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Learn the art of presenting data through quantitative analysis using SPSS, explore various graph types, and understand techniques to create informative and visually appealing graphs.
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The art of presenting data • What makes a good graph? • Show the data. • Induce the reader to think about the data being presented (rather than some other aspect of the graph, like how pink it is). • Avoid distorting the data. • Present many numbers with minimum ink. • Make large data sets (assuming you have one) coherent. • Encourage the reader to compare different pieces of data. • Reveal data. Tufte (2001)
A Bad Graph • 3-D effect • Patterns • Cylindrical bars • Badly labelled y-axis
A Good Graph • A 2-D plot • Informative y-axis • Distractions • Minimum ink
The SPSS Chart Builder Graphs chart Builder • 2 ways • Gallery • Element by element
Histograms: • Simple histogram • Select a variable from the list and drag it into x-axis • Stacked histogram (grouping) • Frequency polygon • Population pyramid (grouping)
Further Histogram options Element Properties: To change statistic Set Parameters: To set Bins
Boxplots (box–whisker diagrams) Characteristics
Boxplots (box–whisker diagrams) • SBP: Single variable for different categories • CBP: To add a second categorical variable • 1-DBP: single variables with no categorical differences • Outliers • Potential Outliers in a normal distribution we’d expect about 5% to have absolute values greater than 1.96 (we often use 2 for convenience), and 1% to have absolute values greater than 2.58, and none to be greater than about 3.29.
Graphing means: bar charts and error bars • To see the means of scores across different groups of cases. For example, you might want to plot the mean ratings of two films. • A second grouping variable. For example, ratings of the two films, but for each film have a bar representing ratings of ‘excitement’ and another bar showing ratings of ‘enjoyment’. • Same as the clustered bar except that the different coloured bars are stacked on top of each other rather than being side by side. • Also the same as the clustered bar except that the second grouping variable is by an additional axis. • This is like the clustered bar chart above except that you can add a third categorical variable on an extra axis. The means will almost certainly be impossible for anyone to read on this type of graph so don’t use it. • This graph is the same as the clustered 3-D graph except the different coloured bars are stacked on top of each • This is the same as the simple bar chart except that instead of bars the mean is represented by a dot, and a line represents the precision of the estimate of the mean (usually the 95% confidence interval is plotted, but you can plot the standard deviation or standard error of the mean also). • This is the same as the clustered bar chart except that the mean is displayed as a dot with an error bar around it. Simple bar: Clustered bar: Stacked bar: Simple 3-D bar: Clustered 3-D bar: Stacked 3-D bar: Simple error bar: Clustered error bar:
Line charts • Simple line: To see the means of scores across different groups of cases • Multiple line: Equivalent to the clustered bar chart to plot means of a particular variable but produce different-coloured lines for each level of a second variable
Graphing relationships: the scatter plot • To plot values of one continuous variable against another. • like a simple scatterplot except that you can display points belonging to different groups in different colours (or symbols). • 1st in 3-D • 2nd in 3-D • Same as a bar chart except that a dot is used instead of a bar. • Also known as density plot, similar to a histogram except that rather than having a summary bar representing the frequency of scores, a density plot shows each individual score as a dot. Useful, like a histogram, for looking at the shape of a distribution. • A grid of scatterplots showing the relationships between multiple pairs of variables. • Similar to a clustered bar chart but with a dot representing a summary statistic (e.g. the mean) instead of a bar, and with a line connecting means of different groups. These graphs can be useful for comparing statistics, such as the mean, across different groups. Simple scatter: Grouped scatter: Simple 3-D scatter: Grouped 3-D scatter: Summary point plot: Simple dot plot: Scatterplot matrix: Drop-line:
