graphical presentation in research

How to develop a graphical framework to chart your research

Graphic representations or frameworks can be powerful tools to explain research processes and outcomes. David Waller explains how researchers can develop effective visual models to chart their work

David Waller

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While undertaking a study, researchers can uncover insights, connections and findings that are extremely valuable to anyone likely to read their eventual paper. Thus, it is important for the researcher to clearly present and explain the ideas and potential relationships. One important way of presenting findings and relationships is by developing a graphical conceptual framework.

A graphical conceptual framework is a visual model that assists readers by illustrating how concepts, constructs, themes or processes work. It is an image designed to help the viewer understand how various factors interrelate and affect outcomes, such as a chart, graph or map.

These are commonly used in research to show outcomes but also to create, develop, test, support and criticise various ideas and models. The use of a conceptual framework can vary depending on whether it is being used for qualitative or quantitative research.

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There are many forms that a graphical conceptual framework can take, which can depend on the topic, the type of research or findings, and what can best present the story.

Below are examples of frameworks based on qualitative and quantitative research.

As shown by the table below, in qualitative research the conceptual framework is developed at the end of the study to illustrate the factors or issues presented in the qualitative data. It is designed to assist in theory building and the visual understanding of the exploratory findings. It can also be used to develop a framework in preparation for testing the proposition using quantitative research.

In quantitative research a conceptual framework can be used to synthesise the literature and theoretical concepts at the beginning of the study to present a model that will be tested in the statistical analysis of the research.

It is important to understand that the role of a conceptual framework differs depending on the type of research that is being undertaken.

So how should you go about creating a conceptual framework? After undertaking some studies where I have developed conceptual frameworks, here is a simple model based on “Six Rs”: Review, Reflect, Relationships, Reflect, Review, and Repeat.

Process for developing conceptual frameworks:

Review: literature/themes/theory.

Reflect: what are the main concepts/issues?

Relationships: what are their relationships?

Reflect: does the diagram represent it sufficiently?

Review: check it with theory, colleagues, stakeholders, etc.

Repeat: review and revise it to see if something better occurs.

This is not an easy process. It is important to begin by reviewing what has been presented in previous studies in the literature or in practice. This provides a solid background to the proposed model as it can show how it relates to accepted theoretical concepts or practical examples, and helps make sure that it is grounded in logical sense.

It can start with pen and paper, but after reviewing you should reflect to consider if the proposed framework takes into account the main concepts and issues, and the potential relationships that have been presented on the topic in previous works.

It may take a few versions before you are happy with the final framework, so it is worth continuing to reflect on the model and review its worth by reassessing it to determine if the model is consistent with the literature and theories. It can also be useful to discuss the idea with  colleagues or to present preliminary ideas at a conference or workshop –  be open to changes.

Even after you come up with a potential model it is good to repeat the process to review the framework and be prepared to revise it as this can help in refining the model. Over time you may develop a number of models with each one superseding the previous one.

A concern is that some students hold on to the framework they first thought of and worry that developing or changing it will be seen as a weakness in their research. However, a revised and refined model can be an important factor in justifying the value of the research.

Plenty of possibilities and theoretical topics could be considered to enhance the model. Whether it ultimately supports the theoretical constructs of the research will be dependent on what occurs when it is tested.  As social psychologist, Kurt Lewin, famously said “ There's nothing so practical as good theory ”.

The final result after doing your reviewing and reflecting should be a clear graphical presentation that will help the reader understand what the research is about as well as where it is heading.

It doesn’t need to be complex. A simple diagram or table can clarify the nature of a process and help in its analysis, which can be important for the researcher when communicating to their audience. As the saying goes: “ A picture is worth 1000 words ”. The same goes for a good conceptual framework, when explaining a research process or findings.

David Waller is an associate professor at the University of Technology Sydney .

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Home » Graphical Methods – Types, Examples and Guide

Graphical Methods – Types, Examples and Guide

Table of Contents

Graphical Methods

Definition:

Graphical methods refer to techniques used to visually represent data, relationships, or processes using charts, graphs, diagrams, or other graphical formats. These methods are widely used in various fields such as science, engineering, business, and social sciences, among others, to analyze, interpret and communicate complex information in a concise and understandable way.

Types of Graphical Methods

Here are some of the most common types of graphical methods for data analysis and visual presentation:

Line Graphs

These are commonly used to show trends over time, such as the stock prices of a particular company or the temperature over a certain period. They consist of a series of data points connected by a line that shows the trend of the data over time. Line graphs are useful for identifying patterns in data, such as seasonal changes or long-term trends.

These are commonly used to compare values of different categories, such as sales figures for different products or the number of students in different grade levels. Bar charts use bars that are either horizontal or vertical and represent the data values. They are useful for comparing data visually and identifying differences between categories.

These are used to show how a whole is divided into parts, such as the percentage of students in a school who are enrolled in different programs. Pie charts use a circle that is divided into sectors, with each sector representing a portion of the whole. They are useful for showing proportions and identifying which parts of a whole are larger or smaller.

Scatter Plots

These are used to visualize the relationship between two variables, such as the correlation between a person’s height and weight. Scatter plots consist of a series of data points that are plotted on a graph and connected by a line or curve. They are useful for identifying trends and relationships between variables.

These are used to show the distribution of data across a two-dimensional plane, such as a map of a city showing the density of population in different areas. Heat maps use color-coded cells to represent different levels of data, with darker colors indicating higher values. They are useful for identifying areas of high or low density and for highlighting patterns in data.

These are used to show the distribution of data in a single variable, such as the distribution of ages of a group of people. Histograms use bars that represent the frequency of each data value, with taller bars indicating a higher frequency. They are useful for identifying the shape of a distribution and for identifying outliers or unusual data values.

Network Diagrams

These are used to show the relationships between different entities or nodes, such as the relationships between people in a social network. Network diagrams consist of nodes that are connected by lines that represent the relationship. They are useful for identifying patterns in complex data and for understanding the structure of a network.

Box plots, also known as box-and-whisker plots, are a type of graphical method used to show the distribution of data in a single variable. They consist of a box with whiskers extending from the top and bottom of the box. The box represents the middle 50% of the data, with the median value indicated by a line inside the box. The whiskers represent the range of the data, with any data points outside the whiskers indicated as outliers. Box plots are useful for identifying the spread and shape of a distribution and for identifying outliers or unusual data values.

Applications of Graphical Methods

Graphical methods have a wide range of applications in various fields, including:

• Business : Graphical methods are commonly used in business to analyze sales data, financial data, and other types of data. They are useful for identifying trends, patterns, and outliers, as well as for presenting data in a clear and concise manner to stakeholders.
• Science and engineering: Graphical methods are used extensively in scientific and engineering fields to analyze data and to present research findings. They are useful for visualizing complex data sets and for identifying relationships between variables.
• Social sciences: Graphical methods are used in social sciences to analyze and present data related to human behavior, such as demographics, survey results, and statistical analyses. They are useful for identifying trends and patterns in large data sets and for communicating findings to a broader audience.
• Education : Graphical methods are used in education to present information to students and to help them understand complex concepts. They are useful for visualizing data and for presenting information in a way that is easy to understand.
• Healthcare : Graphical methods are used in healthcare to analyze patient data, to track disease outbreaks, and to present medical information to patients. They are useful for identifying patterns and trends in patient data and for communicating medical information in a clear and concise manner.
• Sports : Graphical methods are used in sports to analyze and present data related to player performance, team statistics, and game outcomes. They are useful for identifying trends and patterns in player and team data and for communicating this information to coaches, players, and fans.

Examples of Graphical Methods

Here are some examples of real-time applications of graphical methods:

• Stock Market: Line graphs, candlestick charts, and bar charts are widely used in real-time trading systems to display stock prices and trends over time. Traders use these charts to analyze historical data and make informed decisions about buying and selling stocks in real-time.
• Weather Forecasting : Heat maps and radar maps are commonly used in weather forecasting to display current weather conditions and to predict future weather patterns. These maps are useful for tracking the movement of storms, identifying areas of high and low pressure, and predicting the likelihood of severe weather events.
• Social Media Analytics: Scatter plots and network diagrams are commonly used in social media analytics to track the spread of information across social networks. Analysts use these graphs to identify patterns in user behavior, to track the popularity of specific topics or hashtags, and to monitor the influence of key opinion leaders.
• Traffic Analysis: Heat maps and network diagrams are used in traffic analysis to visualize traffic flow patterns and to identify areas of congestion or accidents. These graphs are useful for predicting traffic patterns, optimizing traffic flow, and improving transportation infrastructure.
• Medical Diagnostics: Box plots and histograms are commonly used in medical diagnostics to display the distribution of patient data, such as blood pressure, heart rate, or blood sugar levels. These graphs are useful for identifying patterns in patient data, diagnosing medical conditions, and monitoring the effectiveness of treatments in real-time.
• Cybersecurity: Heat maps and network diagrams are used in cybersecurity to visualize network traffic patterns and to identify potential security threats. These graphs are useful for identifying anomalies in network traffic, detecting and mitigating cyber attacks, and improving network security protocols.

How to use Graphical Methods

Here are some general steps to follow when using graphical methods to analyze and present data:

• Identify the research question: Before creating any graphs, it’s important to identify the research question or hypothesis you want to explore. This will help you select the appropriate type of graph and ensure that the data you collect is relevant to your research question.
• Collect and organize the data: Collect the data you need to answer your research question and organize it in a way that makes it easy to work with. This may involve sorting, filtering, or cleaning the data to ensure that it is accurate and relevant.
• Select the appropriate graph : There are many different types of graphs available, each with its own strengths and weaknesses. Select the appropriate graph based on the type of data you have and the research question you are exploring. For example, a scatterplot may be appropriate for exploring the relationship between two continuous variables, while a bar chart may be appropriate for comparing categorical data.
• Create the graph: Once you have selected the appropriate graph, create it using software or a tool that allows you to customize the graph based on your needs. Be sure to include appropriate labels and titles, and ensure that the graph is clearly legible.
• Analyze the graph: Once you have created the graph, analyze it to identify patterns, trends, and relationships in the data. Look for outliers or other anomalies that may require further investigation.
• Draw conclusions: Based on your analysis of the graph, draw conclusions about the research question you are exploring. Use the graph to support your conclusions and to communicate your findings to others.
• Iterate and refine: Finally, refine your graph or create additional graphs as needed to further explore your research question. Iteratively refining and revising your graphs can help to ensure that you are accurately representing the data and that you are drawing the appropriate conclusions.

When to use Graphical Methods

Graphical methods can be used in a variety of situations to help analyze, interpret, and communicate data. Here are some general guidelines on when to use graphical methods:

• To identify patterns and trends: Graphical methods are useful for identifying patterns and trends in data, which may be difficult to see in raw data tables or spreadsheets. Graphs can reveal trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions.
• To compare data: Graphs can be used to compare data from different sources or over different time periods. Graphical comparisons can make it easier to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• To summarize data : Graphs can be used to summarize large amounts of data in a single visual display. This can be particularly useful when presenting data to a broad audience, as it can help to simplify complex data sets and make them more accessible.
• To communicate data: Graphs can be used to communicate data and findings to a variety of audiences, including stakeholders, colleagues, and the general public. Graphs can be particularly useful in situations where data needs to be presented quickly and in a way that is easy to understand.
• To identify outliers: Graphical methods are useful for identifying outliers or anomalies in the data. Outliers can be indicative of errors or unusual events, and may warrant further investigation.

Purpose of Graphical Methods

The purpose of graphical methods is to help people analyze, interpret, and communicate data in a way that is both accurate and understandable. Graphical methods provide visual representations of data that can be easier to interpret than tables of numbers or raw data sets. Graphical methods help to reveal patterns and trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions. They can also help to identify outliers or unusual data points that may warrant further investigation.

In addition to helping people analyze and interpret data, graphical methods also serve an important communication function. Graphs can be used to present data to a wide range of audiences, including stakeholders, colleagues, and the general public. Graphs can help to simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.

Overall, the purpose of graphical methods is to provide a powerful tool for analyzing, interpreting, and communicating data. Graphical methods help people to better understand the data they are working with, to identify patterns and trends, and to make informed decisions based on the data.

