what is presentation variable

Presentation

Prompts for analyses and dashboards

Content authors

Request

Prompts for analyses and dashboards

Content authors

Global

Analyses

Administrators

PORTALPATH

Identifies the default dashboard a user sees when they sign in (they can override this preference after signing in).

To display ‘mydashboard’ when a user signs in:

TIMEZONE

Specifies the default time zone for a user when they sign in.

A user’s time zone is typically populated from the user’s profile. Users can change their default time zone through preferences (My Account).

To set the time zone when a user signs in:

DATA_TZ

Specifies an offset from the original time zone for data.

This variable enables you to convert a time zone so that users see the appropriate zone.

To convert time data to Eastern Standard Time (EST):

This example means Greenwich Mean Time (GMT) - 5 hours

DATA_DISPLAY_TZ

Specifies the time zone for displaying data.

To display Eastern Standard Time (EST):

This example means Greenwich Mean Time (GMT) - 5 hours

Column prompt

A presentation variable created as part of a column prompt is associated with a column, and the values that it can take come from the column values.

To create a presentation variable as part of a column prompt, in the New Prompt dialog, you must select Presentation Variable in the Set a variable field. Enter a name for the variable in the Variable Name field.

Variable prompt

A presentation variable created as part of a variable prompt isn’t associated with any column, and you define the values that it can take.

To create a presentation variable as part of a variable prompt, in the New Prompt dialog, you must select Presentation Variable in the Prompt for field. Enter a name for the variable in the Variable Name field.

Session

@{biServer.variables['NQ_SESSION.variablename']}

where variablename is the name of the session variable, for example DISPLAYNAME.

@{biServer.variables['NQ_SESSION.SalesRegion]}

Repository

@{biServer.variables.variablename}

or

@{biServer.variables['variablename']}

where variablename is the name of the repository variable, for example, prime_begin

@{biServer.variables.prime_begin}

or

@{biServer.variables['prime_begin']}

Presentation or request

@{variables.variablename}[format]{defaultvalue}

or

@{scope.variables['variablename']}

where:

variablename is the name of the presentation or request variable, for example, MyFavoriteRegion.

(optional) format is a format mask dependent on the data type of the variable, for example #,##0, MM/DD/YY hh:mm:ss. (Note that the format isn’t applied to the default value.)

(optional) defaultvalue is a constant or variable reference indicating a value to be used if the variable referenced by variablename isn’t populated.

scope identifies the qualifiers for the variable. You must specify the scope when a variable is used at multiple levels (analyses, dashboard pages, and dashboards) and you want to access a specific value. (If you don’t specify the scope, then the order of precedence is analyses, dashboard pages, and dashboards.)

When using a dashboard prompt with a presentation variable that can have multiple values, the syntax differs depending on the column type. Multiple values are formatted into comma-separated values and therefore, any format clause is applied to each value before being joined by commas.

@{variables.MyFavoriteRegion}{EASTERN REGION}

or

@{MyFavoriteRegion}

or

@{dashboard.variables['MyFavoriteRegion']}

or

(@{myNumVar}[#,##0]{1000})

or

(@{variables.MyOwnTimestamp}[YY-MM-DD hh:mm:ss]{)

or

(@{myTextVar}{A, B, C})

Global

@{global.variables.variablename}

where variablename is the name of the global variable, for example, gv_region. When referencing a global variable, you must use the fully qualified name as indicated in the example.

The naming convention for global variables must conform to EMCA Scripting language specifications for JavaScript. The name must not exceed 200 characters, nor contain embedded spaces, reserved words, and special characters. If you’re unfamiliar with JavaScripting language requirements, consult a third party reference

@{global.variables.gv_date_n_time}

Chapter 1 - Variables

Note: In Presentation, the end of a statement is always marked by a semi-colon. This allows you to use more than one line for a statement. If you get an error in your scenario, it oftentimes is because of a missing semi-colon.

Presentation has several types of information that can be stored that are considered basic types. The distinction between basic types and other types should become clearer once other types and their parameters are introduced. This section will only use basic type variables in the examples. The basic types in Presentation are strings, integers (ints), doubles, booleans (bools), and rgb colors (rgb_colors).

Basic Types

Note: We will frequently give full scenario examples so you can test out things for yourself. If you are reading this course within the compiled documentation distributed with Presentation, you may open these examples in Presentation by clicking on the links in the example box. We will sometimes use the command term.print( ... ) , which prints to the area labeled "Terminal" in the Editor tab , as a simple way to create output. Also keep an eye on the area labeled "Status", which may have important messages, such as "Press Enter to Start".

Note: We have added lines here to print each of the red/green/blue/alpha values separately, with a tab ("\t") between each.

To review, a variable may be declared and initialized using the following syntax:

The initizializing, in (), is optional, but it is good practice to do so to avoid unexpected behavior. For example, if you declare an integer variable without giving it an initial value, and then start adding to that variable without checking the starting value, you will be adding to an unknown quantity. If some aspect of your experiment is dependent on the value of that variable, the program may not work as you expect.

Naming variables

You can give your variables whatever names you wish, adhering to the following rules:

• Variable names should include only letters, numbers, and underscores (_).
• A variable name must start with a letter.
• Your variable name cannot be the same as any other special word that is part of PCL, like "begin" or "loop".

For ease of programming, it is best to make variable names easy to remember and clear. For example, if you are working on a gambling task and you want a variable to store the total amount of money the user has earned, you might make a variable called total_money or TotalMoney. It also helps that you remain consistent in how you name your variables (e.g., capitalization, underscores, etc.), as it will help you to remember the name of the variable as you continue to work with it in your code.

Assigning values to variables

Changing types

Once you have declared a variable, you cannot change the type of that variable. If you are familiar with programming, you may know that this is not always the case in other programming languages. In Presentation, even though a variable cannot be changed to a different type, there are conversion methods so that you can convert the information held in one variable to another variable. The methods used to convert information from one type to another are called conversion methods . Conversion methods can be useful, for example, if you want to include the value of a number in a string. For instance, if participants play a game for points in your scenario, you might want to display the number of points they had onscreen as text. You can only display strings on screen, so you would need to convert the number of points, stored in an integer variable, into a string. In that case, you might use something like:

Scope of Variables

The examples above are pretty short, so they don't demonstrate the importance of where you declare your variables. Where a variable is declared will determine where it can be used, or its scope . You must declare a variable before using it. A more complicated point about scope is that if you declare a variable inside a sub-part of your scenario - for example a loop or a function - then you can't use it outside that sub-part. So, if you declare a variable inside an conditional, or if statement, that variable does not exist outside of that statement and results in an error:

In this example, because j was declared inside of an if statement, it cannot be used outside of it. However, because k was declared outside of the if statement, you may use it both inside and outside of the following if statement.

Global scope indicates that a variable exists in all parts of the program after the variable is declared. You can establish a variable with global scope by declaring it outside of any subsections of the scenario. Declaring a variable with narrow scope (only when necessary) avoids having to worry about losing track of different points in the scenario where the variable may be changed.

A variable is used to store information. When you declare your variable, you must specify its type (the sort of information it will store, such as int, string or bool) and you can optionally initialize it (give it a starting value). Once you declare a variable, the type of that variable cannot change, but the value can. There are methods for converting information from one type of variable to another type of variable, for example if you need to use an integer value as a string. Finally, we introduced the concept of scope. Scope refers to where in a program a particular variable exists.

In the following exercises, initialize an appropriate variable for the type of data that will be needed for the described portion of an experiment. Print the value of your variable to the terminal window. You may use the experiment/scenario file below.

Example: You are running a task and want to keep track of the number of blocks you have run.

• Your paradigm is a go/no-go paradigm. You want to declare a variable to store whether the next stimulus is supposed to be responded to or not. The default case is that the stimulus should not be responded to.
• You want to have a variable to denote the number of trials of a particular type in your paradigm. There are 32 of those trials in the paradigm.
• You want to print your participant's name to the screen and need a variable to store the name. His name is John Smith.
• In your paradigm, you want to set a stimulus to change color based on user response. You need a variable to keep track of the color of the object so you can set the stimulus to that color. The initial color is red. Note: Please see the rgb_color initialization chapters above for how to print the values to the terminal.
• In your paradigm, you rotate a dot about the origin based on the amount of time the scenario has taken, moving 1.5 degrees every 100 ms. You need a variable to keep track of the current angle of the dot with respect to the origin. The dot starts at a 45 degree angle.
• In your paradigm, participants earn a point for each trial they respond to correctly, and you want a variable to keep track of the number of points they have. They do not have any points when they start.

Additional relevant documentation readings

The following is a page in the Presentation documentation that may be of use to you in further understanding the material presented in chapter 1.

• Basic PCL Types

• Translation

Statistics and data presentation: Understanding Variables

By charlesworth author services.

• Charlesworth Author Services
• 21 January, 2021
• Academic Writing Skills

All science is about understanding variability in different characteristics, and most characteristics vary, hence we call the characteristics that we are studying ‘variables. When we work in a quantitative area, we make measurements. The scale of measurement is very important because one criterion for selecting the appropriate statistical technique is the scale of measurement used to measure whatever it is, we are studying.

There are different statistical techniques to use with each kind of measurement.

✓       Nominal Scale is the lowest level of measurement. Sometimes this is referred to as qualitative data – not to be confused with qualitative research. This scale uses numbers to describe names of discrete categories. One determines for each case whether they have or do not have the attribute in question.

✓       Ordinal Scale is used to rank people in order (e.g. least politically active to most politically active). This is the lowest level of quantitative data and involves the process of assignment of numbers to cases in terms of how much of the attribute is possessed by each subject.

✓       Continuous data can assume different values within a range. Interval Scale is where a number assigned is the amount of attribute possessed. Most statistics procedures can be used with interval data. Ratio Scale is considered the highest level of measurement, because all statistics tools can be used on ratio data.

