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Directly taken from the SPSS bivariate statistics document here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.

Directly taken from a document on bivariate statistics with SPSS that lives here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.

Directly taken from the SPSS bivaraite statistics document here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.

Directly taken from the SPSS bivariate statistics document here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.

Directly taken from the SPSS bivaraite statistics document here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.

Directly taken from the SPSS bivaraite statistics document here:

Chi-square is a useful technique because you can use it to see if there’s a relationship between two ordinal variables, two nominal variables, or between an ordinal and a nominal variable. You look at the assymp. Sig column and if it is less than .05, the relationship between the two variables is statistically significant.