Continuous analogues of the finite linear and stimulus sampling theories are developed for non-determinate reinforcement schedules. Closed-form expressions are derived for the asymptotic response distribution and certain sequential statistics. Computations for a target experiment are given to illustrate the character of the theoretical results.

Two linear operator models are presented for a class of learning situations in which the response is on a numerical scale and the subject is given the magnitude of his error on some or all of the trials. Theoretical expressions are developed for sequential dependencies, mean learning curves, variances, and covariances, which permit a number of tests of goodness of fit.

Note is taken of four related sources of confusion as to the usefulness of Thurstone's factor analysis model and of their resolutions. One resolution uses Tucker's distinction between exploratory and confirmatory analyses. Eight analyses of two sets of data demonstrate the procedures and results of a confirmatory study with statistical tests of some, but not all, relevant hypotheses in an investigation of the stability (invariance) hypothesis. The empirical results provide estimates, as substitutes for unavailable sampling formulations, of effects of variation in diagonal values, in method of factoring, and in samples of cases. Implications of these results are discussed.

Geometrical properties and relationships of the Doolittle and square root methods of multiple correlation, as represented in the variable subspace of an orthogonal person space, are shown. The method of representation is also useful for depicting zero-order and partial correlations, as well as for the more general problem of the combination of variables.

This paper describes a relationship between the variance-covariance matrix of test items and Woodbury's concept of the standard length of a test. An index of item-test relationship is described in standard length terms. The sum of these indices for the items in a test is equal to the square of Jackson's coefficient of sensitivity.

A cosine function is suggested to approximate the normal distribution as a device for simplifying algebraic manipulations of the latter. Numerical evaluations remain straightforward and employ only the commonly available trigonometric tables. A method of visual curve fitting requiring only an oscilloscope is also described.