Factor invariance of the Millon Clinical Multiaxial Inventory

Several factor analyses of the Millon Clinical Multiaxial Inventory (MCMI) have resulted in very similar solutions. Interpretation of this consistency is hampered by the fact that the 20 scales of the inventory share items. Overlapping items cause the scales to be linearly dependent and may create structure in the interscale correlation matrix which is separate from the subject response patterns. A factor analysis was performed on the matrix of item-overlap coefficients which describes the underlying artifactual structure of the instrument. Data from two new subject samples were factor analyzed and compared to previously published studies. Similarity coefficients among factors across studies were calculated.     

Factor structure of the Treatment Evaluation Inventory: Implications for measurement of treatment acceptability

The Treatment Evaluation Inventory (TEI), a frequently used measure of treatment acceptability, was used by 164 undergraduates to rate the acceptability of each of the following treatments: differential reinforcement of other behavior, exclusionary time-out, overcorrection, medical restraint, contingent electric shock, and physical restraint. TEI ratings of each treatment type were grouped separately, variance-covariance matrices were formed and compared, and data were subjected to factor analysis. The results indicated that the factor structure of the TEI varied with the treatment it was used to evaluate. Item analysis of the TEI indicated a high degree of internal consistency, although item-total correlations varied between rated treatments. The findings suggest that although the TEI is a reliable instrument, sensitive assessment of the treatment acceptability construct probably requires multidimensional measurement.     

The social phobia and anxiety inventory: Construct validity

The Social Phobia and Anxiety Inventory (SPAI) is a new instrument composed of social phobia and agoraphobia subscales. The latter scale is used to detect social anxiety that may result from agoraphobia. The SPAI's construct validity was assessed through several procedures. First, confirmatory factor analyses were conducted to validate the existence of the two subscales. Second, exploratory factor analyses examined the underlying structure of the social phobia subscale. Third, a Q factor procedure determined if different anxiety diagnostic groups could be differentiated by their SPAI response pattern. The results confirmed the utility of the two SPAI subscales and identified a number of dimensions contained within the social phobia subscale which differed depending upon the specific subject sample. In addition, the complaints of social phobies appeared more homogeneous than those of an agoraphobic comparison group. The results are discussed in terms of construct validity and the sensitivity of the SPAI to various dimensions of social phobia fears.This study was supported in part by NIMH Grants 41852, 30915, 18269, and 16884.     

Millon Clinical Multiaxial Inventory (MCMI) computer-generated diagnoses: How do they compare to clinician judgment?

During the past several decades, computers have achieved increasing prominence in psychological assessment procedures. This is particularly true for computer-based test interpretation and diagnosis. This study reports on a study designed to compare the accuracy of computer-based diagnoses with clinician-generated diagnoses. The Millon Clinical Multiaxial Inventory (MCMI) was administered to 151 consecutively admitted inpatients at a large private psychiatric hospital. The computer-generated diagnoses were compared with those generated by admitting psychiatrists. The results indicated that the MCMI diagnostic impressions underestimated the severity of depressive disorders when compared with clinician diagnoses on Axis I. Specifically, clinicians diagnosed major depression much more frequently than did the MCMI. In addition, clinicians diagnosed anxiety disorders much less frequently than did the MCMI.     

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person’s time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.     

The detection of malingering in neuropsychological assessment

Although malingering, or the manipulation of data by the patient, is a problem commonly faced by neuropsychologists, there has been little systematic investigation of this problem. This paper reviews the literature on the detection of malingering in assessment instruments commonly used by clinical neuropsychologists. Criticism of previous research is discussed, and suggestions are made both for future research and for clinical practice.     

Factor structure of the mobility inventory for agoraphobia: A validational study with australian samples of agoraphobic patients

This study examines the factor structure of the Mobility Inventory for Agoraphobia and attempts to validate the inventory, using an Australian sample of agoraphobic patients, by comparison with other scales. One hundred twentyfour panic disorder with agoraphobia patients were studied. Factor analysis showed that a two-factor solution was the most suitable both when subjects were accompanied (accompanied condition) and when alone (alone condition). Factor I represented public, crowded, or social situations including department stores, supermarkets, restaurants, theaters, and panics and social gatherings. Factor II reflected enclosed or riding situations such as elevators, parking garages, enclosed spaces, and riding in subways or airplanes. These two factors accounted for 46.6% and 44.3% of the variance on the accompanied condition and the alone condition, respectively. The Mobility Inventory was significantly correlated with the Agoraphobia factor of the Fear Questionnaire and the Social Avoidance and Distress scale. These results indicated that the Mobility Inventory is a valid instrument to measure agoraphobic behavior.     

缺陷儿童人格诊断量表的修订

缺陷儿童人格诊断量表是日本特殊教育领域的一种专门用于测量缺陷儿童人格的著名量表。作者用三年时间对其进行了修订,建立了包括弱智、聋哑、盲等缺陷儿童的中国常模。该量表具有较高的信度与效度,是一个较好的心理测量工具。     

未来时间的心理结构

２２０名大学生被试用秒、分、小时、日、星期、月和年７个时间单位和模糊统计试验方法对３９个不定的未来时间修饰词作经验赋值,结果表明：①３９个不定的未来时间修饰词与秒、分、小时、日、月、年有一定的对应关系,②未来的心理时间分为三个时间段,即以秒和分为计时单位的“较近的未来”,以小时、日和月为计时单位的“近的未来”,以及以年为计时单位的“远的未来”。③离现在越近的未来时间修饰词,词义模糊度越小,评量的把握度越大;而离现在越远的未来时间修饰词,词义模糊度越大,评量的把握度越小。     

Personality disorder correspondence between the million behavioral health inventory (MBHI) and the minnesota multiphasic personality inventory (MMPI)

The MBHI and MMPI personality disorder scales were analyzed for convergent and discriminant validity. Correlational data demonstrated that six of the eight scales were significantly related, while the remaining two scales approached significance. Further analyses of these data, however, demonstrated that none of the scales correlated significantly better with its convergent scale compared to nonconvergent scales. The MBHI classified significantly more of the sample as personality disordered (93%) compared to the MMPI personality disorder scales (17%). Furthermore, the MBHI tended to describe the sample as falling within the Anxious cluster of personality disorders, whereas the MMPI described them within the Dramatic cluster. Single scale codetype correspondence was found to be 15%, while two-point concordance was 12.5%, indicating very low congruence between personality style codetypes. These two measures do not appear to be measuring the same personality style constructs.