Dimensionale versus kategoriale Klassifikation von Pers?nlichkeitsst?rungen

The classification systems DSM-IV and ICD-10 are characterized by a categorical conception of personality disorders. This perspective has long been criticized for a number of reasons and a dimensional perspective plays a major role in the debate. For both approaches, advantages and disadvantages can be discussed and a categorical-dimensional hybrid approach will most likely be established in the revised classification systems ICD-11 and DSM-V. An integration of a categorical and a dimensional??quantifying??perspective has always been a standard in the field of forensic psychiatric assessment. A general hybrid approach of defining personality disorders appears suitable, promising and feasible.     

The modification of agoraphobic avoidance behaviour: some fresh possibilities

A reconsideration of the theoretical basis of the therapy provided for agoraphobic people is warranted on three grounds. Although current methods of behavioural treatment are moderately effective, many patients are left with significant residual problems. Secondly, consideration of the theoretical basis of current behavioural treatments has been neglected and thirdly, there has been insufficient curiosity about the nature of agoraphobia.Three approaches to the problem of persistent avoidance behaviour and/or fear were selected as being of particular promise: Seligman and Johnston's cognitive theory of avoidance behaviour. Gray's elaboration of the safety signal hypothesis and Bandura's self-efficacy theory. Proposals for two new forms of therapy were formulated from an examination of these theories. The most promising possibilities are ‘therapy by repeated disconfirmation’ and ‘safety-signal therapy’. The general lines of these new forms of therapy are set out, examples provided and suggestions put forward for assessment procedures.It is suggested that in many agoraphobic cases, the control of panic attacks is a target of central importance and renewed attempts to find dependable ways of controlling panic attacks by psychological methods should be made. It is also recommended that self-efficacy estimates should be included in experimental investigations of the therapy provided for people with agoraphobic problems.     

Behandlungsleitlinie Persönlichkeitsstörungen

The S2 guidelines for the treatment of personality disorders (PD) are summarized. In the diagnostic assessment of personality disorders a clinical interview should be supplemented by (semi-) structured clinical interviews and self-report measures for the categorical and dimensional assessment of PDs. The results of the assessment process should be communicated to the patient based on a psycho-educational framework. The diagnosis should always be linked to the patient’s individual history. Psychotherapy is the treatment of choice for personality disorders. A detailed analysis of the patient’s problems as well as the definition of a hierarchy of treatment goals are part of the process of treatment planning. For three PDs empirical evidence for treatment approaches is available: (1) dialectical behavior therapy, mentalization-based therapy, schema focused therapy and transference focused therapy all proved beneficial in the treatment of borderline personality disorder. Cognitive-behavior therapy proved helpful in the treatment of (2) dissociative personality disorder and (3) avoidant personality disorder. There is limited evidence for interpersonal therapy and psychodynamic therapies in the treatment of avoidant personality disorder.     

Pathologisch autodestruktives Verhalten und Verhaltenstherapie

The literature on pathological self-destructive behaviour (apart from psychotic, substance-induced or brain disorders) and behaviour therapy is reviewed. An introduction on definition, classification, current typological approaches, occurrence, pathogenesis, and psychopathology is given. From a behavioural theoretic perspective, functional aspects of self-destructive behaviour are emphasised. Basic treatment problems are outlined. They particularly concern covert self-destructive behaviour (artificial disease, Munchhausen-syndrome) that is typically met in a medical hospital setting. Recommendations from the literature are resumed. Most of the relevant publications are psychoanalytically based. Case reports on self-destructive patients predominate even in behaviour therapy publications. These are briefly reviewed. So far, the most elaborate approach is considered to be M. Linehan's Dialectic-Behavioral-Therapy which has been developed for the treatment of borderline personality disorders. A considerable overlap between overtly self-destructive patients and borderline patients underlines the usefulness of this approach. Nonetheless, a further development of behavioural therapeutic methods with regard to different types of self-destructive behaviour is considered to be indicated.     

Dimensional models for research and diagnosis: a current dilemma

This commentary focuses on the current dilemma regarding the application of dimensional models for research and diagnosis. Four articles are used to review the positive contributions of dimensional approaches. Although categorical approaches are integral in deciding the characteristics of the group to be studied (i.e., development of inclusion/exclusion criteria), the generation of cutpoints for categorical classification is, in reality, working with dimensional data. Ultimately, new methodological strategies need to be incorporated that address both categorical and dimensional aspects of the overall diagnostic framework. These refinements will be vital in determining the extent and reality of co-occurrence of disorder and the determination of boundaries across specific disorders.     

Diagnostic categories or dimensions? A question for the Diagnostic And Statistical Manual Of Mental Disorders--fifth edition

The question of whether mental disorders are discrete clinical conditions or arbitrary distinctions along dimensions of functioning is a long-standing issue, but its importance is escalating with the growing recognition of the frustrations and limitations engendered by the categorical model. The authors provide an overview of some of the dilemmas of the categorical model, followed by a discussion of research that addresses whether mental disorders are accurately or optimally classified categorically or dimensionally. The authors' intention is to document the importance of this issue and to suggest that future editions of the Diagnostic and Statistical Manual of Mental Disorders give more recognition to dimensional models of classification. They conclude with a dimensional mental disorder classification that they suggest provides a useful model.     

