Effective Use of Graphs

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# Effective Use of Graphs - PowerPoint PPT Presentation

Effective Use of Graphs. Annie Herbert Medical Statistician Research & Development Support Unit Salford Royal (Hope) Hospitals NHS Foundation Trust annie.herbert@manchester.ac.uk (0161 720) 2227. Timetable. Outline. Graphs for categorical data Graphs for numerical data Comparing groups

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## Effective Use of Graphs

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### Effective Use of Graphs

Annie Herbert

Medical Statistician

Research & Development Support Unit

Salford Royal (Hope) Hospitals NHS Foundation Trust

annie.herbert@manchester.ac.uk

(0161 720) 2227

Outline
• Graphs for categorical data
• Graphs for numerical data
• Comparing groups
• Additional graphs (covered in other courses)
• Final tips & Computer packages
Categorical Data (1)

Examples:

• Sex

– Male/Female

• Blood Group

– A/B/AB/O

• Employment Status

– Unemployed/Part-time/Full-time

Categorical Data (2)
• Record: Frequency (discrete number) per category
• Summary: Frequency OR

percentage/fraction/proportion

• Visually:

- Bar Chart - Pie Chart

- Visually strong.

- Easy to compare between more than one dataset.

• Categories can be ‘re-ordered’ to emphasize certain effects.
• Misleading if not used for counts.
• Misleading if y-axis not from 0.
Bar Charts – Things to consider:
• What group differences are you interested in?
• Frequencies or percentages? If percentage, it’s down to you to specify the totals.
• Is ‘Other’ a large frequency/percentage?
• Consider the categories as un-ordered when using a stacked bar chart.
• Easy to compare categories, are equidistant from each other.
• Ordering of categories does not emphasize certain effects as badly as stacked bar charts do.
• No choice between frequencies and percentages (down to you to specify totals).
• Cannot put more than one data set into a pie chart.
• Lose individual values of the data.
• Limited space: if using more than 5 or 6 categories, chart can look complicated.
Numerical Data (1)

Examples:

• Weight
• Blood Pressure
• Cholesterol Levels
Numerical Data (2)
• Record: Number/Value

(discrete or continuous)

• Summary: - Mean (SD)

- Median (IQR)

• Visually:

- Histogram - Box plot - Spread plot

• Visual display of interval frequencies, easy to compare intervals.
• Can give an idea of the distribution of the data, e.g. shape, typical value, spread.
• Choice of interval width can alter appearance.
• Individual values lost.
• One data set per histogram, difficult to compare data sets.
Box Plot

Extreme Outlier

Upper Quartile

Median

Lower Quartile

Outlier

• Defines many summary statistics in one plot.
• Defines ‘outliers’ explicitly.
• Can have more than one data set in a plot, so easy to compare data sets:
• More complicated visually than some other types of data plots.
• Individual values lost.
• Can give an idea of the distribution of the data, e.g. shape, typical value, spread.
• Shows individual values of the data.
• Not very widely used in journal publications.
• Doesn’t explicitly summarise statistics or outliers as box plot does.
What information does this give?

Mean ± SE, n ≈ 30 per group

Bland-Altman Plots (scatter plots)

How well do two methods of measurement agree?

Final Pointers:
• Before plotting think about the type of data and what you would like to compare.
• Show all data rather than summaries where possible.
• Label axes clearly. Graph should ‘stand alone’.
• Make sure when comparing groups that outcome on the same scale.
• Make sure any colours used are sufficiently different from each other, and not red/green.