Efficient Hessian computation using sparse matrix derivatives in RAM notation

This article proposes a new, more efficient method to compute the minus two log likelihood, its gradient, and the Hessian for structural equation models (SEMs) in reticular action model (RAM) notation. The method exploits the beneficial aspect of RAM notation that the matrix derivatives used in RAM are sparse. For an SEM with K variables, P parameters, and P′ entries in the symmetrical or asymmetrical matrix of the RAM notation filled with parameters, the asymptotical run time of the algorithm is O(P?′?K ²?+?P ² K ²?+?K ³). The naive implementation and numerical implementations are both O(P ² K ³), so that for typical applications of SEM, the proposed algorithm is asymptotically K times faster than the best previously known algorithm. A simulation comparison with a numerical algorithm shows that the asymptotical efficiency is transferred to an applied computational advantage that is crucial for the application of maximum likelihood estimation, even in small, but especially in moderate or large, SEMs.     

Reading about explanations enhances perceptions of inevitability and foreseeability: a cross-cultural study with Wikipedia articles

In hindsight, people often perceive events to be more inevitable and foreseeable than in foresight. According to Causal Model Theory (Nestler et al. in J Exp Psychol Learn Mem Cogn 34: 1043–1054, 2008), causal explanations are crucial for such hindsight distortions to occur. The present study provides further empirical support for this notion but extends previous findings in several ways. First, ecologically valid materials were used. Second, the effect of causal information on hindsight distortions was investigated in the realm of previously known events. Third, cross-cultural differences in reasoning (analytic vs. holistic) were taken into account. Specifically, German and Vietnamese participants in our study were presented with Wikipedia articles about the nuclear power plant in Fukushima Daiichi, Japan. They read either the version that existed before the nuclear disaster unfolded (Version 1) or the article that existed 8 weeks after the catastrophe commenced (Version 2). Only the latter contained elaborations on causal antecedents and therefore provided an explanation for the disaster. Reading that version led participants to perceive the nuclear disaster to be more likely inevitable and foreseeable when compared to reading Version 1. Cultural background did not exert a significant effect on these perceptions. Hence, hindsight distortions were obtained for ecologically valid materials even if the event was already known. Implications and directions for future research are discussed.     

Couples' evaluations of fatherhood in different stages of the family life cycle

Fathers' and mothers' views on mothers' satisfaction with paternal behaviour as well as the respective processes of origination were studied in 393 cohabiting couples from three different stages of the family life cycle. Data on paternal competence and the couples' relationship characteristics were included as predictors in multiple regression analyses, and the stages of the family life cycle were taken into account with multigroup regression analyses. Results showed that the mothers were more satisfied with paternal behaviour than the fathers thought the mothers were. Moreover, mothers were satisfied when the fathers were willing to spend time with the child, whereas fathers thought that their partners were satisfied when they were able to establish and maintain a relationship with the child. Couple relationship satisfaction had a consistent impact on fathers' views, whereas it was not relevant to mothers' views unless the oldest child had reached adolescence.     

Granger Causality—Statistical Analysis Under a Configural Perspective

The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.     

The impact of empathy on burnout in medical students: new findings

Introduction: Research on medical students has shown they are at a higher risk for burnout and that this burnout may become more prevalent as they advance in medical school. The literature, thus far, has not explored the construct of ,emotional empathy and whether this can impact burnout in medical students. Objective: To understand the relationship between empathy (Empathic Concern [EC] and Personal Distress [PD]) and burnout in medical students. Method: Five successive classes of medical students enrolled at a new medical school were given the Maslach Burnout Inventory and Davis’ Interpersonal Reactivity Index over the course of three successive years (n = 353). Two dimensions of empathy were evaluated to determine if they have an impact on three dimensions of burnout (Emotional Exhaustion/EE, Depersonalization/DP, Personal Accomplishment/PA). Results: data was analyzed using a linear mixed model for each of the three components of burnout based on gender, age, year in medical school, and two types of empathy: EC, and PD. Conclusion: It was discovered that students with high levels of EC had statistically lower scores of burnout over time while students with high levels of PD empathy showed statistically higher scores of burnout over three years. Implications for these findings are discussed.     

Parkinson’s Disease: Patients’ Knowledge,Attitudes, and Interest in Genetic Counseling

The objective of this study was to assess the genetics knowledge of patients with Parkinson’s disease (PD), and to explore their attitudes on genetic testing and interest in genetic counseling. We surveyed 158 patients from the University of Maryland Parkinson’s Disease and Movement Disorders Center. Patients averaged a score of 63% on general genetics knowledge and 73% on PD genetics knowledge. Participants had an overall positive attitude toward genetic testing: 80% believed that the use of genetic tests among people should be promoted, and 83% would undertake genetic test for PD if it was available. Patients reported a high interest to discuss the benefits, risks, and impacts of genetic testing for PD (mean sum score?=?26, range?=?9–35), and 43% patients expressed interest in meeting with a genetic counselor. Multivariate regression analysis showed that patients who had more positive attitudes toward genetic testing for PD were more interested in meeting with a genetic counselor (β?=?0.6, p?<?0.001). This study is the first to demonstrate an interest in genetic counseling among patients with PD. Our findings demonstrate a new niche for genetic counselors to support patients in clarifying gaps or misconceptions in knowledge about PD genetics as well as the possible risks, benefits, and limitations of genetic testing.     

Zukunftsperspektiven sexualforensischer Psychotherapie

There is a controversial discussion on to what extent sexual forensic psychotherapy has a positive influence on the legal probation of sexually delinquent people. From this the question arises on how treatment with a perspective for success could be structured. This article assumes that such a perspective can only succeed through close cooperation between science and psychotherapeutic practice. Through the analysis of a methodologically elaborate study, considerations on the motivation and the influence of empirically ascertained risk factors are developed and each placed in relation to scientific and therapeutic practice. In this way various problems are tackled, which range from insufficiently described interventions via too little therapeutic flexibility up to limitations in the scientific evaluation. Finally, possible barriers for the development of such a perspective are discussed and the connection to the so-called risk-need-responsivity (RNR) model is explored.     

Automated Item Generation with Recurrent Neural Networks

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.