How to Present Data

© Dr Andrew Clegg p. 1-11

Data Analysis for Research Presenting Data

1.7 Presenting Data

Presenting numerical data accurately is an important element of essays, reports, presentations and posters.

The aim of the following section is to provide a few basic guidelines on how to incorporate graphs and tables

effectively, and at the same time creatively, into your work.

1.7.1 Using Graphs and Charts

Computer spreadsheets such as Excel, now allow you to produce a range of graphs and charts (bar charts,

column charts, pie charts, graphs) quickly and easily. As such, graphs can be used effectively to enhance the

quality of reports, essays, posters and presentations. Carefully thoughtout graphs can bring to life data from

tables and allow comparisons to be made quickly. However, poorly designed graphs can easily fail and

weaken a piece of work. It is very common for students to rush in and produce a whole plethora of charts and

graphs without giving much thought to the data set they are using or what type of output would be most

appropriate. Therefore is it important to take your time and give careful consideration to what you actually

want to achieve.

First, ask yourself the following questions:

Is a graph or chart necessary?

Students often use diagrams as a means of ‘padding out’ work and as a result graphs not referred to

in the text become ‘window-dressing’. Therefore carefully consider whether the graph is actually

needed - ask yourself whether the graph helps the reader understand a particular point or aspect of the

data. If it does fine - but make sure that is it integrated and referred to fully in your dicussion. If not,

provide a simple verbal description.

What is the purpose/objective/outcome?

Are you producing a graph for an essay/report, poster or presentation? While the basic guidelines and

formatting options are generic, you need to consider the overall purpose and intended audience. For

example graphs produced for a presentation will be different to those produced for inclusion in an

essay or a PowerPoint presentation. Carefully consider the importance of visual impact and clarity,

and the type of media you are using.

What is the nature of the data set you are using?

Graphs often fail because an incorrect chart type has been used or the graph is too complicated.

Therefore before you start carefully consider the actual nature of the data set you are using. Above all

you need to distinguish between ‘continuous’ data and ‘discrete’ quantities. A continuous quantity is

that which can be chosen to any degree



of precision. Examples of continuous quantities include mass (kg), length (m), and time (s). Discrete

quantitites in contrast can only be expressed as integers (whole numbers) for example: 3 computers, 5 cars,

4 houses. In trying to decide if something is continuous or discrete, decide whether it is like a stream (continuous)

or like people (discrete). Continuous variables are usually plotted on a graph as this demonstrates the existence

of a casual relationship between the data points, whereas discrete data series are plotted as bar charts or

histograms.

In addition to the nature of the data set also consider whether you referring to absolute values or percentage

distributions? This will have a significant influence on the chart type that you use. Second, how complicated

is the data set?; is it best represented as a graph or a table?; can the data be manipulated to make it easier to

use, for example by reformatting columns or excluding columns? Be prepared to modify the data set if

necessary. However, make sure that when you do this you do not alter the accuracy or the representativeness

of the data set you are using.

The following graphs highlight the issue of using appropriate chart types.

Figure 2: Car Sales for Rover, BMW, and Jaguar 1995-2000

[Source: Believe, M., 2001]

In Figure 2, car sales for leading manufacturers have been plotted for a 5-year time period. In this instance we

are dealing with discrete data (as you cannot sell half a car!). However, the data has been plotted as a line

graph - is this correct? The answer is YES as there is a logical year to year link and the ‘joining the dots’

technique illustrates the casual relationship between the x-axis variables. This data could have also been

presented as a column chart. Compare this to Figure 3.



Figure 3: Resident Opinions to the Development of New Housing in Greenfield Sites in West Sussex


Figure 3 highlights the attitudes of residents to new housing development in West Sussex. Is this graph the

most effective form of presentation? The answer is NO. In this instance joining the dots is not appropriate as

there is no casual relationship between x-axis variables. In this instance a column chart would have been

more effective - see Figure 4.





While Figure 4 is a definite improvement, is there any way of making the data in Figure 4 more effective so that

it really highlights the differences in resident opinions between the different areas? Again the answer is YES.

So far we have graphed the absolute values relating to resident opinions. If we were to change this to a

percentage distribution we could present the data as a bar chart - see Figure 5.



As you can see in Figure 5, utilising the percentage distribution really succeeds in highlighting the differences

in residents opinions.