When you read an article, you need to figure out what all the variables are in a study. Then you need to identify three things for each variable one at a time: the scale of measurement; the possible score range; and the meaning of high score and low score. Variables take on different functions in a study. We have to be able to tease these functions out. When you are conducting research, you have to recognize the different variables that are at play in your study so you can account for them during your analyses. Variables can take on different functions within the same study, so don’t classify them at the start. Researchers decide on a classification of variables in each analysis. Let’s take a look at the different classifications of variables.

Classification of variables

•          Dependent Variable : The outcome variable of interest is observed to see whether it is influenced by a manipulated variable. This is called a dependent variable. In other words, a characteristic that is dependent on, or thought to be influenced by, an independent variable. This is sometimes called outcome or response variable.

•         Independent Variable :  In experimental research, the researcher can manipulate one variable and measure the effect of that manipulation on another variable. The variable that is manipulated is called an independent variable. In other words, a characteristic that affects, or is thought to influence an outcome or dependent variable, or an antecedent condition. Independent variables are sometimes called factors, treatments, predictors, or manipulated variables.

In a better scenario, the only consistent feature that varies between an intervention and control group would be the outcome variable of interest. However, this is not generally the case, and we often have confounding or extraneous variables that play a part. When we design our research studies, we need to pay attention to and account for these variables also.

•       Control Variable : any variable that is held constant in a research study by observing only one of the instances or levels. Control variables are not necessarily of central interest, but things that a researcher cannot change or remove from participants. They might be known to exert some influence on the dependent variable. We can ’ t study everything, so a researcher may be interested, for example, in how parental education (and some other variable) is related to reading ability in younger children. He/she happens to know through previous research that gender is related to reading. So, for the purposes of the study, they chose to study only girls. Thus, gender is the control variable and is “ held constant ”.

•         Mediator (Intervening) Variable : a hypothetical variable that explains the relationship but is not observed directly in the research study. Rather, it is inferred from the relationship between the independent and dependent variable. This is an important concept to understand because most theory is based on notions of intervening variables and understanding how or why such effects occur. These variables might be clearly identified before doing a study, i.e. measured and analyzed within a study. Often, mediating variables surface as researchers interpret findings and emerge as suggestions for future research.

•         Moderator Variable : a variable/characteristic that moderates or changes the direction and/or strength of the relationship between two other variables. When, under what conditions, a relationship holds; influences on the strength of the relationship. For example, if a researcher were looking at the relationship between Socio economic status and AIDs prevention, age might be a moderator variable such that the relationship is stronger for older kids than younger kids.

Understanding the distinction between mediators and moderators is not always easy. Basically, in a mediation model the independent variable cannot influence the dependent variable directly and does so by means of another variable – the mediator. As a simple example, older people tend to be better drivers than young people. So, age is a predictor of good driving. However, when we think about why this is the case, we see that older people typically make wiser decisions and so wisdom could be seen as the mediating variable.

There are a number of tests that can be used within your statistical software program to test for mediating and moderating effects. Moderated regression is an example. A moderator analysis is used to determine whether the relationship between two variables depends on (is moderated by) the value of a third variable. You can find online tutorials to explore how this is conducted for the statistical package you are using. Regression can also be used to test for a mediating effect.

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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.

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Data Presentation - Bar Charts

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A bar chart is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Bar charts can be used to show comparisons among categories.

The bar chart below shows how the average U.S. diet compares with recommended dietary percentages.

source:ers.usda.gov

Are there any food groups the average person in the U.S. eats too little of (compared to the recommended amount)? Show answer Solution: Find any bars that do not reach the “MyPlate Recommendations” line (which is at 100% on the y-axis). Vegetables, dairy, and fruit do not reach this bar, and this means that the average person in the U.S. does not meet recommended amounts for these food groups.

Making a Bar Chart

To make a bar chart, decide what columns and rows to include. The chart above relates five food groups (food group is the x-axis variable) to a percentage (the y-axis variable). However, bar charts can display more than just two variables. Generally, if multiple things are being compared, one axis will be a variable that links all other parameters.

For example, the chart below shows vegetable type on the x-axis, and shows both pounds per person and whether the food was canned, frozen, fresh, etc. on the y-axis. This graph is able to display two variables on one axis because it uses color to distinguish the state of the food.

To read a bar chart, determine which parameters are being compared by reading the components of the x and y-axes and taking note of how parameters are related. Some parameters are related by space (a taller bar might mean there is more of that element than a shorter bar) while others could be related by color. In the chart above, for example, all frozen foods are denoted by the color red.

Using the chart above, determine the pounds of fresh romaine and leaf lettuce consumed per person. Show answer Solution: We can tell from the key above the bars that the color green indicates a fresh vegetable. Looking at the romaine and leaf lettuce column, we can see that a little more than 5 pounds of fresh lettuce was consumed per person.

What is the third most popular kind of fruit among U.S. consumers according to this bar chart?

source: ers.usda.gov

Good practices for making bar charts Title the chart. Use labels on the axes and to denote categories. Use consistent color unless intentionally differentiating elements. Use consistent spacing between numerical increments. Take care in choosing a scale that best displays the data.

What is wrong with this chart? source: wikipedia Show answer Answer: The scale is too large and the reader cannot discern differences between the bars. Here is the chart with a better scale. source: wikipedia

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

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

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

Click to jump ahead:

10 Essential data presentation examples + methods you should know

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

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

1. Bar graph

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

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

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

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

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

2. Line graph

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

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

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

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

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

3. Pie chart

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

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

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

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

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

4. Scatter plot

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

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

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

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

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

5. Histogram

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

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

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

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

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

6. Stacked bar chart

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

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

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

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

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

7. Area chart

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

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

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

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

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

8. Tabular presentation

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

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

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

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

9. Textual data

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

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

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

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

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

10. Pictogram

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

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

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

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

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

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

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

1. Title and objective

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

2. Key data points

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

3. Context and significance

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

4. Key takeaways

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

5. Visuals and charts

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

6. Implications or actions

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

7. Q&A and discussion

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

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

Overloading with data

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

Assuming everyone’s on the same page

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

Misleading visuals

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

Not providing context

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

Not citing sources properly

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

Not telling a story

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

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

Ignoring data quality

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

Simplify your visuals

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

Missing the emotional connection

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

Skipping the actionable insights

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

Can you provide some data presentation examples for business reports?

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

What are some creative data presentation examples for academic presentations?

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

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

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

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

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

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

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

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

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

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

What is the difference between data visualization and data presentation?

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

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

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

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

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

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9 Data Presentation Tools for Business Success

• By Judhajit Sen
• May 29, 2024

A data presentation is a slide deck that shares quantitative information with an audience using visuals and effective presentation techniques . The goal is to make complex data easily understandable and actionable using data presentation examples like graphs and charts, tables, dashboards, and clear text explanations. 

Data presentations help highlight trends, patterns, and insights, allowing the audience to grasp complicated concepts or trends quickly. This makes it easier for them to make informed decisions or conduct deeper analysis.

Data visualization in presentations is used in every field, from academia to business and industry. Raw data is often too complex to understand directly, so data analysis breaks it down into charts and graphs. These tools help turn raw data into useful information.

Once the information is extracted, it’s presented graphically. A good presentation can significantly enhance understanding and response.

Think of data presentation as storytelling in business presentations with charts. A common mistake is assuming the audience understands the data as well as the presenter. Always consider your audience’s knowledge level and what information they need when you present your data.

To present the data effectively:

1. Provide context to help the audience understand the numbers.

2. Compare data groups using visual aids.

3. Step back and view the data from the audience’s perspective.

Data presentations are crucial in nearly every industry, helping professionals share their findings clearly after analyzing data.

Key Takeaways

• Simplifying Complex Data: Data presentations turn complex data into easy-to-understand visuals and narratives, helping audiences quickly grasp trends and insights for informed decision-making.
• Versatile Tools: Various tools like bar charts, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables each serve unique purposes, enhancing the clarity and impact of the data.
• Audience Consideration: Tailor your presentation to the audience’s knowledge level, providing context and using simple visuals to make the information accessible and actionable.
• Effective Data Storytelling: Combining clear context, organized visuals, and thoughtful presentation ensures that the data’s story is conveyed effectively, supporting better business decisions and success.

Following are 9 data presentation tools for business success.

Bar charts are a simple yet powerful method of presentation of the data using rectangular bars to show quantities or frequencies. They make it easy to spot patterns or trends at a glance. Bar charts can be vertical (column charts) or horizontal, depending on how you want to display your data.

In a bar graph, categories are displayed on one axis, usually the x-axis for vertical charts and the y-axis for horizontal ones. The bars’ lengths represent the values or frequencies of these categories, with the scale marked on the opposite axis.

These charts are ideal for comparing data across different categories or showing trends over time. Each bar’s height (or length in a horizontal chart) is directly proportional to the value it represents. This visual representation helps illustrate differences or changes in data.

Bar charts are versatile tools in business reports, academic presentations, and more. To make your bar charts effective:

• Ensure they are concise and have easy-to-read labels.
• Avoid clutter by not including too many categories, making the chart hard to read.
• Keep it simple to maintain clarity and impact, whether your bars go up or sideways.

Line Graphs

Line graphs show how data changes over time or with continuous variables. They connect points of data with straight lines, making it easy to see trends and fluctuations. These graphs are handy when comparing multiple datasets over the same timeline.

Using line graphs, you can track things like stock prices, sales projections, or experimental results. The x-axis represents time or another continuous variable, while the y-axis shows the data values. This setup allows you to understand the ups and downs in the data quickly.

To make your graphs effective, keep them simple. Avoid overcrowding with too many lines, highlight significant changes, use labels, and give your graph a clear, catchy title. This will help your audience grasp the information quickly and easily.

A data dashboard is a data analysis presentation example for analyzing information. It combines different graphs, charts, and tables in one layout to show the information needed to meet one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs) by displaying visuals you’ve already made in worksheets.

