Choosing a method of data display

General recommendation Before constructing any display of epidemiologic data, it is important to first determine the point to be conveyed. Are you highlighting a change from past patterns in the data? Are you showing a difference in incidence by geographic area or by some predetermined risk factor? What is the interpretation you want he reader to reach? Your answer to these questions will help to determine the choice of display .

As a general recommendation, use a table for precise numbers, for large amounts of numbers, and if there is a great range between the largest and smallest figures. Use a graph for showing trends and relationships, displaying changes over time, and for explaining a point vividly. Use either a table or a graph for comparisons, and for showing parts of a whole .

Often the choice between presenting data in a table or graph is arbitrary as both will work. In general when presenting lots of data e.g. in an annual report it is best to vary how the data is presented by making use of both tables and graphs. A graphical presentation of data has the advantage of enabling a person to visualise a relationship between data i.e. proportions in groups. There is also a subjective nature to this as some individuals find it easier to interpret tables while others find a visual representation more easy to interpret.

If you decide that a graph is the best way to present your information, then no matter what type of graph you use, you need to keep in mind the following 10 tips :

• convey an important message
• decide on a clear purpose
• draw attention to the message, not the source
• experiment with various options and graph styles
• use simple design for complex data
• make the data 'speak'
• adapt graph presentation to suit the target audience
• ensure that the visual perception process is easy and accurate
• avoid distortion and ambiguity
• optimize design and integrate style with text and tables
• describing one variable

The first step is to describe one variable which is crucial before one starts to compare two or more variables. The table below summarises the most common presentation formats for the different types of variables, from the "simplest" to the more "complex".

Describing ONE variable
Variable	Aims	Table	Graph
Binary / dichotomous	Describe with proportions	Frequency distribution table	Bar graph, Pie graph
Nominal (categorical not ordered)	Describe with proportions	Frequency distribution table	Bar graph, Pie graph
Ordinal (categorical (ordered)	Describe with proportions	Frequency distribution table (also cumulative)	Bar graph(also cumulative), Pie graph
Numerical discrete	Describe with proportions, means and standard deviation	Frequency distribution table (also cumulative), Table of descriptive statistics	Bar graph (also cumulative), Histogram (if large number of values)
Numerical continuous	Describe with means, medians, standard deviation, quartiles	Frequency distribution table (group frequencies or cumulative), Table of descriptive statistics	Histogram (also cumulative), Frequency polygon, Box-and-whisker plot, Violin plot, One-way scatter plot

Describing two variables together There are potentially 5x5 = 25 combinations of the types of variables mentioned in the table above; there are many potential graphs and tables to describe these. The important thing is that you understand what you wants to show in those tables or graphs. For describing two variables (X and Y) together the strategy is basically the following:

First, consider one variable as the "outcome" (Y) and the other as the "factor" (X), i.e. explanatory variable. Then describe the outcome (Y) in each group that you can make with the factor (X). Remember that the outcome will be described according to its nature as explained above (univariate description).

Below you find a simple summary of describing two variables together.

Describing TWO variables together
Variable	Aims	Table	Graph
Two categorical variables	Identify relationships, patterns in the data	Contingency table	Grouped bar graph, Stacked bar graph, Component bar graph, Mosaic plot
Two numerical variables	Contingency table (group frequencies)	Line graph (also cumulative), Scatter plot (with or without regression line)
One categorical and one numerical variable	Contingency table, Table of descriptive statistics (mean, median, mode, etc)	Scatter plot, Box-and-whisker plot, Bar graph (showing mean or median with ± standard deviation)

There are typical table formats for presenting results of cohort and case-control studies.

Time series Time series is a special case of describing two variables where the factor (X) variable is always the "time". Selecting a method of displaying time series data is based on certain conditions .

Times series data
Conditions		Aims	Table	Graph
Numbers of cases (epidemic or secular trend)	1 or 2 sets	Display frequency distribution, trends in numbers over time	Frequency table	Histogram
	2 or more sets			Frequency polygon
Rates	Range of values ≤ 2 orders of magnitude	Display trends in rates over time		Arithmetic scale line graph
	Range of values ≥ 2 orders of magnitude	Display rate of change over time		Semi-logarithmic scale line graph

References

FEM PAGE CONTRIBUTORS

Editor

Agnes Hajdu

Original Author

Alain Moren

Contributors

Maarten Hoek

Lisa Lazareck

Agnes Hajdu