Reference group data for the Reitan-Indiana Neuropsychological Test Battery for Young Children

This study presents a data set for a reference group on the Reitan-Indiana Neuropsychological Test Battery for Young Children. The data set is based on a sample of 224 children, ages 5 to 8 years, referred to a special services cooperative for academic or behavioral concerns during the years 1980 through 1993. Data are presented in terms of sample size, means, standard deviations, diagnostic classifications, and population characteristics. Previously published data sets are reviewed in comparison to this newly acquired data set. Potential advantages of this data set include the larger sample, contemporary data collection, and a sample drawn from a United States school-referred population.     

Kicking over the traces: A note in response to Zurif and Piñango (1999)

Zurif and Pi?ango (1999) claimed that they excluded the four agrammatic patients reported by Druks and Marshall (1991) from their review article because two of the patients were nonnative speakers of Hebrew and because the Hebrew sentences we used in our investigations were ungrammatical. In Druks and Marshall (1991) we have shown that the presence or the absence of a trace in two types of Hebrew passives had no effect on the patients' performance. Two patients, without comprehension deficits, performed equally well on both types of passives and two patients, with comprehension deficits, were equally impaired on both types. We remind Zurif and Pi?ango of our previous response to the claims of ungrammaticality of our materials (Druks & Marshall, 1992) and argue that there were no justifiable reasons for excluding these cases from the review. We also comment on Zurif and Pi?ango's (1999) and Grodzinsky's (2000) new proposal that the association of agrammatic comprehension should be with Broca's aphasia and not with agrammatism.     

Neural network modeling of risk assessment in child protective services

The advantages of using neural network methodology for the modeling of complex social science data are demonstrated, and neural network analysis is applied to Washington State Child Protective Services risk assessment data. Neural network modeling of the association between social worker overall assessment of risk and the 37 separate risk factors from the State of Washington Risk Assessment Matrix is shown to provide case classification results superior to linear or logistic multiple regression. The improvement in case prediction and classification accuracy is attributed to the superiority of neural networks for modeling nonlinear relationships between interacting variables; in this respect the mathematical framework of neural networks is a better approximation to the actual process of human decision making than linear, main effects regression. The implications of this modeling advantage for evaluating social science data within the framework of ecological theories are discussed.     

All symptoms are not created equal: the prominent role of hyperarousal in the natural course of posttraumatic psychological distress

This 3-wave longitudinal study examined the natural course of posttraumatic stress disorder symptoms using data collected from young adult survivors of community violence. Three key findings emerged. 1. Mean levels of distress for each symptom cluster decreased over time, with reexperiencing decreasing most rapidly. 2. Cross-lagged panel analysis revealed that hyperarousal strongly influences, but is not generally influenced by, other symptoms clusters. 3. Trajectory analysis demonstrated that respondents for whom hyperarousal was the most pronounced baseline symptom showed lower overall symptom improvement relative to trauma exposed counterparts for whom hyperarousal was a less prominent early symptom. Implications for theory, research, and clinical practice are discussed.     

Trust in the workplace: factors affecting trust formation between team members

The authors used survey data from 127 professional-level employees working in 8 industries to assess the effects of respondent's trusting stance and (a) the trustee's organization membership (internal or external), (b) the hierarchical relationship (supervisor or peer), and (c) the gender of the trustee, on initial trust level for a new project team member. The authors found that trusting stance was positively related to initial trust level. The authors also found an interaction effect between respondent gender and trustee gender on initial trust. Specifically, male initial trust level was higher for a new male team member and lower for a new female team member. The present study provided additional understanding of the formation of initial trust levels and its importance for team functioning.     

Dimensional personality traits and the prediction of DSM-IV personality disorder symptom counts in a nonclinical sample

The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III; APA, 1980) set forth a categorical system of personality psychopathology that is composed of discrete personality disorders (PDs), each with a distinct set of diagnostic criteria. Although this system is widely accepted and highly influential, alternative dimensional approaches to capturing personality psychopathology have been proposed. Three dimensional models of personality have garnered particular attention-the Five-Factor Model (FFM; Costa & McCrae, 1992), the Seven-Factor Psychobiological Model of Temperament and Character (Seven-Factor Model; Cloninger, Svrakic, & Przybeck, 1993); and the 18-factor model of personality pathology (18-factor model; Livesley, 1986). Although the personality traits from each of these models has been examined in relation to the ten personality disorders in the DSM-IV, no study has examined the comparative and incremental validity of these models in predicting PD symptoms for these ten disorders. Using self-report instruments that measure these models and the ten DSM-IV PDs, correlation and linear regression analyses indicate that traits from all three models had statistically significant associations with PD symptom counts. Hierarchical regressions revealed that the 18-factor model had incremental predictive validity over the FFM and Seven-Fac-tor Model in predicting symptom counts for all ten DSM-IV PDs. The FFM had incremental predictive validity over the Seven-Factor Model model for all ten disorders and the Seven-Factor was able to add incremental predictive validity over the 18-factor model for five of the ten PDs and for eight of the ten disorders relative to the FFM.     

Socially desirable responding and the factorial stability of the NEO PI-R

The goal of the present investigation is to compare the factor structure of the revised NEO Personality Inventory (NEO PI-R; P. T. Costa & R. R. McCrae, 1992) in samples of respondents differentially motivated to respond in a socially desirable manner. In the French sample, the authors compared the NEO PI-R structure of job applicants (N=12,631) subgrouped by relative degree of socially desirable responding with that of a normative sample (N=801). In the Belgian sample, the authors compared the NEO PI-R structure in naturally occurring groups, including job applicants (N=3,028), individuals receiving career counseling (N=221), and a normative sample (N=549). Results indicate that the NEO PI-R factor structure remained stable across all samples.     

Distinguishing bipolar depression, major depression, and schizophrenia with the MMPI-2 clinical and content scales

Clinical and content scales from the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) were used to examine the capacity of these scales to assist in the differential diagnosis of a sample of 212 psychiatric patients-137 with major depression; 43 with schizophrenia; and 32 with bipolar disorder, depressed state. Consistent with the previous literature, the clinical scales Depression (D), and Schizophrenia (Sc), and the content scales Depression (DEP), and Low Self-Esteem (LSE) best distinguished major depression from schizophrenia; the content scale DEP proved to be the most powerful predictor in distinguishing bipolar depression from schizophrenia. No clinical or content scale proved to be effective in distinguishing patients with bipolar depression from patients with major depression. In general, the content scales outperformed the clinical scales.