It’s best to keep the number of visuals on a dashboard to three or four. Adding too many can make it hard to see the main points. Dashboards are helpful for business analytics, like analyzing sales, revenue, and marketing metrics. In manufacturing, they help users understand the production scenario and track critical KPIs for each production line.

Dashboards represent vital points of data or metrics in an easy-to-understand way. They are often an  interactive presentation idea , allowing users to drill down into the data or view different aspects of it.

Pie charts are circular graphs divided into parts to show numerical proportions. Each portion represents a part of the whole, making it easy to see each component’s contribution to the total.

The size of each slice is determined by its value relative to the total. A pie chart with more significant points of data will have larger slices, and the whole chart will be more important. However, you can make all pies the same size if proportional representation isn’t necessary.

Pie charts are helpful in business to show percentage distributions, compare category sizes, or present simple data sets where visualizing ratios is essential. They work best with fewer variables. For more variables, it’s better to use a pie chart calculator that helps to create pie charts easily for various data sets with different color slices. 

Each “slice” represents a fraction of the total, and the size of each slice shows its share of the whole. Pie charts are excellent for showing how a whole is divided into parts, such as survey results or demographic data.

While pie charts are great for simple distributions, they can get confusing with too many categories or slight differences in proportions. To keep things clear, label each slice with percentages or values and use a legend if there are many categories. If more detail is needed, consider using a donut chart with a blank center for extra information and a less cluttered look.

A histogram is a graphical presentation of data  to help in understanding the distribution of numerical values. Unlike bar charts that show each response separately, histograms group numeric responses into bins and display the frequency of reactions within each bin. The x-axis denotes the range of values, while the y-axis shows the frequency of those values.

Histograms are useful for understanding your data’s distribution, identifying shared values, and spotting outliers. They highlight the story your data tells, whether it’s exam scores, sales figures, or any other numerical data.

Histograms are great for visualizing the distribution and frequency of a single variable. They divide the data into bins, and the height of each bar indicates how many points of data fall into that bin. This makes it easy to see trends like peaks, gaps, or skewness in your data.

To make your histogram effective, choose bin sizes that capture meaningful patterns. Clear axis labels and titles also help in explaining the data distribution.

Scatter Plot

Using individual data points, a scatter plot chart is a presentation of data in visual form to show the relationship between two variables. Each variable is plotted along the x-axis and y-axis, respectively. Each point on the scatter plot represents a single observation.

Scatter plots help visualize patterns, trends, and correlations between the two variables. They can also help identify outliers and understand the overall distribution of data points. The way the points are spread out or clustered together can indicate whether there is a positive, negative, or no clear relationship between the variables.

Scatter plots can be used in practical applications, such as in business, to show how variables like marketing cost and sales revenue are related. They help understand data correlations, which aids in decision-making.

To make scatter plots more effective, consider adding trendlines or regression analysis to highlight patterns. Labeling key data points or tooltips can provide additional information and make the chart easier to interpret.

A pictogram is the simplest form of data presentation and analysis, often used in schools and universities to help students grasp concepts more effectively through pictures.

This type of diagram uses images to represent data. For example, you could draw five books to show the number of books sold in the first week of release, with each image representing 1,000 books. If consumers bought 5,000 books, you would display five book images.

Using simple icons or images makes the information visually intuitive. Instead of relying on numbers or complex graphs, pictograms use straightforward symbols to depict data points. For example, a thumbs-up emoji can illustrate customer satisfaction levels, with each emoji representing a different level of satisfaction.

Pictograms are excellent for visual data presentation. Choose symbols that are easy to interpret and relevant to the data to ensure clarity. Consistent scaling and a legend explaining the symbols’ meanings are essential for an effective presentation.

Textual Presentation

Textual presentation uses words to describe the relationships between pieces of information. This method helps share details that can’t be shown in a graph or table. For example, researchers often present findings in a study textually to provide extra context or explanation. A textual presentation can make the information more transparent.

This type of presentation is common in research and for introducing new ideas. Unlike charts or graphs, it relies solely on paragraphs and words.

Textual presentation also involves using written content, such as annotations or explanatory text, to explain or complement data. While it doesn’t use visual presentation aids like charts, it is a widely used method for presenting qualitative data. Think of it as the narrative that guides your audience through the data.

Adequate textual data may make complex information more accessible. Breaking down complex details into bullet points or short paragraphs helps your audience understand the significance of numbers and visuals. Headings can guide the reader’s attention and tell a coherent story.

Tabular Presentation

Tabular presentation uses tables to share information by organizing data in rows and columns. This method is useful for comparing data and visualizing information. Researchers often use tables to analyze data in various classifications:

Qualitative classification: This includes qualities like nationality, age, social status, appearance, and personality traits, helping to compare sociological and psychological information.

Quantitative classification: This covers items you can count or number.

Spatial classification: This deals with data based on location, such as information about a city, state, or region.

Temporal classification: This involves time-based data measured in seconds, hours, days, or weeks.

Tables simplify data, making it easily consumable, allow for side-by-side comparisons, and save space in your presentation by condensing information.

Using rows and columns, tabular presentation focuses on clarity and precision. It’s about displaying numerical data in a structured grid, clearly showing individual data points. Tables are invaluable for showcasing detailed data, facilitating comparisons, and presenting exact numerical information. They are commonly used in reports, spreadsheets, and academic papers.

Organize tables neatly with clear headers and appropriate column widths to ensure readability. Highlight important data points or patterns using shading or font formatting. Tables are simple and effective, especially when the audience needs to know precise figures.

Elevate Business Decisions with Effective Data Presentations

Data presentations are essential for transforming complex data into understandable and actionable insights. Data presentations simplify the process of interpreting quantitative information by utilizing data presentation examples like charts, graphs, tables, infographics, dashboards, and clear narratives. This method of storytelling with visuals highlights trends, patterns, and insights, enabling audiences to make informed decisions quickly.

In business, data analysis presentations are invaluable. Different types of presentation tools like bar charts help compare categories and track changes over time, while dashboards consolidate various metrics into a comprehensive view. Pie charts and histograms offer clear views of distributions and proportions, aiding in grasping the bigger picture. Scatter plots reveal relationships between variables, and pictograms make data visually intuitive. Textual presentations and tables provide detailed context and precise figures, which are essential for thorough analysis and comparison.

Consider the audience’s knowledge level to tailor the best way to present data in PowerPoint. Clear context, simple visuals, and thoughtful organization ensure the data’s story is easily understood and impactful. Mastering these nine data presentation types can significantly enhance business success by making data-driven decisions more accessible and practical.

Frequently Asked Questions (FAQs)

1. What is a data presentation?

A data presentation is a slide deck that uses visuals and narrative techniques to make complex data easy to understand and actionable. It includes charts, graphs, tables, infographics, dashboards, and clear text explanations.

2. Why are data presentations important in business?

Data presentations are crucial because they help highlight trends, patterns, and insights, making it easier for the audience to understand complicated concepts. This enables better decision-making and deeper analysis.

3. What types of data presentation tools are commonly used?

Common tools include bar charts, line graphs, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables. Each tool has a unique way of representing data to aid understanding.

4. How can I ensure my data presentation is effective?

To ensure effectiveness, provide context, compare data sets using visual aids, consider your audience’s knowledge level, and keep visuals simple. Organizing information thoughtfully and avoiding clutter enhances clarity and impact.

Transform Your Data into Compelling Stories with Prezentium

Unlock the full potential of your business data with Prezentium ‘s expert presentation services. Our AI-powered solutions turn complex data into clear, actionable insights, helping you easily make informed decisions. 

Prezentium’s Overnight Presentations ensure you wake up to a stunning, ready-to-use presentation in your inbox by 9:30 am PST. Send your requirements by 5:30 pm PST, and let our team combine business acumen, visual design, and data science to craft a presentation that highlights trends and insights seamlessly.

Our Presentation Specialists transform raw ideas and meeting notes into captivating presentations. Whether you need new designs or bespoke templates, our experts bring your vision to life with precision and creativity.

Enhance your team’s skills with Zenith Learning, our interactive workshops that blend structured problem-solving with visual storytelling. Learn to present data effectively and make a lasting impact in your business communications.

Prezentium’s services are designed to help you make the most of your data, from bar charts to dashboards, ensuring your presentations are informative and visually engaging. Let us help you tell your data’s story in a way that resonates. Contact Prezentium today to elevate your business presentations.

Why wait? Avail a complimentary 1-on-1 session with our presentation expert. See how other enterprise leaders are creating impactful presentations with us.

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Corporate communication functions and its importance, barriers to effective communication: 14 common communication barriers.

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The formal presentation of information is divided into two broad categories: Presentation Skills and Personal Presentation .

These two aspects are interwoven and can be described as the preparation, presentation and practice of verbal and non-verbal communication. 

This article describes what a presentation is and defines some of the key terms associated with presentation skills.

Many people feel terrified when asked to make their first public talk.  Some of these initial fears can be reduced by good preparation that also lays the groundwork for making an effective presentation.

A Presentation Is...

A presentation is a means of communication that can be adapted to various speaking situations, such as talking to a group, addressing a meeting or briefing a team.

A presentation can also be used as a broad term that encompasses other ‘speaking engagements’ such as making a speech at a wedding, or getting a point across in a video conference.

To be effective, step-by-step preparation and the method and means of presenting the information should be carefully considered. 

A presentation requires you to get a message across to the listeners and will often contain a ' persuasive ' element. It may, for example, be a talk about the positive work of your organisation, what you could offer an employer, or why you should receive additional funding for a project.

The Key Elements of a Presentation

Making a presentation is a way of communicating your thoughts and ideas to an audience and many of our articles on communication are also relevant here, see: What is Communication? for more.

Consider the following key components of a presentation:

Ask yourself the following questions to develop a full understanding of the context of the presentation.