Let us consider a further example. Figure 6 illustrates the mean monthly temperature and rainfall totals for

Edinburgh. Is the graph appropriate? Again the answer is YES as there is a logical year to year link and the

‘joining the dots’ technique illustrates the casual relationship between the x-axis variables. However, although

this graph allows us to compare monthly temperature and rainfall totals, the high values for temperature have

masked the values for rainfall and a degree of accuracy has been lost. To overcome this we can change the

type of the graph and plot temperature and rainfall on separate axis - see Figure 7.



Figure 6: Mean Monthly Temperature (OC) and Rainfall (mm) for Edinburgh

[Source: Bartholomew, 1987]

Figure 7: Mean Monthly Temperature (OC) and Rainfall (mm) for Edinburgh

[Source: Bartholomew, 1987]

So far our discussion has concentrated on the use of line graphs, column and bar charts. Another type of chart

frequently used is the pie chart. The overall total number of cases represented by the pie chart should equal

the sample size, or aggregate to 100% where segments denote proportional frequencies (Riley et al, 1998, p.

172). Let us consider some specific examples.



Figure 8: The Distribution of Serviced Establishments in Torbay by Size

[Source: Clegg, 1997]

Figure 8 refers to the percentage distribution of serviced establishments in Torbay by size. When using pie

charts it is important to remember that pie charts can only graph the percentage distribution of one specific

variable and cannot be used to analyse time series data. For example, we could not use a pie chart to illustrate

the car sales for Rover, BMW and Jaguar referred to in Figure 2. However, we could use a pie chart to analyse

the market share of car sales for a specific year (see Figure 9).

Figure 9: Market Share of Car Sales for Rover, BMW and Jaguar in 1995




Rover41%

BMW27%

Jaguar32%

By drawing and then combining two or more pie charts we could then compare market share for different

years (see Figure 10).

Figure 10: Market Share of Car Sales for Rover, BMW and Jaguar in 1995 and 1999


Programmes such as Excel will only allow you to draw one pie chart at a time - however once drawn you can

arrange a number of pie charts on a worksheet and print them out. Alternatively, you can cut and paste Excel

charts into Word or Publisher.

Clearly, using the most appropriate type of graph is very important to ensure that the data is presented

accurately. In addition to the type of chart it is also important to ensure that the graph is presented effectively.

Rover27%

BMW32%

Jaguar41%

1995

1999



1.7.2 Producing Graphs

When producing graphs a number of basic rules and guidelines need to be considered. These are:

Is the graph completely self-explanatory?

Is the graph clearly titled, labelled and sourced?

The axes should be labelled, and clear indication given as to the scales being used, and the

numerical quantities being referred to;

All dates and times periods should be explicitly stated in the title, and on the appropriate axis;

In titles do not write ‘A Graph Showing....’. This is obvious - instead refer to the specific content of

the graph (see examples given in this section);

The source of the data should be included, especially if they are drawn from published material.

Are elements of the graph distinguishable?

When using charts it is important that the different data series are clearly distinguishable other-

wise the graph will be meaningless;

Consider carefully the number of data series you intend to graph. Too much data will over

complicate a graph and reduce its impact;

When using pie charts it is recommended that the number of segments should not be too large.

Too many segments make charts confusing and difficult to read;

If charts are to be included in a black and white report, avoid shadings that involve colours as the

distinctions will be clearly lost. Try and keep the use of colours to a minimum. Use one colour and

different shades;

Ensure that each segment of the pie chart is clearly labelled and that the percentage values have

been added to indicate quickly which are the principal groups and by how much;

Avoid repetition; if labels and percentage values have been added to a pie chart there is no need

to include the legend.



1.7.3 2D or 3D Graph Formats

Excel and similar packages allow you to enhance the quality of graphs by making them 3D. However, the

use of 3D formatting needs to be treated with caution. If you are producing graphs on A4 for a presentation 3D

charts can work effectively. However, if you are preparing graphs for inclusion in an essay or report 3D charts

may not be appropriate and you may be better off with a standard 2D version. There are no hard and fast rules

on this issue and, ultimately, the type of chart produced and the type of formatting applied will depend on the

nature of the data set used.

Let me illustrate this by referring to examples included in this section. Below is Figure 4, showing resident

attitudes to housing development in West Sussex. At the moment this is a standard 2D column chart. Let us

convert it into a 3D chart.

