I have a set of variables in an SPSS data set that comprise responses to a set of test variables. A person’s total score would be the sum of their item scores. Is there an SPSS procedure that will compute the correlation between each inter item correlation matrix in stata forex variable and the total score, i. The correlation is corrected in the sense that the value of item i is subtracted from the total for the correlation between the total and item 1.

If you want the uncorrected item-total correlation, with the total score including the item with which it is correlated, you would need to compute the total score and then use the CORRELATIONS procedure to print the item-total correlations. Suppose that your item scores were AITEM1 to AITEM10, as above, and your total score was to be called ATOT. The WITH keyword in the CORRELATIONS command allows you to produce a correlation matrix with the items in the rows and the total as the column, without printing the correlations among the items. Compute menu from the Data Editor. If your item scores are binary variables, you should note that a Pearson correlation between a binary variable and a continuous variable is equal to the point-biserial correlation coefficient.

The latter formula is a shortcut method to computing the correlation when you have the continuous variable means and variances and sample sizes for each value of the binary variable. Therefore, I selected and recoded the 16 items, which belong to this scale. I’m sure about having selected the right items and the frequency-tables show that my recoding was succesful. Reliability The determinant of the covariance matrix is zero or approximately zero. Statistics based on its inverse matrix cannot be computed and they are displayed as system missing values. The inter-item covariance clearly shows that some items have a covariance close to or equal zero. Cronbach’s Alpha is equal to .

I’m surprised to see these results. The items are surely the ones of the scale, so I can’t exclude any of them. Why would this scale be introduced if it has only insufficient reliability within a normative sample? Excluding theses 2 cases did not change neither the descriptives nor the reliability outcomes.

I’m not sure how all this influences my data and how to deal with the warning. Can anyone offer a solution to this problem? The determinate of a matrix being 0 is a result of your one or more of your items being a linear combination of others. That will locate items with a perfect correlation with others. If there are no such pairs of items then the problem is more complex. The items are surely the ones of the scale, so I can’t excluded any of them.