correlation

• correlational analysis 


  • A mathematical technique where the researcher looks to see whether scores for two covariables are related
• co-variables


  • The variables investigated in a correlation (correlations have these instead of an IV and a DV)
• scattergram


  • Used to plot correlations where each pair of values is plotted against each other to see if there is a relationship between them.
• positive correlation 


  • A relationship exists between two covariables where as one increases, so does the other
• negative correlation


  • A relationship exists between two covariables where as one increases, the other decreases
• correlation coefficient 


  • number between -1 and 1 that tells us how strong the relationship is.
  • +1.0 perfect positive correlation
  • +0.8 strong positive correlation
  • +0.5 moderate positive correlation
  • +0.3 weak positive correlation
  • 0 no correlation
  • -0.3 weak negative correlation
  • -0.5 moderate negative correlation
  • -0.8 strong negative correlation
  • -1.0 perfect negative correlatio
• • When writing hypotheses for correlations, use the word relationship when talking about the two variables.
  • Null= no significant relationship between,
  • One-tailed= there will be significant negative/positive a relationship between,
  • Two-tailed= their will be a significant relationship between
• evaluation 


  • Makes a good pilot study to generate a hypothesis for an experiment.
  • Can research variables that would be unethical to manipulate.
  • Can understand the relationship between two variables (positive/negative, weak/strong).
  • Correlations do not show causation.
  • They have the same weakness as whatever method was used to gather the data for the co-variables (i.e: observation/self report).
  • Correlations can’t show/highlight other variables that may be the real cause
  • Often correlations are misleading