Are psychophysiological/psychophysical scores good candidates for diagnosing temperament/personality traits and for a demonstration of the construct validity of psychometrically measured traits?

The aim of this paper is to examine the use of psychophysiological and/or psychophysical measures employed as (a) tools aimed at diagnosing personality/temperament traits and (b) criteria for estimating the construct validity of personality/temperament inventories. The exploration is based on three diferent sources: (1) data collected mainly within the neo-Pavlovian approach on CNS properties; (2) selected examples of studies in the domain of sensation-seeking, neuroticism, and extraversion; and (3) theoretical considerations regarding the specificity of phenomena being measured by means of psychometric and psychophysiological/psychophysical scores. It is concluded that psychophysiological and psychophysical measures may be used for the assessment of personality/temperament traits or for estimating the construct validity of psychometric tools applied in personality (temperament) research under very strict requirements and limited circumstances only. In most of the studies conducted up to now, these circumstances and requirements have not been fulfilled.     

Vibrotactile frequency discrimination

Thresholds for vibrotactile discrimination of pulse interval were determined for pulse frequencies between 1 and 384 Hz. The results point to a temporal resolution significantly more accurate than that demonstrated in earlier studies. Although touch as a vibratory sensor is in general much inferior to audition, the present results show a striking resemblance to those obtained on auditory pitch. The neurophysiological implications for the tactile as well as for the auditory system are discussed.     

Objective measurement of the independent variable: A neglected methodological aspect in community-based behavioral research

The importance of adequately measuring independent as well as dependent variables in studies evaluating therapeutic services provided to antisocial children is indicated. Data were obtained from a study in which 139 antisocial children were stratified according to age and then randomly placed into 14 groups. The children were randomly assigned to either of 2 treatment strategies or to a control treatment strategy. The effect of the treatments was evaluated by 2 pre-post selfrating scales, by a prepost inventory completed by therapists, and by behavioral observations of both children and their therapists. Measurements of the children's and therapists' behaviors were secured at each weekly 2-hr, meeting for approximately 30 weeks. Very few significant differences occurred between the treatment and control groups on the dependent variables. The findings indicated that independent checks are necessary to insure that therapeutic strategies are actually being implemented, thus providing for an adequate test of their effects on the dependent variables.     

Distraction effects in tasks of varying difficulty: Methodological issues in measuring development of distractibility

Effects of distraction on 5- and 8-year-old children's performance on a short-term memory task were examined. Tasks at three difficulty levels were employed to determine whether a floor effect could account for the lack of an age difference reported in an earlier study. A floor effect does not appear to be a key factor; the absolute amount of performance decrement was roughly equal for the two ages here regardless of task difficulty, and performance was well above chance under all conditions. The children adapted somewhat to the presence of distraction as shown in greater performance impairment for the first trial under distraction than for remaining trials. Use of a proportional, rather than an absolute, decrement index was considered, and the relevance of the present data to this issue was discussed.     

Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features

A new procedure is discussed which fits either the weighted or simple Euclidian model to data that may (a) be defined at either the nominal, ordinal, interval or ratio levels of measurement; (b) have missing observations; (c) be symmetric or asymmetric; (d) be conditional or unconditional; (e) be replicated or unreplicated; and (f) be continuous or discrete. Various special cases of the procedure include the most commonly used individual differences multidimensional scaling models, the familiar nonmetric multidimensional scaling model, and several other previously undiscussed variants.The procedure optimizes the fit of the model directly to the data (not to scalar products determined from the data) by an alternating least squares procedure which is convergent, very quick, and relatively free from local minimum problems.The procedure is evaluated via both Monte Carlo and empirical data. It is found to be robust in the face of measurement error, capable of recovering the true underlying configuration in the Monte Carlo situation, and capable of obtaining structures equivalent to those obtained by other less general procedures in the empirical situation.This project was supported in part by Research Grant No. MH10006 and Research Grant No. MH26504, awarded by the National Institute of Mental Health, DHEW. We wish to thank Robert F. Baker, J. Douglas Carroll, Joseph Kruskal, and Amnon Rapoport for comments on an earlier draft of this paper. Portions of the research reported here were presented to the spring meeting of the Psychometric Society, 1975. ALSCAL, a program to perform the computations discussed in this paper, may be obtained from any of the authors.Jan de Leeuw is currently at Datatheorie, Central Rekeninstituut, Wassenaarseweg 80, Leiden, The Netherlands. Yoshio Takane can be reached at the Department of Psychology, University of Tokyo, Tokyo, Japan.     

Multivariate analysis with linearizable regressions

We study the class of multivariate distributions in which all bivariate regressions can be linearized by separate transformation of each of the variables. This class seems more realistic than the multivariate normal or the elliptical distributions, and at the same time its study allows us to combine the results from multivariate analysis with optimal scaling and classical multivariate analysis. In particular a two-stage procedure which first scales the variables optimally, and then fits a simultaneous equations model, is studied in detail and is shown to have some desirable properties.