Reinforcing effects of prerecorded words and delayed speech feedback on the verbal behavior of a neologistic schizophrenic

The possibility of using prerecorded words and 2-sec. delayed speech feedback as reinforcement in the reconditioning of intelligible verbal responses in a chronic, neologistic schizophrenic patient was investigated. A test situation was devised which involved daily presentation of 100 stimulus word-picture cards to elicit verbal responses. The results indicated that both singly presented prerecorded words and delayed speech feedback apparently strengthened correct and intelligible verbal responding.     

Fibrin(ogen degradation product concentration (SFP) as a diagnostic parameter for the differentiation of artifical and essential blood in cerebrospinal fluid

The fibrin(ogen) degradation product concentration is another differential-diagnosis parameter for distinguishing between artificially blood-stained and essentially blood-stained CSF. Determination of the fibrin(ogen) degradation product concentration has been found to be a reliable method of recognizing natively and artificially blood-stained CSF as being different.     

The effect of additional variables on factor indeterminacy in models with a single common factor

Determinate solutions for the indeterminate common factor ofp variables satisfying the single common factor model are not unique. Therefore an infinite sequence of additional variables that conform jointly with the originalp variables to the original single common factor model does not determine a unique solution for the indeterminate factor of thep variables (although the solution is unique for the factor of the infinite sequence). Other infinite sequences may be found to determine different solutions for the factor of the originalp variables. The paper discusses a number of theorems about the effects of additional variables on factor indeterminacy in a model with a single common factor and draws conclusions from them for factor theory in general.     

An alternative to the methodology for analysis of covariance

A general statistical model for simultaneous analysis of data from several groups is described. The model is primarily designed to be used for the analysis of covariance. The model can handle any number of covariates and criterion variables, and any number of treatment groups. Treatment effects may be assessed when the treatment groups are not randomized. In addition, the model allows for measurement errors in the criterion variables as well as in the covariates. A wide variety of hypotheses concerning the parameters of the model can be tested by means of a large sample likelihood ratio test. In particular, the usual assumptions of ANCOVA may be tested.Research reported in this paper has been partly supported by the Swedish Council for Social Science Research under project Statistical methods for analysis of longitudinal data, project director Karl G. Jöreskog, and partly by the Bank of Sweden Tercentenary Foundation under project Structural Equation Models in the Social Sciences, project director Karl G. Jöreskog.     

On various causes of improper solutions in maximum likelihood factor analysis

In the applications of maximum likelihood factor analysis the occurrence of boundary minima instead of proper minima is no exception at all. In the past the causes of such improper solutions could not be detected. This was impossible because the matrices containing the parameters of the factor analysis model were kept positive definite. By dropping these constraints, it becomes possible to distinguish between the different causes of improper solutions. In this paper some of the most important causes are discussed and illustrated by means of artificial and empirical data.The author is indebted to H. J. Prins for stimulating and encouraging discussions.