 12 and 18 months after diabetic self-management program between intervention and control group (Fig 1B)”. [The p values and numbers of patients are already presented in Figure 1B and need not be repeated.]

• Avoid redundant words and information. Do not repeat the result within the text, tables and figures. Well-constructed tables and graphics should be self-explanatory, thus detailed explanation in the text is not required. Only important points and results need to be highlighted in the text.

Tables are useful to highlight precise numerical values; proportions or trends are better illustrated with charts or graphics. Tables summarise large amounts of related data clearly and allow comparison to be made among groups of variables. Generally, well-constructed tables should be self explanatory with four main parts: title, columns, rows and footnotes.

• Title. Keep it brief and relate clearly the content of the table. Words in the title should represent and summarise variables used in the columns and rows rather than repeating the columns and rows’ titles. For example, “Comparing full blood count results among different races” is clearer and simpler than “Comparing haemoglobin, platelet count, and total white cell count among Malays, Chinese and Indians”.

*WC, waist circumference (in cm)

†SBP, systolic blood pressure (in mmHg)

‡DBP, diastolic blood pressure (in mmHg)

£LDL-cholesterol (in mmol/L)

*Odds ratio (95% confidence interval)

†p=0.04

‡p=0.01

• Footnotes. These add clarity to the data presented. They are listed at the bottom of tables. Their use is to define unconventional abbreviation, symbols, statistical analysis and acknowledgement (if the table is adapted from a published table). Generally the font size is smaller in the footnotes and follows a sequence of foot note signs (*, †, ‡, §, ‖, ¶, **, ††, # ). 1 These symbols and abbreviation should be standardised in all tables to avoid confusion and unnecessary long list of footnotes. Proper use of footnotes will reduce the need for multiple columns (e.g. replacing a list of p values) and the width of columns (abbreviating waist circumference to WC as in table 1B )
• Consistent use of units and its decimal places. The data on systolic blood pressure in Table 1B is neater than the similar data in Table 1A .
• Arrange date and timing from left to the right.
• Round off the numbers to fewest decimal places possible to convey meaningful precision. Mean systolic blood pressure of 165.1mmHg (as in Table 1B ) does not add much precision compared to 165mmHg. Furthermore, 0.1mmHg does not add any clinical importance. Hence blood pressure is best to round off to nearest 1mmHg.
• Avoid listing numerous zeros, which made comparison incomprehensible. For example total white cell count is best represented with 11.3 ×10 6 /L rather than 11,300,000/L. This way, we only need to write 11.3 in the cell of the table.
• Avoid too many lines in a table. Often it is sufficient to just have three horizontal lines in a table; one below the title; one dividing the column titles and data; one dividing the data and footnotes. Vertical lines are not necessary. It will only make a table more difficult to read (compare Tables 1A and ​ and1B 1B ).
• Standard deviation can be added to show precision of the data in our table. Placement of standard deviation can be difficult to decide. If we place the standard deviation at the side of our data, it allows clear comparison when we read down ( Table 1B ). On the other hand, if we place the standard deviation below our data, it makes comparison across columns easier. Hence, we should decide what we want the readers to compare.
• It is neater and space-saving if we highlight statistically significant finding with an asterisk (*) or other symbols instead of listing down all the p values ( Table 2 ). It is not necessary to add an extra column to report the detail of student-t test or chi-square values.

Graphics are particularly good for demonstrating a trend in the data that would not be apparent in tables. It provides visual emphasis and avoids lengthy text description. However, presenting numerical data in the form of graphs will lose details of its precise values which tables are able to provide. The authors have to decide the best format of getting the intended message across. Is it for data precision or emphasis on a particular trend and pattern? Likewise, if the data is easily described in text, than text will be the preferred method, as it is more costly to print graphics than text. For example, having a nicely drawn age histogram is take up lots of space but carries little extra information. It is better to summarise it as mean ±SD or median depends on whether the age is normally distributed or skewed. Since graphics should be self-explanatory, all information provided has to be clear. Briefly, a well-constructed graphic should have a title, figure legend and footnotes along with the figure. As with the tables, titles should contain words that describe the data succinctly. Define symbols and lines used in legends clearly.

Some general guides to graphic presentation are:

• Bar charts, either horizontal or column bars, are used to display categorical data. Strictly speaking, bar charts with continuous data should be drawn as histograms or line graphs. Usually, data presented in bar charts are better illustrated in tables unless there are important pattern or trends need to be emphasised.

• Line graphs are most appropriate in tracking changing values between variables over a period of time or when the changing values are continuous data. Independent variables (e.g. time) are usually on the X-axis and dependant variables (for example, HbA 1c ) are usually on the Y-axis. The trend of HbA 1c changes is much more apparent with Figure 1B than Figure 1A , and HbA 1c level at any time after intervention can be accurately read in Figure 1B .
• Pie charts should not be used often as any data in a pie chart is better represented in bar charts (if there are specific data trend to be emphasised) or simple text description (if there are only a few variables). A common error is presenting sex distribution of study subjects in a pie chart. It is simpler by just stating % of male or female in text form.
• Patients’ identity in all illustrations, for example pictures of the patients, x-ray films, and investigation results should remain confidential. Use patient’s initials instead of their real names. Cover or blackout the eyes whenever possible. Obtain consent if pictures are used. Highlight and label areas in the illustration, which need emphasis. Do not let the readers search for details in the illustration, which may result in misinterpretation. Remember, we write to avoid misunderstanding whilst maintaining clarity of data.

Papers are often rejected because wrong statistical tests are used or interpreted incorrectly. A simple approach is to consult the statistician early. Bearing in mind that most readers are not statisticians, the reporting of any statistical tests should aim to be understandable by the average audience but sufficiently rigorous to withstand the critique of experts.

• Simple statistic such as mean and standard deviation, median, normality testing is better reported in text. For example, age of group A subjects was normally distributed with mean of 45.4 years old kg (SD=5.6). More complicated statistical tests involving many variables are better illustrated in tables or graphs with their interpretation by text. (See section on Tables).
• We should quote and interpret p value correctly. It is preferable to quote the exact p value, since it is now easily obtained from standard statistical software. This is more so if the p value is statistically not significant, rather just quoting p>0.05 or p=ns. It is not necessary to report the exact p value that is smaller than 0.001 (quoting p<0.001 is sufficient); it is incorrect to report p=0.0000 (as some software apt to report for very small p value).
• We should refrain from reporting such statement: “mean systolic blood pressure for group A (135mmHg, SD=12.5) was higher than group B (130mmHg, SD= 9.8) but did not reach statistical significance (t=4.5, p=0.56).” When p did not show statistical significance (it might be >0.01 or >0.05, depending on which level you would take), it simply means no difference among groups.
• Confidence intervals. It is now preferable to report the 95% confidence intervals (95%CI) together with p value, especially if a hypothesis testing has been performed.

The main core of the result section consists of text, tables and graphics. As a general rule, text provides narration and interpretation of the data presented. Simple data with few categories is better presented in text form. Tables are useful in summarising large amounts of data systemically and graphics should be used to highlight evidence and trends in the data presented. The content of the data presented must match the research questions and objectives of the study in order to give meaning to the data presented. Keep the data and its statistical analyses as simple as possible to give the readers maximal clarity.

Contributor Information

Tong Seng Fah, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

Aznida Firzah Abdul Aziz, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

FURTHER READINGS

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• Textual Analysis | Guide, 3 Approaches & Examples

Textual Analysis | Guide, 3 Approaches & Examples

Published on November 8, 2019 by Jack Caulfield . Revised on June 22, 2023.

Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.

The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context. Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways.

Table of contents

What is a text, textual analysis in cultural and media studies, textual analysis in the social sciences, textual analysis in literary studies, other interesting articles.

The term “text” is broader than it seems. A text can be a piece of writing, such as a book, an email, or a transcribed conversation. But in this context, a text can also be any object whose meaning and significance you want to interpret in depth: a film, an image, an artifact, even a place.

The methods you use to analyze a text will vary according to the type of object and the purpose of your analysis:

• Analysis of a short story might focus on the imagery, narrative perspective and structure of the text.
• To analyze a film, not only the dialogue but also the cinematography and use of sound could be relevant to the analysis.
• A building might be analyzed in terms of its architectural features and how it is navigated by visitors.
• You could analyze the rules of a game and what kind of behaviour they are designed to encourage in players.

While textual analysis is most commonly applied to written language, bear in mind how broad the term “text” is and how varied the methods involved can be.

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In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analyzed.

Usually working within a particular theoretical framework (for example, using postcolonial theory, media theory, or semiotics), researchers seek to connect elements of their texts with issues in contemporary politics and culture. They might analyze many different aspects of the text:

• Word choice
• Design elements
• Location of the text
• Target audience
• Relationship with other texts

Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating.

In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys , as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.

Textual analysis in the social sciences sometimes takes a more quantitative approach , where the features of texts are measured numerically. For example, a researcher might investigate how often certain words are repeated in social media posts, or which colors appear most prominently in advertisements for products targeted at different demographics.

Some common methods of analyzing texts in the social sciences include content analysis , thematic analysis , and discourse analysis .

Textual analysis is the most important method in literary studies. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories or plays.

Because it deals with literary writing, this type of textual analysis places greater emphasis on the deliberately constructed elements of a text: for example, rhyme and meter in a poem, or narrative perspective in a novel. Researchers aim to understand and explain how these elements contribute to the text’s meaning.

However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyze a classic text in a new and unexpected way.

Some well-known examples of literary analysis show the variety of approaches that can be taken:

• Eve Kosofky Sedgwick’s book Between Men analyzes Victorian literature in light of more contemporary perspectives on gender and sexuality.
• Roland Barthes’ S/Z provides an in-depth structural analysis of a short story by Balzac.
• Harold Bloom’s The Anxiety of Influence applies his own “influence theory” to an analysis of various classic poets.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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The Ultimate Guide to Qualitative Research - Part 3: Presenting Qualitative Data

• Introduction

How do you present qualitative data?

Data visualization.

• Research paper writing
• Transparency and rigor in research
• How to publish a research paper

Table of contents

• Transparency and rigor

Navigate to other guide parts:

Part 1: The Basics or Part 2: Handling Qualitative Data

• Presenting qualitative data

In the end, presenting qualitative research findings is just as important a skill as mastery of qualitative research methods for the data collection and data analysis process . Simply uncovering insights is insufficient to the research process; presenting a qualitative analysis holds the challenge of persuading your audience of the value of your research. As a result, it's worth spending some time considering how best to report your research to facilitate its contribution to scientific knowledge.

When it comes to research, presenting data in a meaningful and accessible way is as important as gathering it. This is particularly true for qualitative research , where the richness and complexity of the data demand careful and thoughtful presentation. Poorly written research is taken less seriously and left undiscussed by the greater scholarly community; quality research reporting that persuades its audience stands a greater chance of being incorporated in discussions of scientific knowledge.

Qualitative data presentation differs fundamentally from that found in quantitative research. While quantitative data tend to be numerical and easily lend themselves to statistical analysis and graphical representation, qualitative data are often textual and unstructured, requiring an interpretive approach to bring out their inherent meanings. Regardless of the methodological approach , the ultimate goal of data presentation is to communicate research findings effectively to an audience so they can incorporate the generated knowledge into their research inquiry.