When and where will you deliver your presentation?

There is a world of difference between a small room with natural light and an informal setting, and a huge lecture room, lit with stage lights. The two require quite different presentations, and different techniques.

Will it be in a setting you are familiar with, or somewhere new?

If somewhere new, it would be worth trying to visit it in advance, or at least arriving early, to familiarise yourself with the room.

Will the presentation be within a formal or less formal setting?

A work setting will, more or less by definition, be more formal, but there are also various degrees of formality within that.

Will the presentation be to a small group or a large crowd?

Are you already familiar with the audience?

With a new audience, you will have to build rapport quickly and effectively, to get them on your side.

What equipment and technology will be available to you, and what will you be expected to use?

In particular, you will need to ask about microphones and whether you will be expected to stand in one place, or move around.

What is the audience expecting to learn from you and your presentation?

Check how you will be ‘billed’ to give you clues as to what information needs to be included in your presentation.

All these aspects will change the presentation. For more on this, see our page on Deciding the Presentation Method .

The role of the presenter is to communicate with the audience and control the presentation.

Remember, though, that this may also include handing over the control to your audience, especially if you want some kind of interaction.

You may wish to have a look at our page on Facilitation Skills for more.

The audience receives the presenter’s message(s).

However, this reception will be filtered through and affected by such things as the listener’s own experience, knowledge and personal sense of values.

See our page: Barriers to Effective Communication to learn why communication can fail.

The message or messages are delivered by the presenter to the audience.

The message is delivered not just by the spoken word ( verbal communication ) but can be augmented by techniques such as voice projection, body language, gestures, eye contact ( non-verbal communication ), and visual aids.

The message will also be affected by the audience’s expectations. For example, if you have been billed as speaking on one particular topic, and you choose to speak on another, the audience is unlikely to take your message on board even if you present very well . They will judge your presentation a failure, because you have not met their expectations.

The audience’s reaction and therefore the success of the presentation will largely depend upon whether you, as presenter, effectively communicated your message, and whether it met their expectations.

As a presenter, you don’t control the audience’s expectations. What you can do is find out what they have been told about you by the conference organisers, and what they are expecting to hear. Only if you know that can you be confident of delivering something that will meet expectations.

See our page: Effective Speaking for more information.

How will the presentation be delivered?

Presentations are usually delivered direct to an audience.  However, there may be occasions where they are delivered from a distance over the Internet using video conferencing systems, such as Skype.

It is also important to remember that if your talk is recorded and posted on the internet, then people may be able to access it for several years. This will mean that your contemporaneous references should be kept to a minimum.

Impediments

Many factors can influence the effectiveness of how your message is communicated to the audience.

For example background noise or other distractions, an overly warm or cool room, or the time of day and state of audience alertness can all influence your audience’s level of concentration.

As presenter, you have to be prepared to cope with any such problems and try to keep your audience focussed on your message.   

Our page: Barriers to Communication explains these factors in more depth.

Continue to read through our Presentation Skills articles for an overview of how to prepare and structure a presentation, and how to manage notes and/or illustrations at any speaking event.

Continue to: Preparing for a Presentation Deciding the Presentation Method

See also: Writing Your Presentation | Working with Visual Aids Coping with Presentation Nerves | Dealing with Questions Learn Better Presentation Skills with TED Talks

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What It Takes to Give a Great Presentation

• Carmine Gallo

Five tips to set yourself apart.

Never underestimate the power of great communication. It can help you land the job of your dreams, attract investors to back your idea, or elevate your stature within your organization. But while there are plenty of good speakers in the world, you can set yourself apart out by being the person who can deliver something great over and over. Here are a few tips for business professionals who want to move from being good speakers to great ones: be concise (the fewer words, the better); never use bullet points (photos and images paired together are more memorable); don’t underestimate the power of your voice (raise and lower it for emphasis); give your audience something extra (unexpected moments will grab their attention); rehearse (the best speakers are the best because they practice — a lot).

I was sitting across the table from a Silicon Valley CEO who had pioneered a technology that touches many of our lives — the flash memory that stores data on smartphones, digital cameras, and computers. He was a frequent guest on CNBC and had been delivering business presentations for at least 20 years before we met. And yet, the CEO wanted to sharpen his public speaking skills.

• Carmine Gallo is a Harvard University instructor, keynote speaker, and author of 10 books translated into 40 languages. Gallo is the author of The Bezos Blueprint: Communication Secrets of the World’s Greatest Salesman  (St. Martin’s Press).

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Presentation

• Written By Gregg Rosenzweig
• Updated: June 4, 2024

We’re here to help you choose the most appropriate content types to fulfill your content strategy. In this series, we’re breaking down the most popular content types to their basic fundamentals so you can start with a solid foundation — simple definitions, clarity on formats, and plenty of examples.

What is a Presentation?

A communication device that relays a topic to an audience in the form of a slide show, demonstration, lecture, or speech, where words and pictures complement each other.

Why should you think of presentations as content?

The beauty of content creation is that almost anything can become a compelling piece of content . It just depends on the creativity used to convert it and the story that brings it to life.

The long and short of it

Although the length of a presentation in terms of time can depend on the overall approach (Are you talking a lot? Are you referring to the screen in detail or not?), consider the number of informational content slides when tallying the overall presentation length. For instance, don’t include title slides in your tally when conveying length to a content creator.

A general guide to presentation length:

• Short Form (5 content slides)
• Standard Form (10 content slides)
• Long Form (20+ content slides)

Popular use cases for presentations…

Let’s consider TED Talks for a minute: one of the best examples (bar none) of how words, pictures, and a narrative can make people care about something they otherwise might not.

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The integrated violin-box-scatter (vbs) plot to visualize the distribution of a continuous variable.

1. The Histogram and Alternatives

2. the vbs plot.

• > d <- Read( " http://web.pdx.edu/~gerbing/data/employee.xlsx " )
• > Plot(Salary, bin=TRUE)

3. VBS Plots across Groups

4. sample size.

• > Plot(Salary, size=0.63, jitter.y=0.57, jitter.x=0, bw=9529.04)
•    n <= 2535: size = 1.096 - 0.134*log(n)
•    n > 2535: size = 0.226 - 0.023*log(n)

5. Bandwidth Smoothing

6. discrete values of a continuous variable.

•    jitter.x <- 1.1 * (1-exp(-0.03*m))

7. Modifying the Stylistic Elements of the Plot

8. conclusions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Gerbing, D.W. The Integrated Violin-Box-Scatter (VBS) Plot to Visualize the Distribution of a Continuous Variable. Stats 2024 , 7 , 955-966. https://doi.org/10.3390/stats7030058

Gerbing DW. The Integrated Violin-Box-Scatter (VBS) Plot to Visualize the Distribution of a Continuous Variable. Stats . 2024; 7(3):955-966. https://doi.org/10.3390/stats7030058

Gerbing, David W. 2024. "The Integrated Violin-Box-Scatter (VBS) Plot to Visualize the Distribution of a Continuous Variable" Stats 7, no. 3: 955-966. https://doi.org/10.3390/stats7030058

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• Open access
• Published: 30 August 2024

Appearance-related cyberbullying and its association with the desire to alter physical appearance among adolescent females

• Taliah Prince 1 ,
• Kate E. Mulgrew 2 ,
• Christina Driver 1 ,
• Lia Mills 1 ,
• Jehan Loza 3 &
• Daniel F. Hermens 1  

Journal of Eating Disorders volume  12 , Article number:  125 ( 2024 ) Cite this article

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Cyberbullying is associated with various mental health concerns in adolescents, including body dissatisfaction and disordered eating behaviours. However, there is a significant research gap concerning the unique effects of appearance-related cyberbullying (ARC) on adolescent mental health. This study examined the prevalence and psychological consequences of ARC among middle to late adolescent females (aged 14–19 years, M age  = 15.98, N  = 336). Participants completed an online survey regarding their experiences of ARC, body image variables, and eating disorder symptomology. Findings indicate the widespread occurrence of ARC among adolescent females, with body shape and size emerging as predominant targets. Experiences of ARC-victimisation positively correlated with increased concerns about body shape, body shame, and eating disorder symptomology. Conversely, experiences of ARC-victimisation were negatively correlated with body esteem and body appreciation. Finally, appearance-related cybervictimisation was significantly associated with adolescent females’ desire to pursue appearance alterations through methods such as dieting and exercising, altering self-presentation, and undergoing cosmetic procedures due to perceived experiences of ARC. These findings highlight the urgent need for preventative measures, such as age-appropriate social media policies and health promotion programs that encourage positive online behaviour, and strategies to address the impacts of ARC to protect the mental well-being of adolescent females.

Plain English Summary

Cyberbullying directed towards appearance is a serious problem for many adolescent females. Our study examined how often this type of cyberbullying happens online and its impact on females aged 14–19. We found that many adolescent females experience appearance-related cyberbullying, where they are teased or insulted about their body shape, weight, or physical features. These experiences make them more likely to feel bad about their bodies, leading to harmful behaviours like extreme dieting or considering cosmetic surgery. These findings highlight the urgent need for action from schools, parents, and social media platforms to prevent this form of cyberbullying and support those affected.

Introduction

Adolescence represents a critical developmental stage characterised by profound changes in neurobiology, cognition, and social-emotional development [ 6 , 7 ]. This period is marked by significant physical maturation and the intricate process of identity formation, rendering adolescents highly responsive to social and environmental influences [ 14 , 17 , 64 ]. Theoretical frameworks, such as those proposed by Thompson et al. [ 70 ] and Cash [ 12 ], highlight how social and environmental factors—including negative affect, low self-esteem, media pressures, social comparison processes, peer influences, internalisation of the societally-based thin-ideal, and parental pressures—intersect to shape adolescents' body image and contribute to body image concerns [ 62 , 80 ].

