Data Analysis for Research
1.7
Presenting Data
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
© Dr Andrew Clegg
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Presenting Data
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.
Š Dr Andrew Clegg
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Data Analysis for Research
Figure 3:
Resident Opinions to the Development of New Housing in Greenfield Sites in West Sussex
[Source: Believe, M., 2001] 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. Figure 4:
Resident Opinions to the Development of New Housing in Greenfield Sites in West Sussex
[Source: Believe, M., 2001] Š Dr Andrew Clegg
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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. Figure 5:
Resident Opinions to the Development of New Housing in Greenfield Sites in West Sussex
[Source: Believe, M., 2001] 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.
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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. Š Dr Andrew Clegg
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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
[Source: Believe, M., 2001] Š Dr Andrew Clegg
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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
1995 Rover 27% Jaguar 41%
BMW 32%
1999 Jaguar 32% Rover 41%
BMW 27%
[Source: Believe, M., 2001] 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.
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Data Analysis for Research
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?
© Dr Andrew Clegg
When using charts it is important that the different data series are clearly distinguishable otherwise 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.
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1.7.3
Presenting Data
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
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Do you think this chart is effective? It looks good but is not quite as easy to read as the standard chart. It is noticeable that in order to create a 3D chart Excel has to shrink the original chart. This is 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 serviced accommodation in Torbay. As before, let us convert this into a 3D chart.
2D
3D In this instance the 3D chart is actually quite effective and has enhanced the standard 2D chart considerably. The basic rule seems to be that simple 2D charts can be converted into 3D charts quite effectively. However, the more detailed and complicated the standard chart the less effective it becomes when you make it 3D. Your best option is to experiment with different data sets and formatting options to find the most effective form of presentation. Š Dr Andrew Clegg
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Data Analysis for Research
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:
Visits Abroad by UK Residents 1994-1997 Area of Destination
Year
Total (‘000) North America Number of Visits (000’s)
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
+9
0
+10
+8
% Change 1996/1997
[Source: ETB, 1999] 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 selfexplanatory. Shading has also been used to try and enhance the visual impact of the table.
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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. Table 3:
The Regional Distribution of Tourism Spending in England, 1997 All
Holidays
Tourism Destination England
Short
Long
Business
Holidays
Holidays
and Work
VFR
(1-3 nights) (4+ nights)
£11,665
£7,725
£2,505
£5,215
£2,055
£1,415
%
%
%
%
%
%
Cumbria
3
5
5
5
1
1
Northumbria
3
3
3
3
3
5
North West England
9
8
11
6
12
10
Yorkshire
8
8
7
8
9
10
Heart of England
11
9
14
7
15
16
East of England
13
14
11
15
14
12
9
6
13
2
15
17
West Country
24
30
17
37
10
10
Southern
11
10
10
11
3
9
9
8
9
7
10
12
London
South East England
[Source: ETB, 1998] 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.
© Dr Andrew Clegg
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Data Analysis for Research
Table 4:
Selected Regional Differentials in the Distribution of Tourism Spending in England, 1997
All
Holidays
Tourism Destination England
Short
Long
Business
Holidays
Holidays
and Work
VFR
(1-3 nights) (4+ nights)
£11,665
£7,725
£2,505
£5,215
£2,055
£1,415
%
%
%
%
%
%
3
3
3
3
3
5
East of England
13
14
11
15
14
12
West Country
24
30
17
37
10
10
9
8
9
7
10
12
Northumbria
South East England
[Source: ETB, 1998]
© Dr Andrew Clegg
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