If your data are not normally distributed or have ordered categories, choose Kendall's tau-b or Spearman, which measure the association between rank orders.

Pearson correlation coefficients assume the data are normally distributed.Р вЂќР вЂЎor quantitative, normally distributed variables, choose the Pearson correlation coefficient. Two variables can be perfectly related, but if the relationship is not linear, Pearson's correlation coefficient is not an appropriate statistic for measuring their association. Pearson's correlation coefficient is a measure of linear association. Ordinal: A variable can be treated as ordinal when its values represent categories with some intrinsic ranking for example, levels of service satisfaction from highly dissatisfied to highly satisfied.Р вЂќР’В«efore calculating a correlation coefficient, screen your data for outliers (which can cause misleading results) and evidence of a linear relationship.

Scale: A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate.

Pairwise: When computing a measure of association between two variables in a larger set, cases are included in the computation when the two variables have non-missing values, irrespective of the values of the other variables in the set. It is useful for determining the strength and direction of the association between two scale or ordinal variables. Р Р†Р вЂљР’В¦procedure computes the pair-wise associations for a set of variables and displays the results in a matrix.

Cara penggunaan spss 16