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2801 Research Methods > Week 9 : Interpreting graphs > Flashcards

Week 9 : Interpreting graphs Flashcards

1
Q

Data

Types of data:

A
  1. categorial data
  2. numerical data
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2
Q

Types of data

Categorial data

A
  • each value represents a discrete category
  • order does not matter (e.g. 1=tiger, 2=lion, 3=ape)
  • represent with pie charts, bar graphs & stacked column charts
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3
Q

Types of data

Numerical data

A
  • each value represents either a real number (e.g., age) or place on a continuum (e.g. a rating scale)
  • Order matters (1= very unhappy, 7= very happy)
  • histograms & scatterplots
  • Time series graphs if data are collected over time
  • discrete vs continuous
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4
Q

numerical data

discrete

A
  • the variable has a discrete, finite number of values
  • e.g. binned volume (e.g. day of month on which u bought ur last avocado… only 31 possibilities)
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5
Q

numerical data

Continuous

A
  • the variable has an infinite number of values
  • e.g. log-transformed volume, average number of avodados you buy each month
  • assumed to be normally distributed
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6
Q

What is a graph?

A
  • visual representation of data that can present complex information quickly and clearly & assist the reader to see patterns and trends in data
  • graph is good when… precise numbers arent required, trend or comparison can be demonstrated and there are relationships between data values
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7
Q

Graphs need to include the following…

A
  • titles
  • axis labels
  • legends
  • footnotes
  • representation of axes
  • scale and error
  • a visualstyle that is easy to interpret
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8
Q

Graphs need to include the following…

Title

A
  • summarize what graph is showing
  • placed in centre above/below graph
  • apply numbering system to the titles of all graphs
  • explain what the X and Y axez represent
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9
Q

Graphs need to include the following…

Labels & legend

A
  • X-axis (horizontal) and Y-azis (vertical)
  • brief
  • explain exactly what each aspect of the graph is showing
  • include units of measurement
  • Legend… key to the various data plotted on a graph (e.g. colours or shading)
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10
Q

Graphs need to include the following…

Footnotes

A
  • further explain data
  • e.g. in sample survey include footnote describing the sample that’s being represented and the number of respondents in the sample (n)
  • incluse a base on th egraph that allows reader to see how many ppl answered the question & make a quick assessment of the likely accuracy of the results based on sample size
  • should mention the source of data
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11
Q

Graphs need to include the following…

Axes & scales

A
  • vertical axis starts at 0
  • Only exception to this rule is when there are negative values, in which case the scale would start at less than 0
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12
Q

Indicating range of error

Confidence intervals

A
  • gives an estimated range of values taken from a set of sample data
  • The range of values is likely to include what the ‘true’ value would be if the entire population were to be surveyed
  • when reporting exact known figures, confidence intervals are not necessary
  • statistical results are often presented using the 95% confidence interval… range of valyes within which there is a 95% chance the true population value lies
  • typically displayed by using error bars… if they do not overlap there is usually a statistically significant difference in the estimates for those response categories
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13
Q

Visual style

how to make the graph look the best

A
  • reduce clutter
  • highlight what is importan
  • data ink… numbers (scale) and vital points representing data (non-data ink is titles, headings, legends, etc. should not be overused)
  • colours and patterns… do not over use to distract
  • dimension… graphs should be 2D whenever possible
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14
Q

Graphs

Bar graphs

A
  • compares a series of categories by representing each one as a bar
  • used to evaluate categorial data
  • simple & easy to interpret
  • easy to include error bars
  • populat in survey reporting
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15
Q

Bar graphs

Vertical/horizontal & clustered bar graph

A
  • vertical… best for comparing estimates (means or percentages) & between 2-7 groups
  • Horizontal… best for showing categorial data when comparing estimates, 8+ different groups, use when category labels are too long to appear neatly on x-axis
  • clustered…
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16
Q

Graphs

Stacked column graphs…

A
  • compare percentage that each value contributes to a total of 100% across categories
  • best used for categorial data when each column is comprised of no more than 3 components
17
Q

Graphs

Line graphs

A
  • used to illustrate trends over time for continuous data
  • can also be used to compare 2 different variables over time
18
Q

Graphs

Histograms

A
  • Shows the distribution (shape) of a numerical variable
  • Provides a visual, intuitive sense of the data (Mean, range, skew, possible outliers )
  • data are grouped into ranges than plotted as connected bars
  • each bar represents a range of data (width of bar proportionate to width of each category, height proportional to frequency/percentage of that cagetory)
  • bars presented in ascending or descending order
  • used for data that are at least at the ordinal level of measurement, and most often for plotting continuous data
19
Q

Graphs

Scatter plots

A
  • used to plot data points on a horizontal and a vertical axis to show relationships between 2 variables
  • plotting continuous data
  • useful when comparing 2 variables in situations when there are so many data points
20
Q

Graphs

Line graphs

A
  • Tells you how two variables are associated by drawing a line through a series of places where X and Y intersect
  • X-axis: discrete variable
  • Y-axis: continuous variable of interest
21
Q

Graphs

Box whisker plot

A
  • displaying variation in a set of data
  • used in exploratory data analysis
  • shows shape of distribution, central value & variability
  • shows 5 number summary
  • useful for indicating whether a distribution is skewed and whether there are unusual observations (outliers) in the data set
  • Ideal for comparing distributions because the centre, spread and overall range are immediately apparent
22
Q

Graphs

Pie charts

A
  • Great for displaying relative frequencies (parts of a whole) but do not really give you much else
  • Not great for displaying absolute frequencies
  • show parts or percentages of a whole
  • limitations… hard to tell difference, error bars & confidence intervals not shown legend & labels hard to read & align & do not work when comparing data
23
Q

Graphs

time-series graphs

A
  • special kind of line graph that shows how something changes over time
  • X-axis: time, usually as a discrete variable
  • Y-axis: continuous variable of interest
24
Q

Importance of y-axis

A
  • Truncating the y-axis (e.g. not starting at baseline) can exaggerate differences
  • A more reasonable y-axis range starts ay 0 and show the whole range
  • But also too broad of a range can minimize differences
  • There should only be one y-axis
25
Q

Importance of X-axis

A
  • Truncating an Axis = Restricting range to maximize differences
  • Expanding an Axis = Using too broad a range to minimize differences
  • Ignoring conventions (E.g., values should go from small to large)
  • Comparing non-equivalent data (E.g., two different Y axes on same graph)

2801 Research Methods (11 decks)

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