correlations

• correlations


  research which analyses strength and direction of a relationship between two co-variables - co-variables name given to variables in correlation that are being analysed to see if they have a relationship - correlations require quantitative data (numerical) plotted on a scatter gram - main types are positive, negative and zero
• correlation and experiment difference

  experiments 1 variable manipulated (IV) whilst one is measured (DV) to establish cause and effect relationship

  correlation no manipulation of 1 variable and it's not possible to establish cause and effect relationship - correlation measured 2 variables (co-variables) and establishes association or relationship
• types of correlation


  positive - as one co-variable increases so does the other - e.g. more caffeine people drink, higher level of anxiety

  negative - as one co- variable increases, the other decreases - e.g. more caffeine people drink, less sleep they have

  zero - no relationship between co-variables - e.g. no relationship between people's height and their intelligence
• correlation coefficients 


  a number that represents strength and direction of relationship between two co-variables - correlation coefficient varies on scale between -1 and +1 - -1 represents perfect negative correlation, 0 represents no correlation and +1 represents perfect positive correlation
• non directional correlation hypothesis example

  There will be a significant correlation between amount of time since participant last ate and the number of words they can recall from a list
• positive correlation hypothesis example


  There will be a significant positive correlation between the amount of time since a participant last ate and the number of words they can recall from a list
• negative correlation hypothesis example


  There will be a significant negative correlation between the amount of time since a participant last ate and the number of words they can recall from a list
• correlational analysis evaluation


  Advantages - easy to analyse (quantitative data) - further research (can expand understanding of world)

  Disadvantages - Doesn't establish cause and effect - third variable problem (relationship could be due to a third variable that isn't measured)