Scales of Measurement, Independent and Dependent Variables

• Scales of Measurement 


  Variables differ from one another in terms of their underlying properties:

  • Nominal (category)
  • Ordinal (ranked/ordered)
  • Interval (equal increments, no real 0)
  • Ratio (real 0)
  • Lowest level -> highest level (most info)
• Nominal (categorical) data

  • Lowest level of information
  • Category membership
  • Numbers assigned serve as labels, but do not indicate numerical relationship, no mathematical reasoning behind it
  • E.g. gender, political party, religion
• Ordinal data

  • Data can be ranked along a continuum
  • Intervals between ranks are not equal
  • E.g. race positions, attractiveness
• Interval Data

  • Intervals between successive values are equal
  • But no true zero point
  • E.g. temperature, shoe size
• Ratio data

  • Highest level of data
  • Equal intervals and a true zero point
  • E.g. Height, Distance
  • We know the zero is nothing
• Experimental Methods


  Research design which allows us to make casual inferences about the influence of one or more variables on a variable of interest, one or more variables is manipulated and effect on the other variable is measured
• Independent variables= The variable that is being manipulated and is hypothesised to bring about change in the variable of interest, have at least 2 levels
• Dependent variable= The variable that is being measured, compare the differences in the DV under the different levels of the IV