Discriminating between alcoholic and nonalcoholic blacks and whites on the MMPI

The purpose of the present study was to investigate the influence of race (black, white) on the MMPI performance of alcoholic and nonalcoholic inpatients. Subjects were 73 (27 black, 46 white) male alcoholic inpatients and 73 (27 black, 46 white) male nonalcoholic psychiatric inpatients. While black and white alcoholics failed to differ on the MMPI, white alcoholics presented as less disturbed and black alcoholics as less defensive and distressed, compared to their respective nonalcoholic counterparts. Furthermore, only white alcoholics were accurately identified by the MMPI 2-4/4-2 high-point pair combination and only white alcoholics achieved more high-point pairs containing Scale 4 relative to nonalcoholic controls.     

Identifying schizophrenia by means of scale 8 Sc of the MMPI

A sample of 38 schizophrenic inpatients was found to achieve significantly higher mean scores on Scale 8 of the MMPI relative to 38 schizophrenia-spectrum and 38 control patients regardless of whether K- or non-K-corrected scores were used. However, when patients were individually classified by means of a cutting score (greater than or equal to 70), only K-corrected scores accurately discriminated between schizophrenic and control patients at a statistically significant level.     

Fixed-interval limited-hold avoidance with and without signalled reinforcement

Rats trained to lever press on a fixed-interval limited-hold avoidance schedule maintained a pattern of responding similar to that maintained by fixed-interval limited-hold schedules of positive reinforcement. But this positively accelerated pattern of behavior was maintained only when the occurrence of reinforcement was signalled by the presentation of a brief flash of light. This result suggests that the discriminative function of the reinforcer in avoidance is less pronounced than the discriminative function of the reinforcer in escape or positive reinforcement. It also suggests that the distinction between positive reinforcement and avoidance is not superfluous. Although the schedule of reinforcement is an important variable in determining the pattern of behavior, other variables, such as the nature (i.e., stimulus presentation, termination, or omission) of the reinforcer, are also potent determinants of behavior.     

Social Anxiety Scale for Adolescents: normative data and further evidence of construct validity

Replicates and extends prior work with the Social Anxiety Scale for Adolescents (SAS-A) by providing psychometric data, further evidence of construct validity, and large-sample based normative data. Participants were 2,937 students (1,431 boys and 1,506 girls) in Grades 6, 7, 8, 9, and 11. Students completed the SAS-A, the Revised Children's Manifest Anxiety Scale (RCMAS), and the Children's Depression Inventory (CDI). Results replicated a three-factor structure for the SAS-A, with good internal consistencies for its subscales. Normative data were subdivided by sex and grade group. Construct validity included replication of prior relations with general anxiety (RCMAS) and depressive symptomatology (CDI). Implications of these results for further use and norming of the SAS-A are discussed.     

Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure

Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to apply some method for estimating a latent structural model such as factor analysis without first verifying that the latent structure type assumed by that method applies to the data. The taxometric method was developed specifically to distinguish between dimensional and 2-class models. This study evaluated the taxometric method as a means of identifying categorical structures in general. We assessed the ability of the taxometric method to distinguish between dimensional (1-class) and categorical (2-5 classes) latent structures and to estimate the number of classes in categorical datasets. Based on 50,000 Monte Carlo datasets (10,000 per structure type), and using the comparison curve fit index averaged across 3 taxometric procedures (Mean Above Minus Below A Cut, Maximum Covariance, and Latent Mode Factor Analysis) as the criterion for latent structure, the taxometric method was found superior to finite mixture modeling for distinguishing between dimensional and categorical models. A multistep iterative process of applying taxometric procedures to the data often failed to identify the number of classes in the categorical datasets accurately, however. It is concluded that the taxometric method may be an effective approach to distinguishing between dimensional and categorical structure but that other latent modeling procedures may be more effective for specifying the model.