As the section on research rigor will suggest, an effective presentation of your research depends on a thorough scientific process that organizes raw data into a structure that allows for a thorough analysis for scientific understanding.

Preparing the data

The first step in presenting qualitative data is preparing the data. This preparation process often begins with cleaning and organizing the data. Cleaning involves checking the data for accuracy and completeness, removing any irrelevant information, and making corrections as needed. Organizing the data often entails arranging the data into categories or groups that make sense for your research framework.

Coding the data

Once the data are cleaned and organized, the next step is coding , a crucial part of qualitative data analysis. Coding involves assigning labels to segments of the data to summarize or categorize them. This process helps to identify patterns and themes in the data, laying the groundwork for subsequent data interpretation and presentation. Qualitative research often involves multiple iterations of coding, creating new and meaningful codes while discarding unnecessary ones , to generate a rich structure through which data analysis can occur.

Uncovering insights

As you navigate through these initial steps, keep in mind the broader aim of qualitative research, which is to provide rich, detailed, and nuanced understandings of people's experiences, behaviors, and social realities. These guiding principles will help to ensure that your data presentation is not only accurate and comprehensive but also meaningful and impactful.

While this process might seem intimidating at first, it's an essential part of any qualitative research project. It's also a skill that can be learned and refined over time, so don't be discouraged if you find it challenging at first. Remember, the goal of presenting qualitative data is to make your research findings accessible and understandable to others. This requires careful preparation, a clear understanding of your data, and a commitment to presenting your findings in a way that respects and honors the complexity of the phenomena you're studying.

In the following sections, we'll delve deeper into how to create a comprehensive narrative from your data, the visualization of qualitative data , and the writing and publication processes . Let's briefly excerpt some of the content in the articles in this part of the guide.

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How often do you read a research article and skip straight to the tables and figures? That's because data visualizations representing qualitative and quantitative data have the power to make large and complex research projects with thousands of data points comprehensible when authors present data to research audiences. Researchers create visual representations to help summarize the data generated from their study and make clear the pathways for actionable insights.

In everyday situations, a picture is always worth a thousand words. Illustrations, figures, and charts convey messages that words alone cannot. In research, data visualization can help explain scientific knowledge, evidence for data insights, and key performance indicators in an orderly manner based on data that is otherwise unstructured.

For all of the various data formats available to researchers, a significant portion of qualitative and social science research is still text-based. Essays, reports, and research articles still rely on writing practices aimed at repackaging research in prose form. This can create the impression that simply writing more will persuade research audiences. Instead, framing research in terms that are easy for your target readers to understand makes it easier for your research to become published in peer-reviewed scholarly journals or find engagement at scholarly conferences. Even in market or professional settings, data visualization is an essential concept when you need to convince others about the insights of your research and the recommendations you make based on the data.

Importance of data visualization

Data visualization is important because it makes it easy for your research audience to understand your data sets and your findings. Also, data visualization helps you organize your data more efficiently. As the explanation of ATLAS.ti's tools will illustrate in this section, data visualization might point you to research inquiries that you might not even be aware of, helping you get the most out of your data. Strictly speaking, the primary role of data visualization is to make the analysis of your data , if not the data itself, clear. Especially in social science research, data visualization makes it easy to see how data scientists collect and analyze data.

Prerequisites for generating data visualizations

Data visualization is effective in explaining research to others only if the researcher or data scientist can make sense of the data in front of them. Traditional research with unstructured data usually calls for coding the data with short, descriptive codes that can be analyzed later, whether statistically or thematically. These codes form the basic data points of a meaningful qualitative analysis . They represent the structure of qualitative data sets, without which a scientific visualization with research rigor would be extremely difficult to achieve. In most respects, data visualization of a qualitative research project requires coding the entire data set so that the codes adequately represent the collected data.

A successfully crafted research study culminates in the writing of the research paper . While a pilot study or preliminary research might guide the research design , a full research study leads to discussion that highlights avenues for further research. As such, the importance of the research paper cannot be overestimated in the overall generation of scientific knowledge.

The physical and natural sciences tend to have a clinical structure for a research paper that mirrors the scientific method: outline the background research, explain the materials and methods of the study, outline the research findings generated from data analysis, and discuss the implications. Qualitative research tends to preserve much of this structure, but there are notable and numerous variations from a traditional research paper that it's worth emphasizing the flexibility in the social sciences with respect to the writing process.

Requirements for research writing

While there aren't any hard and fast rules regarding what belongs in a qualitative research paper , readers expect to find a number of pieces of relevant information in a rigorously-written report. The best way to know what belongs in a full research paper is to look at articles in your target journal or articles that share a particular topic similar to yours and examine how successfully published papers are written.

It's important to emphasize the more mundane but equally important concerns of proofreading and formatting guidelines commonly found when you write a research paper. Research publication shouldn't strictly be a test of one's writing skills, but acknowledging the importance of convincing peer reviewers of the credibility of your research means accepting the responsibility of preparing your research manuscript to commonly accepted standards in research.

As a result, seemingly insignificant things such as spelling mistakes, page numbers, and proper grammar can make a difference with a particularly strict reviewer. Even when you expect to develop a paper through reviewer comments and peer feedback, your manuscript should be as close to a polished final draft as you can make it prior to submission.

Qualitative researchers face particular challenges in convincing their target audience of the value and credibility of their subsequent analysis. Numbers and quantifiable concepts in quantitative studies are relatively easier to understand than their counterparts associated with qualitative methods . Think about how easy it is to make conclusions about the value of items at a store based on their prices, then imagine trying to compare those items based on their design, function, and effectiveness.

Qualitative research involves and requires these sorts of discussions. The goal of qualitative data analysis is to allow a qualitative researcher and their audience to make such determinations, but before the audience can accept these determinations, the process of conducting research that produces the qualitative analysis must first be seen as trustworthy. As a result, it is on the researcher to persuade their audience that their data collection process and subsequent analysis is rigorous.

Qualitative rigor refers to the meticulousness, consistency, and transparency of the research. It is the application of systematic, disciplined, and stringent methods to ensure the credibility, dependability, confirmability, and transferability of research findings. In qualitative inquiry, these attributes ensure the research accurately reflects the phenomenon it is intended to represent, that its findings can be understood or used by others, and that its processes and results are open to scrutiny and validation.

Transparency

It is easier to believe the information presented to you if there is a rigorous analysis process behind that information, and if that process is explicitly detailed. The same is true for qualitative research results, making transparency a key element in qualitative research methodologies. Transparency is a fundamental aspect of rigor in qualitative research. It involves the clear, detailed, and explicit documentation of all stages of the research process. This allows other researchers to understand, evaluate, replicate, and build upon the study. Transparency in qualitative research is essential for maintaining rigor, trustworthiness, and ethical integrity. By being transparent, researchers allow their work to be scrutinized, critiqued, and improved upon, contributing to the ongoing development and refinement of knowledge in their field.

Research papers are only as useful as their audience in the scientific community is wide. To reach that audience, a paper needs to pass the peer review process of an academic journal. However, the idea of having research published in peer-reviewed journals may seem daunting to newer researchers, so it's important to provide a guide on how an academic journal looks at your research paper as well as how to determine what is the right journal for your research.

In simple terms, a research article is good if it is accepted as credible and rigorous by the scientific community. A study that isn't seen as a valid contribution to scientific knowledge shouldn't be published; ultimately, it is up to peers within the field in which the study is being considered to determine the study's value. In established academic research, this determination is manifest in the peer review process. Journal editors at a peer-reviewed journal assign papers to reviewers who will determine the credibility of the research. A peer-reviewed article that completed this process and is published in a reputable journal can be seen as credible with novel research that can make a profound contribution to scientific knowledge.

The process of research publication

The process has been codified and standardized within the scholarly community to include three main stages. These stages include the initial submission stage where the editor reviews the relevance of the paper, the review stage where experts in your field offer feedback, and, if reviewers approve your paper, the copyediting stage where you work with the journal to prepare the paper for inclusion in their journal.

Publishing a research paper may seem like an opaque process where those involved with academic journals make arbitrary decisions about the worthiness of research manuscripts. In reality, reputable publications assign a rubric or a set of guidelines that reviewers need to keep in mind when they review a submission. These guidelines will most likely differ depending on the journal, but they fall into a number of typical categories that are applicable regardless of the research area or the type of methods employed in a research study, including the strength of the literature review , rigor in research methodology , and novelty of findings.

Choosing the right journal isn't simply a matter of which journal is the most famous or has the broadest reach. Many universities keep lists of prominent journals where graduate students and faculty members should publish a research paper , but oftentimes this list is determined by a journal's impact factor and their inclusion in major academic databases.

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This section is part of an entire guide. Use this table of contents to jump to any page in the guide.

Part 1: The Basics

• What is qualitative data?
• 10 examples of qualitative data
• Qualitative vs. quantitative research
• What is mixed methods research?
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research questions
• Conceptual framework
• Conceptual vs. theoretical framework
• Focus groups
• Observational research
• Case studies
• Survey research
• What is ethnographic research?
• Confidentiality and privacy in research
• Bias in research
• Power dynamics in research
• Reflexivity

Part 2: Handling Qualitative Data

• Research transcripts
• Field notes in research
• Research memos
• Survey data
• Images, audio, and video in qualitative research
• Coding qualitative data
• Coding frame
• Auto-coding and smart coding
• Organizing codes
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• What is inductive reasoning?
• Inductive vs. deductive reasoning
• What is data interpretation?
• Qualitative analysis software

Part 3: Presenting Qualitative Data

• Data visualization - What is it and why is it important?

Data Presentation

Josée Dupuis, PhD, Professor of Biostatistics, Boston University School of Public Health

Wayne LaMorte, MD, PhD, MPH, Professor of Epidemiology, Boston University School of Public Health

Introduction

While graphical summaries of data can certainly be powerful ways of communicating results clearly and unambiguously in a way that facilitates our ability to think about the information, poorly designed graphical displays can be ambiguous, confusing, and downright misleading. The keys to excellence in graphical design and communication are much like the keys to good writing. Adhere to fundamental principles of style and communicate as logically, accurately, and clearly as possible. Excellence in writing is generally achieved by avoiding unnecessary words and paragraphs; it is efficient. In a similar fashion, excellence in graphical presentation is generally achieved by efficient designs that avoid unnecessary ink.

Excellence in graphical presentation depends on:

• Choosing the best medium for presenting the information
• Designing the components of the graph in a way that communicates the information as clearly and accurately as possible.

Table or Graph?

• Tables are generally best if you want to be able to look up specific information or if the values must be reported precisely.
• Graphics are best for illustrating trends and making comparisons

The side by side illustrations below show the same information, first in table form and then in graphical form. While the information in the table is precise, the real goal is to compare a series of clinical outcomes in subjects taking either a drug or a placebo. The graphical presentation on the right makes it possible to quickly see that for each of the outcomes evaluated, the drug produced relief in a great proportion of subjects. Moreover, the viewer gets a clear sense of the magnitude of improvement, and the error bars provided a sense of the uncertainty in the data.

