Personality factors as predictors of pathological gambling

The aim of this study was to investigate the relationship between different personality variables and pathological gambling (PG). The NEO-FFI and measures of impulsivity and sensation-seeking were administered to a sample of pathological gamblers (n = 90) and to a contrast group of non-pathological gamblers (n = 66) matched on sex and age. Gender, age, education level and the personality variables were entered into crude and adjusted logistic regression analyses with PG-status as the dependent variable. The results showed that educational level and all personality variables were significant predictors of PG in the crude analyses, however only four of the 12 significant predictor variables (Neuroticism, Openness, Impulsivity, and need for Stimulus Intensity) remained significant in the adjusted analysis. All predictor variables accounted for 71% of the variance in PG-status. Clinical implications of the findings are discussed.     

Leadership and Organizational Culture: Linking CEO Characteristics to Cultural Values

Purpose  The purpose of this study was to empirically examine organizational culture theorists’ assertions about the linkages between leadership and the cultures that emerge in the organizations they lead. Specific hypotheses were developed and tested regarding relationships between chief executive officers’ (CEO’s) personality traits, and the cultural values that are shared among their organization’s members. Design/Methodology/Approach  Thirty-two CEOs completed measures of the Big-Five personality traits and personal values. A total of 467 employees across the 32 organizations completed a competing values measure of organizational culture. Findings  Results indicate support for several hypothesized relationships between CEO personality and cultural values. Exploratory analyses indicated that several CEO personal values were related to culture values. Implications  Organizations need to seriously consider the “fit” between the current or desired organizational culture and CEO characteristics. Organizations attempting to change fundamental aspects of its functioning may need significant behavioral—or personnel—changes at the top of the organization in order to achieve those changes. Originality/Value  This is the first empirical study to establish a link between specific CEO characteristics and the cultural values of their organizations. This study provides evidence that CEO characteristics are felt throughout the organization by impacting the norms that sanction or discourage member behavior and decision making, and the patterns of behavior and interaction among members.     

On the geometric approach to multivariate selection

Multivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation.     

Additive conjoint isotonic probabilistic models (ADISOP)

The ISOP-model or model of twodimensional or bi-isotonicity (Scheiblechner, 1995) postulates that the probabilities of ordered response categories increase isotonically in the order of subject ability and item easiness. Adding a conventional cancellation axiom for the factors of subjects and items gives the ADISOP model where the c.d.f.s of response categories are functions of an additive item and subject parameter and an ordinal category parameter. Extending cancellation to the interactions of subjects and categories as well as of items and categories (independence axiom of the category factor from the subject and item factor) gives the CADISOP model (completely additive model) in which the parallel c.d.f.s are functions of the sum of subject, item and category parameters. The CADISOP model is very close to the unidimensional version of the polytomous Rasch model with the logistic item/category characteristic(s) replaced by nonparametric axioms and statistics. The axioms, representation theorems and algorithms for model fitting of the additive models are presented.     

Statistical inference for multiple choice tests

Finite sample inference procedures are considered for analyzing the observed scores on a multiple choice test with several items, where, for example, the items are dissimilar, or the item responses are correlated. A discrete p-parameter exponential family model leads to a generalized linear model framework and, in a special case, a convenient regression of true score upon observed score. Techniques based upon the likelihood function, Akaike's information criteria (AIC), an approximate Bayesian marginalization procedure based on conditional maximization (BCM), and simulations for exact posterior densities (importance sampling) are used to facilitate finite sample investigations of the average true score, individual true scores, and various probabilities of interest. A simulation study suggests that, when the examinees come from two different populations, the exponential family can adequately generalize Duncan's beta-binomial model. Extensions to regression models, the classical test theory model, and empirical Bayes estimation problems are mentioned. The Duncan, Keats, and Matsumura data sets are used to illustrate potential advantages and flexibility of the exponential family model, and the BCM technique.The authors wish to thank Ella Mae Matsumura for her data set and helpful comments, Frank Baker for his advice on item response theory, Hirotugu Akaike and Taskin Atilgan, for helpful discussions regarding AIC, Graham Wood for his advice concerning the class of all binomial mixture models, Yiu Ming Chiu for providing useful references and information on tetrachoric models, and the Editor and two referees for suggesting several references and alternative approaches.     

Comparing the predictive powers of alternative multiple regression models

Mean squared error of prediction is used as the criterion for determining which of two multiple regression models (not necessarily nested) is more predictive. We show that an unrestricted (or true) model witht parameters should be chosen over a restricted (or misspecified) model withm parameters if (P _t ² ?P _m ² )>(1?P _t ² )(t?m)/n, whereP _t ² andP _m ² are the population coefficients of determination of the unrestricted and restricted models, respectively, andn is the sample size. The left-hand side of the above inequality represents the squared bias in prediction by using the restricted model, and the right-hand side gives the reduction in variance of prediction error by using the restricted model. Thus, model choice amounts to the classical statistical tradeoff of bias against variance. In practical applications, we recommend thatP ² be estimated by adjustedR ² . Our recommendation is equivalent to performing theF-test for model comparison, and using a critical value of 2?(m/n); that is, ifF>2?(m/n), the unrestricted model is recommended; otherwise, the restricted model is recommended.     

A generalized rasch model for manifest predictors

A logistic regression model is suggested for estimating the relation between a set of manifest predictors and a latent trait assumed to be measured by a set ofk dichotomous items. Usually the estimated subject parameters of latent trait models are biased, especially for short tests. Therefore, the relation between a latent trait and a set of predictors should not be estimated with a regression model in which the estimated subject parameters are used as a dependent variable. Direct estimation of the relation between the latent trait and one or more independent variables is suggested instead. Estimation methods and test statistics for the Rasch model are discussed and the model is illustrated with simulated and empirical data.     

A general solution for a class of weakly constrained linear regression problems

This paper contains a globally optimal solution for a class of functions composed of a linear regression function and a penalty function for the sum of squared regression weights. Global optimality is obtained from inequalities rather than from partial derivatives of a Lagrangian function. Applications arise in multidimensional scaling of symmetric or rectangular matrices of squared distances, in Procrustes analysis, and in ridge regression analysis. The similarity of existing solutions for these applications is explained by considering them as special cases of the general class of functions addressed.The author is obliged to Henk Kiers and Willem Heiser for helpful comments.     

On the factor score controversy

A summary and interpretation of the recent literature on the indeterminacy of factor scores is given in simple terms. A good index of factor score determinacy is the squared multiple correlation of the factor with the observed variables.     

Some simple procedures for handling missing data in multivariate analysis

For analyses with missing data, some popular procedures delete cases with missing values, perform analysis with missing value correlation or covariance matrices, or estimate missing values by sample means. There are objections to each of these procedures. Several procedures are outlined here for replacing missing values by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive.This research was supported by NIH Special Research Resources Grant RR-3. The author expresses his gratitude to Robert I. Jennrich and the referees for their suggestions.