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The purpose of this article is to provide simple but accurate methods for comparing correlation coefficients between a dependent variable and set of independent variables. The methods are simple extensions of Dunn & Clark's (1969) work using the Fisher z transformation and include a test and confidence interval for comparing two correlated correlations, a test for heterogeneity, and a test and confidence interval for a contrast among k (>2) correlated correlations. Also briefly discussed is why the traditional Hotelling's t test for comparing correlated correlations is generally not appropriate in practice.

An observed score distribution for a multiple-choice test can often be cogently summarized using 3 statistics that are easily calculated functions of the first 3 moments of the distribution. These statistics can be interpreted as providing a mean ability in terms of probability of obtaining a correct response, a guessing threshold, and a reliability relative to binomial variability. The 3 statistics can also be used, as method-of-moments estimates of the parameters of an extended beta-binomial model, to produce a smoothed score distribution and an estimated score distribution if the test were modified to include infinitely many choices for each test item. Examples of applications to actual Armed Services Vocational Aptitude Battery data and simulated item-response-theory data are included.