Latent distance analysis provides a probability model for the non-perfect Guttman scale; the restricted latent distance structure is simpler to compute than the general structure. Since no sampling theory for latent structure analysis is available, the advantages of the general structure cannot be expressed formally. The two structures are compared in terms of their fit to fifteen sets of empirical data. The computation schemes used are summarized.

The concept of parsimony in factor analysis is discussed. Arguments are advanced to show that this concept bears an analogic relationship to entropy in statistical mechanics and information in communication theory. A formal explication of the term parsimony is proposed which suggests approaches to the final resolution of the rotational problem. This paper provides the rationale underlying Carroll's (2) analytical solution for approximating simple structure, and the solutions of Saunders (7) and Neuhaus and Wrigley (5).

A formula is derived which gives the maximum expected correlation between two multiple-choice tests as a function of the distributions of proportions correct for the items in the two tests and the probability of chance success. The formula is similar to one derived by Carroll based on “true” item difficulties. A numerical example is provided.

Fifty-three tests designed to measure aspects of creative thinking were administered to 410 air cadets and student officers. The scores were intercorrelated and 16 factors were extracted. Orthogonal rotations resulted in 14 identifiable factors, a doublet, and a residual. Nine previously identified factors were: verbal comprehension, numerical facility, perceptual speed, visualization, general reasoning, word fluency, associational fluency, ideational fluency, and a factor combining Thurstone's closure I and II. Five new factors were identified as originality, redefinition, adaptive flexibility, spontaneous flexibility, and sensitivity to problems.

An attempt is made to elaborate upon Lev's results concerning the reliability of the point biserial coefficient of correlation in a manner that will be helpful to the psychological statistician. Procedures required in the use of the non-central t tables prepared by Johnson and Welch are described as they relate to the determination of the fiducial limits for a point biserial coefficient. A normal approximation technique for the estimation of fiducial limits is also suggested. Numerical evidence is presented which shows that relative to a given level of significance the width of the fiducial interval estimated from a point biserial coefficient of any size is smaller than that of the fiducial interval corresponding to an ordinary Pearsonian coefficient of the same magnitude.