Body image concerns are a significant issue for adolescents, with recent survey findings indicating that 90% of Australians aged 12–18 years are concerned with their body image, with more than 38% being very or extremely concerned [ 10 ]. Additionally, studies have found that 24–46% of adolescent girls and 12–26% of adolescent boys’ report marked body dissatisfaction [ 8 , 52 , 67 ]. Notably, females, gender diverse youth, and those in the LGBTQIA + community report the highest levels of body dissatisfaction [ 30 ]. Body dissatisfaction, encompassing negative subjective evaluations of the weight and shape of one's own body, is closely linked with various mental health disorders among adolescent females, including depression, anxiety, and eating disorders [ 56 , 66 , 74 ].

The widespread use of social media has revolutionised communication, self-expression, and identity exploration among adolescents [ 47 ]. However, alongside these opportunities, social media platforms also pose significant threats to adolescent mental and social well-being, particularly for females, by shaping and perpetuating societal beauty ideals, fostering peer comparisons, and facilitating bullying [ 3 , 53 ]. Recent sociocultural frameworks suggest that features of social media—such as idealised images of peers, celebrities, and influencers, along with the quantifiable feedback on these images—intersect with adolescent developmental factors and sociocultural gender socialisation processes to shape body image [ 15 ]. Cross-sectional studies have found that these interactions, particularly through online appearance-related activities, exacerbate body image concerns and negatively impact mental health among adolescence, particularly females [ 61 , 65 ].

Of particular concern is appearance-related cyberbullying (ARC), which has emerged as the predominant form of bullying experienced by adolescent females [ 5 , 13 , 51 ]. ARC involves online harassment or criticism focused on physical appearance, including aspects such as weight, body shape, and facial features [ 5 ]. Notably, emerging research suggests that ARC is linked to increased emotional distress, perpetuation of negative societal beauty standards, and heightened vulnerability to body image concerns, including body dissatisfaction [ 5 , 25 , 57 ].

Despite the established association between cyberbullying and negative body image outcomes [ 34 , 46 , 54 , 57 ], research specifically focusing on ARC remains sparse, particularly concerning vulnerable adolescent females. Understanding the distinct impacts of ARC is crucial for comprehending its implications on adolescent body image and mental health outcomes [ 5 , 25 ]. Therefore, the aim of the current study is to conduct a quantitative investigation of ARC among adolescent females, focusing on its prevalence and psychological impacts.

Cyberbullying and body image outcomes among adolescent females

Cyberbullying and traditional bullying share commonalities in their intent to inflict harm through repeated aggressive behaviours and a power imbalance between the perpetrator and victim [ 29 , 32 , 50 , 75 ]. However, while traditional bullying occurs in face-to-face contexts, cyberbullying occurs within the digital realm, utilising electronic communication to perpetrate harm [ 63 , 71 ]. Further distinguishing features of cyberbullying include anonymity, rapid communication, lack of direct supervision, frequent repetition of incidents, and the potential to reach a broad audience [ 55 ]. Both forms of bullying involve three roles: perpetrators, victims, and bystanders. In cyberbullying dynamics, perpetrators initiate online aggression, causing distress to victims who suffer psychological and emotional harm [ 71 ]. Bystanders, on the other hand, observe these incidents and can inadvertently amplify them within the online environment [ 36 ].

Estimating cyberbullying prevalence is challenging due to underreporting, variability in definitions, rapid technological advances, cultural and social influences, and age-related development factors [ 82 ]. Research indicates that cyberbullying incident rates among adolescence range from 5.6–46.3% for perpetrators, 8.0–57.5% for victims and 62.3–75% for bystanders [ 2 , 27 , 82 ]. Gender disparities reveal higher rates of cybervictimisation among females and greater perpetration among males [ 33 , 68 ].

Emerging neurobiological research highlights the profound impact of cyberbullying on adolescent mental health, suggesting parallels between peer victimisation and physical pain processing in the adolescent brain [ 41 , 73 ]. Such experiences may become biologically embedded, heightening adolescents' vulnerability to mental health problems associated with cyberbullying, including increased social anxiety and withdrawal [ 16 ], generalised anxiety and depression [ 48 ], body dissatisfaction and disordered eating behaviours [ 18 , 23 , 57 ], self-harm and suicide attempts [ 37 ], and reduced psychological well-being [ 22 ].

Experimental research further highlights a reciprocal relationship between cyberbullying and body dissatisfaction. Cross-sectional studies show that adolescent females targeted by cyberbullying tend to internalise negative perceptions of their appearance more intensely than similarly victimised boys [ 24 , 38 , 42 ]. Longitudinal studies reveal that victims internalise online negativity, leading to lower self-esteem, body dissatisfaction, and increased depressive symptoms [ 57 , 77 ]. Conversely, a systematic review by Holland and Tiggemann [ 35 ] shows that adolescents with elevated body dissatisfaction are more likely to seek social media validation, exposing themselves to negative comments and feedback, which exacerbates body dissatisfaction and compromises well-being [ 24 , 57 ]. This cycle perpetuates the negative impacts of cyberbullying and body dissatisfaction, reinforcing one another.

As summarised above, current research has established an association between adolescents' experiences of cyberbullying and the development of body image issues and eating disorders [ 24 , 34 , 54 , 57 ]. However, it is important to note that many studies addressing cyberbullying conflate evidence across online (i.e., cyberbullying) and offline (i.e., traditional bullying) experiences, thus complicating the ability to isolate and understand the unique impacts of cyberbullying in its original form, online [ 82 ]. Nevertheless, findings from cyberbullying studies parallel those observed in offline settings, whereby traditional bullying related to physical appearance has been shown to significantly heighten body dissatisfaction and the development of disordered eating behaviours among adolescent females [ 18 ]. More specifically, research shows that bullying targeting aspects of appearance, such as body shape, heighten adolescents’ tendencies toward disordered eating behaviours [ 4 , 44 ], while bullying that targets facial features increases adolescents' likelihood of considering cosmetic procedures later in life [ 18 , 43 ]. Given the pervasive influence of digital spaces, research emphasises that ARC may be potentially more harmful to adolescent mental health than traditional forms of bullying, necessitating the importance of ongoing investigation [ 57 ].

Appearance-related cyberbullying and body image outcomes among adolescent females

Acknowledging the distinction between general cyberbullying and ARC is crucial for understanding the nuanced impacts and consequences on adolescent body image and mental health outcomes. This differentiation has guided significant research efforts, such as those by Berne et al. [ 5 ], Frisén and Berne [ 25 ] and Wang and Ding [ 76 ], who explored ARC's nuanced effects. In their focus group study involving 27 adolescence, Berne et al. [ 5 ] discovered a gender-specific pattern, showing that females were more frequently targeted regarding weight and shape, while boys were more likely to be cyberbullied about their muscularity. Additionally, the study revealed that adolescent females experienced more severe outcomes from ARC, including higher rates of depression, lower self-esteem, and greater body concerns. In a more recent study, Frisén and Berne [ 25 ] conducted a survey with 482 adolescents, examining the relationship between cybervictimisation and body image concerns, such as body-esteem, self-objectification, and internalisation of societal body ideals. Their findings indicated that victims of ARC experienced significantly worse body image outcomes compared to those subjected to general cyber victimisation. This included reduced satisfaction with their appearance, increased body shame, and deeper internalisation of thinness ideals, exacerbated by media representations of idealised body images.

Overview of the current study

Building upon foundational research, this study aims to address gaps in understanding the effects of ARC on body image and associated mental health outcomes among Australian adolescent females aged 14–18 years. Specifically, we investigate the prevalence, forms, and psychological impacts of ARC, and hypothesise that greater experiences of ARC victimisation will be associated with increased body dissatisfaction and heightened vulnerability to eating disorder symptoms.

Participants

Participants were N  = 336 individuals who identified as female, aged 14–19 years old ( M age  =  15.98 SD  = 1.27), and were recruited from the general community and the university of the research team. All participants outside of the age bracket, who did not identify as female, or those that did not provide explicit consent, were excluded from the study. The majority of participants were attending secondary school (85.4%) at the time of completing the survey.

This study was approved by the Human Research Ethics committee at the home institution of the lead author (approval number S221703). Participants were recruited through social media paid advertisements, networks of the research team, and a first-year psychology student research participation scheme. The study was described as examining the relationship between ARC and body image outcomes. Participants were asked to carefully consider their participation and withdraw if topics of cyberbullying, body image, or disordered eating were triggering for them.

Data collection took place from March to June, 2023. Participants entered the online survey (hosted by Qualtrics), read the project information sheet, and before commencing were required to provide consent. To ensure informed consent, participants were required to complete a brief questionnaire aimed at assessing their comprehension and understanding of their participation in the research project before commencing the survey [ 45 ]. To maintain both anonymity and informed consent, a two-question strategy was implemented. One example question inquired, "Is my participation in this study voluntary?" Participants were required to select one of two options: (1) "My participation is not voluntary, and I am expected to complete the study," or (2) "My participation is voluntary, and it is perfectly okay to decline or withdraw at any point." Importantly, participants needed to answer both questions correctly (i.e., “My participation is voluntary, and it is perfectly okay to decline or withdraw at any point") to proceed to the main survey, ensuring a robust verification of their understanding and consent.

Once participants commenced the survey, they were required to first complete a set of demographic questions, followed by the above-mentioned self-report questionnaires in the following sequence: BCyQ, ARC measures (roles, types and impact), BSQ, BESSA, BISS, BAS-2 and chEDE-Q. After completing the self-report questionnaires, participants received a debriefing statement. Additionally, they were provided with links to relevant information from the Butterfly Foundation, a non-profit Australian organisation dedicated to supporting individuals with eating disorders and promoting body positivity [ 10 ], as well as links to other important mental health services. Participants were also offered a follow-up call with the research team member if required, and a list of resources were made available to participants to access further support if needed. Participants were provided with a separate link which allowed them to leave their details to enter a prize draw for six products worth AUD $50.