Characteristics of Graphical Methods

Here are some characteristics of graphical methods:

• Visual Representation: Graphical methods provide a visual representation of data, which can be easier to interpret than tables of numbers or raw data sets. Graphs can help to reveal patterns and trends that may not be immediately apparent in the data.
• Simplicity : Graphical methods simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.
• Comparability : Graphical methods can be used to compare data from different sources or over different time periods. This can help to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• Flexibility : Graphical methods can be adapted to different types of data, including continuous, categorical, and ordinal data. Different types of graphs can be used to display different types of data, depending on the characteristics of the data and the research question.
• Accuracy : Graphical methods should accurately represent the data being analyzed. Graphs should be properly scaled and labeled to avoid distorting the data or misleading viewers.
• Clarity : Graphical methods should be clear and easy to read. Graphs should be designed with the viewer in mind, using appropriate colors, labels, and titles to ensure that the message of the graph is conveyed effectively.

Advantages of Graphical Methods

Graphical methods offer several advantages for analyzing and presenting data, including:

• Clear visualization: Graphical methods provide a clear and intuitive visual representation of data that can help people understand complex relationships, trends, and patterns in the data. This can be particularly useful when dealing with large and complex data sets.
• Efficient communication: Graphical methods can help to communicate complex data sets in an efficient and accessible way. Visual representations can be easier to understand than numerical data alone, and can help to convey key messages quickly.
• Effective comparison: Graphical methods allow for easy comparison between different data sets, making it easier to identify trends, patterns, and differences. This can help in making decisions, identifying areas for improvement, or developing new insights.
• Improved decision-making: Graphical methods can help to inform decision-making by presenting data in a clear and easy-to-understand format. They can also help to identify key areas of focus, enabling individuals or teams to make more informed decisions.
• Increased engagement: Graphical methods can help to engage audiences by presenting data in an engaging and interactive way. This can be particularly useful in presentations or reports, where visual representations can help to maintain audience attention and interest.
• Better understanding: Graphical methods can help individuals to better understand the data they are working with, by providing a clear and intuitive visual representation of the data. This can lead to improved insights and decision-making, as well as better understanding of the implications of the data.

Limitations of Graphical Methods

Here are a few limitations to consider:

• Misleading representation: Graphical methods can potentially misrepresent data if they are not designed properly. For example, inappropriate scaling or labeling of the axes or the use of certain types of graphs can create a distorted view of the data.
• Limited scope: Graphical methods can only display a limited amount of data, which can make it difficult to capture the full complexity of a data set. Additionally, some types of data may be difficult to represent visually.
• Time-consuming : Creating graphs can be a time-consuming process, particularly if multiple graphs need to be created and analyzed. This can be a limitation in situations where time is limited or resources are scarce.
• Technical skills: Some graphical methods require technical skills to create and interpret. For example, certain types of graphs may require knowledge of specialized software or programming languages.
• Interpretation : Interpreting graphs can be subjective, and the same graph can be interpreted in different ways by different people. This can lead to confusion or disagreements when using graphs to communicate data.
• Accessibility : Some graphical methods may not be accessible to all audiences, particularly those with visual impairments. Additionally, some types of graphs may not be accessible to those with limited literacy or numeracy skills.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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• Oct 27, 2021
• 13 min read

How to design an effective graphical abstract: the ultimate guide

All researchers know this story by now.

We spend months writing and revising our manuscript to absolute perfection. We feel pretty proud of our work, and we’re certain our target journal is going to roll out the red carpet and embrace it with open arms.

Alas, something unexpected happens…

We hit a massive roadblock at the tail-end of the manuscript submission process, simply because we don’t have a “graphical abstract.”

“A graphical what?!” we exclaim, throwing our arms in the air.

“What on earth IS this thing preventing us from submitting our amazing manuscript? This is going to be a massive waste of time!”

So, what’s the point you say?

You’re about to learn exactly how important graphical abstracts are, AND how to nail them right the first time!

Let’s talk about the purpose of graphical abstracts, what they should look like, and how you can easily create one to stay competitive with your research.

What on earth is a graphical abstract?

Let’s start by clarifying what a graphical abstract (GA) is NOT.

But first imagine this. . . it’s late at night.

After several hours of reformatting your paper to the guidelines, you finally hit that SUBMIT button, go to bed, and pray that your manuscript is accepted.

Then at the last minute, something really (really!) frustrating happens. Your target journal requires a “graphical abstract” to be submitted along with your paper, and you can’t move forward without one!

So, what do you do?

You have three choices:

1) Scream and damn the day you decided to become an academic (oh the memories…)

2) Design a graphical abstract from scratch (remember, it’s half-past midnight already).

3) Grab the prettiest figure from your paper and pretend it’s a graphical abstract (you know, the statistically significant graph from Figure 3.1A!)

Look, chances are you’re not a graphic designer, and creating a masterpiece with PowerPoint is out of the question. So I’m certain you’d choose Option 1 or Option 3. . . and then pay a VERY steep price for it. If your journal allows it, there may be an Option 4 for submitting a video abstract . We can compare and contrast the options later. Today we’re talking specifically about graphical abstracts.

And on that note, let’s get one thing straight: a graphical abstract should not be a copy of the best figure in your paper. N E V E R.

So if it’s now 3 am and you’re tempted to do that, go to bed! Or, keep reading.

What’s the purpose of a Graphical Abstract?

Now that we’ve clarified what a GA should not be, let’s nail down its purpose.

A graphical abstract is used to visually and concisely summarise your manuscript and its main message. It tells a clear and concise story , and how it works in your favour depends on who is reading.

If your peers are reading: A GA becomes a promotional tool that positions your paper to stand out in places like social media . As the name suggests, a GA has the same purpose as a traditional abstract. But with 7,000+ peer-reviewed articles being published daily, nobody has the time to read a 250-word abstract. GAs work like movie posters: to grab attention and drive traffic to your paper (the equivalent of the movie). What’s more, they even have the power to double the number of times your article is read . Incredible!

If a non-academic is reading: They don’t speak the scientific jargon, and the blocks of text and the boring black-and-white figures just don’t do it for them (can you blame them?). Instead with a well-designed GA, these people can finally become acquainted with, understand and appreciate, your research. A GA extends the reach of your research beyond your peers. A GA is clear and to the point, just like if you were to explain your scientific profession at a dinner party . The lay person appreciates short and sweet explanations, not a full lecture!

Do they really work?

Graphical abstracts have been shown to improve the reach of new scientific publications.

One study used Twitter to quantify the effect of including a graphical abstract in the promotion of new publications. The researchers compared Twitter posts with and without GAs over one year, using each post as its own control. They found that the reach of posts with GAs were dramatically greater than those without.

Tweets with GAs received a 7.7-fold increase in Twitter impressions , a 8.4-fold increase in retweets , and a 2.7-fold increase in article visits . We’ve even compiled this same study into a GA below, check it out!

Who will read them?

The first question you should ask yourself is, who do I want to reach with this GA? Am I just interested in reaching my small community of peers interested in my obscure science or am I interested in going beyond?

Expert audiences

There is a lot of research out there that is hyper-technical and interests only a limited number of experts. If that’s your case, great! You know who you are talking to: the big cheeses of the field.

If you feel that this is your case, I have a surprise for you. You have total freedom in the style of graphical abstract you can use. Because your audience has an expert level of understanding of the subject, you have the freedom to go technical or not. You can decide to show them complex diagrams and p -values or hook them in with a funny comic with a highly nerdy joke that maybe 8 people in the world will understand.

It is up to you.

Non-expert audiences

But what if you wanted to share your work with your next door neighbour, or your grandma?

(… assuming neither of them are scientists in your field…)

Science has traditionally been for (guess what) scientists. That’s why Open Access publishing is a super trendy topic. The idea of removing paywalls is great… for scientists. However, is this really enough to make science truly “open”? The paywall is one barrier, but what are the others? And how can a GA help?

Comprehension is the greatest barrier of all. And it’s the barrier that the general public or layman audience can’t break on their own.

Let’s help them out by using these tips on your GA.

Context: you need to provide some context because otherwise a non-expert won’t be able to appreciate the relevance of your research.

No jargon: Some people call it Jargon Monoxide because it asphyxiates audiences. It is true, not being able to understand a few words will cause the reader to switch off and think that this is just not for them.

The “so what?” factor. The reason why your research is relevant might be obvious to your peers, but it is definitely not obvious to Joe and Jane next door. Tell them in plain English why this matters to their lives.

Styles of graphical abstracts

Let’s now talk about the fun stuff! Style!

When it comes to GA’s, there isn’t a one-size-fits-all, cookie-cutter template. Scientists and artists from around the world have explored a variety of approaches and styles .

So while there are no concrete rules about what a GA should look like, we’re familiar with a number of popular styles and how each one fits a certain audience.

Let’s have a look at a few examples of some different styles and where they sit in the Experts-Public spectrum .

Style 1: The classic diagram

This is a more traditional style of GA that’s been around for a while. Using GAs like this wasn’t uncommon in the chemistry field a few decades ago, given that chemistry is such a visual topic.

You’d notice that there is no background context and it’s full of technical jargon. If the target audience is other experts then great, they’ll get it. But this is not suitable for any other kind of audience.

Style 2: The p-value aficionado

This is called a ‘Visual Abstract’. It’s very popular in the medical field, and usually consists of vertical or horizontal panels. It’s a little more accessible than the previous style, with some easily recognisable icons and some text to guide the reader. But, it’s still geared towards other scientists. ​

Style 3: The infographic​

In the infographic style, there’s less emphasis on data and more emphasis on the main scientific message and the “so what” factor.

As the most versatile style, it provides a good middle ground on the accessibility spectrum. It starts with a sentence that provides some background context, and the images are clear and interesting. What’s important is the use of a large eye-catching graphic that draws people’s attention.

Style 4: The comic strip​

Here we can play with our knowledge of pop culture, humour and artistic freedom.

A comic-style is perfect for telling your scientific story in a fun, whimsical way which can include metaphors or real-world references. This is by far the most accessible way for the public to understand the intentions behind the science, without going into the nitty-gritty detail.

The last style is a comic style and is clearly aimed at the general public. It’s visually appealing with some custom graphics, and it uses humour to convey the key scientific message: opening up the target audience to engage with everyone.

How can I design one?

Before we dive in, let’s establish one unbreakable rule.

Your GA will be CLEAR and CONCISE . Got it? Good.

What’s that? You’ve got an awesome multi-dimensional plot with 8 colours? Great!

Keep it in the paper, that’s where it belongs.

Got a beautiful table with 20 rows of significant p-values? Amazing!

Let’s keep this rule in mind as we work through the following steps.

Step 1: Planning the content

Once you’ve identified your target audience, let’s decide on the content, starting with the text.

While you do need some text to provide context and to guide the reader through the graphics, you’ll need to keep it as short as possible: definitely less than 80 words.

What to write

We’re huge advocates of the And - But - Therefore format of storytelling invented by Dr Randy Olson in his book “Connection” which one of our favourite science communication books of all time!

The ‘And’ is the context (background), the ‘But’ is the hook that holds the reader’s attention (knowledge gap), and the ‘Therefore’ is what you found (results and conclusions). You can read more about this format of storytelling here . We can leave out the methods (unless you’re writing a methods paper!). If your reader is interested, they can find them in all their nitty-gritty glory in the full paper.

Now that you have your target audience in mind, let’s decide on the content, starting with the text.

You do need some text to provide context and to guide the reader through the graphics, but keep it as short as possible. And anyway, the clearer your graphics are, the fewer words you’ll need!

How to write it

If you’re talking to experts, you might have some technical words, but if you’re engaging with the public you’ll need to stay away from all jargon. Remember that jargon monoxide is lethal!

Step 2: Concept

Crack your knuckles because now we’re getting to work on how your GA will look. On paper, or in your design software, make the first draft.

If you’re particularly arty, roughly draw the key graphics that you’ll polish up later. If not, don’t worry, just keep in mind where you want to put in the graphics, and afterwards, we’ll track down the best the internet has to offer.

Ask yourself where your GA is going to be distributed most, because this will determine its size. If you’re submitting it to a journal, you’ll need to follow their instructions. Or maybe you just want to make a splash on social media. Twitter, Instagram etc. each have their own preferred sizes, and this determines whether or not your GA will be cropped when viewed on mobile devices etc. Decide which platform will give your GA the best chance of being seen, and size it accordingly.

Most things are either read left to right, or top to bottom. The easiest way to lay things out neatly are by arranging text and figures in panels, which could be connected by an arrow or numbering system. We’ve covered this in detail for scientific posters , and luckily the same principles apply.

Negative space

No, this isn’t astronaut terminology. Negative space just means space on your GA that’s not filled with stuff. It's a resting spot for the eyes.

