What are Correlation and Cronbach Alpha?

To help gauge the "agreement" among Q-Sort results, two statistical measures are shown.

First, the correlation between your result and that of the manager’s own Q-Sort can provide a measure of how closely you and the manager agree on their culture. In general, a correlation of over .70 would be considered to show a reasonable measure of agreement.

Second, the Cronbach Alpha is used to measure the level to which your responses suggest you agree with the combined "judgment" of all other non-managers who have previously Q-Sorted this manager.

Cronbach Alpha in detail: Cronbach Alpha compensates and adjusts for overlapping responses. Expressed as a function of the number of test items and the average inter-correlation among the items, the formula for standardized Cronbach Alpha is provided here:

N is equal to the number of items and r-bar is the average inter-item correlation among the items. One can see from this formula that if you increase the number of items, you increase Cronbach Alpha. Additionally, if the average inter-item correlation is low, alpha will be low. As the average inter-item correlation increases, Cronbach Alpha increases as well. This makes sense intuitively -- if the inter-item correlations are high, then there is evidence that the items are measuring the same underlying construct. This is really what is meant when someone says they have "high" or "good" reliability. They are referring to how well their items measure a single uni-dimensional latent construct. Source: http://www.ats.ucla.edu/stat/spss/faq/alpha.html

Similar to correlation, a higher Cronbach Alpha reflects greater agreement among respondents.