Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…

This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…

Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size…

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…

This article describes and proposes an unrestricted factor analytic procedure to: (a) assess the dimensionality and structure of a balanced personality scale taking into account the potential effects of acquiescent responding, and (b) correct the individual trait estimates for acquiescence. The procedure can be considered as an extension of ten…

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

The purpose of this study was to examine the location of Zukerman's sensation seeking (SS) construct in the personality domain as measured by Cattell's 16 personality factors (16PF). Results of the factor analytic study indicated that the global construct of sensation seeking is related to the broad personality factor of independence. (Author/LMO)

Responses of male prisoners to the Personal Orientation Inventory were clustered, using hierarchical linkage analysis. Six second-order clusters accounted for all the items. Reliabilities of these clusters were comparable to those of the first-order clusters. Relative validity of cluster scores and scale scores remains to be determined. (RC)

The relation of the general factor of the Inventory of Interpersonal Problems (IIP) to several response set and personality measures and the circumplex structure was studied with 105 and 1,093 undergraduates. Results support the general factor of the IIP as having a substantial nonbiasing interpretation and indicative of general interpersonal…

D.V. Budescu and J.L. Rogers (1981) proposed a method of adjusting correlations of scales to eliminate spurious components resulting from the overlapping of scales. Three reliability correction formulas are derived in this article that are based on more tenable assumptions. (SLD)

Scores were obtained on 31 item-clusters from the California Psychological Inventory for 490 identical and 317 same-sex fraternal twin pairs from the National Merit twin sample. Ordinary and cross-pair correlations were calculated and used to derive three matrices hypothesized to reflect the influence of genes, shared environment, and unshared…

A three subscale inventory designed by Fenigstein, Scheier, and Buss to measure self-consciousness was administered to 297 college students. Fenigstein et al.'s representation was found to fit the data in its original form. Items on the subscales differ nearly as much statistically as they do substantively. (Author/LMO)

Item overlap coefficient (IOC) formulas are discussed, providing six warnings about their calculation and interpretation and some explanations of why item overlap influences the Minnesota Multiphasic Personality Inventory and the Millon Clinical Multiaxial Inventory factor structures. (SLD)