Step 3: Designing

This is the most important part. This is what first grabs the reader’s attention when they start scrolling through Twitter, still half-asleep, while they eat breakfast. It should be big, bold, and capable of landing a solid impression. One glance should give your topic away. So, naturally, this isn’t the place to put Figure 3.1A of your manuscript!

The reason we’re choosing your image first is because, unless you’re making your own from scratch, the image will determine which colours you can use for the rest of the GA. We’ll go into more detail in the next section.

You can outsource modifiable images legally through The Creative Commons Search Engine , and there are sites dedicated to this, including PixaBay and PNG Tree . For photos, check out Unsplash . Some sites may ask for accreditation, so make sure to follow individual guidelines.

Or maybe you’re keen on drawing everything from scratch? We’ve got handy tips for that too .

So now, what software will you use to produce your GA? We’ve previously covered our personal recommendations for free and paid illustration software , so check out what suits your skill level and/or budget!

If you’re using an image you found on the web, then this step is easy. You’re going to sample the colours from that image using the Eyedropper Tool . It exists in every design software (even Microsoft PowerPoint!). Doing this will keep a consistent palette of colours throughout your GA.

Choosing colours from scratch? It is great fun to go freestyle, but there are literally an infinite number of colours out there, so how do we choose the 3 to 5 that we need?

Simple. Search “infographic colour palette” in Google Images and find one that you like and that is appropriate to your theme.

Marine biology? Well then, you can’t go wrong with some shades of blue.

Plant ecologist? How about a couple of greens and a nice brown?

Once you’ve found a colour combination that you like, use the Eyedropper tool to sample them, and hey presto, you’ve got your palette.

Pro tip 1: You can even install an eyedropper tool on your web browser. ColorZilla is a good one for Google Chrome.

Pro tip 2: Adobe Colour Wheel is a nice way of getting complementary colours based on colour theory - don’t worry, it’s easy to use.

OK, background, we want something eye-catching, so that means a photo, right? Nope! A texture? Double nope. Anything too busy will make your text and graphics hard to read.

A solid colour is perfect . We can be a bit more adventurous than white, but let’s not get carried away: save the hot pink for your underwear drawer.

Have you ever stared at a blank Microsoft Word page for over an hour, just because you were busy choosing a font?

Good. Because font choice is incredibly important!

We’ve covered fonts in detail before , but in a nutshell, this is what you’ll need to consider:

You’ll need a font without serif, that is sans serif.

Not only does sans serif sound cool (hey, look at you speaking French), these fonts are easier to read and appear more modern. So it’s goodbye Mr. Times New Roman , hello Mrs. Arial .

Wait. Comic Sans is sans serif , does that mean you can use it? N O P E. Just don’t! Every time a scientist uses Comic Sans a graphic designer dies

What about font size? Well, it depends on how large you make your GA in your software. Here’s a guide. Make your GA full-screen on your computer monitor. Can you read the text from a metre or two back? If so, then your text is probably big enough.

Do you need a title? Not necessarily. You might not have enough space. But, if you think it’ll help your GA to be CLEAR and CONCISE, go for it. You have my blessing.

Key information

If your GA is shared and used by other people, then you want your audience to be able to find your work. Include the title of your paper, the names of the authors, the year of publication, the journal, the DOI, and maybe even a QR code !

If you are a Microsoft aficionado, you can use PowerPoint to make your GA - just be aware that it has its limits. If you fancy your design skills and have time to invest in the steep learning curve, use Affinity Designer, Adobe Illustrator or Indesign. But if you want something more user-friendly (and free!) then check out Canva .

Step 4: Getting ready to release your GA into the wild

Congratulations on putting together your masterpiece. This is new territory, so you should be proud. But what’s next?

Take a break and come back to your GA with fresh eyes. Note what your eyes are drawn to first. Is this the first thing you want your audience to see? If so, then you’ve planned your GA well.

Do the elements of your GA align well? Good alignment will give your GA a professional look, and it’ll keep my Obsessive-Compulsive Disorder under control too, so thanks.

Get some feedback

Different people interpret images, symbols and icons differently. So something you think is obvious might not be to others. Remember the first part of our unbreakable rule? ‘CLEAR’.

Get feedback from people within your target audience. Your friends, if you’re targeting the public, and your colleagues if you’re targeting other academics. Even if this is the case, your friends are a good tool here too. If they can understand it, then you’ve done your job well.

Colour profile

If you designed your poster with professional software, you’ll have the ability to control the colour profile. Nothing complicated, there are two options: RGB and CMYK. The first one is for digital use, and the second one is for printing — pick the first one. That’s all you need to know.

Saving the file

Always keep your source file, in case you need to edit it later. But save your output as a .PNG (this is best for screens). If this isn’t available a .JPEG is good too.

Posting to social media

When posting on social media don’t forget to include the URL link pointing to the article’s page. This will not only help drive traffic to your paper but will also make your social media post visible by the Altmetric algorithm. If you don’t know what Altmetric is…let us fill you in, check out our awesome infographic.

Include any relevant hashtags in your post, and tag your co-authors. You should mention the journal, your institute and funding bodies too. This is not only good practice but could lead to a powerful re-tweet by an account with a large following. Garnish your post with some emojis and serve.

And that's the whole recipe!

Main take-aways

A graphical abstract is a visual summary of your work. Not a recycled Figure 3.1A!

Plan your design around your desired target audience.

Less is more! Recite after me. Your GA will be CLEAR and CONCISE.

Haven’t got the time to make one yourself?

No worries, we’re here to help!

At Animate Your Science we help researchers from all around the world stand out and have an impact. And an eye-catching, show-stopping graphical abstract is exactly what you’ll need to get started!

Our team of science communicators and designers can turn your research into an infographic or animation that will turn heads. Check out our gallery to find a style that suits you!

Explore how we can help you to unleash your impact by contacting us today !

Dr Juan Miguel Balbin

Dr Tullio Rossi

#graphicalabstract #Twitter #infographic

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Best examples of graphical abstracts

What’s better: graphical abstracts or video abstracts?

• Correspondence
• Open access
• Published: 11 April 2008

Graphical presentation of diagnostic information

• Penny F Whiting 1 ,
• Jonathan AC Sterne 1 ,
• Marie E Westwood 2 ,
• Lucas M Bachmann 3 ,
• Roger Harbord 1 ,
• Matthias Egger 4 &
• Jonathan J Deeks 5  

BMC Medical Research Methodology volume  8 , Article number:  20 ( 2008 ) Cite this article

21k Accesses

101 Citations

6 Altmetric

Metrics details

Graphical displays of results allow researchers to summarise and communicate the key findings of their study. Diagnostic information should be presented in an easily interpretable way, which conveys both test characteristics (diagnostic accuracy) and the potential for use in clinical practice (predictive value).

We discuss the types of graphical display commonly encountered in primary diagnostic accuracy studies and systematic reviews of such studies, and systematically review the use of graphical displays in recent diagnostic primary studies and systematic reviews.

We identified 57 primary studies and 49 systematic reviews. Fifty-six percent of primary studies and 53% of systematic reviews used graphical displays to present results. Dot-plot or box-and- whisker plots were the most commonly used graph in primary studies and were included in 22 (39%) studies. ROC plots were the most common type of plot included in systematic reviews and were included in 22 (45%) reviews. One primary study and five systematic reviews included a probability-modifying plot.

Graphical displays are currently underused in primary diagnostic accuracy studies and systematic reviews of such studies. Diagnostic accuracy studies need to include multiple types of graphic in order to provide both a detailed overview of the results (diagnostic accuracy) and to communicate information that can be used to inform clinical practice (predictive value). Work is required to improve graphical displays, to better communicate the utility of a test in clinical practice and the implications of test results for individual patients.

Peer Review reports

Readers of a research report evaluating a diagnostic test may wish to assess the test's characteristics (diagnostic accuracy) or evaluate the impact that its use has on diagnostic decisions (predictive value) for individual patients. Graphical displays of results of test accuracy studies allow researchers to summarise and communicate the key findings of their study. We discuss the types of graphical display commonly encountered in primary diagnostic accuracy studies and systematic reviews of such studies, and systematically review the use of graphical displays in recent diagnostic systematic reviews and primary studies. Table 1 defines the various measures of diagnostic accuracy used.

Types of graphical display

Primary studies.

Figure 1 illustrates four types of graphical display commonly used to present data on diagnostic accuracy for primary diagnostic accuracy studies. We used data from a study of the biochemical tumour marker CA-19-9 antigen to diagnose pancreatic cancer to construct these graphs [ 1 ].

Example graphical displays for primary study data . a. Dot plot. b. Box-and-whisker plot. c. ROC Plot. d. Flow diagram.

Dot plots (Figure 1a ) and Box-and-whisker plots (Figure 1b )

Dot plots are used for test results that take many values, and display the distribution of results in patients with and without the target condition. Box and whisker plots summarise these distributions: the central box covers the interquartile range with the median indicated by the line within the box. The whiskers extend either to the minimum and maximum values or to the most extreme values within 1.5 interquartile ranges of the quartiles, in which case more extreme values are plotted individually [ 2 ]. Sometimes an indication of the threshold used to define a positive test result is included, for example by adding a horizontal line or shading at the relevant point. Such plots can be used to clearly summarise a large volume of data, but are only able to display differences in the distribution of test values between patients with and without the target condition; they do not directly display the diagnostic performance of the test.

Although the CA-19-9 antigen test to diagnose pancreatic cancer (used to construct Figure 1 ) is an example of continuous data, it is also possible to construct similar graphs for categorical test results providing that the number of categories is reasonably large. Alternatively, for smaller numbers of categories, similar information can be conveyed using paired bar charts/histograms. Paired histograms show the distribution of test results in patients with the target condition above the x-axis and the distribution in patients without the target condition below the x-axis. These types of graphical display are less commonly used. It is not possible to construct any of these graphs for truly dichotomous test results. However, truly dichotomous tests rarely occur in practice. Examples of dichotomous tests include dipstick tests that change colour if the target condition is said to be present (although these are based on an underlying implicit threshold) or the presence/absence of certain clinical symptoms.

Receiver operating characteristic (ROC) plot (Figure 1c )

ROC plots show values of sensitivity and specificity at all of the possible thresholds that could be used to define a positive test result [ 3 ]. Typically, sensitivity (true positive rate) is plotted against 1-specificity (false positive rate): each point represents a different threshold in the same group of patients. Stepped lines are used for continuous test results while sloping lines are used for ordered categories. ROC curves may be derived directly from the observed sensitivity and specificity corresponding to different test thresholds, or by fitting curves based on parametric [ 4 ], semi-parametric [ 5 , 6 ], or non-parametric methods [ 7 ]. The area under the ROC curve (AUC) is a summary of diagnostic performance, and takes values between 0.5 and 1. The more accurate the test, the more closely the curve approaches the top left hand corner of the graph (AUC = 1). A test that provides no diagnostic information (AUC = 0.5) will produce a straight line from the bottom left to the top right. ROC curves may be restricted to a range of sensitivities or specificities of clinical interest.

ROC plots show how estimated sensitivity and specificity vary according to the threshold chosen, and can be used to identify suitable thresholds for clinical practice if the points on the curve are labelled with the corresponding threshold as in Figure 1c , which shows for example that the sensitivity and specificity corresponding to a threshold of 39.3 are 74% and 90%, respectively. Confidence intervals can be added to indicate the uncertainty in estimates of test performance at each point. ROC plots also allow comparison of the performance of several tests independently of choice of threshold, by plotting data sets for multiple tests in the same ROC space. However, they are thought to be difficult to interpret as they describe the characteristics of the test in a way which does not relate directly to its usefulness in clinical practice; research has shown that ROC plots are generally poorly understood by clinicians [ 8 ].

Flow charts (Figure 1d )

These depict the flow of patients through the study: for example how many patients were eligible, how many entered the study, how many of these had the target condition, and the numbers testing positive and negative. Such charts require categorisation of test results, for example as "positive" and "negative". Although flow charts do not directly present diagnostic accuracy data, addition of percentages to the test result boxes (as in Figure 1d ) can be used to report test sensitivity (68/90 = 76%) and specificity (46/51 = 90%). Charts that first separate individuals according to test result before classification by disease status may similarly be used to depict positive and negative predictive values. The STARD (standards for reporting of diagnostic accuracy) statement, an initiative to improve the reporting of diagnostic test accuracy studies similar to the CONSORT statement for clinical trials, recommends the inclusion of a flow diagram in all reports of primary diagnostic accuracy studies [ 9 ]. This should illustrate the design of the study and provide information on the numbers of participants at each stage of the study as well as the results of the study. The example flow chart in Figure 1d is not a full STARD flow diagram as we do not have data on numbers of withdrawals or uninterpretable results from this study. It does, however, show the design (diagnostic case-control) and results of the study.

