In large-scale psychological testing programs, it happens frequently that a group of items must be checked for accuracy with respect to certain characteristics. In many cases it is not feasible to check each item in the group separately, so that a decision concerning the entire group is usually reached by examining in detail only a small portion of the group. This paper proposes some sequential sampling plans for use in this type of work. A general discussion of the problem is followed by an illustrative example of the method applied to checking scores on groups of psychological test papers.

A method is described for the calculation of the sum of squares for a second-order interaction. It is then shown that the method is general and can be used for the calculation of the sum of squares for any higher-order interaction.

In a centroid factor analysis of the Multiphasic, Strong, Kuder, and Bell inventories using a population of 400 adult males, eight common factors dealing with aspects of personality as measured by these instruments were isolated. Seven of the factors were meaningful and one was a residual. This study indicates little overlap between the two personality and the two interest inventories. It would appear that factors found in these instruments measuring aspects of personality are dichotomous in nature and are not common to the two types of instruments included in this study. That is, two of the factors were common to the two personality inventories, and five of the factors were common to the two interest inventories.

A mechanical device is described which materially reduces the time, labor, and probability of errors which are involved in the computation of certain moments about the mean. The use of the device is illustrated with the computation of the mean, standard deviation, g1 as a measure of skewness, and g2 as a measure of kurtosis of data from a given study.

Weights may be determined for combining tests so that the composite has maximum reliability, but the calculation of these weights by means of the original equations is cumbersome. It is shown that the desired weighted composite is the first principal axis of a matrix closely related to the intercorrelation matrix. Thus, simple and straightforward procedures are available for calculating the weights.

The contradictory findings in the classical literature on the rate of reversal of perspective illusions stem, at least partly, from a failure to isolate three major sources of variance: set, subjects, and sequence. This paper provides a model for the statistical isolation of these variables. It consists of the square-root transformation of reversible data which appear to constitute a Poisson distribution. Such a transformation permits the full utilization of the analysis of variance.