What Carries a Mediation Process? Configural Analysis of Mediation

Mediation is a process that links a predictor and a criterion via a mediator variable. Mediation can be full or partial. This well-established definition operates at the level of variables even if they are categorical. In this article, two new approaches to the analysis of mediation are proposed. Both of these approaches focus on the analysis of categorical variables. The first involves mediation analysis at the level of configurations instead of variables. Thus, mediation can be incorporated into the arsenal of methods of analysis for person-oriented research. Second, it is proposed that Configural Frequency Analysis (CFA) can be used for both exploration and confirmation of mediation relationships among categorical variables. The implications of using CFA are first that mediation hypotheses can be tested at the level of individual configurations instead of variables. Second, this approach leaves the door open for different types of mediation processes to exist within the same set. Using a data example, it is illustrated that aggregate-level analysis can overlook mediation processes that operate at the level of individual configurations.     

Dimensional Assessment of Emotional Disorder Outcomes in Transdiagnostic Treatment: A Clinical Case Study

Current classification systems for mental disorders emphasize categorical assessment. In the domain of emotional disorders, this is inconsistent with a growing consensus that anxiety, depressive, and related disorders are best conceptualized as variations on shared underlying processes, chiefly heightened negative affect, and aversion to unwanted emotional experiences. Dimensional assessment of transdiagnostic emotional disorder constructs offers advantages for clinical and research applications, including increased parsimony and improved validity, yet there are drawbacks to many dimensional assessment systems for emotional disorders. The case presented in this paper illustrates the potential clinical utility of a new self-report instrument—the Multidimensional Emotional Disorders Inventory (MEDI)—which assesses nine dimensions characteristic of emotional disorders. MEDI scores for a highly comorbid patient are examined over the course of transdiagnostic emotion-focused cognitive-behavioral treatment. At baseline, midtreatment, and posttreatment, the MEDI offered information above and beyond DSM categorical assessment, including detection of subclinical symptoms and symptom change, while remaining parsimonious. Implications for possible use as a treatment planning instrument, integration with categorical assessment, and future research directions are discussed.     

An overview of issues related to categorical and dimensional models of personality disorder assessment

Despite long-standing efforts to improve the current diagnostic system for Axis II, problems remain with the categorical conceptualization of personality disorders (PDs). Due in part to these problems, interest has developed in dimensional models of PD classification. In this article, we discuss four issues relevant to categorical vs. dimensional assessment of PDs: (a) problems with self-reports in PD patients, (b) methodological issues in behavioral and clinician assessment of PDs, (c) challenges that arise when dimensional models are applied to patient and nonpatient samples, and (d) clinical implications of categorical and dimensional PD models. We suggest that researchers and clinicians address these concerns to avoid implementing a new PD assessment model that-although different from the current system-would otherwise remain fraught with difficulties.     

An Overview of Issues Related to Categorical and Dimensional Models of Personality Disorder Assessment

Despite long-standing efforts to improve the current diagnostic system for Axis II, problems remain with the categorical conceptualization of personality disorders (PDs). Due in part to these problems, interest has developed in dimensional models of PD classification. In this article, we discuss four issues relevant to categorical vs. dimensional assessment of PDs: (a) problems with self-reports in PD patients, (b) methodological issues in behavioral and clinician assessment of PDs, (c) challenges that arise when dimensional models are applied to patient and nonpatient samples, and (d) clinical implications of categorical and dimensional PD models. We suggest that researchers and clinicians address these concerns to avoid implementing a new PD assessment model that—although different from the current system—would otherwise remain fraught with difficulties.     

A framework for integrating dimensional and categorical classifications of personality disorder

Although empirical evidence strongly supports a dimensional representation of personality disorder, there is strong resistance to dimensional classification due in part to concerns about clinical utility. Acceptance of an evidence-based dimensional classification would be facilitated by information on how such a system would map onto existing diagnoses. With this objective in mind, an integrated framework is proposed that combines categorical and dimensional diagnoses. A two-component classification is adopted that distinguishes between the diagnosis of general personality disorder and the assessment of individual differences in the form the disorder takes. Then, the DSM definition of personality disorders is extended by defining individual disorders as categories of trait dimensions. This makes it possible to develop an integrated classification organized around a set of empirically derived primary traits. Assessments of these traits may then be combined to generate categorical and dimensional diagnoses. It is argued that this approach would introduce an etiological perspective into the classification of personality disorder and improve categorical classification by providing an explicit definition of each diagnosis. The clinical utility of incorporating a dimensional classification is discussed in terms of convenience and acceptability, value in predicting outcomes and treatment planning, and usefulness in organizing and selecting interventions.     

Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data

Ambulatory assessment methods provide a rich approach for studying daily behaviour. Too often, however, these data are analysed in terms of averages, neglecting patterning of this behaviour over time. This paper describes recurrence quantification analysis (RQA), a non-linear time series technique for analysing dynamic systems, as a method for analysing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively assessed social behaviour (e.g. talking to another person) coded from the Electronically Activated Recorder. Conceptual interpretations of RQA parameters, and an analysis of Electronically Activated Recorder data in adults going through a marital separation, are provided. Using machine learning techniques to avoid model overfitting, we find that adding RQA parameters to models that include just average amount of time spent talking (a static measure) improves prediction of four Big Five personality traits: extraversion, neuroticism, conscientiousness, and openness. Our strongest results suggest that a combination of average amount of time spent talking and four RQA parameters yield an R² = .09 for neuroticism. Neuroticism is shown to be associated with shorter periods of extended conversation (periods of at least 12 minutes), demonstrating the utility of RQA to identify new relationships between personality and patterns of daily behaviour. Materials: https://osf.io/5nkr9/ . © 2020 European Association of Personality Psychology     

Commentary on “Trauma to the Psyche and Soma”

This case report addresses assessment and treatment considerations for a patient suffering from posttraumatic stress disorder (PTSD) in the context of mild traumatic brain injury and ongoing pain. Management of this case is based on the application of evidence-based therapy, and of cognitive behaviour therapy, for PTSD reduction. Assessment and treatment approaches recognize that delivery of treatment requires modification to allow exposure therapy for individuals who are partially amnesic of their traumatic experience. Further, administering exposure and cognitive therapy for patients with cognitive impairment requires specific means of treatment delivery to allow adequate compliance with therapy tasks. This case highlights that evidence-based approaches can be usefully applied to complex presentations but they may require modifications that recognize obstacles associated with comorbid conditions.     

多维测验项目参数的估计：基于SEM与MIRT方法的比较

作者简要回顾了SEM框架下分类数据因素分析(CCFA)模型和MIRT框架下测验题目和潜在能力的关系模型, 对两种框架下的主要参数估计方法进行了总结。通过模拟研究, 比较了SEM框架下WLSc和WLSMV估计方法与MIRT框架下MLR和MCMC估计方法的差异。研究结果表明：(1) WLSc得到参数估计的偏差最大, 且存在参数收敛的问题; (2)随着样本量增大, 各种项目参数估计的精度均提高, WLSMV方法与MLR方法得到的参数估计精度差异很小, 大多数情况下不比MCMC方法差; (3)除WLSc方法外, 随着每个维度测验题目的增多参数估计的精度逐渐增高; (4)测验维度对区分度参数和难度参数的影响较大, 而测验维度对项目因素载荷和阈值的影响相对较小; (5)项目参数的估计精度受项目测量维度数的影响, 只测量一个维度的项目参数估计精度较高。另外文章还对两种方法在实际应用中应该注意的问题提供了一些建议。     

A structure-based approach to psychological assessment: matching measurement models to latent structure

The present article sets forth the argument that psychological assessment should be based on a construct's latent structure. The authors differentiate dimensional (continuous) and taxonic (categorical) structures at the latentand manifest levels and describe the advantages of matching the assessment approach to the latent structure of a construct. A proper match will decrease measurement error, increase statistical power, clarify statistical relationships, and facilitate the location of an efficient cutting score when applicable. Thus, individuals will be placed along a continuum or assigned to classes more accurately. The authors briefly review the methods by which latent structure can be determined and outline a structure-based approach to assessment that builds on dimensional scaling models, such as item response theory, while incorporating classification methods as appropriate. Finally, the authors empirically demonstrate the utility of their approach and discuss its compatibility with traditional assessment methods and with computerized adaptive testing.     

Assigning cases to groups using taxometric results: an empirical comparison of classification techniques

Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic and dimensional models, but it is not clear how best to assign individuals to groups using taxometric results. The present study compares the performance of two classification techniques-Bayes' theorem and a base-rate technique--across a wide range of data conditions. The base-rate technique achieves greater classification accuracy and a more even balance between sensitivity and specificity. In addition, the base-rate classification technique is easier to implement than Bayes' theorem and is more versatile in that it can be used when the context of assessment requires that cases be classified despite the absence of latent classes.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

Contributions and novelties from Functional Analytic Psychotherapy

Functional Analytic Psychotherapy is based on the principles of radical behaviourism. It emphasises the impact of events occurring during therapeutic sessions, the therapist-client interaction context, functional equivalence of environments, natural reinforcement, and shaping by the therapist. Functional Analytic Psychotherapy makes use of both the basic principles of behaviour analysis: individual functional assessment and application of in vivo treatment. This paper analyses novelties and new contributions of this therapy. New contributions are classified in various categories: integration with other psychotherapies, improvement of therapeutic skills, methods for evaluation and data recording in therapy, its application to several clinical problems, and studies of its efficacy.     

Assessing Growth in a Diagnostic Classification Model Framework

A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth. This study introduces the Transition Diagnostic Classification Model (TDCM), which combines latent transition analysis with the log-linear cognitive diagnosis model to provide methodology for analyzing growth in a general DCM framework. Simulation study results indicate that the proposed model is flexible, provides accurate and reliable classifications, and is quite robust to violations to measurement invariance over time. The TDCM is used to analyze pre-test/post-test data from a diagnostic mathematics assessment.