Systematic reviews

Figure 2 illustrates two graphical displays commonly used to present data on diagnostic accuracy in diagnostic systematic reviews. Data from a systematic review of dipstick tests for urinary nitrite and leukocyte esterase to diagnose urinary tract infections were used to construct these graphs [ 10 ].

Example graphs for systematic review data . a. Paired forest plots of sensitivity and specificity for LE dipstick. b. ROC plot with SROC curves.

Forest plots (Figure 2a )

Forest plots are commonly used to display results of meta-analysis. They display results from the individual studies together with, optionally, a summary (pooled) estimate. Point estimates are shown as dots or squares (sometimes sized according to precision or sample size) and confidence intervals as horizontal lines [ 11 ]. The pooled estimate is displayed as a diamond whose centre represents the estimate and tips the confidence interval.

For diagnostic accuracy studies, measures of test performance (sensitivity, specificity, predictive values, likelihood ratios or diagnostic odds ratio) are plotted on the horizontal axis. Diagnostic test performance is often described by pairs of summary statistics (e.g. sensitivity and specificity; positive and negative likelihood ratios), and these are depicted side-by-side. Between-study heterogeneity can readily be assessed by visual examination. Results may be sorted by one of a pair of test performance measures, usually that which is most important to the clinical application of the test. A disadvantage of paired forest plots is that they do not directly display the inverse association between the two measures that commonly results from variations in threshold between studies.

ROC plots and summary ROC (SROC) curves (Figure 2b )

ROC plots can be used to present the results of diagnostic systematic reviews, but differ from those used in primary studies as each point typically represents a separate study or data set within a study (individual studies may contribute more than one point). A summary ROC (SROC) curve can be estimated using one of several methods [ 12 – 15 ] and quantifies test accuracy and the association between sensitivity and specificity based on differences between studies. As with forest plots, ROC plots provide an overview of the results of all included studies. However, unless there are very few studies, it is not feasible to display confidence intervals as the plot would become cluttered. Results for several tests can be displayed on the same plot, facilitating test comparisons. It is also possible to display pooled estimates of sensitivity and specificity together with associated confidence intervals or prediction regions. ROC plots may also be used to investigate possible explanations for differences in estimates of accuracy between studies, for example those arising from differences in study quality. Figure 3 shows results for a recent review that we conducted on the accuracy of magnetic resonance imaging (MRI) for the diagnosis of multiple sclerosis (MS) [ 16 ]. By using different symbols to illustrate studies that did (diagnostic cohort studies) and did not (other study designs) include an appropriate patient spectrum we were able to show that studies that included an inappropriate patient spectrum grossly overestimated both sensitivity and specificity.

Sensitivity plotted against specificity, separately for cohort studies and for studies of other designs for MRI for diagnosis of multiple sclerosis.

Other plots

Various other graphical methods have been developed to display the results of systematic reviews and meta-analyses [ 17 , 18 ]. Although not generally developed specifically for diagnostic test reviews these can be adapted to display the results of such reviews. Funnel plots [ 19 ] and Galbraith plots [ 20 ] are often used to assess evidence for publication bias or small study effects in systematic reviews of the effects of medical interventions assessed in randomized controlled trials. However, their application to systematic reviews of diagnostic test accuracy studies is problematic [ 20 ]. Diagnostic odds ratios are typically far from 1, and it has been shown that, for data of this type, sampling variation can lead to artefactual associations between log odds ratios and their standard errors [ 21 ]. It is therefore recommended that the effective sample size funnel plot be used in reviews of test accuracy studies [ 20 ].

Predictive value

A number of graphical displays aim to put results of diagnostic test evaluations into clinical context, based either on primary studies or systematic reviews. Two graphical displays commonly used for this purpose are the likelihood ratio nomogram (Figure 4a ) and the probability-modifying plot (Figure 4b ). Each allows the reader to estimate the post-test probability of the target condition in an individual patient, based on a selected pre-test probability. To use the likelihood ratio nomogram, the reader needs an estimate of the likelihood ratios for the test. He then draws a line through the appropriate likelihood ratio on the central axis, intersecting the selected pre-test probability, to derive the post-test probability of disease. The probability-modifying plot depicts separate curves for positive and negative test results. The reader draws a vertical line from the selected pre-test probability to the appropriate likelihood ratio line and then reads the post-test probability off the vertical scale. Both graph types are based on a single estimate of test accuracy (likelihood ratio), although it is possible to plot separate curves on the probability-modifying plot or lines on the nomogram to depict confidence intervals around the estimated likelihood ratios. Each assumes constant likelihood ratios across the range of pre-test probabilities. However, this assumption may be violated in practice [ 22 ], because populations in which the test is used may have different spectrums of disease to those in which estimates of test accuracy were derived.

Example graphs for interpreting diagnostic study result . a. Likelihood ratio nomogram. b. Probability modifying plot.

Use of graphical displays in the literature

We systematically reviewed how graphical displays are currently incorporated in studies of test performance. We included primary diagnostic accuracy studies published in 2004, identified by hand searching 12 journals (Table 2 ), and diagnostic systematic reviews published in 2003, identified from DARE (Database of Abstracts of Reviews of Effects) [ 23 ]. Searches were conducted in 2005 and so these years were the most complete available years for searching (there is a delay in adding studies to DARE). Diagnostic accuracy studies were studies that provided data on the sensitivity and specificity of a diagnostic test and that focused on diagnostic (whether the patient had the condition of interest) rather than prognostic (disease severity/risk prediction) questions. Journals were selected to provide a mixture of the major general medical and specialty journals. We particularly aimed to select journals that clinicians read. We extracted data on the different graphical displays used to summarise information about test performance, defined as any graphical method of summarising data on diagnostic accuracy or the predictive value of a test (Table 1 ).

We located 56 primary studies and 49 systematic reviews (Web Appendix). Fifty-seven percent of primary studies and 53% of systematic reviews used graphical displays to present results. In publications using graphics, the number of graphs per publication ranged from 1 to 51 (median 2, IQR 1 to 3 for primary studies and median 4, IQR 2 to 7 for systematic reviews). Table 3 summarises the categories of tests evaluated in the primary studies and systematic reviews. None of the tests evaluated in any of the primary studies were truly dichotomous: they all gave continuous or categorical results. Three of the eight systematic reviews that assessed clinical examination looked at whether a variety of signs or symptoms were present or absent: these can be considered as truly dichotomous tests. All other reviews evaluated continuous or categorical tests.

Dot-plots or box-and-whisker plots were the most commonly used graphic and were included in 22 (39%) studies. Generally the plots showed individual test results separately for patients with and without the target condition, with four including an indication of the threshold used to define a positive test result. Three studies included both a dot plot and a box-and-whisker plot on the same figure. Other variations included separate plots for different patient subgroups, different symbols to indicate different stages of disease, or separate plots for different tests. The majority of studies using these types of plots were of laboratory tests. An ROC curve was displayed in 15 (26%) studies. All of these plotted full ROC curves; only two provided any indication of the thresholds corresponding to one or more of the points. Thirteen studies included separate ROC curves for different tests, either on the same plot (10 studies) or on separate plots (3 studies). Five studies included separate ROC plots for different patient subgroups. Although all the primary studies were published in 2004, after the publication of the STARD guidelines, only one included a STARD flow diagram.

ROC plots were included in 22 (45%) reviews. Twenty showed individual study estimates of sensitivity and specificity, 14 fitted SROC curves, and two displayed a summary point. One study, which did not fit an SROC curve, added a box and whisker plot to each axis to show the distributions of sensitivity and specificity. One study plotted only summary estimates of sensitivity and specificity in ROC space, with no SROC curves. Some reviews included separate plots for different tests, for different patient subgroups, or for different thresholds used to define a positive test result.

Ten reviews (20%) used forest plots to display individual study results. One study provided a plot of diagnostic odds ratios, while all others displayed paired plots of sensitivity and specificity (8 reviews), positive and negative likelihood ratios (3 reviews), or positive and negative predictive values (1 review). Several studies displayed more than one set of forest plots, including plots for more than one summary measure, for different stages of diagnosis, different test thresholds or for different tests. One study included a forest plot of summary data only, showing how pooled estimates of positive and negative likelihood ratios varied for different patient subgroups.

None of the studies included a likelihood ratio nomogram. One primary study and five systematic reviews included a probability-modifying plot.

Research in the area of cognitive psychology suggests that sensitivity and specificity are generally poorly understood by doctors [ 8 , 24 ] and are often confused with predictive values [ 8 , 25 , 26 ]. Doctors tend to overestimate the impact of a positive test result on the probability of disease [ 27 , 28 ] and this overestimation increases with decreasing pre-test probabilities of disease [ 29 ]. This research suggests that the most informative measures for doctors may be estimates of the post-test probability of disease (predictive value), which can be presented as a range corresponding to different pre-test probabilities. However, graphical displays that facilitate the derivation of post-test probabilities, such as likelihood ratio nomograms, are usually based on summary estimates of test characteristics (positive and negative likelihood ratios) without allowing for the precision of the estimate, or its applicability to a given population. Use of summary estimates in this way is questionable in the context of reviews of diagnostic accuracy studies, which typically find substantial between-study heterogeneity [ 30 ]. It is particularly problematic if the summary estimate is the only information conveyed in a graphic and the graphic is taken as the key message of the paper.

The inclusion of some form of graphical presentation of test accuracy data has a number of advantages compared to not using such displays. It allows fuller reporting of results, for example (S)ROC plots can display results for multiple thresholds whereas reporting test accuracy results in a text or table generally requires the selection of one or more thresholds. In addition, (S)ROC plots depict the trade-off between sensitivity and specificity at different thresholds. Use of such displays also have the advantage of presenting all of the results of a primary study or systematic review without the need for selected analyses, which may be biased depending on the analyses selected. The inclusion of graphical displays, such as SROC plots or forest plots, in systematic reviews of test accuracy studies allows a visual assessment of heterogeneity between studies by showing the results from each individual study included in the review. There is also a suggestion that graphical displays may be easier to interpret than text or tabular summaries of the same data.

Diagnostic accuracy studies will usually need to include more than one graphic in order both to provide a detailed description of results (diagnostic accuracy) and to communicate appropriate summary measures that can be used to inform clinical practice (predictive value); the more detailed graphic provides context for the interpretation of summary measures. Further work is required to improve on existing graphical displays. The starting point for this should be further evaluation of the types of graphical display most helpful to assessing the utility of a test in clinical practice and the implications of test results for individual patients.

We hope that this paper will contribute to an increase in the use and quality of graphical displays in diagnostic accuracy studies and systematic reviews of these studies. To achieve this, journal guidelines and the STARD statement need to encourage the use of graphs in reports of test accuracy. Currently, journal guidelines say very little about this issue. A brief review of the instructions for authors from a selection of leading medical journals ( Annals of Internal Medicine , BMJ , Clinical Chemistry , JAMA , Lancet , New England Journal of Medicine ) found that these only provide formatting guidelines rather than discussing when and what type of graphical displays should be used, although all except the New England Journal of Medicine recommend that the STARD guidelines be followed and include references to the STARD flow diagram. STARD itself does not comment on how graphical displays should be used to convey results of test accuracy studies other than to recommend the inclusion of a flow diagram and to provide an illustration of a dot-plot as a suggestion for how individual study results may be displayed. Guidelines on the type of graphical displays that should be included in reports of test accuracy studies could be considered when STARD is next updated, and should be considered by journals in their instructions for authors.

Our review suggests that graphical displays are currently underused in primary diagnostic accuracy studies and systematic reviews of such studies. Graphical displays of diagnostic accuracy data should provide an easily interpretable and accurate representation of study results, conveying both diagnostic accuracy and predictive value. This is not usually possible in a single graphic: the type of information presented in the most commonly used graphs does not directly allow clinicians to assess the implications of test results for an individual patient.

Web Appendix: Studies included in the review

A. primary studies.

1. Arvanitakis M, Delhaye M, De Maertelaere V, Bali M, Winant C, Coppens E, et al. Computed tomography and magnetic resonance imaging in the assessment of acute pancreatitis. Gastroenterology 2004;126:715–723.

2. Baldas V, Tommasini A, Santon D, Not T, Gerarduzzi T, Clarich G, et al. Testing for Anti-Human Transglutaminase Antibodies in Saliva Is Not Useful for Diagnosis of Celiac Disease. Clin Chem 2004;50:216–219.