Principles for Table Display

• Sort table rows in a meaningful way
• Avoid alphabetical listing!
• Use rates, proportions or ratios in addition (or instead of) totals
• Show more than two time points if available
• Multiple time points may be better presented in a Figure
• Similar data should go down columns
• Highlight important comparisons
• Show the source of the data

Consider the data in the table below from http://www.cancer.gov/cancertopics/types/commoncancers

Our ability to quickly understand the relative frequency of these cancers is hampered by presenting them in alphabetical order. It is much easier for the reader to grasp the relative frequency by listing them from most frequent to least frequent as in the next table.

However, the same information might be presented more effectively with a dot plot, as shown below.

Data from http://www.cancer.gov/cancertopics/types/commoncancers

Principles of Graphical Excellence from E.R. Tufte

Pattern Perception

Pattern perception is done by

• Detection: recognition of geometry encoding physical values
• Assembly: grouping of detected symbol elements; discerning overall patterns in data
• Estimation: assessment of relative magnitudes of two physical values

Geographic Variation in Cancer

As an example, Tufte offers a series of maps that summarize the age-adjusted mortality rates for various types of cancer in the 3,056 counties in the United States. The maps showing the geographic variation in stomach cancer are shown below.

These maps summarize an enormous amount of information and present it efficiently, coherently, and effectively.in a way that invites the viewer to make comparisons and to think about the substance of the findings. Consider, for example, that the region to the west of the Great Lakes was settled largely by immigrants from Germany and Scand anavia, where traditional methods of preserving food included pickling and curing of fish by smoking. Could these methods be associated with an increased risk of stomach cancer?

John Snow's Spot Map of Cholera Cases

Consider also the spot map that John Snow presented after the cholera outbreak in the Broad Street section of London in September 1854. Snow ascertained the place of residence or work of the victims and represented them on a map of the area using a small black disk to represent each victim and stacking them when more than one occurred at a particular location. Snow reasoned that cholera was probably caused by something that was ingested, because of the intense diarrhea and vomiting of the victims, and he noted that the vast majority of cholera deaths occurred in people who lived or worked in the immediate vicinity of the broad street pump (shown with a red dot that we added for clarity). He further ascertained that most of the victims drank water from the Broad Street pump, and it was this evidence that persuaded the authorities to remove the handle from the pump in order to prevent more deaths.

Humans can readily perceive differences like this when presented effectively as in the two previous examples. However, humans are not good at estimating differences without directly seeing them (especially for steep curves), and we are particularly bad at perceiving relative angles (the principal perception task used in a pie chart).

The use of pie charts is generally discouraged. Consider the pie chart on the left below. It is difficult to accurately assess the relative size of the components in the pie chart, because the human eye has difficulty judging angles. The dot plot on the right shows the same data, but it is much easier to quickly assess the relative size of the components and how they changed from Fiscal Year 2000 to Fiscal Year 2007.

Consider the information in the two pie charts below (showing the same information).The 3-dimensional pie chart on the left distorts the relative proportions. In contrast the 2-dimensional pie chart on the right makes it much easier to compare the relative size of the varies components..

More Principles of Graphical Excellence

Exclude Unneeded Dimensions

These 3-dimensional techniques distort the data and actually interfere with our ability to make accurate comparisons. The distortion caused by 3-dimensional elements can be particularly severe when the graphic is slanted at an angle or when the viewer tends to compare ends up unwittingly comparing the areas of the ink rather than the heights of the bars.

It is much easier to make comparisons with a chart like the one below.

Source: Huang, C, Guo C, Nichols C, Chen S, Martorell R. Elevated levels of protein in urine in adulthood after exposure to

the Chinese famine of 1959–61 during gestation and the early postnatal period. Int. J. Epidemiol. (2014) 43 (6): 1806-1814 .

Omit "Chart Junk"

Consider these two examples.

Here is a simple enumeration of the number of pets in a neighborhood. There is absolutely no reason to connect these counts with lines. This is, in fact, confusing and inappropriate and nothing more than "chart junk."

Source: http://www.go-education.com/free-graph-maker.html

Moiré Vibration

Moiré effects are sometimes used in modern art to produce the appearance of vibration and movement. However, when these effects are applied to statistical presentations, they are distracting and add clutter because the visual noise interferes with the interpretation of the data.

Tufte presents the example shown below from Instituto de Expansao Commercial, Brasil, Graphicos Estatisticas (Rio de Janeiro, 1929, p. 15).

 While the intention is to present quantitative information about the textile industry, the moiré effects do not add anything, and they are distracting, if not visually annoying.

Present Data to Facilitate Comparisons

Here is an attempt to compare catches of cod fish and crab across regions and to relate the variation to changes in water temperature. The problem here is that the Y-axes are vastly different, making it hard to sort out what's really going on. Even the Y-axes for temperature are vastly different.

http://seananderson.ca/courses/11-multipanel/multipanel.pdf1

The ability to make comparisons is greatly facilitated by using the same scales for axes, as illustrated below.

Data source: Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease:

the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279-81. PMID: 14819398

It is also important to avoid distorting the X-axis. Note in the example below that the space between 0.05 to 0.1 is the same as space between 0.1 and 0.2.

Source: Park JH, Gail MH, Weinberg CR, et al. Distribution of allele frequencies and effect sizes and

their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011; 108:18026-31.

Consider the range of the Y-axis. In the examples below there is no relevant information below $40,000, so it is not necessary to begin the Y-axis at 0. The graph on the right makes more sense.

Also, consider using a log scale. this can be particularly useful when presenting ratios as in the example below.

Source: Broman KW, Murray JC, Sheffield VC, White RL, Weber JL (1998) Comprehensive human genetic maps:

Individual and sex-specific variation in recombination. American Journal of Human Genetics 63:861-869, Figure 1

We noted earlier that pie charts make it difficult to see differences within a single pie chart, but this is particularly difficult when data is presented with multiple pie charts, as in the example below.

Source: Bell ML, et al. (2007) Spatial and temporal variation in PM2.5 chemical composition in the United States

for health effects studies. Environmental Health Perspectives 115:989-995, Figure 3

When multiple comparisons are being made, it is essential to use colors and symbols in a consistent way, as in this example.

Source: Manning AK, LaValley M, Liu CT, et al.  Meta-Analysis of Gene-Environment Interaction:

Joint Estimation of SNP and SNP x Environment Regression Coefficients.  Genet Epidemiol 2011, 35(1):11-8.

Avoid putting too many lines on the same chart. In the example below, the only thing that is readily apparent is that 1980 was a very hot summer.

Data from National Weather Service Weather Forecast Office at

http://www.srh.noaa.gov/tsa/?n=climo_tulyeartemp

Make Efficient Use of Space

Reduce the Ratio of Ink to Information

This isn't efficient, because this graphic is totally uninformative.

Source: Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers.  Journal of the American Statistical Association 90:233-241, Figure 1

Bar graphs add ink without conveying any additional information, and they are distracting. The graph below on the left inappropriately uses bars which clutter the graph without adding anything. The graph on the right displays the same data, by does so more clearly and with less clutter.

Multiple Types of Information on the Same Figure

Choosing the Best Graph Type

Bar Charts, Error Bars and Dot Plots

As noted previously, bar charts can be problematic. Here is another one presenting means and error bars, but the error bars are misleading because they only extend in one direction. A better alternative would have been to to use full error bars with a scatter plot, as illustrated previously (right).

Consider the four graphs below presenting the incidence of cancer by type. The upper left graph unnecessary uses bars, which take up a lot of ink. This layout also ends up making the fonts for the types of cancer too small. Small font is also a problem for the dot plot at the upper right, and this one also has unnecessary grid lines across the entire width.

The graph at the lower left has more readable labels and uses a simple dot plot, but the rank order is difficult to figure out.

The graph at the lower right is clearly the best, since the labels are readable, the magnitude of incidence is shown clearly by the dot plots, and the cancers are sorted by frequency.

Single Continuous Numeric Variable

In this situation a cumulative distribution function conveys the most information and requires no grouping of the variable. A box plot will show selected quantiles effectively, and box plots are especially useful when stratifying by multiple categories of another variable.

Histograms are also possible. Consider the examples below.

Two Variables

 The two graphs below summarize BMI (Body Mass Index) measurements in four categories, i.e., younger and older men and women. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. This is easy to interpret, but the viewer cannot see that the data is actually quite skewed. The graph on the right shows the same information presented as a box plot. With this presentation method one gets a better understanding of the skewed distribution and how the groups compare.

The next example is a scatter plot with a superimposed smoothed line of prediction. The shaded region embracing the blue line is a representation of the 95% confidence limits for the estimated prediction. This was created using "ggplot" in the R programming language.

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf (page 121)

Multivariate Data

The example below shows the use of multiple panels.

Source: Cleveland S. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.

Displaying Uncertainty

• Error bars showing confidence limits
• Confidence bands drawn using two lines
• Shaded confidence bands
• Bayesian credible intervals
• Bayesian posterior densities

Confidence Limits

Shaded Confidence Bands

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf

Source: Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Forest Plot

This is a Forest plot summarizing 26 studies of cigarette smoke exposure on risk of lung cancer. The sizes of the black boxes indicating the estimated odds ratio are proportional to the sample size in each study.

Data from Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Summary Recommendations

• In general, avoid bar plots
• Avoid chart junk and the use of too much ink relative to the information you are displaying. Keep it simple and clear.
• Avoid pie charts, because humans have difficulty perceiving relative angles.
• Pay attention to scale, and make scales consistent.
• Explore several ways to display the data!

12 Tips on How to Display Data Badly

Adapted from Wainer H.  How to Display Data Badly.  The American Statistician 1984; 38: 137-147. 

• Show as few data as possible
• Hide what data you do show; minimize the data-ink ratio
• Ignore the visual metaphor altogether
• Only order matters
• Graph data out of context
• Change scales in mid-axis
• Emphasize the trivial;  ignore the important
• Jiggle the baseline
• Alphabetize everything.
• Make your labels illegible, incomplete, incorrect, and ambiguous.
• More is murkier: use a lot of decimal places and make your graphs three dimensional whenever possible.
• If it has been done well in the past, think of another way to do it

Additional Resources

• Stephen Few: Designing Effective Tables and Graphs. http://www.perceptualedge.com/images/Effective_Chart_Design.pdf
• Gary Klaas: Presenting Data: Tabular and graphic display of social indicators. Illinois State University, 2002. http://lilt.ilstu.edu/gmklass/pos138/datadisplay/sections/goodcharts.htm (Note: The web site will be discontinued to be replaced by the Just Plain Data Analysis site).

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4 Presenting Data

• Published: July 2009
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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

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Analyzing Text Data

• Overview of Text Analysis and Text Mining

Choosing a Method

How much data do you need, word frequency analysis, machine learning/natural language processing, sentiment analysis.

• Library Databases
• Social Media
• Open Source
• Language Corpora
• Web Scraping
• Software for Text Analysis
• Text Data Citation

Library Data Services

Choosing the right text mining method is crucial because it significantly impacts the quality of insights and information you can extract from your text data. Each method provides different insights and requires different amounts of data, training, and iteration. Before you search for data, it is essential that you:

• identify the goals of your analysis
• determine the method you will use to meet those goals
• identify how much data you need for that method
• develop a sampling plan to build a data set that accurately represents your object of study.