Cyberbullying and cybervictimisation

The Berlin Cyberbullying-Cybervictimisation Questionnaire (BCyQ) [ 60 ] was used to assess experiences of cyberbullying (20 items) and cybervictimisation (19 items). The BCyQ has been validated and has good psychometric properties in young people aged 9–17 years. Participants were asked if they had experienced a list of behaviours over the previous six months, as well as if they had acted in that way. The scale ranged from 1 ( has not happened to me at all) to 5 ( several times a week ). Combined scores were then created to allow a total score for both cyberbullying (score range = 18–90) and cybervictimisation (score range = 17–85), with higher scores indicating greater frequency of either bullying or victimisation. These scales were shown to have good internal consistency in this study (cyberbullying α = 0.883; cybervictimisation α = 0.94).

Appearance-related cyberbullying (ARC)

Due to the limited research on ARC, there is a scarcity of tailored questionnaires that adequately measure its dimensions. Thus, this study adopted an approach to examine the various dimensions of ARC through three distinct measures, based on the frameworks established in previous research [ 5 , 25 , 76 ].

The first measure of ARC utilised six items from the BCyQ to evaluate the different roles in ARC dynamics. From the BCyQ's original set of 17 victimisation and 18 bullying items, we carefully selected and adapted three items from each measure based on their applicability to ARC. This selection process facilitated the categorisation of participants as either ARC-bullies, ARC-victims, ARC-bully-victims, or those with no experience of ARC. A key modification in this adaptation was the integration of the phrase "my appearance or body" into the text of the six chosen statements, thereby tailoring the measure to focus specifically on appearance-related aspects of cyberbullying. For example, the BCyQ statement 'Others spread embarrassing, insulting, or humiliating video clips/photos of me without my permission on the Internet or by mobile phone' was revised to 'Others spread embarrassing, insulting, or humiliating video clips/photos of my appearance or body without my permission on the Internet or by mobile phone.' The instructions and response scales are the same as the BCyQ. Participants were asked if they had experienced a list of behaviours over the previous six-months, as well as if they had acted in that way. The 5-point Likert scales ranged from 1 (has not happened to me at all) to 5 ( several times a week). Combined scores were then created to allow a total score (analogous to the scoring methodology of the BCyQ) for ARC-bullies (score range = 4–15) and ARC-victims (score range = 4–15) or ARC-bully-victims (score range = 4–15 on both victim and bully scale), with higher scores indicating greater frequency of ARC. Cronbach's alpha for this category was calculated at 0.76, indicating acceptable internal consistency.

Types of ARC experienced

The second measure comprised ten items formulated by the research team to delineate specific types of ARC. Drawing from established body image, eating disorder, and appearance-related questionnaires [ 19 , 40 , 79 ] these items explored several aspects of appearance, including body shape, body size, specific body attributes (e.g., breasts), disability, changes to appearance (e.g., scars), facial features, clothing or style, skin colour, age, and body tone or muscularity. Participants were asked if they had ever experienced these types of ARC. An example question is, “Have you ever been made fun of or teased online because of your body shape?” The 5-point Likert scales ranged from 1 (has not happened to me at all) to 5 ( several times a week). The internal consistency for this measurement was robust, with a Cronbach's alpha of 0.92.

Impact of ARC on the desire to change physical appearance

The third measure aimed to investigate the emotional and psychological impact of ARC. Informed by research that suggests an association between experiences of appearance-related bullying and the desire to change physical appearance [ 18 ], three items were utilised to evaluate the extent to which individuals desired to alter their physical appearance due to their experience with ARC. Participants were asked whether and how often they had felt a certain way about their appearance because of their experience with ARC. An example question is, “Have you ever felt the need to change your appearance through cosmetic procedures (e.g., nose job, boob job) because of appearance-related cyberbullying?” The 5-point Likert scale ranged from 1 (never) to 5 ( frequently). The internal consistency of this category was evidenced by a Cronbach's alpha of 0.86.

Body satisfaction

The Body Shape Questionnaire subscale (BSQ-8) was used to assess body dissatisfaction and body shape/weight concerns. The BSQ-8 contains 8 items that assess factors such as preoccupation with weight, dissatisfaction with specific body parts, and the desire to change one's body shape over the past month (4 weeks). Examples of items in the BSQ-8 include “Have you felt so bad about your shape that you have cried?” and “Have you avoided running because your flesh might wobble?” Participants indicated their agreement with each statement on a 6-point Likert scale from 1 ( never ) to 6 ( always ). Total scores for the BSQ ranged from 8 to 48, with higher scores indicating higher levels of body dissatisfaction and body shape/weight concerns. This scale was shown to have good internal consistency in this study (α = 0.91) and in past research [ 79 ].

Body esteem

The Body Esteem Scale for Adolescents and Adults (BESAA) [ 49 ] is a 23-item tool designed to assess an individual's cognitive, emotional, and behavioural responses to their body image, providing insights into their self-perception and body esteem. The BESAA has three subscales: BE-Appearance (general feelings about one's appearance; e.g., “I like what I see when I look in the mirror”) (ten items), BE-Weight (satisfaction with one's weight; e.g., “I really like what I weigh”) (eight items) and BE-Attribution (evaluations attributed to others about one's body and appearance (e.g., “People my own age like my looks”) (five items). Using a five-point a five-point Likert-scale ranging from 0 ( never ) to 4 ( always ), participants were asked to indicate the degree to which they agreed with each statement on. Total scores for the BESAA ranged from 23 to 115, with higher scores indicating higher body esteem. The BESAA is a widely used measure in research and clinical settings and was shown to have good internal consistency in this study (α = 0.94) and in past research [ 40 ].

The Body Image Shame Scale (BISS) [ 19 ] was used to measure experiences of body image shame. The BISS is a 14-item scale comprising a two-factor structure that assesses both externalised and internalised dimensions of body shame. The externalised dimension consists of seven items that assess judgments of being negatively evaluated or criticised by others based on one's physical appearance, such as feeling uncomfortable in social situations due to fear of criticism (e.g., " The relationship I have with my physical appearance makes it difficult for me to feel comfortable in social situations"). The internalised dimension includes seven items that focus on negative self-evaluations based on one's physical appearance, such as feeling like a defective person when seeing one's body in the mirror (e.g., "I choose clothes that hide parts of my body that I consider ugly or disproportional"). Additionally, a combined score for overall body shame was calculated. Participants rated each item based on the frequency of experiencing body image shame, ranging from 0 ( never ) to 4 ( almost always ). Total scores for the BISS ranged from 14 to 70, with higher scores indicating higher levels of body shame. This scale was shown to have good internal consistency in this study (α = 0.95) and in past research with a female adolescent sample [ 78 ].

Body appreciation

Feelings of body appreciation and acceptance were measured by the Body Appreciation Scale-2 (BAS-2) [ 72 ]. The BAS-2 is a 10-item scale consisting of a series of statements that capture positive attitudes and behaviours related to body image, such as valuing one's body for its functionality, expressing gratitude towards one's body, and treating one's body with kindness and respect. Using a 5-point Likert scale with responses ranging from 1 ( never ) to 5 ( always ), participants were asked to rate their level of agreement or disagreement with each statement (i.e., “I respect my body”) on. Total scores for the BAS-2 ranged from 10 to 50, with higher scores indicating greater body appreciation. The BAS-2 has shown good internal consistency in this study (α = 0.95) and in previous studies [ 72 ].

Eating disorder (ED) symptomology

The 8-item Child Eating Disorder Examination Questionnaire (chEDE-Q8) [ 39 ] was used to assess ED symptomology. The chEDE-Q8 is based on the EDE-Q (a gold standard diagnostic interview for EDs) but is modified for use in younger populations. The chEDE-Q includes 8-items that assess key attributes of eating disorders over the past 14-days, such as dietary restraint, eating concern, shape concern, and weight concern. Participants were asked questions such as “How many times in the past 14-days have you been trying to cut down on food to control your weight or shape?” in which they responded to on a 7-point Likert scale ranging from 1 ( no days ) to 7 ( every day ). Total scores for the chEDE-Q8 ranged from 8 to 56, with higher scores indicating greater eating disorder symptomatology. The chEDE-Q8 has been shown to have good internal consistency in this study (α = 0.92) and good convergent reliability, validity and internal consistency in young people aged 13–18 years [ 39 ].

Statistical analysis

Data analysis was conducted using SPSS® (version 27, IBM Corporation, Chicago, IL, USA). Prior to analysis, a series of assessments were performed to ensure data integrity, including checking for normality, homogeneity of variance, and identification of any potential outliers.

Descriptive statistics assessed the means and standard deviations (SD) for various body image and ED measures across different ARC groups (bullies, victims, bully-victims, and those with no experience of ARC). Notably, ARC-bullies were excluded from further analysis due to the small sample size. Pearson’s correlations were used to assess relationships between these measures, and ANOVA identified differences in body image and ED variable measures between ARC groups (i.e., ARC-victims, ARC-bully-victims, and no experience of ARC). The assumption of normality was met for ANOVA testing. Levene’s statistic was non-significant, indicating that the assumption of homogeneity of variances was met for all measures ( p  > 0.05).

Three separate multiple regression analyses (MRA) were conducted for each of the following criterion variables: the desire to change physical appearance through diet or exercise, the desire to change self-presentation (e.g., changing hair, makeup, or clothing), and the desire to change appearance through cosmetic procedures. Each model incorporated 11 predictor variables, including one demographic factor (i.e., age) and 10 distinct types of ARC, including ARC directed towards: body shape, body size, specific body attributes, facial appearance, changes to appearance, clothing or style, skin colour, age, disability and body tone or muscularity. the predictor variables included age and experiences of ARC-victimisation directed towards: body shape, body size, specific body attributes, changes to appearance, facial appearance, clothing or style, body tone or muscularity, skin colour, age, and disability.

