A Structural Modeling Examination of the Executive Decline Hypothesis of Cognitive Aging Through Reanalysis of Crawford et al.'s (2000) Data

This study presents a new analysis of data previously published by Crawford, Bryan, Luszcz, Obansawin, and Stewart (2000) examining the role of executive function in age-related declines in general cognitive ability and memory. Although the original authors' question is relevant to understanding the role of executive function in aging, this paper argues that the methods used can be substantially improved to more accurately answer the question. Thus, Crawford et al.'s data are here analyzed using a more parsimonious methodological approach (structural equation modeling), and the results obtained depart from the authors' results but are in line with their original hypotheses. First, for younger individuals, age was differently associated with verbal measures of cognitive ability than with performance measures. Although the relation of age to executive function was stronger than the relation of age to verbal abilities, this difference was not apparent with regard to performance abilities. Second, across samples (i.e., younger and older individuals), memory was not accounted for by cognitive ability but was related to age and executive function. Finally, also across samples, executive function was strongly linked to recall and recognition measures, accounting for the largest variance in memory. These results are discussed in relation to previous findings as well as their theoretical significance for aging research.     

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to describe the steps and properties of HS. We then use empirical data on daily affect from one couple to illustrate the use of HS for describing the affective dynamics of the dyad. First, we partition the data into three periods that represent different affective states and show different dynamics between both individuals’ affect. We then examine the synchrony between both individuals’ affective states and identify different patterns of coherence across the periods. Finally, we discuss the possibilities of using results from HS to construct confirmatory dynamic models with multiple change points or regime-specific dynamics.     

Reducing teachers' psychological distress through a mindfulness training program

Teachers constitute one of the professional collectives most affected by psychological problems. The purpose of this quasi-experimental study is to examine the efficacy of a mindfulness training programme to reduce psychological distress in a group of teachers. The sample comprised 68 teachers of Secondary School Education, from various public schools; half of them formed the experimental group, and the another half the control group. The levels of psychological distress were measured, in both groups, by the Symptom Checklist-90-R (SCL-90-R) before and after the application of the programme. Statistical analysis shows the significant reduction of three general measures of psychological distress (Global Severity Index, Positive Symptom Distress Index, and Positive Symptom Total), as well in all its dimensions (somatization, obsessive-compulsive, interpersonal sensibility, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism), in the experimental group compared with the control group. Follow-up measures show that these results were maintained for four months after termination of the intervention in the experimental group.     

Fear as a Mediator for the Relationship Between Child Sexual Abuse and Victimization of Relationship Violence

Childhood sexual abuse (CSA) has been identified as a predictor for dating violence victimization; however, the mechanism for this relationship is unexplored. The current study examined whether fearful dating experiences may help explain the relationship between CSA and dating violence victimization. Participants (N =?327 college women) completed self-report measures of CSA, fearful dating experiences, and dating violence. In a mediational model test, CSA was found to be positively associated with fear in dating relationships and with dating violence victimization, fear was associated with dating violence victimization, and the effect of CSA on dating violence victimization scores was reduced after controlling for fear. A Sobel's test showed that fear partially mediated the relationship between CSA and current dating violence victimization.     

Bayesian Estimation of Random Coefficient Dynamic Factor Models

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across units. The aims of this study are (a) to propose a random coefficient DFM (RC-DFM) to statistically model variations of dynamics across multiple units using the Bayesian method, (b) to illustrate the use of the proposed procedure by applying RC-DFMs to affect data collected from multiple dyads in romantic relationships, and (c) to evaluate the performance of the RC-DFMs with Bayesian estimation through simulation analyses. The results from the simulation analyses show that the Bayesian estimation of RC-DFMs works well in recovering parameters including both fixed and random effects. A number of practical considerations are provided to guide future research on using Bayesian methods for estimating multivariate time series from multiple units.     

Analyzing the Dynamics of Affective Dyadic Interactions Using Patterns of Intra- and Interindividual Variability

There are many compelling accounts of the ways in which the emotions of 1 member of a romantic relationship should influence and be influenced by the partner. However, there are relatively few methodological tools available for representing the alleged complexity of dyad level emotional experiences. In this article, we present an algorithm for examining such affective dynamics based on patterns of variability. The algorithm identifies periods of stability based on length of time and amplitude of emotional fluctuations. The patterns of variability and stability are quantified at the individual and dyadic level, and the approach is illustrated using data of the daily emotional experiences of individuals in romantic couples. With this technique, we examine the fluctuations of the emotions for each person and inspect the overlap fluctuations between both individuals in the dyad. The individual and dyadic indices of variability are then used to predict the status of the dyads (i.e., together, apart) 1 year later.     

Response to Oud & Folmer: Randomness and Residuals

This article presents our response to Oud and Folmer's “Modeling Oscillation, Approximately or Exactly?” (2011), which criticizes aspects of our article, “Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes” (2011). In this response, we present a conceptual explanation of the derivative-based estimation that we implemented in our original article, as well as the exact discrete model promoted by Oud &; Folmer. We describe relevant differences between each of the two methods, highlight some of their benefits and limitations, and offer justifications for our choice.