Changes in Catastrophizing and Depressed Mood During and After Early Cognitive Behaviorally Oriented Interventions for Pain

Catastrophizing and depressed mood are risk factors for poor outcome in treatments for pain and appear to act as mediators for favorable outcome. However, little is known about how catastrophizing and depressed mood co-occur within individuals and how these patterns change during treatment, which is the focus of the current study. The study uses data from a randomized controlled trial about early cognitive behaviorally oriented interventions for patients with nonspecific spinal pain (N = 84). Cluster analyses were used to extract subgroups of individuals with similar scoring patterns on catastrophizing and depressed mood at pretreatment, mid-treatment, posttreatment, and at 6 months' follow-up. To track individual progress, the clusters were linked over time. The analyses revealed four clusters: “low depression and catastrophizing”, “high depression and catastrophizing”, “high depression”, and “high catastrophizing”. There was little individual transition from one scoring pattern to another across time, not at least for those scoring high on both depressed mood and catastrophizing. Moreover, high stability within this cluster was related to low levels of psychological flexibility at baseline. It is concluded that catastrophizing and depressed mood at the start of treatment were likely to remain high despite a cognitive behavioral intervention and that a lack of psychological flexibility may have a role.     

Presurgical MMPI-2 Cluster Profiles Predict 1-Year Low-Back Surgery Outcomes

The primary purpose of this study was to examine whether empirically derived cluster profiles based on scores from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) predicted outcomes of surgery at 1 year for patients with low-back pain. On the basis of hierarchical cluster analysis of presurgery MMPI-2 scores for 60 patients, three interpretable and significant subgroups were formed. These consisted of a pathological/neurotic type, a double V-code type, and a normal type. The patents in the normal type were significantly more likely to report beneficial outcomes of surgery in terms of disability and pain than those in the other 2 types. These findings extend previous research and suggest (1) low-back surgery candidates are heterogeneous in their psychological profiles, and (2) MMPI-2 profiles are predictive of low-back surgery outcome. Future research should focus on identifying physiological substrates for these distinct profiles and investigating the effectiveness of presurgical psychological interventions based on profile type.     

Modeling Clustered Data with Very Few Clusters

Small-sample inference with clustered data has received increased attention recently in the methodological literature, with several simulation studies being presented on the small-sample behavior of many methods. However, nearly all previous studies focus on a single class of methods (e.g., only multilevel models, only corrections to sandwich estimators), and the differential performance of various methods that can be implemented to accommodate clustered data with very few clusters is largely unknown, potentially due to the rigid disciplinary preferences. Furthermore, a majority of these studies focus on scenarios with 15 or more clusters and feature unrealistically simple data-generation models with very few predictors. This article, motivated by an applied educational psychology cluster randomized trial, presents a simulation study that simultaneously addresses the extreme small sample and differential performance (estimation bias, Type I error rates, and relative power) of 12 methods to account for clustered data with a model that features a more realistic number of predictors. The motivating data are then modeled with each method, and results are compared. Results show that generalized estimating equations perform poorly; the choice of Bayesian prior distributions affects performance; and fixed effect models perform quite well. Limitations and implications for applications are also discussed.     

Behavioural Profiles and Offender Characteristics across 111 Korean Sexual Assaults

Although the potential usefulness of the offence action–offender characteristic (A–C) relationships is widely accepted and operational ‘offender profiling’ units now exist around the world, few such relationships have been empirically established. To explore this, the offending action patterns within 111 sexual assault cases from South Korea were coded in terms of 16 distinctive, objective crime scene criteria and subjected to an agglomerative hierarchical cluster analysis. Background psychiatric and general characteristics, Personality Assessment Inventory (PAI) scale scores, and criminal histories were described for each cluster. The cluster analysis drew attention to six clusters or behavioural profiles within the sexual assaults. Cluster 1 included serial offenders who aggressively raped and robbed adult women, with some pseudo‐intimate sexual behaviour, in their homes. Two thirds of these offenders had histories of sexual assault. Cluster 2 included offenders who again targeted adults in their homes, but without pseudo‐intimate sexual behaviour. Cluster 3 included offenders who targeted adults outdoors at night. These offenders showed high antisocial personality PAI scores and psychiatric histories of sexual sadism. Cluster 4 included unarmed offenders who targeted adults in their homes without robbery. These offenders often had psychiatric histories of depression. Cluster 5 included offenders who targeted adults outdoors with a blitz‐style attack, and Cluster 6 included offenders who targeted minors outdoors, without weapons, using a confidence‐trick style of approach. Paedophilia and histories of psychiatric treatment were prominent amongst these offenders. The results indicate therefore some of the key empirical relationships that future research may develop as the basis for sexual assault ‘profiles’. Copyright © 2014 John Wiley & Sons, Ltd.     

Distance stability analysis in multidimensional scaling using the jackknife method

Stability or sensitivity analysis is an important topic in data analysis that has received little attention in the application of multidimensional scaling (MDS), for which the only available approaches are given in terms of a coordinate‐based analytical jackknife methodology. Although in MDS the prime interest is in assessing the stability of the points in the configuration, this methodology may be influenced by imprecisions resulting from the inherently necessary Procrustes method. This paper proposes an analytical distance‐based jackknife procedure to study stability and cross‐validation in MDS in terms of the jackknife distances, which is not influenced by the Procrustes method. For each object, the corresponding jackknife estimated points are considered as naturally clustered points, and stability and cross‐validation are analysed in terms of the MDS distances arising from the jackknife procedure, on the basis of a weighted cluster‐MDS algorithm. A jackknife‐relevant configuration is also proposed for cross‐validation in terms of coordinates, in a cluster‐MDS framework.     

儿童学校适应的分类及判定

以6579名小学三、五年级学生为被试,采用聚类分析和判别分析方法,结果表明:(1)根据儿童学校适应的指标将儿童的学校适应状况分为良好型、闲适型、紧张型、高危型四种类型.(2)良好型的儿童,在学业自我评估、学业动机和心理健康水平三个水平上的得分为正向,在问题行为上的得分为负;高危型儿童的结果与适应良好型的儿童完全相反;而在闲适型的儿童中,学业动机为负、心理健康水平为正;在紧张型的儿童中,学业动机为正向、而心理健康水平是负向的.(3)建立儿童学校适应的判别函数,为儿童在教育和心理问题进行指导提供了初步诊断依据,同时也对有关学校适应的研究提供新的视角.     

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.     

A variable-selection heuristic for K-means clustering

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.