Assumption testing included normality of the predictor variables, which was verified through normal probability plots; homoscedasticity, assessed via scatterplots of residuals; absence of multivariate outliers, confirmed using Mahalanobis distance (threshold ≤ 13.82 for df = 2, α = 0.001); and multicollinearity, assessed through tolerance values (all > 0.2) [ 1 ].

Preliminary analysis

A total of N  = 612 survey responses were collected. Missing data were handled using a combination of deletion and imputation methods. Responses flagged by Qualtrics as bot responses or empty surveys ( n  = 199) were excluded from the dataset. Additionally, responses with substantial missing self-report data were excluded ( n  = 77); this included participants who did not complete any items within one or more of the three ARC measures ( n  = 35) and those with more than 25% missing data across all four body image questionnaires and the eating disorder questionnaire ( n  = 42). Mean replacement was applied to participants with smaller amounts of missing data (17 data points across 5 participants). A Missing Completely at Random (MCAR) test was conducted to ensure the appropriateness of our data handling methods, and the results indicated that the missing data were random. After exclusions and imputations, a final total of N  = 336 survey responses were included in the analysis.

To ensure that the final sample was representative, we conducted an analysis comparing demographic data collected (i.e., age and education status) between the included ( N  = 336) and excluded ( N  = 77) participants. An independent samples t-test revealed no significant difference in age between included ( Mage  = 15.98, SD = 1.27) and excluded participants ( Mage  = 15.83, SD = 1.13); t(411) = − 0.957, p  = 0.339, with a mean difference of − 0.151 (95% CI − 0.461 to 0.159). Effect size measures indicated a small effect size (Cohen's d = − 0.121, 95% CI − 0.369 to 0.127). A chi-square test also showed no significant difference in the distribution of education status (secondary school, university, or neither) between included and excluded participants; χ 2 (2, N = 412) = 1.366, p  = 0.505.

An a priori power analysis was conducted using G*Power [ 21 ]. The analysis indicated that 152 participants was recommended to achieve adequate statistical power considering an effect size of 0.15, error probability of 0.05, and desired power level of 0.90. With our final sample size of 336 participants, we can confidently affirm that our study possesses ample statistical power. Descriptive statistics are summarised in Table  1 .

Participants were categorised based on their experiences relating to ARC, aligning with the reporting of the BCyQ. Those with a total score exceeding 17 on the ARC-victimisation scale ( N  = 167) were categorised as 'ARC-victims.' Individuals scoring above 18 on the ARC-bully scale ( N  = 9) were categorised as 'ARC-bullies ' [ 60 ]. Participants who scored above these thresholds on both scales ( N  = 33) were classified as ‘ARC-bully-victims.’ Those scoring below 17 on the ARC-victimisation scale and below 18 on the ARC-bully scale were categorised as ‘no experience’ ( N  = 127).

Prevalence and characteristics of cyberbullying experiences

Most of the sample reported at least one experience of cyberbullying ( n  = 320; 98.2%), and 62.2% ( n  = 209) reported ARC. Of those that reported general cyberbullying, 20.6% ( n  = 67) reported cybervictimisation, 1.5% ( n  = 5) reported cyberbullying perpetration, 76.1% ( n  = 248) reported cyberbullying and cybervictimisation, and 1.8% ( n  = 6) reported no experience of cyberbullying. Of those that reported ARC, 49.7% ( n  = 167) reported ARC-victimisation, 2.7% ( n  = 9) reported ARC-perpetration, 9.8% ( n  = 33) reported ARC-perpetration and ARC-victimisation and 37.8% ( n  = 127) reported no prior experience of ARC.

Among the participants with reported experiences of ARC-victimisation, the majority indicated that it was directed at aspects of their physical appearance, with body shape and size (fatness, thinness) being the most commonly targeted attributes. Specific body attributes, such as breasts and bottom, facial appearance, clothing or style, changes to appearance (e.g., scars, burns, skin conditions) and body tone and muscularity were also frequently reported. Less commonly, ARC-victimisation focused on age, skin colour and disabilities.

Furthermore, 96.2% of participants who reported an experience of ARC reported feeling like they would like to change their body shape, size, or physical appearance (i.e., through diet or exercise) due to ARC, with 74.2% reporting experiencing this feeling often or always. Additionally, 95.2% of participants felt like they would like to change how they present themselves (i.e., changing hair, makeup, or clothing) because of ARC, with 69.4% reporting feeling this way often or always. Finally, 81.3% of participants felt like they needed to change their appearance through cosmetic procedures (i.e., nose job, boob job etc.) due to ARC experiences, with 40.7% indicating experiencing such feelings often or always.

Analysis of ARC effects on body image outcomes and desire to change appearance

The subsequent analyses examined the impact of ARC experiences (i.e., ARC-victim, ARC-bully-victim, ARC-bully, and no experience of ARC) on psychological constructs such as body shape concern, body esteem, body shame, body appreciation, and eating disorder symptomology. As shown in Table  2 , participants categorised as ARC-victims and both ARC-bully-victims had higher mean scores for body shape concerns, body shame, and eating disorder symptomology, and lower mean scores of body esteem and appreciation, compared to ARC-bullies and those without ARC experiences. Pearson's correlation analysis (Table  3 ) revealed significant associations, indicating that higher rates of ARC-victimisation correlated with increased body shape concerns, body shame, and eating disorder symptomology, while negatively correlating with body esteem and body appreciation.

Due to the small sample size of reported ARC-bullies ( n  = 9; 2.7%), this group was excluded from further analysis.

The ANOVAs revealed statistically significant differences between the three groups (ARC-victims, ARC-bully-victims, and no experience of ARC) on scores related to body shape concern body esteem body shame body appreciation and eating disorder symptomology. Subsequent post-hoc analyses utilising Bonferroni correction indicated significant pairwise differences between groups ( p  < 0.05). Specifically, ARC-victims and ARC-bully-victims demonstrated significantly higher body shape concern, body shame and eating disorder symptomology, compared to those with no experience. Additionally, ARC-victims and ARC-bully-victims also reported lower body esteem and body appreciation than those with no experience. The results suggest that individuals who have encountered ARC, whether as cybervictims or cyberbully-victims, exhibit more negative body image and higher ED symptomology compared to those with no such experiences.

ARC-victimisation on the desire to change appearance

To investigate the association between specific types of ARC-victimisation and the desire to change aspects of appearance in adolescent females because of perceived ARC, Pearson’s correlations (Table  4 ) and three standard MRAs were performed (Table  5 ).

The model incorporated 11 predictor variables, including one demographic factor (i.e., age) and 10 distinct types of ARC, including ARC directed towards: body shape, body size, specific body attributes, facial appearance, changes to appearance, clothing or style, skin colour, age, disability and body tone or muscularity. The analysis was based on data from 209 participants with a reported experience of ARC.

Association between ARC-victimisation and the desire to change physical appearance through diet or exercise

Significant positive correlations were found between the desire to change one’s body shape, size, or elements of their physical appearance (e.g., through diet or exercise) because of perceived ARC, and experiences of ARC-victimisation directed towards body shape, body size, specific body attributes, changes to appearance, facial appearance, clothing or style, and body tone or muscularity ( p  < 0.001). Additionally, age and ARC directed towards skin colour and age, also showed significant, albeit weaker ( p  < 0.05) positive correlations. No significant correlations were found between experiences of ARC directed towards a disability ( p  = 0.06) and the desire to change the abovementioned elements of physical appearance.

The overall model was statistically significant, accounting for 37.4% of the variance in the desire to change one's body shape, size, or aspects of physical appearance (for instance, via dieting or exercising) because of perceived ARC and experiences of ARC-victimisation ( F (11,197) = 10.688, p  < 0.001). The regression coefficients for the predictors in the model indicated that ARC directed towards one’s body shape ( β  = 0.280, t  = 2.904, p  = 0.004) and specific body attributes ( β  = 0.166, t  = 2.302, p  = 0.022) were the only individual significant predictors of an individual’s desire to change their body shape, size, or elements of physical appearance (e.g., through diet or exercise).

Association between ARC-victimisation and the desire to change self-presentation

Significant positive correlations were found between the desire to change self-presentation (e.g., changing hair, make up, or clothing) because of perceived ARC and experiences of ARC directed towards body shape, body size, specific body attributes, changes to appearance, facial appearance, clothing or style, and body tone or muscularity ( p  < 0.001). Additionally, ARC directed towards a disability, skin colour, and age, also showed significant, albeit weaker ( p  < 0.05) positive correlations.

The second regression model, incorporating the abovementioned predictors, was also significant, and explained 23.3% of the variance in the desire to change self-presentation due to ARC-victimisation ( R 2  = 0.233, F (11, 197) = 5.426, p  < 0.001) in adolescent females. However, the regression coefficients for the predictors in the model indicated that no individual variables contributed significantly to the prediction of the desire to change self-presentation due to ARC-victimisation.

Association between ARC-victimisation and the desire to change appearance through cosmetic procedures

Significant positive correlations were found between the desire to change one’s appearance through cosmetic procedures because of perceived ARC, and experiences of ARC directed towards body shape, body size, specific body attributes, changes to appearance, facial appearance, skin colour, and body tone or muscularity ( p  < 0.001). Additionally, ARC directed towards a disability, clothing or style, and age, also showed significant, albeit weaker ( p  < 0.05) positive correlations. No significant correlations were seen between age of participants ( p  = 0.172) and the desire to change one’s appearance through cosmetic procedures.

The third regression model, incorporating the abovementioned predictors, was statistically significant and explained 22.3% of the variance in the desire to change appearance through cosmetic procedure ( R 2  = 0.223, F (11, 197) = 5.132, p  < 0.001) in adolescent females. The regression coefficients for the predictors in the model indicated that, individually, ARC directed towards specific body attributes ( β  = 0.161, t  = 2.01, p  = 0.046) was the only variable to significantly contribute to the prediction of the desire to change one’s appearance through cosmetic procedures.