Starting with this information in mind will make your project go more quickly and smoothly, and help you overcome a lot of hurdles such as incomplete data, too much or too little data, or problems with access to data.

More Resources:

• Content Analysis Method and Examples, from Mailman School of Public Health, Columbia University
• Qualitative Research Methods Overview, from Northeastern University
• O'Reilly Online Learning: Nvivo
• Sage Research Methods: NVivo
• Sage Research Methods: Atlas.ti

Before you start collecting data, think about how much data you really need. New researchers in text analysis often want to collect every source mentioning their topic, but this is usually not the best approach. Collecting so much data takes a lot of time, uses many computational resources, often goes against platform terms of service, and doesn't necessarily improve analysis.

In text analysis, an essential idea is saturation , where adding more data doesn't significantly improve performance. Saturation is when the model has learned as much as it can from the available data, and no new patterns are themes are emerging with additional data. Researchers often use experimentation and learning curves to determine when saturation occurs; you can start by analyzing a small or mid-sized dataset and see what happens if you add more data.

Once you know your research question, the next step is to create a sampling plan . In text analysis, sampling means selecting a representative subset of data from a larger dataset for analysis. This subset, called the sample, aims to capture the diversity of sentiments in the overall dataset. The goal is to analyze this smaller portion to draw conclusions about the information in the entire dataset.

For example, in a large collection of customer reviews, sampling may involve randomly selecting a subset for sentiment analysis instead of analyzing every single review. This approach saves computational resources and time while still providing insights into the overall sentiment distribution of the entire dataset. It's crucial to ensure that the sample accurately reflects the diversity of sentiments in the complete dataset for valid and reliable generalizations.

Example Sampling Plans

Sampling plans for text analysis involve selecting a subset of text data for analysis rather than analyzing the entire dataset. Here are two common sampling plans for text analysis:

Random Sampling:

• Description: Randomly select a subset of text documents from the entire dataset.
• Process: Assign each document a unique identifier and use a random number generator to choose documents for inclusion in the sample.

Stratified Sampling:

• Description: Divide the dataset into distinct strata or categories based on certain characteristics (e.g., product types, genres, age groups, race or ethnicity). Then, randomly sample from each stratum.
• Process: Divide the dataset into strata, and within each stratum, use random sampling to select a representative subset.

Remember, the choice of sampling plan depends on the specific goals of the analysis and the characteristics of the dataset. Random sampling is straightforward and commonly used when there's no need to account for specific characteristics in the dataset. Stratified sampling is useful when the dataset has distinct groups, and you want to ensure representation from each group in the sample.

Exactly How Many Sources do I need?

Determining the amount of data needed for text analysis involves a balance between having enough data to train a reliable model and avoiding unnecessary computational costs. The ideal dataset size depends on several factors, including the complexity of the task, the diversity of the data, and the specific algorithms or models being used.

• Task Complexity:  If you are doing a simple task, like sentiment analysis or basic text classification, a few dozen articles might be enough. More complex tasks, like language translation or summarization, often require datasets on the scale of tens of thousands to millions.
• Model Complexity:  Simple models like Naive Bayes often perform well with smaller datasets, whereas complex models, such as deep learning models with many parameters, will require larger datasets for effective training.
• Data Diversity:  Ensure that the dataset is diverse and representative of the various scenarios the model will encounter. A more diverse dataset can lead to a more robust and generalizable model. A large dataset that is not diverse will yield worse results than a smaller, more diverse dataset.
• Domain-Specific Considerations:  Sometimes there is not a lot of data available, and it is okay to make do with what you have!

Start by taking a look at articles in your field that have done a similar analysis. What approaches did they take? You can also schedule an appointment with a Data Services Librarian to get you started.

More Readings on Sampling Plans for Text Analysis:

• How to Choose a Sample Size in Qualitative Research, from LinkedIn Learning  (members of the GW community have free access to LinkedIn Learning using their GW email account)
• Sampling in Qualitative Research , from Saylor Academy
• Lowe, A., Norris, A. C., Farris, A. J., & Babbage, D. R. (2018). Quantifying Thematic Saturation in Qualitative Data Analysis. Field Methods, 30(3), 191-207. https://doi.org/10.1177/1525822X17749386

Software for Word Frequency Analysis

• NVivo via GW's Virtual Computer Lab NVivo is a software package used for qualitative data analysis. It includes tools to support the analysis of textual data in a wide array of formats, as well as and audio, video, and image data. NVivo is available through the Virtual Computer Lab. Faculty and staff may find NVivo available for download from GW's Software Center.
• Analyzing Word and Document Frequency in R This chapter explains how to use tidy to analyze word and document frequency using Tidy Data in R.
• word clouds in R R programming functionality to create pretty word clouds, visualize differences and similarities between documents, and avoid over-plotting in scatter plots with text.
• ATLAS.ti Trial version of qualitative analysis workbench for processing text, image, audio, and video data. (Note: Health science students may have access to full version through Himmelfarb Library)

Related Tools Available Online

• Google ngram Viewer When you enter phrases into the Google Books Ngram Viewer, it displays a graph showing how those phrases have occurred in a corpus of books (e.g., "British English", "English Fiction", "French") over the selected years.
• HathiTrust This link opens in a new window HathiTrust is a partnership of academic and research institutions, offering a collection of millions of titles digitized from libraries around the world. To log in, select The George Washington University as your institution, then log in with your UserID and regular GW password.
• Voyant Voyant is an online point-and-click tool for text analysis. While the default graphics are impressive, it allows limited customizing of analysis and graphs and may be most useful for exploratory visualization.

Related Library Resources

• HathiTrust and Text Mining at GWU HathiTrust is an international community of research libraries committed to the long-term curation and availability of the cultural record. Through their common efforts and deep commitment to the public good, the libraries support the teaching and learning activities of the faculty, students or researchers at their home institutions, and the scholarly needs of the broader public as well.
• HathiTrust+Bookworn From the University of Illinois Library: HathiTrust+Bookworm is an online tool for visualizing trends in language over time. Developed by the HathiTrust Research Center using textual data from the HathiTrust Digital Library, it allows you to track changes in word use based on publication country, genre of works, and more.
• Python for Natural Language Processing A workshop offered through GW Libraries on natural language processing using Python.
• Text Mining Tutorials in R A collection of text mining course materials and tutorials developed for humanists and social scientists interested to learn R.
• Oxford English Dictionary This link opens in a new window The Oxford English Dictionary database will provide a word frequency analysis over time, drawing both from Google ngrams and the OED's own databases.

Example Projects Using Word Frequency Analysis

Robinson, J. S. and D. (n.d.). 3 Analyzing word and document frequency: Tf-idf | Text Mining with R . Retrieved November 21, 2023, from https://www.tidytextmining.com/tfidf.html

• Zhang, Z. (n.d.). Text Mining for Social and Behavioral Research Using R . Retrieved November 21, 2023, from https://books.psychstat.org/textmining/index.html
• Exploring Fascinating Insights with Word Frequency Analysis In the realm of data analysis, words hold immense power. They convey meaning, express ideas, and shape our understanding of the world. In this article, we’ll explore the fascinating world of textual data analysis by examining word frequencies. By counting the occurrence of words in a text, we can uncover interesting insights and gain a deeper understanding of the underlying themes and patterns. Join us on this word-centric journey as we dive into the realm of word frequency analysis using Python.

Machine learning for text analysis is a technology that teaches computers to understand and interpret written language by exposing them to examples. There are two types of machine learning for text analysis: supervised learning, in which a human helps to train the computer to detect patterns, and unsupervised learning, which enables computers to automatically categorize, analyze, and extract information from text without needing explicit programming.

One type of machine learning for text analysis is  Natural Language Processing (NLP).  NLP for text analysis is a field of artificial intelligence that involves the development and application of algorithms to automatically process, understand, and extract meaningful information from human language in textual form. NLP techniques are used to analyze and derive insights from large volumes of text data, enabling tasks such as sentiment analysis, named entity recognition, text classification, and language translation. The aim is to equip computers with the capability to comprehend and interpret written language, making it possible to automate various aspects of text-based information processing.

Software for Natural Language Processing

• NLTK for Python NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
• scikit Simple and efficient tools for predictive data analysis, using Python.

Related Resources Available Online

• [Large Language Models] LLMs in Scientific Research
• HathiTrust and Text Mining at GWU Information on text data mining using HathiTrust
• Social Feed Manager Social Feed Manager software was developed to support campus research about social media including Twitter, Tumblr, Flickr, and Sina Weibo platforms. It can be used to track mentions of you or your articles and other research products for the previous seven days and on into the future.. Email [email protected] to get started with Social Feed Manager or to schedule a consultation

Example Projects using Natural Language Processing

• Redd D, Workman TE, Shao Y, Cheng Y, Tekle S, Garvin JH, Brandt CA, Zeng-Treitler Q. Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?  Medical Sciences . 2023; 11(2):37. https://doi.org/10.3390/medsci11020037
• Nguyen D, Liakata M, DeDeo S, Eisenstein J, Mimno D, Tromble R, Winters J. How We Do Things With Words: Analyzing Text as Social and Cultural Data. Front Artif Intell. 2020 Aug 25;3:62. doi: 10.3389/frai.2020.00062. 

Sentiment analysis is a method of analyzing text to determine whether the emotional tone or sentiment expressed in a piece of text is positive, negative, or neutral. Sentiment analysis is commonly used in businesses to gauge customer feedback, social media monitoring, and market research.

Software for Sentiment Analysis

• Sentiment Analysis using NLTK for Python NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
• Sentiment Analysis with TidyData in R This chapter shows how to implement sentiment analysis using tidy data principles in R.
• Tableau Tableau works with numeric and categorical data to produce advanced graphics. Browse the Tableau public gallery to see examples of visuals and dashboards. Tableau offers free one-year Tableau licenses to students at accredited academic institutions, including GW. Visit https://www.tableau.com/academic/students for more about the program or to request a license.
• Qualtrics Text iQ Qualtrics is a powerful tool for collecting and analyzing survey data. Qualtrics Text iQ automatically performs sentiment analysis on collected data.

Related Resources Available Online

• finnstats. (2021, May 16). Sentiment analysis in R | R-bloggers. https://www.r-bloggers.com/2021/05/sentiment-analysis-in-r-3/

Example Projects Using Sentiment Analysis

• Duong, V., Luo, J., Pham, P., Yang, T., & Wang, Y. (2020). The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 126–130. https://doi.org/10.1109/ASONAM49781.2020.9381379
• Ali, R. H., Pinto, G., Lawrie, E., & Linstead, E. J. (2022). A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00633-z
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• Next: Finding Text Data >>
• Last Updated: Sep 4, 2024 11:45 AM
• URL: https://libguides.gwu.edu/textanalysis

Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro. We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

• Clarity : Data presentations make complex information clear and concise.
• Engagement : Visuals, such as charts and graphs, grab your audience's attention.
• Comprehension : Visual data is easier to understand than long, numerical reports.
• Decision-making : Well-presented data aids informed decision-making.
• Impact : It leaves a lasting impression on your audience.

