In defence of the criticism of the Defence Mechanism Test (DMT): A reply to Kragh (1998)

Kragh ( Scandinavian Journal of Psychology , 1998, 39, 123–124) criticises a recent empirical study of the Defence Mechanism Test (DMT) conducted by the present authors (Zuber & Ekehammar, 1997) concerning methodological and other shortcomings. The present paper reviews and responds to Kragh's critical points. As to the most serious criticism, the methodological, the main conclusion is that Kragh's own published empirical studies of the DMT, and also those of other researchers, can be criticised along similar lines. Furthermore, and surprisingly, in contrast to our study, Kragh's main DMT studies do not employ the DMT picture(s) at all.     

Matching models in the analysis of cross-classifications

Inference models motivated by the combinatorial chance literature and the concept of object matching may be used in the analysis of a contingency table if the conditional assumption of fixed row and column totals is imposed. More specifically, by developing a matching reinterpretation for several problems of interest in the prediction analysis of cross-classifications—as defined by Hildebrand, Laing and Rosenthal, appropriate significance tests can be given that may differ from those justified by the more common multinomial models. In the course of the paper the distinction between a degree-1 statistic (based on the relationship between single objects) and a degree-2 statistic (based on the relationship between object pairs) is reviewed in some detail. Also, several specializations are presented to topics of current methodological importance in psychology; for instance, a number of references are made to the measurement of nominal scale response agreement between two raters.Partial support for this research was provided by the National Science Foundation through GSOC-77-28227.     

Likelihood methods for testing group problem solving models with censored data

Problem solving models relating individual and group solution times under time limit censoring are presented. Maximum likelihood estimates of parameters of the resulting censored distributions are derived and goodness of fit tests for the individual-group models are constructed. The methods are illustrated on data previously analyzed by Restle and Davis.This research was supported by grants from the Graduate School, University of Minnesota. Also, work of the second author was aided by a Single Quarter Leave granted by the Regents of the University of Minnesota.     

画概念图对高中二年级学生认知结构形成的影响

运用联想测试方法探索高二学生学习新陈代谢一章后的认知结构。运用相关系数值绘制出了学生的认知结构图。结果显示,运用概念图策略进行教学的实验班学生更容易形成认知结构。实验班学生所形成的认知结构更良好、更合理。     

Assessment of executive function in ADHD adolescents: contribution of performance tests and rating scales

This study aimed to analyze performance on measures of neuropsychological and behavioral executive functions (EF) in adolescents with attention deficit hyperactivity disorder (ADHD), and to evaluate the utility of performance-based tests for predicting scores on behavioral EF ratings. One hundred eighteen adolescents (75 ADHD and 43 controls) aged 12–16 years performed neuropsychological tests and completed a behavior rating scale of EF. The ADHD group presented significantly lower scores than controls on Full Scale IQ (FSIQ) and all indexes of the WISC-IV, except the verbal comprehension index (VCI). The ADHD group had significantly lower scores on performance-based tests of working memory, planning and inhibition, and on EF rating scales. Scores on the cognitive EF working memory, planning and flexibility modestly predicted performance on behavioral EF. The results suggest that the combined use of performance-based tests and rating scales provides valuable complementary information that can improve the assessment of executive domains in ADHD.     

Bayesian Gaussian distributional regression models for more efficient norm estimation

A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test.     

Exact tests for the rasch model via sequential importance sampling

Rasch proposed an exact conditional inference approach to testing his model but never implemented it because it involves the calculation of a complicated probability. This paper furthers Rasch’s approach by (1) providing an efficient Monte Carlo methodology for accurately approximating the required probability and (2) illustrating the usefulness of Rasch’s approach for several important testing problems through simulation studies. Our Monte Carlo methodology is shown to compare favorably to other Monte Carlo methods proposed for this problem in two respects: it is considerably faster and it provides more reliable estimates of the Monte Carlo standard error.This Research was supported in part by National Science Foundation grant DMS-0203762 and a University of Pennsylvania Research Foundation grant.The authors are grateful to Don Burdick for helpful comments. In addition, the authors wish to thank the editor, the associate editor, and the referees for their helpful suggestions.This revised article was published online in August 2005 with the PDF paginated correctly.     

Neuropsychological Differences Between Frontotemporal Dementia and Alzheimer's Disease: A Review

This paper surveys the similarities and differences between frontotemporal dementia (FTD) and Alzheimer's disease (AD). The review covers findings primarily from neuropsychological studies on memory, language, attention/executive function, and visuospatial abilities. However, neuropsychiatric and neuroimaging data are also briefly discussed. Distinguishing features of both FTD and AD are described in order to present a comprehensive clinical picture of these dementing diseases, which is essential for the process of differential diagnosis. The cause of specific cognitive deficits is also considered. Our comprehensive review of the empirical literature reveals that AD is characterized by early memory loss and visuospatial problems, while among the main features of FTD are behavioral abnormalities and executive dysfunctions.     

A data-driven approach to neuropsychological features in isolated REM behaviour disorder: A latent class analysis

Recent evidence demonstrated that neuropsychological assessment may be considered a valid marker of neurodegeneration in idiopathic REM sleep behaviour disorder (iRBD). However, little is known about the possible neuropsychological heterogeneity within the iRBD population. This retrospective study aimed to identify and describe different neuropsychological phenotypes in iRBD patients by means of a data-driven approach using latent class analysis. A total of 289 iRBD patients underwent a neuropsychological assessment evaluating cognitive domains: global cognition, language, short- and long-term memory, executive functions and visuospatial abilities. The presence of mild cognitive impairment (MCI) was also assessed. Latent class analysis was carried out to identify iRBD subtypes according to neuropsychological scores. The most parsimonious model identified three latent classes. Groups were labelled as follows: Class 2 “severely impaired” (n = 83/289): mean pathological scores in different tests, a high percentage of MCI multiple-domain and impairment in all neuropsychological domains. Class 1 “moderately impaired” (n = 44/289): mean neuropsychological score within the normal value, a high percentage of MCI (high risk to phenoconversion) and great impairment in the visuospatial domain. Class 3 “slightly impaired” (n = 162/289): no deficit worthy of attention except for short- and long-term memory. Our results suggest three different clinical phenotypes within the iRBD population. These findings may be relevant in the future for predicting the clinical trajectories of phenoconversion in iRBD.     

Compromised item detection: A Bayesian change-point perspective

Psychometric methods for accurate and timely detection of item compromise have been a long-standing topic. While Bayesian methods can incorporate prior knowledge or expert inputs as additional information for item compromise detection, they have not been employed in item compromise detection itself. The current study proposes a two-phase Bayesian change-point framework for both stationary and real-time detection of changes in each item's compromise status. In Phase I, a stationary Bayesian change-point model for compromise detection is fitted to the observed responses over a specified time-frame. The model produces parameter estimates for the change-point of each item from uncompromised to compromised, as well as structural parameters accounting for the post-change response distribution. Using the post-change model identified in Phase I, the Shiryaev procedure for sequential testing is employed in Phase II for real-time monitoring of item compromise. The proposed methods are evaluated in terms of parameter recovery, detection accuracy, and detection efficiency under various simulation conditions and in a real data example. The proposed method also showed superior detection accuracy and efficiency compared to the cumulative sum procedure.