3. Banks E, Reeves G, Beral V, Bull D, Crossley B, Simmonds M, et al. Influence of personal characteristics of individual women on sensitivity and specificity of mammography in the Million Women Study: cohort study. BMJ 2004;329:477.

4. Baschat AA, Guclu S, Kush ML, Gembruch U, Weiner CP, Harman CR. Venous Doppler in the prediction of acid-base status of growth-restricted fetuses with elevated placental blood flow resistance. Am J Obstet Gynecol 2004;191:277–284.

5. Biel SS, Nitsche A, Kurth A, Siegert W, Ozel M, Gelderblom HR. Detection of Human Polyomaviruses in Urine from Bone Marrow Transplant Patients: Comparison of Electron Microscopy with PCR. Clin Chem 2004;50:306–312.

6. Bluemke DA, Gatsonis CA, Chen MH, DeAngelis GA, DeBruhl N, Harms S, et al. Magnetic Resonance Imaging of the Breast Prior to Biopsy. JAMA 2004;292:2735–2742.

7. Brugge WR, Lewandrowski K, Lee-Lewandrowski E, Centeno BA, Szydlo T, Regan S, et al. Diagnosis of pancreatic cystic neoplasms: a report of the cooperative pancreatic cyst study. Gastroenterology 2004;126:1330–1336.

8. Bulterys M, Jamieson DJ, O'Sullivan MJ, Cohen MH, Maupin R, Nesheim S, et al. Rapid HIV-1 Testing During Labor: A Multicenter Study. JAMA 2004;292:219–223.

9. Carnevale V, Dionisi S, Nofroni I, Romagnoli E, Paglia F, De Geronimo S, et al. Potential Clinical Utility of a New IRMA for Parathyroid Hormone in Postmenopausal Patients with Primary Hyperparathyroidism. Clin Chem 2004;50:626–631.

10. Chye SM, Lin SR, Chen YL, Chung LY, Yen CM. Immuno-PCR for Detection of Antigen to Angiostrongylus cantonensis Circulating Fifth-Stage Worms. Clin Chem 2004;50:51–57.

11. Cotton PB, Durkalski VL, Pineau BC, Palesch YY, Mauldin PD, Hoffman B, et al. Computed Tomographic Colonography (Virtual Colonoscopy): A Multicenter Comparison With Standard Colonoscopy for Detection of Colorectal Neoplasia. JAMA 2004;291:1713–1719.

12. DeWitt J, Devereaux B, Chriswell M, McGreevy K, Howard T, Imperiale TF, et al. Comparison of Endoscopic Ultrasonography and Multidetector Computed Tomography for Detecting and Staging Pancreatic Cancer. Ann Intern Med 2004;141:753–763.

13. Esteban A, Fernandez-Segoviano P, Frutos-Vivar F, Aramburu JA, Najera L, Ferguson ND, et al. Comparison of Clinical Criteria for the Acute Respiratory Distress Syndrome with Autopsy Findings. Ann Intern Med 2004;141:440–445.

14. Foxman EF, Jarolim P. Use of the Fetal Fibronectin Test in Decisions to Admit to Hospital for Preterm Labor. Clin Chem 2004;50:663–665.

15. Gibot S, Cravoisy A, Levy B, Bene MC, Faure G, Bollaert PE. Soluble Triggering Receptor Expressed on Myeloid Cells and the Diagnosis of Pneumonia. NEJM 2004;350:451–458.

16. Greenough A, Thomas M, Dimitriou G, Williams O, Johnson A, Limb E, et al. Prediction of outcome from the chest radiograph appearance on day 7 of very prematurely born infants. Eur J Pediatr 2004;163:14–18.

17. Grenache DG, Hankins K, Parvin CA, Gronowski AM. Cervicovaginal Interleukin-6, Tumor Necrosis Factor-, and Interleukin-2 Receptor as Markers of Preterm Delivery. Clin Chem 2004;50:1839–1842.

18. Hammerer-Lercher A, Ludwig W, Falkensammer G, Muller S, Neubauer E, Puschendorf B, et al. Natriuretic Peptides as Markers of Mild Forms of Left Ventricular Dysfunction: Effects of Assays on Diagnostic Performance of Markers. Clin Chem 2004;50:1174–1183.

19. Hattori H, Kujiraoka T, Egashira T, Saito E, Fujioka T, Takahashi S, et al. Association of Coronary Heart Disease with Pre-[beta]-HDL Concentrations in Japanese Men. Clin Chem 2004;50:589–595.

20. Herget-Rosenthal S, Poppen D, Husing J, Marggraf G, Pietruck F, Jakob HG, et al. Prognostic Value of Tubular Proteinuria and Enzymuria in Nonoliguric Acute Tubular Necrosis. Clin Chem 2004;50:552–558.

21. Hetzel M, Hetzel J, Arslandemir C, Nussle K, Schirrmeister H. Reliability of symptoms to determine use of bone scans to identify bone metastases in lung cancer: prospective study. BMJ 2004;328:1051–1052.

22. Hift RJ, Davidson BP, van der Hooft C, Meissner DM, Meissner PN. Plasma Fluorescence Scanning and Fecal Porphyrin Analysis for the Diagnosis of Variegate Porphyria: Precise Determination of Sensitivity and Specificity with Detection of Protoporphyrinogen Oxidase Mutations as a Reference Standard. Clin Chem 2004;50:915–923.

23. Hong KM, Najjar H, Hawley M, Press RD. Quantitative Real-Time PCR with Automated Sample Preparation for Diagnosis and Monitoring of Cytomegalovirus Infection in Bone Marrow Transplant Patients. Clin Chem 2004;50:846–856.

24. Imperiale TF, Ransohoff DF, Itzkowitz SH, Turnbull BA, Ross ME, the Colorectal Cancer Study Group. Fecal DNA versus Fecal Occult Blood for Colorectal-Cancer Screening in an Average-Risk Population. NEJM 2004;351:2704–2714.

25. Jung K, Reiche J, Boehme A, Stephan C, Loening SA, Schnorr D, et al. Analysis of Subforms of Free Prostate-Specific Antigen in Serum by Two-Dimensional Gel Electrophoresis: Potential to Improve Diagnosis of Prostate Cancer. Clin Chem 2004;50:2292–2301.

26. Kageyama S, Isono T, Iwaki H, Wakabayashi Y, Okada Y, Kontani K, et al. Identification by Proteomic Analysis of Calreticulin as a Marker for Bladder Cancer and Evaluation of the Diagnostic Accuracy of Its Detection in Urine. Clin Chem 2004;50:857–866.

27. Kiesslich R, Burg J, Vieth M, Gnaendiger J, Enders M, Delaney P, et al. Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 2004;127:706–713.

28. Kramer H, van Putten JWG, Post WJ, van Dullemen HM, Bongaerts AHH, Pruim J, et al. Oesophageal endoscopic ultrasound with fine needle aspiration improves and simplifies the staging of lung cancer. Thorax 2004;59:596–601.

29. Kriege M, Brekelmans CTM, Boetes C, Besnard PE, Zonderland HM, Obdeijn IM, et al. Efficacy of MRI and Mammography for Breast-Cancer Screening in Women with a Familial or Genetic Predisposition. NEJM 2004;351:427–437.

30. Lacey JM, Minutti CZ, Magera MJ, Tauscher AL, Casetta B, McCann M, et al. Improved Specificity of Newborn Screening for Congenital Adrenal Hyperplasia by Second-Tier Steroid Profiling Using Tandem Mass Spectrometry. Clin Chem 2004;50:621–625.

31. Lennon PV, Wingerchuk DM, Kryzer TJ, Pittock SJ, Lucchinetti CF, Fujihara K, et al. A serum autoantibody marker of neuromyelitis optica: distinction from multiple sclerosis. Lancet 2004;364:2106–2112.

32. Leung Sf, Tam JS, Chan ATC, Zee B, Chan LYS, Huang DP, et al. Improved Accuracy of Detection of Nasopharyngeal Carcinoma by Combined Application of Circulating Epstein-Barr Virus DNA and Anti-Epstein-Barr Viral Capsid Antigen IgA Antibody. Clin Chem 2004;50:339–345.

33. Leung GM, Rainer TH, Lau FL, Wong IOL, Tong A, Wong TW, et al. A Clinical Prediction Rule for Diagnosing Severe Acute Respiratory Syndrome in the Emergency Department. Ann Intern Med 2004;141:333–342.

35. Liebeschuetz S, Bamber S, Ewer K, Deeks J, Pathan AA, Lalvani A. Diagnosis of tuberculosis in South African children with a T-cell-based assay: a prospective cohort study. Lancet 2004;364:2196–2203.

36. Llorente MJ, Sebastián M, Fernández-Aceñero MJ, Prieto G, Villanueva S. IgA Antibodies against Tissue Transglutaminase in the Diagnosis of Celiac Disease: Concordance with Intestinal Biopsy in Children and Adults. Clin Chem 2004;50:451–453.

36. McLean RG, Carolan M, Bui C, Arvela O, Ford JC, Chew M, et al. Comparison of new clinical and scintigraphic algorithms for the diagnosis of pulmonary embolism. Br J Radiol 2004;77:372–376.

37. Miglioretti DL, Rutter CM, Geller BM, Cutter G, Barlow WE, Rosenberg R, et al. Effect of Breast Augmentation on the Accuracy of Mammography and Cancer Characteristics. JAMA 2004;291:442–450.

38. Mikolajczyk SD, Catalona WJ, Evans CL, Linton HJ, Millar LS, Marker KM, et al. Proenzyme Forms of Prostate-Specific Antigen in Serum Improve the Detection of Prostate Cancer. Clin Chem 2004;50:1017–1025.

39. Minguez M, Herreros B, Sanchiz V, Hernandez V, Almela P, AnonAnon R, et al. Predictive value of the balloon expulsion test for excluding the diagnosis of pelvic floor dyssynergia in constipation. Gastroenterology 2004;126:57–62.

40. Palomaki GE, Neveux LM, Knight GJ, Haddow JE, Pandian R. Maternal Serum Invasive Trophoblast Antigen (Hyperglycosylated hCG) as a Screening Marker for Down Syndrome during the Second Trimester. Clin Chem 2004;50:1804–1808.

41. Palomaki GE, Knight GJ, Roberson MM, Cunningham GC, Lee JE, Strom CM, et al. Invasive Trophoblast Antigen (Hyperglycosylated Human Chorionic Gonadotropin) in Second-Trimester Maternal Urine as a Marker for Down Syndrome: Preliminary Results of an Observational Study on Fresh Samples. Clin Chem 2004;50:182–189.

42. Papadopoulos MC, Abel PM, Agranoff D, Stich A, Tarelli E, Bell PBA, et al. A novel and accurate diagnostic test for human African trypanosomiasis. Lancet 2004;363:1358–1363.

43. Parsi MA, Shen B, Achkar JP, Remzi FF, Goldblum JR, Boone J, et al. Fecal lactoferrin for diagnosis of symptomatic patients with ileal pouch-anal anastomosis. Gastroenterology 2004;126:1280–1286.

44. Raad I, Hanna HA, Alakech B, Chatzinikolaou I, Johnson MM, Tarrand J. Differential Time to Positivity: A Useful Method for Diagnosing Catheter-Related Bloodstream Infections. Ann Intern Med 2004;140:18–25.

45. Rathbun SW, Whitsett TL, Raskob GE. Negative D-dimer Result To Exclude Recurrent Deep Venous Thrombosis: A Management Trial. Ann Intern Med 2004;141:839–845.

46. Rietveld RP, Riet Gt, Bindels PJE, Sloos JH, van Weert HCPM. Predicting bacterial cause in infectious conjunctivitis: cohort study on informativeness of combinations of signs and symptoms. BMJ 2004;329:206–210.

47. Rosenberg WMC, Voelker M, Thiel R, Becka M, Burt A, Schuppan D, et al. Serum markers detect the presence of liver fibrosis: A cohort study. Gastroenterology 2004;127:1704–1713.

Schwertz E, Kahlenberg F, Sack U, Richter T, Stern M, Conrad K, et al. Serologic Assay Based on Gliadin-Related Nonapeptides as a Highly Sensitive and Specific Diagnostic Aid in Celiac Disease. Clin Chem 2004;50:2370–2375.

49. van Gelder RE, Nio CY, Florie J, Bartelsman JF, Snel P, de Jager SW, et al. Computed tomographic colonography compared with colonoscopy in patients at increased risk for colorectal cancer. Gastroenterology 2004;127:41–48.