Types of data presentation:

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

• Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
• Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
• Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
• Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation?

When creating your data presentation, remember these key components:

• Data points : Clearly state the data points you're presenting.
• Comparison : Highlight comparisons and trends in your data.
• Graphical methods : Choose the right chart or graph for your data.
• Infographics : Use visuals like infographics to make information more digestible.
• Numerical values : Include numerical values to support your visuals.
• Qualitative information : Explain the significance of the data.
• Source citation : Always cite your data sources.

How to structure an effective data presentation?

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

• Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
• High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
• Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
• Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

• Use visuals : Incorporate charts and graphs to enhance understanding.
• Keep it simple : Avoid clutter and complexity.
• Highlight key points : Emphasize crucial data.
• Engage the audience : Encourage questions and discussions.
• Practice : Rehearse your presentation.

Don'ts:

• Overload with data : Less is often more; don't overwhelm your audience.
• Fit Unrelated data : Stay on topic; don't include irrelevant information.
• Neglect the audience : Ensure your presentation suits your audience's level of expertise.
• Read word-for-word : Avoid reading directly from slides.
• Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

• Definition : Data presentation is the art of visualizing complex data for better understanding.
• Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
• Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
• Choosing methods : Select the right method based on data, audience, and purpose.
• Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
• Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
• Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

FAQ's on a data presentation

1. what is data presentation, and why is it important in 2024.

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

• Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
• On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
• Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
• Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
• Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive! Sign up for our free trial or book a demo ! ‍

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Data Collection, Presentation and Analysis

• First Online: 25 May 2023

Cite this chapter

• Uche M. Mbanaso 4 ,
• Lucienne Abrahams 5 &
• Kennedy Chinedu Okafor 6  

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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

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Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

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• Textual And Tabular Presentation Of Data

Think about a scenario where your report cards are printed in a textual format. Your grades and remarks about you are presented in a paragraph format instead of data tables. Would be very confusing right? This is why data must be presented correctly and clearly. Let us take a look.

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Presentation of data.

Presentation of data is of utter importance nowadays. Afterall everything that’s pleasing to our eyes never fails to grab our attention. Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted. The three main forms of presentation of data are:

• Textual presentation
• Data tables
• Diagrammatic presentation

Here we will be studying only the textual and tabular presentation, i.e. data tables in some detail.

Textual Presentation

The discussion about the presentation of data starts off with it’s most raw and vague form which is the textual presentation. In such form of presentation, data is simply mentioned as mere text, that is generally in a paragraph. This is commonly used when the data is not very large.

This kind of representation is useful when we are looking to supplement qualitative statements with some data. For this purpose, the data should not be voluminously represented in tables or diagrams. It just has to be a statement that serves as a fitting evidence to our qualitative evidence and helps the reader to get an idea of the scale of a phenomenon .

For example, “the 2002 earthquake proved to be a mass murderer of humans . As many as 10,000 citizens have been reported dead”. The textual representation of data simply requires some intensive reading. This is because the quantitative statement just serves as an evidence of the qualitative statements and one has to go through the entire text before concluding anything.

Further, if the data under consideration is large then the text matter increases substantially. As a result, the reading process becomes more intensive, time-consuming and cumbersome.

Data Tables or Tabular Presentation

A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.

Components of  Data Tables

• Table Number : Each table should have a specific table number for ease of access and locating. This number can be readily mentioned anywhere which serves as a reference and leads us directly to the data mentioned in that particular table.
• Title:  A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study and the nature of classification of data .
• Headnotes:  A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.
• Stubs:  These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row.
• Caption:  A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column.
• Body or field:  The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’.
• Footnotes:  Footnotes are rarely used. In effect, they supplement the title of a table if required.
• Source:  When using data obtained from a secondary source, this source has to be mentioned below the footnote.

Construction of Data Tables

There are many ways for construction of a good table. However, some basic ideas are:

• The title should be in accordance with the objective of study:  The title of a table should provide a quick insight into the table.
• Comparison:  If there might arise a need to compare any two rows or columns then these might be kept close to each other.
• Alternative location of stubs:  If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table.
• Headings:  Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’.
• Footnote:  A footnote should be given only if needed.
• Size of columns:  Size of columns must be uniform and symmetrical.
• Use of abbreviations:  Headings and sub-headings should be free of abbreviations.
• Units: There should be a clear specification of units above the columns.

The Advantages of Tabular Presentation

• Ease of representation:  A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation.
• Ease of analysis:  Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion etc.
• Helps in comparison:  In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value.
• Economical:  Construction of a data table is fairly easy and presents the data in a manner which is really easy on the eyes of a reader. Moreover, it saves time as well as space.

Classification of Data and Tabular Presentation

Qualitative classification.

In this classification, data in a table is classified on the basis of qualitative attributes. In other words, if the data contained attributes that cannot be quantified like rural-urban, boys-girls etc. it can be identified as a qualitative classification of data.

Quantitative Classification

In quantitative classification, data is classified on basis of quantitative attributes.

Temporal Classification

Here data is classified according to time. Thus when data is mentioned with respect to different time frames, we term such a classification as temporal.

Spatial Classification

When data is classified according to a location, it becomes a spatial classification.

A Solved Example for You

Q:  The classification in which data in a table is classified according to time is known as:

• Qualitative
• Quantitative

Ans:  The form of classification in which data is classified based on time frames is known as the temporal classification of data and tabular presentation.

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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

If you need a quick method to create a data presentation, check out our  AI presentation maker . A tool in which you add the topic, curate the outline, select a design, and let AI do the work for you.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Activity Sheet-Organization and Presentation of Data in Textual, Tabular and Graphical Form

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This activity sheet about the Organization and Presentation of Data in Textual, Tabular and Graphical Form for Basic Statistics Class.

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Textual and Tabular Presentation

The textual presentation uses words to present the data. Tabular data is self-explanatory as there are segments that depict what the data wants to convey. The textual data need to be explained with words

Textual & Tabular Presentation

Raw data must be collected and organized correctly to derive actionable conclusions from voluminous data. Majorly, data is represented using these three presentations: 

Textual presentation

Tabular presentation, diagrammatic presentation .

These presentations organize data in a ready-to-use form for decision-making or generating statistics. In this article, you will learn about the most used forms – textual and tabular presentation of data . 

Textual Presentation 

Just as the name says, Textual (meaning in the form of text) or Descriptive Presentation data is described in the text form. This is one of the most primary forms of data presentation as it simply takes the data and writes it. Once the reader reads the paragraphs, it becomes easy to comprehend the data. 

You can choose to represent your data in textual form when you’re consecutively making statements. These must be qualitative statements that need corroboration from organized data. However, it is essential to note that the data presented in the textual form must not be too large. 

Let’s consider some examples to understand textual presentation.

Note: The statements/facts in the examples throughout the articles are not accurate. They are only used for demonstrating an example of the concept.  

• Example 1: The workers union called for a nationwide strike on November 23, 2017, protesting the unjust laws passed during the winter session of the Parliament. In Kerala, 13 factories were closed, and two were found open. Even the schools supported the strike of the workers in the town. Out of 7 schools, five were found to be shut on the day of the protest. 
• Example 2: According to the 2011 Census of India, the country’s population is 130 billion, out of which 57 crores were females and 73 crores were men. 71% of the population lived in rural areas, and 19% lived in cities. There were 84 crores quick lurkers in the entire country, against 46 crores workers. 

Limitations & Scope of Textual Presentation 

As you can see, the data presented in textual form does not give away the facts quickly. You must read the entire paragraph to get the complete information. Judging the content based on one or two facts in the first lines will increase your probability of making incorrect conclusions. 

Countering these limitations is that data presented in textual format allows the writer to stress the importance of a particular point. To meet this purpose, the phenomenon’s scale becomes difficult to comprehend if the writer chooses to present data in any other form.

For example, you chose to present the data of the number of deaths due to a cyclone in a coastal town in a tabular format. While this may communicate the exact number in an easy-to-view manner, the reader will not understand the intensity of the disaster without a qualitative statement. On the contrary, writing “Cyclone Meena caused severe destruction in coastal Andhra Pradesh by killing 294 people” effectively communicates the intensity of the catastrophe. 

Despite the clarity offered by textual data, it falls short of describing voluminous data. To avoid the cumbersome process of reading multiple paragraphs, you can use the following type of data presentation. 

Tabular Presentation of Data or Data Tables

Tabular presentation is probably the most used (and more advanced) form of data presentation. Here, you can read the data horizontally and vertically as it is divided into rows and columns. For example, given below is a 3 x 3 table presenting information about the literacy rates in 3 Indian states. 

Classifications

As demonstrated in the previous example, the data in the tabular format is classified into rows and columns. There are four types of classifications used in data tables. They are: 

• Qualitative : The classification used in the last example is the appropriate example of Qualitative classification. In such cases, you can use visible attributes (like location and sex in the previous example) to categorize data. 
• Quantitative : Here, the data table’s data is categorized based on numbers or quantitative factors. For instance, if a table shows the percentage of voters in any place based on their age, the table uses quantitative classification. In this example, the percentage of voters is calculated based on the total number of voters in the town. 
• Temporal : The classifying variable of a temporal data table is time. This time can be measured in minutes, hours, dates, weeks, months, or years. For instance, when represented in a table format, the monthly sales of a grocery shop would use temporal classification. Here, you will be listing every month and the corresponding sales amount in a table. 
• Spatial : Every classification that you perform based on a place is called spatial classification. The place could be a street, town, district, village, or country. For instance, the data of imports received by a country from other countries would use spatial classification. Here, countries would be the classifying variable. 

Parts of a Data Table

With a strong understanding of the parts of a data table, you can construct the best table for statistical purposes. As per experience, you can judge the three essential parts that make a tabular presentation – a title, table, and explanatory note. Along with these, other detailed parts contribute to the perfection of a table. They are listed below. 

• Location: Beginning of the table title
• Nature: Whole or subscripted numbers (Example, 4 or 4.1. Here 4.1 could describe the first table of the fourth chapter)
• Location: Below the table number or next to it
• Nature: Brief, clear, and accurately worded
• Location: At the top of every column of a data table
• Nature: Brief 
• Stubs : Stubs are commonly known as row headings. You use them for describing the type of data inserted in a particular row. The heading given to the rows is also called stub items, and the column where these headings are written is called a stub column. If required, you can also talk about the row headings in the leftmost corner cell of the table. 
• Measurement Unit : For every row or column heading, it is good to include the measurement unit of the type of data. 

Conclusion  

With a clear understanding of the tabular format and textual meaning , you can present any data effectively. 

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• Tabular Presentation of Data

What is Tabular Presentation of Data in Detail

The presentation of data is essential. A tabular presentation of data helps the viewer to understand and to interpret the information better. Take, for example, your annual report card that is presented in a tabular format. You have your subjects written in one column of the table and your grades on the other. The third column mentions any teachers’ remarks. A single glance at your report card lets you read through the grades and subjects as well as the remarks with ease.

Now think, what would have happened if the same information was presented to you in the form of a paragraph. You would have to go through each line to know the grade that you got and the teachers’ remarks on a particular subject. This would make it tedious and also confusing to understand the report card.