2D

3D



Do you think this chart is effective? It looks good but is not quite as easy to read as the standardchart. It is noticeable that in order to create a 3D chart Excel has to shrink the original chart. Thisis where problems lie, as in making the graph smaller the overall impact of the graph is diminished.

Let us try another example. Below is Figure 8, which refers to the distribution of servicedaccommodation in Torbay. As before, let us convert this into a 3D chart.

In this instance the 3D chart is actually quite effective and has enhanced the standard 2D chartconsiderably. The basic rule seems to be that simple 2D charts can be converted into 3D chartsquite effectively. However, the more detailed and complicated the standard chart the less effectiveit becomes when you make it 3D. Your best option is to experiment with different data sets andformatting options to find the most effective form of presentation.

2D

3D



1.7.3 Using Tables

In addition to charts, tables are also an effective way of presenting information. Again when producing tables

a number of guidelines can be followed:

Consider the purpose of presenting the data as a table as there may be better ways of presenting it;

Avoid the temptation of just photocopying tables out of text books and sticking into essays. In many

cases, tables often contain information superfluous to the reader. Be prepared to modify data sets so

that only relevant information is included in your table;

Make sure that tables are completely self-explanatory. Provide a table number and title for each table.

If abbreviations are used when labelling then provide a key;

Make sure that the content of the table is fully referred to in the text - make sure that tables are not

basically ‘window-dressing’;

Allow sufficient space when designing the table for all figures to be clearly written;

Make sure that the table/data is fully sourced.

Again let me illustrate with a number of examples.

Table 2 is an example of a table I created for the Arun Tourism Strategy document. Does the table meet the

guidelines highlighted above? The answer is YES. The table is clear, well laid out, titled, sourced and self-

explanatory. Shading has also been used to try and enhance the visual impact of the table.

Table 2: Visits Abroad by UK Residents 1994-1997

Area of Destination

Year Total (‘000) North America Western Europe Rest of World

1994 39,630 2,927 32,375 4,328

1995 41,345 3,120 33,821 4,404

1996 42,050 3,584 33,566 4,900

1997 45,957 3,594 37,060 5,303

% Change

1996/1997 +9 0 +10 +8

[Source: ETB, 1999]

Number of Visits (000’s)



Now consider Table 3 which refers to regional tourism spending in England in 1997. Again this is a clear table

that for the purposes of the tourism strategy had to contain a lot of detail. If you were using this table to

illustrate patterns of regional spending it could be simplified to show the most obvious or important patterns.

For example in Table 1 it is evident that tourism spending is highest in the West Country and lowest in

Northumbria.

The table could therefore be easily modified to really reinforce this message (see Table 4). Notice that in the

amended Table 4, I have also changed the title so that the content of the new table becomes self-explanatory

and reflects the actual purpose of the table. Table 3 could have also been modified by removing specific

columns thereby emphasising the patterns of spending in particular market areas.

Table 3: The Regional Distribution of Tourism Spending in England, 1997

[Source: ETB, 1998]

Destination

England

Cumbria

Northumbria

North West England

Yorkshire

Heart of England

East of England

London

West Country

Southern

South East England

All

Tourism

£11,665

%

3

3

9

8

11

13

9

24

11

9

Holidays

£7,725

%

5

3

8

8

9

14

6

30

10

8

Short

Holidays

(1-3 nights)

£2,505

%

5

3

11

7

14

11

13

17

10

9

Long

Holidays

(4+ nights)

£5,215

%

5

3

6

8

7

15

2

37

11

7

Business

and Work

£2,055

%

1

3

12

9

15

14

15

10

3

10

VFR

£1,415

%

1

5

10

10

16

12

17

10

9

12



Destination

England

Northumbria

East of England

West Country

South East England

All

Tourism

£11,665

%

3

13

24

9

Holidays

£7,725

%

3

14

30

8

Short

Holidays

(1-3 nights)

£2,505

%

3

11

17

9

Long

Holidays

(4+ nights)

£5,215

%

3

15

37

7

Business

and Work

£2,055

%

3

14

10

10

VFR

£1,415

%

5

12

10

12

Table 4: Selected Regional Differentials in the Distribution of Tourism Spending in England, 1997

[Source: ETB, 1998]

Documents

How to Present Data