50. Van Meensel B, Hiele M, Hoffman I, Vermeire S, Rutgeerts P, Geboes K, et al. Diagnostic Accuracy of Ten Second-Generation (Human) Tissue Transglutaminase Antibody Assays in Celiac Disease. Clin Chem 2004;50:2125–2135.

51. Vasbinder GB, Nelemans PJ, Kessels AGH, Kroon AA, Maki JH, Leiner T, et al. Accuracy of Computed Tomographic Angiography and Magnetic Resonance Angiography for Diagnosing Renal Artery Stenosis. Ann Intern Med 2004;141:674–682.

52. Vlahou A, Giannopoulos A, Gregory BW, Manousakas T, Kondylis FI, Wilson LL, et al. Protein Profiling in Urine for the Diagnosis of Bladder Cancer. Clin Chem 2004;50:1438–1441.

53. Warner E, Plewes DB, Hill KA, Causer PA, Zubovits JT, Jong RA, et al. Surveillance of BRCA1 and BRCA2 Mutation Carriers With Magnetic Resonance Imaging, Ultrasound, Mammography, and Clinical Breast Examination. JAMA 2004;292:1317–1325.

54. Wasmuth J-C, Grün B, Terjung B, Homrighausen, Spengler A, Spengler U. ROC Analysis Comparison of Three Assays for the Detection of Antibodies against Double-Stranded DNA in Serum for the Diagnosis of Systemic Lupus Erythematosus. Clin Chem 2004;50:2169–2171.

55. Wildi SM, Judson MA, Fraig M, Fickling WE, Schmulewitz N, Varadarajulu S, et al. Is endosonography guided fine needle aspiration (EUS-FNA) for sarcoidosis as good as we think? Thorax 2004;59:794–799.

56. Zehentner BK, Persing DH, Deme A, Toure P, Hawes SE, Brooks L, et al. Mammaglobin as a Novel Breast Cancer Biomarker: Multigene Reverse Transcription-PCR Assay and Sandwich ELISA. Clin Chem 2004;50:2069–2076.

b. Systematic reviews

1. Arbyn M, Schenck U, Ellison E, Hanselaar A. Metaanalysis of the accuracy of rapid prescreening relative to full screening of pap smears. Cancer Cytopathology 2003;99(1):9–16.

2. Austin MP, Lumley J. Antenatal screening for postnatal depression: a systematic review. Acta Psychiatr Scand 2003;107:10–17.

3. Babu AN, Kymes SM, Fryer SM. Eponyms and the diagnosis of aortic regurgitation: what says the evidence. Ann Intern Med 2003;138:736–742.

4. Bachmann LM, Kolb E, Koller MT, Steurer J, ter RG. Accuracy of Ottawa ankle rules to exclude fractures of the ankle and mid-foot: systematic review. BMJ 2003;326:417–419.

5. Bastian LA, Smith CM, Nanda K. Is this woman perimenopausal? JAMA 2003;289:895–902.

6. Bipat S, Glas AS, van d, V, Zwinderman AH, Bossuyt PM. Computed tomography and magnetic resonance imaging in staging of uterine cervical carcinoma: a systematic review. Gynecol Oncol 2003;91:59–66.

7. Boustani, M., Peterson, B., Hanson, L., Harris, R., and Lohr, K. N. Screening for dementia. 2003.

8. Cardarelli R, Lumicao TG. B-type natriuretic peptide: a review of its diagnostic, prognostic, and therapeutic monitoring value in heart failure for primary care physicians. J Am Board Fam Pract 2003;16:327–333.

9. Carnero-Pardo C. Systematic review of the value of positron emission tomography in the diagnosis of Alzheimer's disease. Rev Neurol 2003;37:860–870.

10. Chunilal SD, Eikelboom JW, Attia J, Miniati M, Panju AA, Simel DL. Does this patient have pulmonary embolism? JAMA 2003;290:2849–2858.

11. Dinnes J, Loveman E, McIntyre L, Waugh N. The effectiveness of diagnostic tests for the assessment of shoulder pain due to soft tissue disorders: a systematic review. Health Technol Assess 2003;7:1–178.

12. Farquhar C, Ekeroma A, Furness S, Arroll B. A systematic review of transvaginal ultrasonography, sonohysterography and hysteroscopy for the investigation of abnormal uterine bleeding in premenopausal women. Acta Obstet Gynecol Scand 2003;82:493–504.

13. Framarin, A. First-trimester prenatal screening for Down syndrome and other aneuploidies. Montreal, PQ, Canada. Agence d'Evaluation des Technologies et des Modes d'Intervention en Sante (AETMIS) 2003: 81.

14. Gilbert DL, Sethuraman G, Kotagal U, Buncher CR. Meta-analysis of EEG test performance shows wide variation among studies. Neurology 2003;60:564–570.

15. Glas AS, Roos D, Deutekom M, Zwinderman AH, Bossuyt PM, Kurth KH. Tumor markers in the diagnosis of primary bladder cancer: a systematic review. J Urol 2003;169:1975–1982.

16. Goerres GW, Mosna-Firlejczyk K, Steurer J, von Schulthess GK. Assessment of clinical utility of F-18-FDG PET in patients with head and neck cancer: a probability analysis. Eur J Nucl Med Mol Imaging 2003;30:562–571.

17. Goto M, Noguchi Y, Koyama H, Hira K, Shimbo T, Fukui T. Diagnostic value of adenosine deaminase in tuberculous pleural effusion: a meta-analysis. Ann Clin Biochem 2003;40:374–381.

18. Gould MK, Kuschner WG, Rydzak CE, Maclean CC, Demas AN, Chan JK, et al. Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer. Ann Intern Med 2003;139:879–892.

19. Grady, D., McDonald, K., Bischoff, K., Cabou, A., Chaput, L., Hoerster, K., Shahpar, C., Walsh, J., Sorrough, G., and Won, G. Results of systematic review of research on diagnosis and treatment of coronary heart disease in women. Rockville, MD, USA, Agency for Healthcare Research and Quality. 2003; 268

20. Gupta S, Bent S, Kohlwes J. Test characteristics of alpha-fetoprotein for detecting hepatocellular carcinoma in patients with hepatitis C. Ann Intern Med 2003;139:46–50.

21. Heffner JE, Highland K, Brown LK. A meta-analysis derivation of continuous likelihood ratios for diagnosing pleural fluid exudates. Am J Respir Crit Care Med 2003;167:1591–1599.

22. Hollingworth W, Nathens AB, Kanne JP, Crandall ML, Crummy TA, Wang MC, et al. The diagnostic accuracy of computed tomography angiography for traumatic or atherosclerotic lesions of the carotid and vertebral arteries: a systematic review. Eur J Radiol 2003;48:88–102.

23. Honest H, Bachmann LM, Coomarasamy A, Gupta JK, Kleijnen J, Khan KS. Accuracy of cervical transvaginal sonography in predicting preterm birth: a systematic review. Ultrasound Obstet Gynecol 2003;22:305–322.

24. Ioannidis JP, Lau J. F-18-FDG PET for the diagnosis and grading of soft-tissue sarcoma: a meta-analysis. J Nucl Med 2003;44:717–724.

25. Jackson JL, O'Malley PG, Kroenke K. Evaluation of acute knee pain in primary care. Ann Intern Med 2003;139:575–588.

26. Johnston R, V, Burrows E, Raulli A. Assessment of diagnostic tests to inform policy decisions-visual electrodiagnosis. Int J Technol Assess Health Care 2003;19:373–383.

27. Liberman M, Sampalis F, Mulder DS, Sampalis JS. Breast cancer diagnosis by scintimammography: a meta-analysis and review of the literature. Breast Cancer Res Treat 2003;80:115–126.

28. Makrydimas G, Sotiriadis A, Ioannidis JP. Screening performance of first-trimester nuchal translucency for major cardiac defects: a meta-analysis. Am J Obstet Gynecol 2003;189:1330–1335.

29. Neumayer L, Kennedy A. Imaging in appendicitis: a review with special emphasis on the treatment of women. Obstet Gynecol 2003;102:1404–1409.

30. Olaniyan OB. Validity of colposcopy in the diagnosis of early cervical neoplasia: a review. Afr J Reprod Health 2002;6:59–69.

31. Pai M, Flores LL, Pai N, Hubbard A, Riley LW, Colford JM. Diagnostic accuracy of nucleic acid amplification tests for tuberculous meningitis: a systematic review and meta-analysis. Lancet Infect Dis 2003;3:633–643.

32. Pasternack I, I, Malmivaara A, Tervahartiala P, Forsberg H, Vehmas T. Magnetic resonance imaging findings in respect to carpal tunnel syndrome. Scand J Work Environ Health 2003;29:189–196.

33. Pastor-Gomez J, Pulido-Rivas P, De Sola RG. Review of the literature on the value of magnetoencephalography in epilepsy. Rev Neurol 2003;37:951–996.

34. Patton LL. The effectiveness of community-based visual screening and utility of adjunctive diagnostic aids in the early detection of oral cancer. Eur J Cancer 2003;39:708–723.

35. Pirozzo S, Papinczak T, Glasziou P. Whispered voice test for screening for hearing impairment in adults and children: systematic review. BMJ 2003;327:967–970.

36. Rietveld RP, Van Weert HC, ter RG, Bindels PJ. Diagnostic impact of signs and symptoms in acute infectious conjunctivitis: systematic literature search. BMJ 2003;327:789.

37. Riley RD, Burchill SA, Abrams KR, Heney D, Lambert PC, Jones DR, et al. A systematic review and evaluation of tumour markers in paediatric oncology: Ewing's sarcoma and neuroblastoma. Health Technol Assess 2003;7:1–162.

38. Romagnuolo J, Bardou M, Rahme E, Joseph L, Reinhold C, Barkun AN. Magnetic resonance cholangiopancreatography: a meta-analysis of test performance in suspected biliary disease. Ann Intern Med 2003;139:547–557.

39. Rosado B, Menzies S, Harbauer A, Pehamberger H, Wolff K, Binder M, et al. Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch Dermatol 2003;139:361–367.

40. Rothman R, Owens T, Simel DL. Does this child have acute otitis media? JAMA 2003;290:1633–1640.

41. Scholten RJ, Opstelten W, van der Plas CG, Bijl D, Deville WL. Accuracy of physical diagnostic tests for assessing ruptures of the anterior cruciate ligament: a meta-analysis. J Fam Pract 2003;52:689–694.

42. Sotiriadis A, Makrydimas G, Ioannidis JP. Diagnostic performance of intracardiac echogenic foci for Down syndrome: a meta-analysis. Obstet Gynecol 2003;101:1009–1016.

43. Takata GS, Chan LS, Morphew T, Mangione-Smith R, Morton SC. Evidence assessment of the accuracy of methods of diagnosing middle ear effusion in children with otitis media with effusion. Pediatrics 2003;112:1379–1387.

44. Toloza EM, Harpole L, McCrory DC. Noninvasive staging of non-small cell lung cancer: a review of the current evidence. Chest 2003;123:137S-146S.

45. Toloza EM, Harpole L, Detterbeck F, McCrory DC. Invasive staging of non-small cell lung cancer: a review of the current evidence. Chest 2003;123:157S-166S.

46. Trowbridge RL, Rutkowski NK, Shojania KG. Does this patient have acute cholecystitis? JAMA 2003;289:80–86.

47. van Gelder JM. Computed tomographic angiography for detecting cerebral aneurysms: Implications of aneurysm size distribution for the sensitivity, specificity, and likelihood ratios. Neurosurgery 2003;53:597–605.

48. Watson LC, Pignone MP. Screening accuracy for late-life depression in primary care: a systematic review. J Fam Pract 2003;52:956–964.

49. Watson EJ, Templeton A, Russell I, Paavonen J, Mardh PA, Stary A. The accuracy and efficacy of screening tests for Chlamydia trachomatis: a systematic review. J Med Microbiol 2002;51:1021–1031.

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This work was supported by the MRC Health Services Research Collaboration. Jonathan Deeks is funded by a Senior Research Fellowship in Evidence Synthesis from the Department of Health.

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Graphic presentation is considered the preferred way of presentation of data over diagrammatic presentation as graphs are always more accurate and precise, whereas diagrams are generally used for the purpose of publicity and propaganda. Relationship between two variables can be studied by graphs. These can be drawn more easily than diagrams. Graphs are considered very useful for studying time series and frequency distribution.