Presentation of Data

Data must be presented properly. If the information is pleasing to the eyes, then it immediately gets attention. Data presentation is about using the same information to exhibit it in an attractive and useful way that can be read and interpreted easily. Data presentation is of three broad kinds. These are:

Textual presentation.

Data tables.

Diagrammatic presentation.

On this presentation of data Class 11 page, you will get to understand the textual and tabular data presentation or the data tables.

Textual Presentation

Data is first obtained in a textual format. It is a vague and raw format of the data. The data is mentioned in the text form, which is usually written in a paragraph. The textual presentation of data is used when the data is not large and can be easily comprehended by the reader just when he reads the paragraph.

This data format is useful when some qualitative statement is to be supplemented with data. The reader does not want to read volumes of data to be represented in the tabular format. Does he want to understand the data in a diagrammatic form? All that the reader wants to know is the data that provides evidence to the statement written. This is enough to let the reader gauge the intensity of the statement.

The textual data is evidence of the qualitative statement, and one needs to go through the complete text before he concludes anything.

For example, the coronavirus death toll in India today is 447. The reader does not need a lot of data here. The entire text of the state-wise breakup is accumulated to arrive at the national death figure. This is enough information for the reader.

Data Tables or Tabular Presentation

Data Tables or Tabular presentation of data is known to be the arrangement of certain values recorded in tables such that they are easy to manage and read. It is mostly done for a reader to gain the idea about the data without making it too complicated. The data presentation can be used for proper matter which is informative and creative at the same time.

What is Data Presentation?

If the reader has to interpret a lot of data, then this has to be organized in an easy to read format. The data should be laid out in rows and columns so that the reader can get what he wants at a single glance. Data tables are easy to construct and also easy to read, which makes them popular.

Components of Data Tables

Below are the key components of the data table.

Table Number - Each table has a table number that makes it easy to locate it. This number serves as a reference and leads one to a particular table.

Title - The table should also have a title that lets the reader understand what information the table provides. The place of study, the period, and the nature of data classification are also mentioned in the title.

Headnotes - The headnotes give further information. It provides the unit of data in brackets which is mentioned at the end of the title. The headnote aids the title to offer more information that the reader would need to interpret the data.

Stubs - These are the titles that tell you what the row represents. In other words, the stubs give information about what data is contained in each row.

Caption - The caption is the column title in the data table. It gives information about what is contained in each column.

Body or Field - The body or the field is the entire content in the table. Each item that is present in the body is the cell.

Footnotes - Footnotes are not commonly used, but these are used to supplement the table title if needed.

Source - If the data used in the table is taken from a secondary source, then that has to be mentioned in the footnote.

Construction of Data Tables

Tabular presentation can be constructed in many ways. Here are some ways that are commonly followed.

The title of the table should be able to reflect on the table content.

If two rows or columns have to be compared, then these should be placed adjacent to each other.

If the rows in the table are lengthy, then the stub can be placed on the right-hand part of the table.

Headings should always be in the singular.

Footnotes are not compulsory and should be provided only if required.

The column size should be symmetrical and uniform.

There should be no abbreviations in the headings and the subheadings.

The units should be specified above the column.

The Advantages of Tabular Presentation

Makes representation of data easy.

Makes it easy to analyze the data.

Makes it easy to compare data.

The data is represented in a readable manner which saves space and the reader’s time.

Classification of Data and Tabular Presentation

Classification of data and Tabular presentation is needed to arrange complex, heterogeneous data into a more simple and sophisticated manner. This is done for the convenience of the audience studying the data so the values are easy to distinguish. There are four ways in which one can classify the data and Tabular presentation. These are as follows.

Qualitative Classification

In qualitative classification, the data is classified based on its qualitative attributes. This is when the data has attributes that cannot be quantified. These could be boys-girls, rural-urban, etc.

Quantitative Classification

In quantitative classification, the data is classified based on the quantitative attributes. These could be marks where the data is categorized into 0-50, 51-100, etc.

Temporal Classification

In this tabular presentation, the data is classified according to the time. Here the data is represented in varied time frames like in the year 2016, 2018, etc.

Spatial Classification

In this method of classification, the data is classified according to location, like India, Pakistan, Russia, etc.

FAQs on Tabular Presentation of Data

1. What do you Mean by the Tabular Presentation of Data?

When data is presented in a tabular form, it makes the information easy to read and to engage. The data is arranged in rows and columns. The tabular method of presenting data is the most widely used. The tabular representation of data coordinates the information for decision making, and any presentation of data in statistics use. Data in the tabular format is divided into 4 kinds. These are the Qualitative (based on traits), Quantitative (based on quantitative features), Temporal (based on time), and spatial (based on location) presentation of data.

2. Explain the Difference Between the Tabular and Textual Presentation of Data ? 

In the tabular representation of data, the data is presented in the form of tables and diagrams. The textual presentation uses words to present the data.Tabular data is self-explanatory as there are segments that depict what the data wants to convey. The textual data need to be explained with words.The key difference thus is that the textual representation of data is subjective. In a tabular format, the data is mentioned in the form of tables. This makes tabular data perfect for the vast amount of data which makes it easy for the reader to read and interpret the information.

3. Where can I get the most appropriate Textual and Tabular Presentation of Data - Advantages, Classification and FAQs?

At Vedantu, the students can find different types of study material which help them ace their exams. Whether it is sample tests, mock tests, important questions, notes you want, Vedantu has it all. All of these are curated by our master teachers who make sure that you score the highest of marks. For finding the Textual and Tabular Presentation of data - Advantages, Classification and FAQs, all students have to do is sign in Vedantu.com using the Vedantu app or website.

4. What is meant by textual and Tabular Presentation? 

Data around us is represented in different ways to us on an everyday basis. Two of these methods are either presenting it via texts which are known as textual presentation and the other one is known as Tabular Presentation by which the data is presented using tables. The tabular presentation is attractive and helps one to visualize the given data, although some may consider textual presentation for a detailed and proper explanation. It depends entirely on the individual how they want their data to be produced, however, most people consider the tabular presentation.

5. Why should I know about textual and Tabular Presentation?

We need data to share information with others, for this, it is important for the students to know how to use the different ways of data presentation. Knowing about Textual and Tabular presentation of data helps an individual to choose how they need their information to be conveyed. Textual data representation is basic and it is important that a student already knows about it completely when they move on to studying the tabular presentation of data. This makes sure that you have your concepts clear and for your progress to attain great heights. 

Refer to Vedantu for free solutions chapter wise and get free access to other online resources to improve your learning in several folds.

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Analysis of correlation features in material application for architectural design based on big data text mining.

1. Introduction

2. materials and methods, 2.1. materials, 2.1.1. data collection, 2.1.2. data statistics overview, 2.2. methods, 2.2.1. research process and screening method, 2.2.2. text mining and vocabulary co-occurrence network, 3. results and discussion, 3.1. systematic sorting of material occurrence frequency, 3.2. overview of material lexical co-occurrence network, 3.3. concrete, glass, and steel, 3.4. wood and timber, 3.5. brick, stone, and metal, 4. cases of study program, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Yi, T.; Liou, S.-R.; Tai, J.; Zhou, J. Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining. Buildings 2024 , 14 , 2832. https://doi.org/10.3390/buildings14092832

Yi T, Liou S-R, Tai J, Zhou J. Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining. Buildings . 2024; 14(9):2832. https://doi.org/10.3390/buildings14092832

Yi, Teng, Shuenn-Ren Liou, Jie Tai, and Jie Zhou. 2024. "Analysis of Correlation Features in Material Application for Architectural Design Based on Big Data Text Mining" Buildings 14, no. 9: 2832. https://doi.org/10.3390/buildings14092832

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• Published: 03 September 2024

Utility of pelvic examination in assessing women with bleeding in early pregnancy: a multicenter Canadian emergency department study

• Steven Fisher 1 ,
• Stephanie Couperthwaite 1 ,
• Esther H. Yang 1 , 2 ,
• Nana Owusu Mensah Essel 1 &
• Brian H. Rowe 1 , 3  

International Journal of Emergency Medicine volume  17 , Article number:  110 ( 2024 ) Cite this article

Metrics details

Bleeding in early pregnancy is a common emergency department (ED) presentation. Although variability in approaches has been demonstrated, research is relatively uncommon on practices and outcomes. This study investigated the influence of clinical pattern of care, utility, and contribution of pelvic examination aimed at diagnosing and managing bleeding in early pregnancy at three Canadian EDs.

After obtaining informed consent, data were collected from adult women who were pregnant and from treating ED physicians using a structured questionnaire. We defined the change in management based on the initial clinical plan at the time of the initial physician assessment in the ED and any subsequent changes made after the pelvic examination was performed. Patient telephone follow-up was supplemented by linking with provincial administrative data for births. Univariable and multivariable binary logistic regression analyses were performed to identify factors associated with a change in patient management following pelvic examination in the ED.

Overall, 200 women were enrolled. The mean age was 31 years, patients had been bleeding for a median of 1 day and stayed in the ED for a median of 5 h. Of these, 166 (83.0%) received a pelvic examination, including speculum examination and/or bimanual palpation. Pregnancy outcome data were available for 192 pregnancies; 107 (56%) experienced a miscarriage. Factors significantly associated with a change in management after pelvic examination in the univariate logistic regression analysis were brown/dark-red bleeding per vaginam (physician determined), tachycardia, right lower quadrant tenderness, and bimanual palpation. In the multivariate logistic regression analysis, brown/dark-red bleeding per vaginam was independently associated with a reduced likelihood of a change in management after pelvic examination (aOR = 0.37; 95% CI: 0.14–0.98).

Among women presenting to the ED with bleeding in early pregnancy prior to 20 weeks gestation, only brown/dark-red vaginal bleeding, potentially indicative of bleeding resolution, significantly independently influenced the baseline odds of a change in management after pelvic examination. Until the debate on the utility of pelvic examination in the ED for this presentation is resolved, physician preferences and shared decision making with patients should guide practice regarding speculum examination/bimanual palpation for the management of bleeding in early pregnancy.

Bleeding in early pregnancy is a common problem, and many women present for assessment at emergency departments (EDs) primarily due to timing, symptomatic urgency, and want of reassurance out of concern that they may have miscarried. It accounts for approximately 500,000 annual visits to the ED in the United States [ 1 , 2 ], typically preceding the first prenatal visit [ 3 ]. In countries such as Canada and the United Kingdom, early pregnancy units have become commonplace for assessing and managing women with bleeding in early pregnancy; however, such units do not operate 24 h a day, not all pregnant women can access them, and they are not universally available [ 4 ]. Thus, the ED remains a common entry point for women experiencing bleeding in early pregnancy. In the ED, the management of bleeding in early pregnancy primarily focuses on excluding rare, albeit serious, conditions such as ectopic gestations, assessing fetal viability, and management of symptoms [ 5 , 6 , 7 ]. Previous authors have questioned whether abdominopelvic ultrasound can replace a formal pelvic examination, while studies conducted in non-ED settings have described the two diagnostic tools as complementary [ 8 ]. Nevertheless, in the ED, the pelvic examination is performed for a number of reasons: to document the vaginal and cervical appearance (e.g., presence/absence of tissue in the cervical os, cervical lesions that would explain the blood loss), the color and quantity of bleeding, and to rule out other serious causes of bleeding. Overall, the goal of the examination is to confirm the history and refine the diagnosis, at the same time as guiding future laboratory testing, imaging, and disposition (e.g., admit, discharge, refer) [ 9 ].