The impact of ARC on adolescent mental health is an emerging and critical area of research. Our study contributes valuable insights to the growing body of cyberbullying and body image literature by examining the intricate relationship between ARC and various psychological variables in adolescent females. Collectively, our findings reveal that experiences of ARC-victimisation are associated with significantly elevated levels of body shape concern, body shame, and ED symptomology, coupled with diminished levels of body esteem and body appreciation in adolescent females. Additionally, such experiences were associated with a heightened desire among adolescent females to alter their physical appearance through practices such as dieting, exercise, changing self-presentation, and/or cosmetic procedures. These findings support our hypothesis, and highlight the detrimental effects of ARC on body image and eating disorder-related variables in adolescent females.

The proliferation of social media use among adolescents has coincided with a surge in cyberbullying, particularly concerning body image and EDs among adolescent females. While valuable, prior research into ARC is limited [ 5 ] and often faces methodological challenges such as the absence of validated tools for assessing ARC's prevalence, frequency, types, and consequences.

Our investigation into ARC utilises three targeted measures to explore its different dimensions among adolescent females. These measures allow us to identify the prevalence and specific roles individuals assume in ARC situations, the types of ARC experienced, and the extent to which ARC is perceived to impact an individual’s desire to alter their physical appearance. This approach facilitates a deeper understanding of ARC and its influence on adolescent body image and ED variables, emphasising the critical need for methodological progress in the study of cyberbullying. The lack of suitable measures necessitated the curation of items which captured the varied outcomes of ARC. We acknowledge that while these measures were based on frameworks established in previous research [ 5 , 25 , 76 ], these measures have not undergone rigorous testing to ensure their psychometric properties are suitable. Accordingly, we encourage researchers to develop measures in this space which are generalisable across diverse populations and cultural contexts.

In line with earlier results from Berne et al. [ 5 ], body shape and size emerged as the primary focus of ARC encounters reported by our sample of adolescent females. Building on this foundation, our findings indicate that ARC targeting body shape and size is significantly associated with the desire among adolescent females to change their body shape, size, or other physical attributes through means such as dieting or exercising because of perceived ARC. Considering the established link between dieting behaviours and the increased risk of developing long-term disordered eating behaviours and EDs during adolescence, it is critical to identify the risk factors that lead to dieting. Our study uniquely contributes to this understanding by highlighting the potential role of ARC as an important factor associated with dieting behaviours among adolescent females.

Furthermore, encounters with ARC were linked to a heightened desire among adolescent females to change their self-presentation, involving adjustments to their hair, makeup, and/or clothing. Drawing from existing literature, the inclination to alter one's appearance has been recognised as a potential catalyst for the development of negative body image perceptions, reduced self-esteem, and other adverse psychological outcomes [ 12 , 58 ]. Moreover, societal pressures to conform to prevailing beauty standards have been associated with the adoption of unhealthy weight control behaviours and disordered eating patterns [ 20 ]. Hence, it is essential for both research efforts and intervention strategies to thoroughly acknowledge the influential role of ARC in shaping these dynamics.

Finally, our study aligns with existing research that has indicated a connection between bullying and an increased desire for cosmetic procedures among adolescent females [ 43 ]. Importantly, we extend current research by revealing that ARC-victimisation is significantly associated with the desire to alter aspects of physical appearance through cosmetic procedures because of perceived ARC. Specifically, cyberbullying targeting specific body attributes emerged as the sole individual variable significantly associated with this desire, demonstrating the intricate consequences of ARC among adolescent females. Collectively, these findings highlight the urgent need for targeted interventions, such as age-appropriate social media policies, health promotion programs encouraging positive online behaviour, and strategies to address the impacts of ARC to protect the mental well-being of adolescent females.

Limitations and implications

While our study provides valuable insights into the impacts of ARC on adolescent females, it is not without limitations. Firstly, the sample consisted exclusively of female (sex assigned at birth) participants within a narrow age range. Although this specificity is a strength, allowing for a detailed exploration within a highly affected group, it also limits the applicability of our results to broader, more diverse populations. Thus, diverse, and intersectional analyses, incorporating various genders, ethnicities, and socioeconomic backgrounds, are essential to contributing to a more comprehensive understanding of how ARC affects different demographic groups. This is especially important considering the prevalence of body image disturbances and eating disorders among gender and sexually diverse communities [ 11 ] and racial minority groups [ 8 , 9 ]. Additionally, the reliance on self-report data may introduce bias, and the cross-sectional nature of the study limits our ability to establish causal relationships. The cross-sectional design hinders causal inferences, emphasising the need for longitudinal investigations to uncover the temporal dynamics of ARC and its psychological consequences.

Moreover, our study did not include a measure for ARC bystander experiences, a vital component considering the prevalence of bystander involvement in cyberbullying incidents, especially within social media contexts [ 28 ]. Addressing this gap is crucial, as bystanders' exposure to ARC could amplify societal beauty standards, contributing to the internalisation of the thin ideal and body dissatisfaction among adolescents. Specifically, observing cyberbullying that targets body image and appearance may exacerbate young females' concerns over being subjected to similar negative feedback, potentially driving them towards unhealthy behaviours to conform to societal body image expectations. Future research should incorporate assessments of cyberbystander experiences to gain a comprehensive understanding of the broader impact of cyberbullying on adolescent well-being. Including the perspective of bystanders will enhance our insight into the ways in which witnessing ARC influences adolescents’ perceptions of body image, self-esteem, and overall mental health. Expanding the scope to include ARC-bystanders is essential for a more complete evaluation of the role of social and cultural norms in perpetuating body image issues and for crafting solutions that foster healthier online interactions.

Another potential limitation of our study is the design of one of the ARC measures, which was intended to capture participants' perceptions of the impact of ARC on their desire to change their appearance through diet, exercise, or cosmetic procedures. Accordingly, our findings likely reflect perceived impacts rather than a causal relationship . Future research should employ longitudinal designs or alternative methodologies to better disentangle the relationships between cyberbullying experiences, body dissatisfaction, and body modification desires. For instance, future studies could include measures of general body dissatisfaction and desire for appearance changes without directly attributing these to ARC experiences, providing a clearer understanding of these comparative dynamics.

Additionally, the absence of a standard definition of ARC in our measure presents another limitation. This omission may lead to varied interpretations of what constitutes cyberbullying, potentially impacting the consistency and reliability of our findings. Including a clear definition of ARC before presenting the questions could help ensure a more uniform understanding among participants and improve the reliability of self-reported data. Future studies should incorporate such definitions to enhance the clarity and accuracy of the data collected.

Future research will provide the opportunity to enhance our comprehension of ARC among adolescents and its complex effects on mental health. One direction involves integrating neurobiological approaches to investigate the neural mechanisms involved in ARC, body dissatisfaction, and eating disorders on the developing adolescent brain. This approach is pivotal in identifying how ARC impacts key developing brain areas involved in emotional regulation, self-esteem, and body image processing, such as the limbic system, prefrontal cortex, and the visual and somatosensory cortices. These areas are critical for managing emotions, self-perception, and integrating visual and somatic information related to one's body image.

Employing advanced neuroimaging techniques like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), researchers have been able to map the specific brain changes and neural circuits altered by the negative experiences associated with ARC and body image concerns. For example, studies utilising fMRI have revealed significant alterations in the prefrontal cortex (PFC) and limbic system—areas crucial for emotional regulation and stress response—in adolescents exposed to victimisation [ 23 , 69 ]. These changes highlight the profound impact of cyberbullying on the emotional and psychological well-being of adolescents. Similarly, research has identified aberrant activation in these regions among adolescents with body image concerns and EDs, highlighting the neural basis of such issues [ 59 , 81 ]. Further, investigations employing DTI have documented changes in white matter integrity in brain areas pivotal for body image and self-perception in adolescents with EDs [ 26 , 31 ]. This comprehensive mapping not only corroborates the subjective psychological distress reported by victims of ARC, but also begins to reveal the underlying neural mechanisms, laying a foundational basis for the development of targeted interventions. Thus, neuroimaging research offers critical insights into the intricate relationship between ARC and adolescent brain development, charting a path toward more effective prevention and treatment strategies. By incorporating these neuroimaging findings into therapeutic practices, we can more adeptly address the multifaceted impacts of ARC on adolescent mental health, contributing to the advancement of nuanced and effective interventions.

To summarise, this is the first study to provide a robust examination of appearance-related cyberbullying and its implications for the mental health and well-being of adolescent females. The research identified a strong positive correlation between experiences of appearance-related cybervictimisation and heightened concerns about body shape, body shame, and symptoms of eating disorders. Inversely, these experiences were linked to lower body esteem and body appreciation. Additionally, ARC-victimisation was associated with the inclination among adolescent females towards changing their appearance through dieting, exercising, altering self-presentation, and considering cosmetic procedures. The nuanced insights into specific targets of ARC and desires for change, coupled with a detailed analysis of psychological outcomes and regression models, enhance our understanding of the complex interplay between ARC and its effects on self-perception, body image, and eating disorders in adolescent females. These findings highlight the need for future research to explore potential prevention interventions to support the mental well-being of adolescent females affected by ARC.

Availability of data and materials

No datasets were generated or analysed during the current study.

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The first author was supported by an Australian Government Research Training Program (RTP) Scholarship. This research is supported by the Australian Commonwealth Government’s ‘Prioritising Mental Health Initiative’ (2018-25).

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Prince, T., Mulgrew, K.E., Driver, C. et al. Appearance-related cyberbullying and its association with the desire to alter physical appearance among adolescent females. J Eat Disord 12 , 125 (2024). https://doi.org/10.1186/s40337-024-01083-z

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Received : 17 May 2024

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DOI : https://doi.org/10.1186/s40337-024-01083-z

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