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46 Presentation of data II – Graphical representation

Pa . Raajeswari

Graphical representation is the visual display of data using plots and charts. It is used in many academic and professional disciplines but most widely so in the fields of mathematics, medicine and sciences. Graphical representation helps to quantify, sort and present data in a method that is understandable to a large variety of audiences. A graph is the representation of data by using graphical symbols such as lines, bars, pie slices, dots etc. A graph does represent a numerical data in the form of a qualitative structure and provides important information.

Statistical surveys and experiments provides valuable information about numerical scores. For better understanding and making conclusions and interpretations, the data should be managed and organized in a systematic form.

Graphs also enable in studying both time series and frequency distribution as they give clear account and precise picture of problem. Above all graphs are also easy to understand and eye catching and can create a storing impact on memory.

General Principles of Graphic Representation:

There are some algebraic principles which apply to all types of graphic representation of data. In a graph there are two lines called coordinate axes. One is vertical known as Y axis and the other is horizontal called X axis. These two lines are perpendicular to each other. Where these two lines intersect each other is called ‘0’ or the Origin. On the X axis the distances right to the origin have positive value and distances left to the origin have negative value. On the Y axis distances above the origin have a positive value and below the origin have a negative value.

TYPES OF GRAPHICAL REPRESENTATON:

The various types of graphical representations of the data are

• Circle Graph
• Histogram and Frequency Polygon

1. Dot Plots

The dot plot is one of the most simplest ways of graphical representation of the statistical data. As the name itself suggests, a dot plot uses the dots. It is a graphic display which usually compares frequency within different categories. The dot plot is composed of dots that are to be plotted on a graph paper.

In the dot plot, every dot denotes a specific number of observations belonging to a data set. One dot usually represents one observation. These dots are to be marked in the form of a column for each category. In this way, the height of each column shows the corresponding frequency of some category. The dot plots are quite useful when there are small amount of data is given within the small number of categories.

2. Bar Graph

A bar graph is a very frequently used graph in statistics as well as in media. A bar graph is a type of graph which contains rectangles or rectangular bars. The lengths of these bars should be proportional to the numerical values represented by them. In bar graph, the bars may be plotted either horizontally or vertically. But a vertical bar graph (also known as column bar graph) is used more than a horizontal one.

A vertical bar graph is shown below:

Number of students went to different countries for study:

The rectangular bars are separated by some distance in order to distinguish them from one another. The bar graph shows comparison among the given categories.

Mostly, horizontal axis of the graph represents specific categories and vertical axis shows the discrete numerical values.

3.Line Graph

A line graph is a kind of graph which represents data in a way that a series of points are to be connected by segments of straight lines. In a line graph, the data points are plotted on a graph and they are joined together with straight line.

A   sample   line   graph   is    illustrated    in    the   following   diagram:

The line graphs are used in the science, statistics and media. Line graphs are very easy to create. These are quite popular in comparison with other graphs since they visualize characteristics revealing data trends very clearly. A line graph gives a clear visual comparison between two variables which are represented on X-axis and Y-axis.

4.Circle Graph

A circle graph is also known as a pie graph or pie chart. It is called so since it is similar to slice of a “pie”. A pie graph is defined as a graph which contains a circle which is divided into sectors. These sectors illustrate the numerical proportion of the data.

A pie chart are shown in the following diagram:

The arc lengths of the sectors, in pie chart, are proportional to the numerical value they represent.Circle graphs are quite commonly seen in mass media as well as in business world.

5. Histogram and Frequency Polygon

The histograms and frequency polygons are very common graphs in statistics. A histogram is defined as a graphical representation of the mutually exclusive events. A histogram is quite similar to the bar graph. Both are made up of rectangular bars. The difference is that there is no gap between any two bars in the histogram. The histogram is used to represent the continuous data.

A histogram may look like the following graph:

The frequency polygon is a type of graphical representation which gives us better understanding of the shape of given distribution. Frequency polygons serve almost the similar purpose as histograms do. But the frequency polygon is quite helpful for the purpose of comparing two or more sets of data. The frequency polygons are said to be the extension of the histogram. When the midpoints of tops of the rectangular bars are joined together, the frequency polygon is made.

Few   examples    of    graphical    representation    of    statistical    data    are    given    below:

Example 1: Draw a dot plot for the following data.

Solution: The pie graph of the above data is:

Methods to Represent a Frequency Distribution:

Generally four methods are used to represent a frequency distribution graphically. These are Histogram, Smoothed frequency graph and Ogive or Cumulative frequency graph and pie diagram.

1. Histogram:

Histogram is a non-cumulative frequency graph, it is drawn on a natural scale in which the representative frequencies of the different class of values are represented through vertical rectangles drawn closed to each other. Measure of central tendency, mode can be easily determined with the help of this graph.

How to draw a Histogram:

Represent the class intervals of the variables along the X axis and their frequencies along the Y-axis on natural scale.

Start X axis with the lower limit of the lowest class interval. When the lower limit happens to be a distant score from the origin give a break in the X-axis n to indicate that the vertical axis has been moved in for convenience.

Now draw rectangular bars in parallel to Y axis above each of the class intervals with class units as base: The areas of rectangles must be proportional to the frequencies of the corresponding classes.

In this graph we shall take class intervals in the X axis and frequencies in the Y axis. Before plotting the graph we have to convert the class into their exact limits.

Advantages of histogram:

1.  It is easy to draw and simple to understand.

2.  It helps us to understand the distribution easily and quickly.

3.  It is more precise than the polygene.

Limitations of histogram:

1.  It is not possible to plot more than one distribution on same axes as histogram.

2.  Comparison of more than one frequency distribution on the same axes is not possible.

3.  It is not possible to make it smooth.

Uses of histogram:

1.Represents the data in graphic form.

2.Provides the knowledge of how the scores in the group are distributed. Whether the scores are piled up at the lower or higher end of the distribution or are evenly and regularly distributed throughout the scale.

3.Frequency Polygon. The frequency polygon is a frequency graph which is drawn by joining the coordinating points of the mid-values of the class intervals and their corresponding fre-quencies.

How to draw a frequency polygon:

Draw a horizontal line at the bottom of graph paper named ‘OX’ axis. Mark off the exact limits of the class intervals along this axis. It is better to start with i. of lowest value. When the lowest score in the distribution is a large number we cannot show it graphically if we start with the origin. Therefore put a break in the X axis to indicate that the vertical axis has been moved in for convenience. Two additional points may be added to the two extreme ends.

Draw a vertical line through the extreme end of the horizontal axis known as OY axis. Along this line mark off the units to represent the frequencies of the class intervals. The scale should be chosen in such a way that it will make the largest frequency (height) of the polygon approximately 75 percent of the width of the figure.

Plot the points at a height proportional to the frequencies directly above the point on the horizontal axis representing the mid-point of each class interval.

After plotting all the points on the graph join these points by a series of short straight lines to form the frequency polygon. In order to complete the figure two additional intervals at the high end and low end of the distribution should be included. The frequency of these two intervals will be zero.

Illustration: No. 7.3:

Draw a frequency polygon from the following data:

Advantages of frequency polygon:

2.  It is possible to plot two distributions at a time on same axes.

3.  Comparison of two distributions can be made through frequency polygon.

4.  It is possible to make it smooth.

Limitations of frequency polygon:

1.  It is less precise.

2.  It is not accurate in terms of area the frequency upon each interval.

Uses of frequency polygon:

1. When two or more distributions are to be compared the frequency polygon is used.

2. It represents the data in graphic form.

3. It provides knowledge of how the scores in one or more group are distributed. Whether the scores are piled up at the lower or higher end of the distribution or are evenly and regularly distributed throughout the scale.

2. Smoothed Frequency Polygon:

When the sample is very small and the frequency distribution is irregular the polygon is very jig-jag. In order to wipe out the irregularities and “also get a better notion of how the figure might  look if the data were more numerous, the frequency polygon may be smoothed.”

In this process to adjust the frequencies we take a series of ‘moving’ or ‘running’ averages. To get an adjusted or smoothed frequency we add the frequency of a class interval with the two adjacent intervals, just below and above the class interval. Then the sum is divided by 3. When these adjusted frequencies are plotted against the class intervals on a graph we get a smoothed frequency polygon.

Illustration 7.4:

Draw a smoothed frequency polygon, of the data given in the illustration No. 7.3:

Here we have to first convert the class intervals into their exact limits. Then we have to determine the adjusted or smoothed frequencies.

3. Ogive or Cumulative Frequency Polygon:

Ogive is a cumulative frequency graphs drawn on natural scale to determine the values of certain factors like median, Quartile, Percentile etc. In these graphs the exact limits of the class intervals  are shown along the X-axis and the cumulative frequencies are shown along the Y-axis. Below are given the steps to draw an ogive.

Get the cumulative frequency by adding the frequencies cumulatively, from the lower end (to get a less than ogive) or from the upper end (to get a more than ogive).

Mark off the class intervals in the X-axis.

Represent the cumulative frequencies along the Y-axis beginning with zero at the base.

Put dots at each of the coordinating points of the upper limit and the corresponding frequencies.

Join all the dots with a line drawing smoothly. This will result in curve called ogive.

Illustration No. 7.5:

Draw an ogive from the data given below:

To plot this graph first we have to convert, the class intervals into their exact limits. Then we have to calculate the cumulative frequencies of the distribution.

Uses of Ogive:

1.  Ogive is useful to determine the number of students below and above a particular score.

2.  When the median as a measure of central tendency is wanted.

3.  When the quartiles, deciles and percentiles are wanted.

4.  By plotting the scores of two groups on a same scale we can compare both the groups.

4. The Pie Diagram:

Figure given below shows the distribution of elementary pupils by their academic achievement in a school. Of the total, 60% are high achievers, 25% middle achievers and 15% low achievers. The construction of this pie diagram is quite simple. There are 360 degree in the circle. Hence, 60% of 360′ or 216° are counted off as shown in the diagram; this sector represents the proportion of high achievers students.

Ninety degrees counted off for the middle achiever students (25%) and 54 degrees for low achiever students (15%). The pie-diagram is useful when one wishes to picture proportions of the total in a striking way. Numbers of degrees may be measured off “by eye” or more accurately with a protractor.

Uses of Pie diagram:

1.  Pie diagram is useful when one wants to picture proportions of the total in a striking way.

2.  When a population is stratified and each strata is to be presented as a percentage at that time pie diagram is used.

PURPOSE OF GRAPHICAL REPRESENTATION:

The purpose of graphical presentation of data is to provide a quick and easy-to-read picture of information that clearly shows what otherwise takes a great deal of explanation. The impact of graphical data is typically more pointed and memorable than paragraphs of written information

For example, a person making a presentation regarding sales in various states across the country establishes the point of the presentation to the audience more quickly by using a color-coded map rather than merely stating the sales figures for each state. Observers quickly determine which states are ahead and which are behind in sales, and they know where emphasis needs to be placed. Alternatively, when making a presentation on sales by age groups using a pie chart that divides the pie into various ages, the audience quickly sees the results of sales by age. This  means that the audience is more likely to retain that information than if the presenter simply reads the results aloud or puts it into writing.

GENERAL RULES DISPLAYING DATA

• Simpler is Better
• Graphs, Tables and charts can be used together
• Use clear Description, title and labels
• Provide a narrative Description of the highlights
• Don’t compare variables with different scales of magnitude.
• A Diagram must be attractive, well proportioned,neat and pleasing to the eyes.
• They should be geometrically Accurate
• Size of the diagram should be proportional to paper should not be too big or too small
• Different colors should be used to classify data’s.

ADVANTAGES:

• Acceptability: graphical report is acceptable to the busy persons because it easily highlights about the theme of the report. This helps to avoid wastage of time.
• Comparative Analysis : Information can be compared in terms of graphical representation. Â Such comparative analysis helps for quick understanding and attention.
• Less cost : Information if descriptive involves huge time to present properly. It involves more money to print the information but graphical presentation can be made in short but catchy view to make the report understandable. It obviously involves less cost.
• Decision Making: Business executives can view the graphs at a glance and can make decision very quickly which is hardly possible through descriptive report.
• Logical Ideas: If tables, design and graphs are used to represent information then a logical sequence is created to clear the idea of the audience.
• Helpful for less literate Audience: Less literate or illiterate people can understand graphical representation easily because it does not involve going through line by line of any descriptive report.
• Less Effort and Time: To present any table, design, image or graphs require less effort and time. Furthermore, such presentation makes quick understanding of the information.
• Less Error and Mistakes: Qualitative or informative or descriptive reports involve errors or mistakes. As graphical representations are exhibited through numerical figures, tables or graphs, it usually involves less error and mistake.
• A complete Idea: Such representation creates clear and complete idea in the mind of audience. Reading hundred pages may not give any scope to make decision. But an instant view or looking at a glance obviously makes an impression in  the mind of audience regarding the topic or subject.
• Use in the Notice Board: Such representation can be hanged in the notice board to quickly raise the attention of employees in any organization.