The utility of pelvic examination has been debated, with opponents citing the literature and proponents noting several methodological issues calling into question the reliability of the evidence [ 9 , 10 ]. For example, using a composite calculation to compare 30-day morbidity outcomes in an underpowered equivalence study [ 11 ], and suggesting that the actual issue could be provider unease with the procedure, rather than one based on solid evidence [ 10 ]. Rosenberg also posits that to discard the pelvic examination, it should be proven to lead to more misleading outcomes than helpful ones [ 12 ].

Although the evidence remains inconclusive, preliminary studies reveal that omitting the pelvic examination in the ED has benefits without significant untoward outcomes, especially with the given improvements in diagnostic capacity within the ED setting. Thus, despite providing additional clinical information under specific circumstances, the existing practice of always including pelvic examinations while evaluating women presenting to the ED with bleeding in early pregnancy may no longer be appropriate. Due to conflicting information in the literature, this study aimed to investigate the clinical pattern of care, utility, and contribution of pelvic examination in the ED toward diagnosing and managing bleeding in early pregnancy. To achieve this, factors that determine a change in clinical management following pelvic examination in the ED were explored. Our primary hypothesis was that the findings on pelvic examinations (speculum and bimanual palpation) performed by ED physicians would not significantly alter the clinical decision-making process or the formulation of a care plan for women presenting with bleeding in early pregnancy.

Study setting and design

This prospective cohort study was conducted at three EDs located in Edmonton, Alberta, Canada (urban population ≈ 1 million) which are three of seven high-volume EDs within the region. The study participants were recruited between January 2014 and January 2018. The University of Alberta Hospital (UAH) is a major urban, academic, tertiary care center assessing approximately 67,000 adult patients per year with an admission proportion of 23% and access to ultrasound services; however, it has no obstetric in-patient capacity and no in-house obstetric services. The Royal Alexandra Hospital (RAH) is a mixed adult and pediatric urban hospital assessing approximately 72,000 adult patients per year with in-patient obstetric beds, an obstetric service, and access to ultrasound services. The Northeast Community Health Centre (NECHC) is a mixed adult and pediatric urban emergency department assessing approximately 41,000 patients per year with no in-patient beds, ultrasound services, or obstetric consultants. All sites are staffed with full-time emergency physicians, are teaching sites for emergency medicine and other academic programs, and operate 24 h per day.

Ethical considerations

The study complied with the Declaration of Helsinki (1964) and its later amendments. Written informed consent was obtained from all study participants before enrollment. The study protocol and materials were approved by the Health Research Ethics Board (HREB; reference ID: Pro00047076) at the University of Alberta in Edmonton, Alberta, Canada . Operational and administrative approval was obtained from Alberta Health Services and a data sharing agreement was signed.

Study procedures and definitions

Pregnancy was primarily diagnosed through a combination of clinical assessment, patient history, and quantitative serum beta-human chorionic gonadotropin (β-HCG) levels. We defined bleeding in early pregnancy as the occurrence of vaginal bleeding in the first 20 weeks of pregnancy (calculated from the last normal menstrual period). When available, we also incorporated sonographic confirmation for pregnancy dating; specifically, if an ultrasound was performed during the ED visit or had been conducted recently, we used sonographic dating to refine the estimated gestational age (EGA).

For the purposes of this study, pelvic examination was defined as a composite of bimanual palpation and/or speculum examination. The primary outcome of our study was defined as a change in the management plan following the pelvic examination. We defined the change in management based on the ED physician’s response to the question: “Did the findings on pelvic examination change your management plan?” comparing their initial assessment to their thoughts after the pelvic examination was performed. Emergency physicians were not asked to record their management approach before and after completing the pelvic examination. These subjective changes included, but were not limited to: a revised diagnosis and management plan (e.g., diagnosing an ectopic pregnancy or incomplete miscarriage that necessitated different clinical actions); decisions to request further diagnostic procedures such as additional imaging (e.g., transvaginal or pelvic ultrasound) or laboratory tests; a decision to refer the patient to an obstetrician for further evaluation and management; a decision to admit the patient to the hospital for observation, treatment, or surgical intervention; and a decision to extend the period of observation within the ED to monitor the patient’s condition and response to initial management.

Sample size

No a priori sample size determination was performed. Rather, a convenience sample of women presenting to any of the three EDs with vaginal bleeding at an EGA < 20 weeks was utilized.

Physician/patient recruitment

Emergency physicians at each study site were invited to participate by completing a paper-based form to identify patients who presented with bleeding in early pregnancy. Patients were eligible if they were aged 17 years or older and presented to the ED with proven pregnancy and vaginal bleeding under 20 weeks gestation. We excluded patients who were hemodynamically unstable or experiencing excessive vaginal bleeding (passing large clots and/or soaking at least one pad per hour), non-English speaking women, and patients who did not consent to the study. In some cases, a research assistant identified potentially eligible patients using the Emergency Department Information System (EDIS), who then confirmed the patient’s eligibility with the attending physician. A general patient survey was completed by ED physicians and/or a research assistant following written informed consent.

Data collection

Completed screening forms were retrieved by research assistants and the results were entered into REDCap (Vanderbilt University, Nashville, TN, USA), a secure web-based platform, licensed through the Women and Children’s Health Research Institute (WCHRI) at the University of Alberta [ 13 ]. For each patient successfully enrolled, data were collected from the patient’s paper chart and EDIS. Characteristics of interest are shown in Table  1 and included: (1) patients’ demographics; (2) ED presentation (e.g., Canadian Triage and Acuity Scale [CTAS] score, mode of arrival, vital signs); (3) ED investigations (e.g., complete blood count, β-HCG levels, ultrasound, blood typing); (4) ED management (e.g., interventions, procedures, and drugs administered); (5) imaging findings; (6) ED length of stay (LOS) (triage to discharge) and time to assessment (triage to physician initial assessment [PIA]); (7) patient disposition and repeat visit to the ED within 30 days of discharge from the ED or discharge from hospital admission; and (8) consultations requested in the ED and post-hospital referrals. All participating EDs utilize CTAS, a five-level acuity assessment scoring system, as follows: CTAS 1 (resuscitation), CTAS 2 (emergent), CTAS 3 (urgent), CTAS 4 (semi-urgent), and CTAS 5 (non-urgent) [ 14 , 15 ]. Duplicate data extraction was completed on the first 10 charts and reviewed by a clinical research nurse to identify potential disagreements and ensure a unified data collection methodology.

Linkage of administrative data

To validate the existing follow-up data and secure missing data, four population-based linked health administrative databases from Alberta Health Services (AHS) were obtained. All databases are hosted in the AHS Enterprise Data Warehouse. The EDIS dataset captured information on all ED visits in the Edmonton area. Each EDIS record represents a unique service and includes a unique identifier, start and end dates and times, presenting complaints, the number and type of consultation services, and admitting services. These data were linked to the study cohort to secure any missing ED data on the factors mentioned above. The provincial laboratory data captured all general laboratory tests performed across the province and was used to identify women who underwent hemoglobin and β-HCG testing within the index early pregnancy visit to the ED for bleeding. The Alberta Perinatal Data captured information on maternal and perinatal data from the provincial delivery records for all deliveries occurring ≥ 20 weeks of gestation. Pregnancy-related outcomes (e.g., live birth, pregnancy loss, or stillbirth) that occurred within 9 months of the index ED visit were searched. Stillbirths were identified using both follow-up and administrative data. Finally, the Provincial Registry captured Alberta residents covered by the Alberta Health Care Insurance Plan. Alberta residents at the time of the ED visit were identified by records in this dataset during the fiscal year of the ED visit.

Statistical methods

The distributions of continuous and discrete data were tested using the Kolmogorov–Smirnov test. Normally distributed data are reported as means and standard deviations (SDs) while skewed variables are reported as medians and interquartile ranges (IQRs). Categorical variables are reported as frequencies and percentages. To investigate factors associated with a change in management after pelvic examination, we performed univariable binary logistic regression (with the primary outcome being the physician’s perceived change in management following pelvic examination) and reported odds ratios (ORs) and 95% confidence intervals (CIs). For β-HCG level, which had a right-skewed distribution, we selected an approximation of the median (10,404.5 mIU/mL) as the cut-off value. In the multivariable logistic regression analysis, we calculated adjusted ORs (aORs) for variables that showed marginal or significant associations ( p- value < 0.1) in the univariable analyses. These included the color of bleeding (based on the physician’s assessment), presence of abdominal cramping, tachycardia at presentation, right lower quadrant (RLQ) tenderness, and bimanual palpation within the ED. None of the variables included in the logistic regression analyses had more than 10% data missing for the 166 women included. All statistical computations were performed using R version 4.2.2 (The R Development Core Team, Vienna, Austria). Hypothesis tests were two-sided and considered statistically significant at p -value < 0.05.

Patients’ clinical and ED characteristics

Overall, 200 unique patients who presented to one of the study EDs with a complaint consistent with bleeding in early pregnancy were included in the study (Fig.  1 ). Selected patient demographic, clinical, and ED characteristics are shown in Table  1 . The mean age at presentation was 30.7 years (SD: 5.7), with a majority of women between 26 and 35 years of age. Most women (79%) presented with an EGA of ≤ 10 weeks, a median gravidity of 2 (IQR: 1, 3), and a median parity of 1 (IQR: 0, 1). Five (2.5%) women had a history of prior ectopic gestation, 61 (31%) had experienced bleeding in early pregnancy before the index pregnancy, and 55 (28%) had a history of spontaneous miscarriage. Thirteen women (7.8%) had undergone fertility treatment for their current pregnancy.

Flow chart showing variations in the management of 200 women who presented to three Canadian emergency departments with bleeding in early pregnancy

At presentation, the median duration of bleeding was 1 day (IQR: 0.3, 2.0), with a median pain score of 3 on a verbal analog scale of 0 to 10, and 53% had experienced abdominal cramping accompanying the bleeding episode. The median CTAS score was 3 and 21% presented afterhours. During their ED stay, 83% of women underwent at least one β-HCG measurement. On physical examination, 18% presented with either a systolic BP > 140 mmHg or a diastolic BP > 90 mmHg, and 19% were tachycardic (pulse > 100/min) (included in Table  1 ). Eighty-one women (41%) showed normal findings on abdominal examination; nearly equal proportions (~ 12%) showed diffuse tenderness, left lower quadrant tenderness, or RLQ tenderness; and 11 (6%) showed guarding.

Overall, 166 (83%) received a pelvic examination; 123 (62%) women received a speculum examination and 123 (62%) received a bimanual examination. 70% of women subjected to speculum examination in the ED showed bleeding at the cervical os and 14% showed normal findings. Similarly, bimanual palpation revealed normal findings among 31%, adnexal mass/tenderness in 8%, and cervical motion tenderness/uterine tenderness in 7%. At discharge, the median ED LOS was 5 h (IQR: 3, 6). Regarding outcomes, 107/192 (56%) patients with available pregnancy outcome data experienced a miscarriage.

