Correlations
Cards (8)
- Correlation - illustrates the strength and direction of an association between two co variables.
- Scattergram - one co variable is on the x axis and the other is on the y
- Types of correlationPositive - co variables increase togetherNegative - one co variable increases and the other decreasesZero - no relationship between variables
- Difference between correlations and experimentsIn an experiment the research manipulates the IV and records the effect on the DV. In a correlation there is no manipulation of variables and so cause and effect cannot be demonstrated.
- Evaluation - strengths of correlations
- Useful starting point for research - by assessing the strength and direction of a relationship , correlations provide a measure of how two variables are related. If variables are strongly related it may suggest hypothesis for future research.
- Relatively economical - Unlike a lab study, there is no need for a controlled environment and can use secondary data. So correlations are less time consuming than experiments.
- Evaluation - Limitations
- No cause and effect - Correlations are often presented as casual which tends to lead to false conclusions about causes of behaviour.
- Intervening variables - Another untested variable may explain relationship between co variables. This may also lead to false conclusions
- Correlation coefficient strengthStatistical tests of correlation produce a numerical value somewhere between -1 and +1. This is the correlation coefficient. This value tells us the strength of the relationship between the two variables. The closer the coefficient is to 1 the stronger the relationship between the co variables. The closer to 0, the weaker the relationship is.
- Correlation coefficient directionvalue of +1 represents a perfect positive correlationvalue of -1 represents a perfect negative correlationThe sign informs us of the direction.Correlation coefficient are calculated using an inferential test like Pearson's r or Spearman's rho.