A potential source of confusion in working out what statistics to use in analysing data is whether your data allows for parametric or non-parametric statistics.
The importance of this issue cannot be underestimated!
If you get it wrong you risk using an incorrect statistical procedure or you may use a less powerful procedure.
Non-paramteric statistical procedures are less powerful because they use less information in their calulation. For example, a parametric correlation uses information about the mean and deviation from the mean while a non-parametric correlation will use only the ordinal position of pairs of scores.
The basic distinction for paramteric versus non-parametric is:
There are other considerations which have to be taken into account:
The best way to do this is to check the skew and Kurtosis measures from the frequency output from SPSS. For a relatively normal distribution:
Descriptive
Name | For what | Notes |
Mode | Central tendancy | Greatest frequency |
Median | Central tendancy | 50% split of distribution |
Range | Distribution | lowest and highest value |
Name | For what | Notes |
Spearman's Rho | Correlation | based on rank order of data |
Kendall's Tau | Correlation | |
Chi square | Tabled data |