Exploratory analysis of factors associated with a change in management after pelvic examination

The performance of pelvic exam did not significantly differ among facilities with and without ultrasound services (83.2% vs. 81.8%, chi-squared p -value = 0.88). On the other hand, the facility with in-house obstetric services (RAH) performed significantly more pelvic examinations than the other two facilities without (94.7% vs. 76.0%, chi-squared p -value < 0.001). There was no significant difference in the proportions of eventual miscarriage between patients who underwent vs. did not undergo pelvic examination (chi-squared p -value = 0.75). The results of univariate nominal logistic regression analysis seeking factors associated with a change in management after pelvic examination are shown in Table  2 . In the multivariable logistic regression analysis, only brown/dark-red bleeding per vaginam (determined by the examining ED physician) was independently associated with a reduced likelihood of a change in management after pelvic examination in the ED (aOR = 0.37; 95% CI: 0.14–0.98). Controlling for other factors (that showed marginal to significant associations in the univariable analyses: presence of abdominal cramps, tachycardia, RLQ tenderness, bimanual palpation, and color of the blood) failed to identify other factors independently associated with a change in management based on pelvic examination findings.

This prospective observational cohort study identified and characterized the management of 200 women with vaginal bleeding in early pregnancy at three Canadian EDs and further described factors associated with a change in management after pelvic examination. A large majority of included patients were young and had experienced a very short period of bleeding (~ 1 day) in their current pregnancy. Practice was variable among the treating clinicians; however, the majority (83%) performed a vaginal examination, 83% obtained serum β-HCG tests, and nearly all patients were referred for follow-up assessment. The decision to perform a bedside ultrasound was left to the discretion of the treating clinician and was documented in 63 (31.5%) women. These patients experienced prolonged ED stays, with some patients lingering for up to 15 h. Overall, their outcomes were poor (more than 50% experienced a miscarriage); however, it is important to recognize that this population is inherently at higher risk for adverse outcomes, given that a majority of spontaneous miscarriages in early pregnancy occur primarily due to chromosomal abnormalities in the embryo, for which no first trimester treatment exists.

Factors initially identified to be associated with a change in management after pelvic examination were: brown/dark-red bleeding per vaginam, tachycardia, RLQ tenderness, and the performance of bimanual examination in the ED. Among these, only brown/dark-red bleeding per vaginam, was independently associated with a change in management. There was no significant difference in the percentage of women receiving pelvic examinations between facilities with ultrasound services and those without; however, physicians practicing at the facility with in-house obstetric services tended to perform more pelvic examinations than those without. Both facility characteristics (the availability of ultrasound services and in-house obstetrics/gynecology services) were also not associated with a change in management following pelvic examination.

Among the 166 (83%) cases where a pelvic examination was completed, a significant majority of ED physicians reported that pelvic examination changed their approach to management (72.3% vs. 27.7%; difference, 44.6%; 95% CI: 33.4–55.7). While cervical motion tenderness and the presence of adnexal masses are important findings on pelvic examination, they have low sensitivity (45% and 10%, respectively) and their subsequent positive likelihood ratios are unhelpful [ 16 ].

These findings agreed with a recent randomized controlled trial [ 17 ], which failed to meet its goals of enrolling patients with early pregnancy confirmed on ultrasound according to whether pelvic examination was performed during evaluation in the ED. Although the appropriateness of deriving practice-changing conclusions from an underpowered study has been called into question [ 11 ], it is worth noting that the researchers found no statistically significant difference in 30-day morbidity when 202 women underwent pelvic examination in the ED [ 17 ]. Upholding the aforementioned results would likely be based on the fact that ED ultrasound provides much more quantitative and qualitative information than the pelvic examination, particularly in ruling out ectopic gestation. Among such women, a meta-analysis showed that ultrasound performed by ED physicians led to a pooled sensitivity of 99.3%, a negative predictive value of 99.95%, and a negative likelihood ratio of 0.08, without substantial heterogeneity [ 18 ]. Similarly, upon detecting an adnexal mass and no intrauterine pregnancy, the positive likelihood ratio for ectopic gestation exceeded 1.00 while the negative likelihood ratio was 0.12 [ 16 ]. In the presence of ultrasound-confirmed intrauterine gestation, pelvic examination has neither been shown to add information to the management of patients with early pregnancy nor does it affect their disposition, even when the pelvic findings are unexpected [ 19 , 20 ].

For a healthy, hemodynamically stable woman without clinical concern about bleeding in early pregnancy secondary to cervical neoplasia, vaginal trauma, vaginitis, or cervical polyps, some believe conducting a pelvic examination may be invasive, offers little diagnostic benefit, increases health care costs, decreases throughput, and increases the ED LOS [ 17 , 19 , 21 ]. Moreover, from a patient’s perspective, women who did not undergo pelvic examination were half as likely to report a feeling of discomfort, compared to those who did, and when given a choice to participate, many (42%) eligible patients refused study enrollment because they preferred not to undergo pelvic examination [ 17 ]. Further, point-of-care ultrasonography is currently considered a crucial skill for emergency care providers and is recommended in the ED to improve diagnoses and outcomes [ 22 , 23 ]. In another prospective cohort study, pelvic examination led to a change in management in only 6% of cases [ 20 ]. Determining the definitive impact and safety of omitting the ED pelvic examination when there is bedside ultrasound evidence of an intrauterine pregnancy needs to be assessed for equivalence with performing it to evaluate women with abdominal pain and/or bleeding per vaginam in early pregnancy.

Study limitations

Our study had some methodological limitations. First, this study relied on the support of participating ED physicians to identify patients with bleeding in early pregnancy; however, form completion was not universal and often incomplete. While some patients may have been misidentified as having bleeding in pregnancy, a majority of participating ED physicians had many years of clinical experience, so this risk was likely minimal.

We could not complete the analysis of some outcomes as planned due to poor reporting in the patient’s charts or variability in the care provided by clinicians in these three ED settings. A standardized protocol for bleeding in early pregnancy was not in place at any site during this study. The proportion of patients lost to follow-up was high and would have invalidated these findings; however, the use of linked administrative data mitigated this problem (e.g., pregnancy outcomes could be ascertained for 192 of 200 included patients).

ED physicians were not asked to indicate their management approach before and after completing the pelvic examination. Instead, they were asked a general question as to whether the examination changed their management without specifying what changes might have occurred. We also did not record information about how any unexpected findings altered the treatment approach and thus could not ascertain how any such finding would correlate with a change in the management approach.

This study was conducted in Canada, where access to health care is assured by government funding and no co-payments exist for ED care. As a result, ED management and disposition strategies used among such patients may not be representative of outcomes in studies conducted in other countries. Also, some social determinants of health (e.g., supports, race, income, housing, employment), behavioral factors (e.g., alcohol consumption, use of cigarettes/vaping, cannabis use, diet), and comorbidities (e.g., body mass index, diabetes mellitus) that may have affected pregnancy outcomes were also not recorded. Finally, the sample size limited the inclusion of molar pregnancy and other rare presentations.

Notwithstanding these limitations, we believe that our findings raise some important questions and contribute to the discourse regarding the routine performance of pelvic examinations in the ED among women with bleeding in early pregnancy. This is one of the first studies to comprehensively assess the management of patients with early pregnancy loss.

Conclusions

This pilot observational cohort study found that women presenting to the ED with bleeding in early pregnancy (EGA < 20 weeks) who were hemodynamically stable undergo variable assessment and approximately 56% suffer a miscarriage. These presentations appear to be an appropriate target for interventions to standardize care, as well as provide support for patients and their families. Moreover, only brown/dark-red bleeding per vaginam, potentially indicative of bleeding resolution, significantly independently influenced the baseline odds of a change in management after pelvic examination. Although our study primarily explores improving ED efficiency, until the debate regarding the utility of speculum/bimanual examination in the ED is resolved, physician preferences, availability of obstetric services, and shared decision making with patients should guide practice regarding speculum and bimanual pelvic examination for the management of bleeding in early pregnancy.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon request.

Abbreviations

Alberta Health Services

Adjusted Odds Ratio

Confidence Interval

Canadian Triage and Acuity Scale

Emergency Department

Emergency Department Information System

Estimated Gestational Age

Human Chorionic Gonadotropin

Interquartile Range

Left Lower Quadrant

Length of Stay

Northeast Community Health Centre

Royal Alexandra Hospital

Right Lower Quadrant

Standard Deviation

University of Alberta Hospital

Women and Children’s Health Research Institute

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Acknowledgements

We would like to thank all of the emergency physicians who volunteered to participate in the study at the RAH, NECHC, and UAH Emergency Departments. We would also like to thank Ms. Natalie Runham for her support and assistance with the study.

Dr Rowe’s research is supported by a Scientific Director’s Grant (SOP 168483) from the Canadian Institutes of Health Research (CIHR) through the Government of Canada (Ottawa, ON). Dr Fisher’s research was supported by the NECHC Emergency Medicine Special Purposes Fund. The funding organizations were not involved in any aspect of the conduct, analysis, and manuscript preparation of this study; the funders take no responsibility for the conduct or results of this study.

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Department of Emergency Medicine, Faculty of Medicine & Dentistry, College of Health Sciences, University of Alberta, 1G1.43 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada

Steven Fisher, Stephanie Couperthwaite, Esther H. Yang, Nana Owusu Mensah Essel & Brian H. Rowe

SPOR SUPPORT Unit, Alberta Health Services (AHS), Edmonton, AB, Canada

Esther H. Yang

School of Public Health, College of Health Sciences, University of Alberta, Edmonton, AB, Canada

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Contributions

Conceptualization: S.F. and B.H.R. Data collection: S.F., S.C., E.H.Y., and B.H.R. Data cleaning and formal analysis: N.O.M.E. and E.H.Y. Data interpretation: N.O.M.E., E.H.Y., and B.H.R. Writing-original draft: N.O.M.E., S.F., S.C., E.H.Y., and B.H.R. All the authors have reviewed and approved the manuscript in its current form.

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Correspondence to Brian H. Rowe .

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Ethics approval and consent to participate.

The study complied with the Declaration of Helsinki (1964) and its later amendments. Written informed consent was obtained from all study participants before enrollment. The study protocol and materials were approved by the Health Research Ethics Board (HREB; reference ID: Pro00047076) at the University of Alberta in Edmonton, Alberta, Canada. Operational and administrative approval was obtained from Alberta Health Services and a data sharing agreement was signed.

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The authors declare no competing interests.

Notation of prior abstract publication/presentation

Partial results from this study were presented at the 2023 Annual Meeting of the Canadian Association of Emergency Physicians (CAEP), Toronto, ON, Canada , May 2023.

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Fisher, S., Couperthwaite, S., Yang, E.H. et al. Utility of pelvic examination in assessing women with bleeding in early pregnancy: a multicenter Canadian emergency department study. Int J Emerg Med 17 , 110 (2024). https://doi.org/10.1186/s12245-024-00686-2

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Received : 16 May 2024

Accepted : 22 August 2024

Published : 03 September 2024

DOI : https://doi.org/10.1186/s12245-024-00686-2

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