Preface

Sociological Forum -     

Interprofessional Client-Centered Reasoning Processes in Home Modification Practice

We investigated the complex reasoning processes of professional home evaluators involved with home safety assessments. Twenty evaluators with varied professional training engaged in in-depth qualitative interviews. Two primary themes emerged: integrating expertise and tailoring interventions. Within these themes, evaluators expressed differences and similarities in how they obtained information, developed interventions, and addressed professional-client interactive reasoning as they identified needs. We propose an adapted ecological model to describe best practices for personalizing home modifications through an interprofessional lens. Differences among professional home evaluators reveal unique, yet overlapping reasoning processes. Interprofessional teams may better meet the holistic needs of home modification consumers.     

Couple Differentiation: Mediator or Moderator of Depressive Symptoms and Relationship Satisfaction?

The purpose of this investigation was to determine whether differentiation at the couple level would act as a moderator or a mediator in the association between marital satisfaction and depressive symptoms over time. In a sample of 412 couples, a latent profile analysis was performed to determine how couple differentiation scores were clustered. An Actor/Partner Interdependence Model was then estimated via a group comparison procedure in structural equation modeling. There was no evidence of a moderating effect of differentiation. A mediating model was then estimated and there was evidence that differentiation mediated the association between depressive symptoms and relationship satisfaction via actor and partner effects.     

Synthetic data method to incorporate external information into a current study

We consider the situation where there is a known regression model that can be used to predict an outcome, Y, from a set of predictor variables X . A new variable B is expected to enhance the prediction of Y. A dataset of size n containing Y, X and B is available, and the challenge is to build an improved model for Y| X ,B that uses both the available individual level data and some summary information obtained from the known model for Y| X . We propose a synthetic data approach, which consists of creating m additional synthetic data observations, and then analyzing the combined dataset of size n + m to estimate the parameters of the Y| X ,B model. This combined dataset of size n + m now has missing values of B for m of the observations, and is analyzed using methods that can handle missing data (e.g., multiple imputation). We present simulation studies and illustrate the method using data from the Prostate Cancer Prevention Trial. Though the synthetic data method is applicable to a general regression context, to provide some justification, we show in two special cases that the asymptotic variances of the parameter estimates in the Y| X ,B model are identical to those from an alternative constrained maximum likelihood estimation approach. This correspondence in special cases and the method's broad applicability makes it appealing for use across diverse scenarios. The Canadian Journal of Statistics 47: 580–603; 2019 © 2019 Statistical Society of Canada     

M-estimators of some GARCH-type models; computation and application

In this paper, we consider robust M-estimation of time series models with both symmetric and asymmetric forms of heteroscedasticity related to the GARCH and GJR models. The class of estimators includes least absolute deviation (LAD), Huber’s, Cauchy and B-estimator as well as the well-known quasi maximum likelihood estimator (QMLE). Extensive simulations are used to check the relative performance of these estimators in both models and the weighted resampling methods are used to approximate the sampling distribution of M-estimators. Our study indicates that there are estimators that can perform better than QMLE and even outperform robust estimator such as LAD when the error distribution is heavy-tailed. These estimators are also applied to real data sets.     

Return to work among employees with mental health problems: Development and validation of a self-efficacy questionnaire

Abstract

Because of the costs to both the organization and the individual, it is important that employees who are sick-listed with mental health problems are facilitated in their return to work (RTW). In order to provide adequate interventions, it is necessary to obtain a better understanding of the RTW process of people with mental health problems. Work-related self-efficacy (SE) might play a key role within this process. This paper describes the development and validation of the return-to-work self-efficacy’ scale (RTW-SE) for employees with mental health problems. Three Dutch samples of sick-listed employees were used to validate the 11-item instrument (N=2214). Based on the factor structure and reliability results, RTW-SE was conceptualized as a unitary construct. The associations with general SE, locus of control, coping, physical workload and mental health problems support the construct validity of this scale. Most importantly, RTW-SE proved to be a robust predictor of actual return to work within three months. The encouraging preliminary psychometric properties of the scale make it a potentially valuable tool in research and in clinical practice and occupational health care settings, both before and after employees have returned to work.     

Social cognitive neuroscience and leadership

We provide an overview of how the emerging field of social cognitive neuroscience can be linked to leadership theory and practice. A number of challenges are addressed, including theory development, as well as technical, measurement and methodological issues. In addition, we review recent leadership research that involves neuroscience applications, as well as areas that are closely related to leadership, such as emotional regulation and ethical reasoning and decision-making. Consideration is also given to how neuroscience might inform leadership development processes. We conclude with a discussion of the institutional challenges in conducting leadership research that incorporates neuroscience, and we consider potential limitations of such applications.