Robert J. Wherry

Hierarchical factor solutions without rotation

A method is presented for securing a hierarchical factor solution which achieves simple structure at each hierarchical level without rotation or even preliminary arbitrary orthogonal or oblique solutions. The method is based upon the assumption that if overlap is removed from clusters the remaining specifics will achieve simple structure automatically. The problem presented earlier by Schmid and Leiman, using oblique simple structural rotation as a basis, is reworked by this new approach.

The Use of Simulated Stimuli and the "Jan" Technique to Capture and Cluster the Policies of Raters

he is being required to evaluate. For example, if we ask k judges to rank a group of n individuals with regard to general job competence, the way in which they are ordered by each judge would represent that judge’s policy. To the extent that two judges did not order people in the same fashion their policies would be considered to be different. Also, the extent to which one could predict the rank order for a given judge using information about the individuals being ranked would represent the degree to which that judge’s policy has been captured. The process of capturing policies and of determining their general similarities and differences are two distinctly separate problems and have received considerably different emphasis on the literature. Generally speaking, almost nothing has been done concerning policy capturing, while a fair amount of research is available pertaining to clustering raters in terms of the similarities of their judgment. The intercorrelation and factor analysis of this type of data is the most

Generating multiple samples of multivariate data with arbitrary population parameters

A method of generating any number of score and correlation matrices with arbitrary population parameters is described. EitherZ scores or stanines are sampled from a normal population to represent factor scores by an IBM 1620 program. These are converted to variates from a population with an a priori factor structure. The effectiveness of the method is illustrated from research data. Some further modifications and uses of the method are discussed.

A method for factoring large numbers of items

The computation of intercorrelation matrices involving large numbers of variables and the subsequent factoring of these matrices present a formidable task. A method for estimating factor loadings without computing the intercorrelation matrix is developed. The estimation procedure is derived from a theoretical model which is shown to be a special case of the multiple-group centroid method of factoring. Empirical checks have indicated that the model, even though it makes some stringent assumptions, can be applied to a variety of variables found in psychological factoring problems. It has been found to be particularly useful in factoring test items.

An empirical verification of the Wherry-Gaylord iterative factor analysis procedure

A comparison of the Wherry-Gaylord iterative factor analysis procedure and the Thurstone multiple-group analysis of sub-tests shows that the two methods result in the same factors. The Wherry-Gaylord method has the advantage of giving factor loadings for items. The number of iterations needed can be reduced by doing a factor analysis of sub-tests, re-grouping sub-tests according to factors, and using each group as a starting point for iterations.

Hierarchical Factor Structure of the Wechsler Intelligence Scale for Children

A Wherry-Wherry (1969) hierarchical factor analysis was performed on WISC subtest intercorrelations reported by Wechsler (1949). An hierarchical ability arrangement congruent with Vernons (1950) structural paradigm was obtained. A strong general factor (g) was defined by positive loadings from all subtests and two relatively weak subgeneral factors; a subgeneral factor corresponding to Vernons (1950) verbal-educational (v:ed) factor was defined by the verbal subtests and another by the performance sub-rests. The latter seemed to correspond to the spatial-perceptual (k:m) factor of Vernons (1950) paradigm. These data not only provided strong support for the construct validity of the WISC as a measure of g but also provided some conditional support for Wechslers (1949) decision to maintain separate verbal and performance IQs.

Comparison of Two Approaches—Jan and Prof—for Capturing Rater Strategies

IN an earlier paper the present authors (Naylor and Wherry, 1965) have described the use of the Bottenberg-Christal JAN technique (Bottenberg and Christal, 1961) for the isolation of rater policies. In that paper we reported in detail the JAN outcome for one of four air force specialties analyzed. Actually four specialties were studied and the present paper is based upon the complete study. The four specialties consisted of two supervisory levels of a mechanical specialty and two versions of an administrative (housekeeping) type of specialty, both versions being at the same supervisory level. The JAN technique is based upon defining the capturing of rater policy as the extent to which one can predict the actions of a rater

A new iterative method for correcting erroneous communality estimates in factor analysis

A new method for correcting erroneous communality estimates is applicable to any completed orthogonal factor solution. It seeks, by direct correction of factor loadings, to make the residuals conform to the chance error criteria of zero mean and zero skewness for each row separately. Two numerical examples, with one and two factors, respectively, are presented. The method can be used as a short cut for Dwyers extension in adding variables to a matrix. It can also be used as a short cut in cross-validation factor studies. Successful use on problems with many variables and numerous factors is claimed. Factors can be made oblique,after correction, if desired.

IV. Comparison of Cross-Validation with Statistical Inference of Betas and Multiple R From a Single Sample

inference can be developed along two different lines of emphasis. The first of these is concerned largely with the problem of correction for the fitting of error, while the second is concerned principally with the nature of the sampling involved. If the correction for the fitting-of-error aspect is emphasized, the two approaches appear as competitors and we must attempt to answer the question, &dquo;Which is the best method to determine

Motivational Constructs: A Factorial Analysis of Feelings

THE present study was based upon dissatisfaction with the present state of testing in the area of motivation. Both the man on the street and theoreticians seem agreed on the premise that motivation must play a large role in productivity. However the negative, or at least ambivalent, results of research in this area are well known. Personality tests, morale surveys, and depth techniques have yielded quite minimal results in most cases. One of the more promising findings to date has arisen from work