Data Presentation

Research Methods

Data Presentation:Figures and Tables

Consider your audience. The reader should understand (generally)

the figure or table without reading the text.The reader should understand (generally)

the text without looking at a figure or table.Text and figures and tables should be

coordinated—each improves the others. But, a picture (here, a figure or a table) is

worth a thousand words. More is communicated with figure or table than without.

Is Style Important for Communication?

Consider the following text example, without punctuation: A woman without her man is nothing.

With punctuation: A woman, without her man, is nothing.

Or, with punctuation: A woman: without her, man is nothing.

Figures or Charts

Pie ChartBar Chart (including Bilateral Bar Chart)HistogramLine GraphScatterplotAny method to communicate [empirical] data

through graphical means

Pie Chart

Percentage Use for Sources of Electricity, U.S. 2003

Coal51%

Nuclear21%

Natural Gas16%

Hydropow er6%

Petroleum3%

Other3%

Bar Chart/Bar Graph

Number of Hours of TV Watching on the Average DayGeneral Social Surveys, 2004

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Figures and Charts:Good Practices I

Title Identify topic and purpose of figure The research question or the relationships shown

in the chart Unique, distinguish between other related charts

Axis Titles and Labels Y-axis = Dependent Variable, X-axis =

Independent Variable Be precise, but minimize clutter

Figures and Charts:Good Practices II

Legend Comparison of two or more categorical variables

only Series or category not labeled elsewhere in the

figureData Labels

Use sparingly Identify reference points Report absolute level for pie or stacked bar chart If exact values necessary for reader, use a table

not a chart

Bad Line Graph

Figure 2

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Better Line Graph

Figure 2Percentage of Collaboration for Research Articles,

By Journal and Decade

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AJP APSR JACS PR QRB

Scatterplot

Canadian Provincial Legislature Size by Population

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0 2000000 4000000 6000000 8000000 10000000 12000000

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Data Transformation Quantitative data may not be reported in a

manner that is most appropriate for theory a/o hypothesis to be tested

Should legislature size increase uniformly with population? No, say Taagepera (1972) and Stigler (1976)

Natural Logarithm of X values (population size)

Transformed X and Scatterplot

Canadian Provincial Legislature Size by Population (Logged)

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Political Transformation and Scatterplot

Canadian Provincial Legislature Size by Population (Logged):With Ontario Change

Ontario (old)

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Some Bad Practices for Figures or Charts I

Good practices (earlier) not satisfiedTokens or acronyms where inappropriate

(V0003059, LGINFR2, VAR3, X and Y)Zero is not included on vertical axisUsing two-dimensional figures in place of

bars or points (one-dimensional)Comparing dissimilar groups on the same

figureThree-dimensions for one or two variables

Some Bad Practices for Figures or Charts II

Enhanced features/colors/designs included that do not communicate the point of the figure

Inconsistent scale for a series of chartsIncorrect chart for the data (e.g. line chart

for bar chart)

An Example

Is partisanship stable or subject to short-term forces (such as the economy)?

Tradition view: individuals develop long-standing attachments to a political party

Result: Macropartisanship changes only at the margins

Challenge: Macropartisanship varies with “considerable magnitude” and varies systematically over time

Macropartisanship I

Erikson-McKuen-Stimson macropartisanhip measuresEMS Scaling

“Percent” is the Democratic share of party identif iers. Independents are deleted f rom the calculation.

19501952

19541956

19581960

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19661968

19701972

19741976

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19861988

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20022004

200645

55

65

75Percent

Their series

Recent Gallup

GSS

Macropartisanship II

Erikson-McKuen-Stimson macropartisanhip measures

“Percent” is the Democratic share of party identif iers. Independents are deleted f rom the calculation.

19501952

19541956

19581960

19621964

19661968

19701972

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19781980

19821984

19861988

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Their seriesRecent Gallup

GSS

Really Interesting Data Presentation I (Minard)

http://www.stat.ucla.edu/history/march.htm

“I came to fight men, not Nature” - Napoleon

Minard

Probably the best statistical graphic ever drawn, this map by Charles Joseph Minard portrays the losses suffered by Napoleon's army in the Russian campaign of 1812.

Beginning at the Polish-Russian border, the thick band shows the size of the army at each position. The path of Napoleon's retreat from Moscow in the bitterly cold winter is depicted by the dark lower band, which is tied to temperature and time scales. Exquisitely printed in two colors on fine archival paper, 22” by 15”.

Really Interesting Data Presentation II (Nightingale)

Nightingale

Nightingale was a pioneer in the visual presentation of information. Among other things she used the pie chart, which had first been developed by William Playfair in 1801.

After the Crimean War, Nightingale used the polar area chart, equivalent to a modern circular histogram or rose diagram, to illustrate seasonal sources of patient mortality in the military field hospital she managed.

Nightingale called a compilation of such diagrams a "coxcomb", but later that term has frequently been used for the individual diagrams.

She made extensive use of coxcombs to present reports on the nature and magnitude of the conditions of medical care in the Crimean War to Members of Parliament and civil servants who would have been unlikely to read or understand traditional statistical reports.

Figures for Distribution ofOne Variable

Task Type of Figure Comments Nominal, few

categories (≤ 5)

Pie chart Arrange categories by frequency or theoretical criteria

Nominal, many categories (> 5)

Bar chart Arrange categories by theoretical criteria or frequency

Ordinal

Bar chart or histogram

Arrange categories in order

Continuous, with few (< 20) values

Histogram Arrange values in numeric order

Continuous, with many (≥ 20) values

Line graph Arrange values in numeric order

Figures for Relationship among Two (or More)

Variables

Task Type of Figure Comments

Both categorical Bar chart Bar height to show percentage or frequency of each variables

Clustered bar chart, or clustered histogram

Within each category, create a frequency for each group

Stacked bar chart Within each category, group of second variable stacked in a single bar

One categorical, one continuous

Bar chart Categorical variable on x-axis, continuous variable on y-axis

Both continuous Line graph Trend; one Y for each X, dependent variable on Y-axis, independent variable on X-axis

Scatterplot Individual points that are not linked, more than one Y for an X, DV on Y-axis, IV on X-axis

Effective Tables

Title; Row and Column Headings; Data; NotesPurpose of the table (title)Context of the table (title, notes)Location of specific variables in the table

(headings)Coding or units of measurement for each

variableData sourcesDefinitions of important terms

Bad Practices:Tables

TABLE II

RMI turnout% 18 – 24 -.08 -.35% Over 65 -.12 .09% Bachelor’s degree

-.21 -.08

Types of Tables

Univariate TableDescriptive StatisticsComparison of two distributions (such as sample and population)

Bivariate TableCrosstabulationBivariate Statistics (Pearson correlation)

N-way TableCompare three or more variables

Table 7 Transferable and Nontransferable Votes by Count/Round

1997 Irish General Election

Count Number of Districts Transferred Votes

Nontransferable Vote

Percent Nontransferable

2nd 41 59583 1773 2.98 % 3rd 41 50194 2931 5.84 4th 39 77936 7959 10.21 5th 37 81391 9062 11.13 6th 31 65244 6016 9.22 7th 29 99272 12744 12.84 8th 22 57461 9664 16.82 9th 16 37151 7041 18.95

10th 11 29068 6474 22.27 11th 7 20058 6039 30.11 12th 2 2491 388 15.58 13th 1 2457 298 12.13 14th 1 2574 729 28.32 15th 1 5659 507 8.96

TOTAL 41 590539 71625 12.13 % Transferable votes are ballots for which the preference is transferred to the next, ordered available candidate.