PSYC2063 WEEK 2

WHY GRAPHS ARE USEFUL

• Graphs help us simplify information while highlighting important ideas 

• Graphs help us communication information quickly 

• Cost = some information is inevitably lost 

GRAPH TERMINOLOGY

NUMERICAL DATA

• Represents an amount of something 

• Discrete = counted, not measured 

• Continuous = measure, not counted 

CATEGORICAL DATA

• Items are assigned to labelled categories 

• Nominal = categories can be rearranged 

• Ordinal = categories have order 

CONTENT VS SCAFFOLDING

VISUAL ENCODINGS

Properties of graphs that vary according to the data. 

• Length/height

• position

• area

• angle

• colour hue

• colour shade

• shape

• width/thickness

LENGTH/HEIGHT

The length/height of the data visualisations varies in proportion to the underlying data 

POSITION

The position of each dot on x-axis corresponds to y-axis 

AREA

Ex. Area of bubble/segment proportional to population in each county  

ANGLE

• Ex. Pie chart - the angle of each segment is proportional to % of world population 

• The slope of each line segment is proportional to the rate of change between data points 

COLOUR HUE

Different colours to represent different variables 

COLOUR SHADE

Intensity of colour to show variation 

SHAPE

Different shapes represent different variables 

WIDTH/THICKNESS

Line width proportional to number of values per variable 

GOOD GRAPH GUIDELINES

• Sensible axes

• complete data

• consistent scale intervals

• consistent bin sizes

• principle of proportional ink

SENSIBLE AXES

• In bar graphs, dependent variable axis should start at zero  

• In line graphs, dependent variable axis shouldn't start at zero 

• Graphs being compared should have same axes 

COMPLETE DATA

If data is available for multiple scale points, it should be included 

CONSISTENT SCALE INTERVALS

Axes should have consistent intervals between scale points 

CONSISTENT BIN SIZES

When data is grouped in categories (bins), they should be equally sized 

PRINCIPLE OF PROPORTIONAL INK

When a shaded region is used to represent data, the size of the shaded region should be directly proportional should be directly proportional to the data 

GRAPH INTERPRETATION

• Red = with calf, Blue = without calf 

• X axis = individual dolphins 

• Y axis = frequencies 

• Dolphins use a higher pitch voice when their with their calf vs without 

TUTORIAL GRAPH 1 INTERPRETATION

G : B = number wines receiving a maximum score of Gold and a minimum score of bronze (gold, silver or bronze) 

G : NA = number wines receiving a maximum score of Gold and a minimum score of not placing (gold, silver, bronze, fourth or below) 

G : S = number wines receiving a maximum score of Gold and a minimum score of silver (gold or silver) 

Other : did not score a gold 

 

• If wine was to receive gold it is most likely to not place again 

• 47% scored gold at least once 

• This is not reliable results/scoring of the wines 

TUTORIAL GRAPH 2 INTERPRETATION

• How consistently the wines place across all competitions 

• 0.33 is highest correlation between SC and GH (this is small still) 

• Average correlation is around 0.1 which is very low  

TUTORIAL GRAPH 3 INTERPRETATION

• Predictions are similar to observed 

• Predicted that gold medals awarded will not be consistent over 5 competitions 

• Since predictions were by chance alone, that means the observed winnings are no better than chance  

• Non-significant graph (no significance bars but make decision)