A maximum likelihood procedure for combining standardized mean differences based on a noncentratt-distribution is proposed. With a proper data augmentation technique, an EM-algorithm is developed. Information and likelihood ratio statistics are discussed in detail for reliable inference. Simulation results favor the proposed procedure over both the existing normal theory maximum likelihood procedure and the commonly used generalized least squares procedure.

A technique of analysis utilizing frequencies of differences is described and applied to a hypothetical experiment involving two methods of instruction. A nomograph is provided for computing the chi-square values applicable to the method.

The results of J. P. Guilford's card-sorting experiment by the method of equal appearing intervals are analyzed mathematically. The constants of the psychometric functions are obtained by processes analogous to the constant process. When these constants are determined (a) independent of the supposition that the series of groups into which the cards were sorted is quantitative in character and (b) under this supposition, good agreement is found between them. Guilford's results agree with Weber's and Fechner's laws in both cases.

Changes in dichotomous data caused by treatments can be analyzed by means of the so-called linear logistic model with relaxed assumptions (LLRA). The LLRA does not require observable criteria representing a single underlying latent trait, but it postulates the generalizability of the treatment effects over criteria and subjects. To test this latter crucial assumption, the mixture LLRA was proposed that allows directly unobservable types of subjects to have different treatment effects. As the earlier methods for estimating the parameters of the mixture LLRA have specific drawbacks, a further method based on the conditional maximum likelihood principle will be presented here. In contrast to the earlier conditional methods, it uses all of the dichotomous change data while having fewer parameters. Further, its goodness-of-fit tests become more sensitive to a falsely specified number of change-types even though the treatment effects are biased. For typically occurring small to moderate sample sizes, however, parametric bootstrapping of the distributions of the fit statistics is recommended for performing hypotheses tests. Finally, three applications of the new method to empirical data are described: first, about the effect of the so-called Trager psychophysical integration, second, about the effect of autogenic therapy on patients with psychosomatic symptoms, and, third, about the effect of religious education on the attitude towards sects.

A transformation of a set of partial regression equations to a potentially simple form is shown to be permissible for personnel classification purposes when all individuals are assigned to jobs. Simplification is achieved by maximizing the number of near zero partial regression weights.

Allport's J-curve hypothesis of conforming behavior and its attendant treatment of appropriate data are criticized on the following points: (1) narrowness of application; (2) flexibility of interpretation of results; (3) arbitrary selection of a criterion of conformity; (4) lack of a means by which the extent of conformity in one situation can be compared with that in another; (5) inequality of “telic” units. As an alternative treatment of such data, the method of higher moments is suggested and rationalized. Data from Allport and Solomon are reworked by this method and results compared.

When errors of test scoring obey a Poisson frequency law (theoretical considerations suggest that they do), the method described may be used for finding the upper fiducial limits of scoring errors per paper. A criterion is suggested for establishing tolerance limits on scoring errors, and a method is given (1) for finding the probability of being wrong in the statement that the tolerance limit is being met for a given size sample or (2) for finding the size of sample that will make this probability not greater than some fixed value.

In many decision problems the decision-maker must ask a series of questions or select cues relevant to but prior to the decision. There are many strategies which would prescribe an order for asking such questions or selecting such cues. It is the purpose of the paper to describe a manner in which the strategy or strategies of the decision-maker can be analyzed. From this model can be derived limits corresponding to the possible use of a particular strategy, and the effective or observed use of the strategy or strategies by the decision-maker.