DISADVANTAGES:

Graphical representation of reports is not free from limitations. The following are the problems of graphical representation of data or reports:

• Costly : Graphical representation pf reports are costly because it involves images, colors and paints. Combination of material with human efforts makes the graphical presentation expensive.
• More time : Normal report involves less time to represent but graphical representation involves more time as it requires graphs and figures which are dependent to more time.
• Errors and Mistakes : Since graphical representations are complex, there is- each and every chance of errors and mistake. This causes problems for better understanding to general people.
• Lack of Secrecy: Graphical representation makes full presentation of information which may hamper the objective to keep something secret.
• Problems to select the suitable method: Information can be presented through various graphical methods and ways. Which should be the suitable method is very hard to select.
• Problem of Understanding: All may not be able to get the meaning of graphical representation because it involves various technical matters which are complex to general people.

Last of all it can be said that graphical representation does not provide proper information to general people.

CONCLUSION:

Graphical representation makes the datamore possible to easily draw; visual impression of data. Graphical representation of data enhances the understandings of the observer. It makes comparisons easy. This kind of method creates an imprint on mind for a long period of time. Well in this chapter we have discussed about the definition ,types ,advantages and disadvantages in detail with relevant examples which will have an impact in the power of understanding. I request you all to go through the various types of graphs commonly used in research studies in with reference to home science research studies to explore new ideas in the field of research.

• http://shodhganga.inflibnet.ac.in/bitstream/10603/143688/2/file%202%20chapter%201 %20data%20representation%20techniques.pdf
• http://www.mas.ncl.ac.uk/~ndah6/teaching/MAS1403/notes_chapter2.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5453888/
• http://cec.nic.in/wpresources/module/Anthropology/PaperIX/9/content/downloads/file1. pdf
• https://www.kluniversity.in/arp/uploads/2096.pdf

• Graphic Presentation of Data

Apart from diagrams, Graphic presentation is another way of the presentation of data and information. Usually, graphs are used to present time series and frequency distributions. In this article, we will look at the graphic presentation of data and information along with its merits, limitations , and types.

Suggested Videos

Construction of a graph.

The graphic presentation of data and information offers a quick and simple way of understanding the features and drawing comparisons. Further, it is an effective analytical tool and a graph can help us in finding the mode, median, etc.

We can locate a point in a plane using two mutually perpendicular lines – the X-axis (the horizontal line) and the Y-axis (the vertical line). Their point of intersection is the Origin .

We can locate the position of a point in terms of its distance from both these axes. For example, if a point P is 3 units away from the Y-axis and 5 units away from the X-axis, then its location is as follows:

Browse more Topics under Descriptive Statistics

• Definition and Characteristics of Statistics
• Stages of Statistical Enquiry
• Importance and Functions of Statistics
• Nature of Statistics – Science or Art?
• Application of Statistics
• Law of Statistics and Distrust of Statistics
• Meaning and Types of Data
• Methods of Collecting Data
• Sample Investigation
• Classification of Data
• Tabulation of Data
• Frequency Distribution of Data
• Diagrammatic Presentation of Data
• Measures of Central Tendency
• Mean Median Mode
• Measures of Dispersion
• Standard Deviation
• Variance Analysis

Some points to remember:

• We measure the distance of the point from the Y-axis along the X-axis. Similarly, we measure the distance of the point from the X-axis along the Y-axis. Therefore, to measure 3 units from the Y-axis, we move 3 units along the X-axis and likewise for the other coordinate .
• We then draw perpendicular lines from these two points.
• The point where the perpendiculars intersect is the position of the point P.
• We denote it as follows (3,5) or (abscissa, ordinate). Together, they are the coordinates of the point P.
• The four parts of the plane are Quadrants.
• Also, we can plot different points for a different pair of values.

General Rules for Graphic Presentation of Data and Information

There are certain guidelines for an attractive and effective graphic presentation of data and information. These are as follows:

• Suitable Title – Ensure that you give a suitable title to the graph which clearly indicates the subject for which you are presenting it.
• Unit of Measurement – Clearly state the unit of measurement below the title.
• Suitable Scale – Choose a suitable scale so that you can represent the entire data in an accurate manner.
• Index – Include a brief index which explains the different colors and shades, lines and designs that you have used in the graph. Also, include a scale of interpretation for better understanding.
• Data Sources – Wherever possible, include the sources of information at the bottom of the graph.
• Keep it Simple – You should construct a graph which even a layman (without any exposure in the areas of statistics or mathematics) can understand.
• Neat – A graph is a visual aid for the presentation of data and information. Therefore, you must keep it neat and attractive. Choose the right size, right lettering, and appropriate lines, colors, dashes, etc.

Merits of a Graph

• The graph presents data in a manner which is easier to understand.
• It allows us to present statistical data in an attractive manner as compared to tables. Users can understand the main features, trends, and fluctuations of the data at a glance.
• A graph saves time.
• It allows the viewer to compare data relating to two different time-periods or regions.
• The viewer does not require prior knowledge of mathematics or statistics to understand a graph.
• We can use a graph to locate the mode, median, and mean values of the data.
• It is useful in forecasting, interpolation, and extrapolation of data.

Limitations of a Graph

• A graph lacks complete accuracy of facts.
• It depicts only a few selected characteristics of the data.
• We cannot use a graph in support of a statement.
• A graph is not a substitute for tables.
• Usually, laymen find it difficult to understand and interpret a graph.
• Typically, a graph shows the unreasonable tendency of the data and the actual values are not clear.

Types of Graphs

Graphs are of two types:

• Time Series graphs
• Frequency Distribution graphs

Time Series Graphs

A time series graph or a “ histogram ” is a graph which depicts the value of a variable over a different point of time. In a time series graph, time is the most important factor and the variable is related to time. It helps in the understanding and analysis of the changes in the variable at a different point of time. Many statisticians and businessmen use these graphs because they are easy to understand and also because they offer complex information in a simple manner.

Further, constructing a time series graph does not require a user with technical skills. Here are some major steps in the construction of a time series graph:

• Represent time on the X-axis and the value of the variable on the Y-axis.
• Start the Y-value with zero and devise a suitable scale which helps you present the whole data in the given space.
• Plot the values of the variable and join different point with a straight line.
• You can plot multiple variables through different lines.

You can use a line graph to summarize how two pieces of information are related and how they vary with each other.

• You can compare multiple continuous data-sets easily
• You can infer the interim data from the graph line

Disadvantages

• It is only used with continuous data.

Use of a false Base Line

Usually, in a graph, the vertical line starts from the Origin. However, in some cases, a false Base Line is used for a better representation of the data. There are two scenarios where you should use a false Base Line:

• To magnify the minor fluctuation in the time series data
• To economize the space

Net Balance Graph

If you have to show the net balance of income and expenditure or revenue and costs or imports and exports, etc., then you must use a net balance graph. You can use different colors or shades for positive and negative differences.

Frequency Distribution Graphs

Let’s look at the different types of frequency distribution graphs.

A histogram is a graph of a grouped frequency distribution. In a histogram, we plot the class intervals on the X-axis and their respective frequencies on the Y-axis. Further, we create a rectangle on each class interval with its height proportional to the frequency density of the class.

Frequency Polygon or Histograph

A frequency polygon or a Histograph is another way of representing a frequency distribution on a graph. You draw a frequency polygon by joining the midpoints of the upper widths of the adjacent rectangles of the histogram with straight lines.

Frequency Curve

When you join the verticals of a polygon using a smooth curve, then the resulting figure is a Frequency Curve. As the number of observations increase, we need to accommodate more classes. Therefore, the width of each class reduces. In such a scenario, the variable tends to become continuous and the frequency polygon starts taking the shape of a frequency curve.

Cumulative Frequency Curve or Ogive

A cumulative frequency curve or Ogive is the graphical representation of a cumulative frequency distribution. Since a cumulative frequency is either of a ‘less than’ or a ‘more than’ type, Ogives are of two types too – ‘less than ogive’ and ‘more than ogive’.

Scatter Diagram

A scatter diagram or a dot chart enables us to find the nature of the relationship between the variables. If the plotted points are scattered a lot, then the relationship between the two variables is lesser.

Solved Question

Q1. What are the general rules for the graphic presentation of data and information?

Answer: The general rules for the graphic presentation of data are:

• Use a suitable title
• Clearly specify the unit of measurement
• Ensure that you choose a suitable scale
• Provide an index specifying the colors, lines, and designs used in the graph
• If possible, provide the sources of information at the bottom of the graph
• Keep the graph simple and neat.

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Graphical presentation of research results: How to place accurate LSD bars in graphs

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Data are collected often in raw form. These are then not useable unless summarized. The techniques of presentation in tabular and graphical forms are introduced. Some illustrations provided are real-world examples. Graphical presentations cover bar chart, pie chart, histogram, frequency polygon, pareto chart, frequency curve and line diagram. Data are often collected in raw form. These are then not useable unless summarized. There are certain guidelines for data summarization such as summarization-should be as useful as possible,-should represent data fairly, and-should be easy to interpret. After collection of data (primary or secondary), it is necessary to summarize them suitably and present in such forms as can facilitate subsequent analysis and interpretation. There are two major tools/techniques for presentation of data as follows:-Presentation in tabular form-Presentation in graphical form. 2.1 Tabular Presentation Data may be presented in the form of statistical tables. In one table only simple frequencies can be shown. Also, in the same table cumulative frequencies, relative frequencies, and cumulative relative frequencies can be shown. Relative frequencies and cumulative frequencies are defined as follows: Relative frequency: It means the ratio of the frequency in the category of concern to the total frequency in the reference set.

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In research, there are the different methods of measuring data to be analyzed. The reason for these is to measure the level of dispersion (Eboh, 2009). Dispersion is the tendency of values of a variable to scatter away from the mean or midpoint. The data are measured majorly with basic statistical tools such as mean, median and mode. To arrive at accurate measurement, the use of standard deviation is employed. Standard deviation is a measurement that is designed to find the disparity between the calculated mean.it is one of the tools for measuring dispersion. To have a good understanding of these, it is of general interest to give a better light to the following terms (mean, median, mode) and variance) also their uses. MEAN Panneerslvam (2008) defined mean as the ratio between the sum of the observations and the number of the observation.in his study, he termed it as arithmetic mean. .Eboh (2009) said it is sum of observations divided by the number of observations. Mathematically, t...

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Measurement scale is an important part of data collection, analysis, and presentation. In the data collection and data analysis, statistical tools differ from one data type to another. There are four types of variables, namely nominal, ordinal, discrete, and continuous, and their nature and application are different. Graphs are a common method to visually present and illustrate relationships in the data. There are several statistical diagrams available to present data sets. However, their use depends on our objectives and data types. We should use the appropriate diagram for the data set, which is very useful for easily and quickly communicating summaries and findings to the audience. In the present study, statistical data type and its presentation, which are used in the field of biomedical research, have been discussed.

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Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

Thanks very much for the information

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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.

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How to Make a “Good” Presentation “Great”

• Guy Kawasaki

Remember: Less is more.

A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others. Here are some unique elements that make a presentation stand out.

• Fonts: Sans Serif fonts such as Helvetica or Arial are preferred for their clean lines, which make them easy to digest at various sizes and distances. Limit the number of font styles to two: one for headings and another for body text, to avoid visual confusion or distractions.
• Colors: Colors can evoke emotions and highlight critical points, but their overuse can lead to a cluttered and confusing presentation. A limited palette of two to three main colors, complemented by a simple background, can help you draw attention to key elements without overwhelming the audience.
• Pictures: Pictures can communicate complex ideas quickly and memorably but choosing the right images is key. Images or pictures should be big (perhaps 20-25% of the page), bold, and have a clear purpose that complements the slide’s text.
• Layout: Don’t overcrowd your slides with too much information. When in doubt, adhere to the principle of simplicity, and aim for a clean and uncluttered layout with plenty of white space around text and images. Think phrases and bullets, not sentences.

As an intern or early career professional, chances are that you’ll be tasked with making or giving a presentation in the near future. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others.

• Guy Kawasaki is the chief evangelist at Canva and was the former chief evangelist at Apple. Guy is the author of 16 books including Think Remarkable : 9 Paths to Transform Your Life and Make a Difference.

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