Presurgical Coping, Depression, and Quality of Life in Persons with Parkinson's Disease

This study describes utilization of coping strategies and evaluates the interaction between coping strategies, depression, and quality of life (QOL) in patients with Parkinson's disease (PD) who are being considered for neurosurgical intervention. Eighty patients (mean age 61.7 years) with PD being evaluated for possible deep brain stimulation completed self-report instruments of coping strategies (Coping Responses Inventory), depression (Beck Depression Inventory), and disease-specific QOL (Parkinson's Disease Questionnaire-39). Analyses showed that patients with PD cope with the acute stressor of approaching neurosurgery through a variety of strategies, but particularly avoidant and behavioral strategies. When the correlated but apparently opposing effects of cognitive and behavioral strategies were teased apart, greater use of cognitive strategies was associated with more severe depressive symptomatology (and poorer QOL), while greater use of behavioral strategies appeared to be associated with less depression. Depressive symptomatology, in turn, was associated with poorer QOL. However, coping had minimal direct association with QOL. From this it was concluded that patients with advanced PD generate a variety of coping responses to an acute stressor such as surgery, and the use of behavioral strategies, in particular seeking of alternative enjoyable activities, may be associated with better mood if salutary effects are not overwhelmed by less helpful cognitive coping techniques. The minimization of depressive symptomatology, in turn, is associated with better QOL.     

Brain Oxidative Damage Following Acute Immobilization and Mild Emotional Stress

We studied the role of free radicals on brain oxidative damage in rats after acute immobilization stress (restraint) and mild emotional stress (handling). To investigate brain oxidative damage, CuZn and Mn dependent superoxide dismutase (CuZn SOD, Mn SOD) activities, lipid peroxidation (TBARs), Na ⁺ K ⁺ ATPase activity, protein carbonyl (PrC), and reduced and oxidized glutathione (GSH, GSSG) levels were measured in the cerebral cortex (CTX), hippocampus (HIP), and striatum (ST) of the animals after the two different stress stimuli. Because stress produces abnormalities in the hypothalamic-pituitary-adrenal axis, the intensity of the two stress conditions were measured by plasmatic corticosteroid (COR) levels: particularly, COR levels doubled in handled rats and increased 15-fold in restrained animals. The SOD activities increased in CTX and decreased in HIP of the handled rats, while in ST a significant decrease in handled animals but an increase in restrained animals occurred. TBARs, GSH, and GSSG levels remained unchanged, while an index of glutathione redox decreased significantly in ST of handled animals and in CTX of restrained ones. Na ⁺ K ⁺ ATPase activity increased significantly in the HIP and ST of both groups of stressed rats. The stress induced a remarkable increase in PrC levels in all studied cerebral areas. These findings provide evidence to support the idea that stress produces oxidants but that the oxidative damage in stress differs in cerebral areas and could contribute to the degenerative mechanism of aging.     

Do people reason rationally about causally related events? Markov violations,weak inferences,and failures of explaining away

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X₁ → Y → X₂, common cause structures X₁ ← Y → X₂, and common effect structures X₁ → Y ← X₂, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative “explaining away” pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations.     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.     

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.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

Temporal dynamics of need satisfaction and need frustration. Two sides of the same coin?

We study the dynamics of need satisfaction and need frustration by examining how need satisfaction and need frustration change over time and how these changes relate to changes in motivation. To investigate this, volunteers were assessed daily during a delineated activity, resulting in a total sample of 77 volunteers and 467 completed daily diaries. Bayesian confirmatory factor analysis on the within-person level confirmed a bifactor solution: autonomy, competence, and relatedness were distinguished by three specific factors, but the majority of variance was explained by two strongly correlated (need satisfaction and need frustration) factors. The strong correlation between need satisfaction and need frustration further translated in the development of their relationship over time. That is, Bayesian latent growth curve modelling revealed a simultaneous, yet opposite, growth curve. Contrary to our expectations, neither of them could be related to behavioural internalization or externalization. These results imply that, at the within-person level, momentary need satisfaction and frustration are difficult to distinguish, and affecting one may automatically affect the other. Also, the relationships within the self-determination framework, such as the proposed link between need satisfaction and behavioural internalization may be better reframed with attention to the time-frame in which they occur.     

Counterfactual thinking as an issue between language and pretend play / El pensamiento contrafactual como un problema entre el lenguaje y el juego de simulación

Discussions on counterfactual thinking (CT) have been focused on whether it is a language skill or it emerges spontaneously before language acquisition. This paper surveys the most compelling arguments regarding these frameworks: (1) the approach ‘CT as a language skill’; (2) those who claim that pretending shows that children have CT; (3) those who consider pretend play is a rehearsal for cognitive dispositions. I shall point out that the three approaches on CT are incomplete: (1) neglects pretend play (which prelinguistic children perform) as an instantiation of the CT; (2) puts too much emphasis on the linguistic dimension of pretense; (3) is highly demanding on the cognitive architecture that we ought to have by nature — the so-called children as scientists.