0.3 Calculating correlations: parametric and non parametric  (Page 3/4)

* Skewness [Note. Skewness refers to the extent to which the data are normally distributed around the mean. Skewed data involve having either mostly high scores with a few low ones or having mostly low scores with a few high ones.] Readers are referred to the following sources for a more detailed definition of skewness: (External Link)&term_id=356 and (External Link)

To standardize the skewness value so that its value can be constant across datasets and across studies, the following calculation must be made: Take the skewness value from the SPSS output (in this case it is -.177) and divide it by the Std. error of skewness (in this case it is .071). If the resulting calculation is within -3 to +3, then the skewness of the dataset is within the range of normality (Onwuegbuzie&Daniel, 2002). If the resulting calculation is outside of this +/-3 range, the dataset is not normally distributed.

* Kurtosis [Note. Kurtosis also refers to the extent to which the data are normally distributed around the mean. This time, the data are piled up higher than normal around the mean or piled up higher than normal at the ends of the distribution.] Readers are referred to the following sources for a more detailed definition of kurtosis: (External Link)&term_id=326 and (External Link)

To standardize the kurtosis value so that its value can be constant across datasets and across studies, the following calculation must be made: Take the kurtosis value from the SPSS output (in this case it is .072) and divide it by the Std. error of kurtosis (in this case it is .142). If the resulting calculation is within -3 to +3, then the kurtosis of the dataset is within the range of normality (Onwuegbuzie&Daniel, 2002). If the resulting calculation is outside of this +/-3 range, the dataset is not normally distributed.

• Standardized Coefficients Calculator
• Copy variable #1 and #2 into the skewness and kurtosis calculator

Step four:

• Calculate a Correlation Procedure on the Data
• √ Analyze
• √ Correlate
• √ Bivariate

• √ Send Over Variables on which you want to calculate a correlation by clicking on the variables in the left hand cell and then clicking on the middle arrow to send them to the right hand cell.
• √ Perform a Pearson r if the standardized skewness coefficients and standardized kurtosis coefficients are within normal limits—the Pearson r is the default
• √ Calculate a Spearman rho if the standardized skewness coefficients and standardized kurtosis coefficients are outside of the normal limits of +/- 3
• √ To calculate a Spearman rho, click on the Spearman button and unclick the Pearson r
• √ Use the default two-tailed test of significance
• √ Use the Flag significant Correlation

• √ OK

Step five:

• Check for Statistical Significance
• 1. Go to the correlation box
• 2. Follow Sig. (2-tailed) row over to chosen variable column
• 3. If you have any value less than .05 or less than your Bonferroni adjustment, if you are calculating multiple correlations on the same sample in the same study, then you have statistical significance.