Presentation and display of quantitative data (graphs)

• Why do we use graphs?

  • They provide visual representations of findings = patterns can be seen in data
  • Clearly show distribution of scores within sets
• Types of graphs:

  • Linear graph/frequency polygon
  • Bar chart
  • Histogram
  • Scattergram
  • Distributions
• Linear graphs/frequency polygons

  • Useful to map quantitative IVs + DVs
  • Line segment connecting 2 points expresses a slope -> can be interpreted visually relative to slope of other lines/expressed as a mathematical formula

  
• Bar charts

  • Useful to compare classes/groups of data
  • Can have 1 or more data series
  • X axis can represent frequency/single statistic, e.g. a sample's mean
  • Must have a clear title
  • Equal intervals + bars not touching

  
• Histograms

  • Provides visual illustration of data items' distribution in data sets
  • Each bar = frequency of x axis' variable
  • All categories of data represented
  • Columns = equal width per category
  • No intervals missed just because they are empty
  • Column areas proportional to area they represent
  • Calculated as: frequency density = class width ➗ frequency (frequency = class width x FD)
  • Different from bar charts -> histograms used for continuous data + no gaps

  
• Scattergrams

  • Gives visual picture of relationship between co-variables + aids interpretation of correlational coefficient
  • Each piece of data provides point on scattergram
  • Points plotted but not joined
  • Pattern indicates type + strength of relationship
  • More clustered points around a straight line = stronger relationship between co-variables
  • Clear title needed
  • Both axes made up of numbers -> no categories
  • Clustered line from bottom left to top right = positive relationship (direct)
  • Clustered line from top left to bottom right = negative relationship (indirect)

  
• Distributions

  • Distribution of data = how data is grouped together
  • Data gathered on how often an event happens -> forms patterns which can be seen on graphs
  • Values can be spread throughout entire range of data or group towards bottom/top of range of data
  • Different distributions tell us different things about the data
• Normal distributions

  
• Positively and negatively skewed distributions