analysis of quantitative data - RM

• Nominal

   

  Where data forms discrete categories for example hair colour can only be nominal data as it can only be described in its categories of blonde, brown, red or black
• Ordinal

   

  A level of measurement where numbers are rankings rather than scores in themselves eg a rank order for attractiveness on a scale of 1-5
• Interval/ Ratio

  Where an individual score for each participant is gathered and the score can be identified using a recognised scale with equal distances between scores eg time or height
• Mean

   

  Add up all the values and divide by the number of values

  Is the most powerful measure of central tendency as it takes all the score into account. 

  Can be distorted by extreme scores so unrepresentative of the data set.  Also assumes interval level data.
• Median

   

  Find the middle value

  Unaffected by extreme scores, so more appropriate measure when there are extreme scores.

  Only takes into account one or two score – the middle values.  Generally used with ordinal level data.
• Mode

   

   

  Find the most commonly given value

  Similar to the median, the mode is unaffected by extreme scores.

  Can be affected by the change in one score, making it unrepresentative.  Used with nominal data.
• The range is quick to calculate BUT - It does not provide any idea of the distribution of values around the centre, nor does it take individual values into account (remember that the only values used when calculating the range are the two most extreme values). Therefore it also means the range is seriously affected by extreme scores (outliers).
• Standard deviation

  What it tells us about the data

  Large

   A large standard deviation tells us that there was much variation around the mean

  Small

  A small standard deviation tells us that the data was closely clustered around the mean

  Zero

  All the data values were the same