The Importance of Positive Social Support During Reentry From Prison: Examining the Role of Volunteer Mentoring

Social support appears to be important in improving outcomes for incarcerated individuals during the reentry process not only in terms of general wellbeing but also in gaining employment and avoiding recidivism. Mentoring programs have become increasingly popular interventions that are intended to provide such support during reentry. However, research on mentoring programs is limited and tends to focus solely on the programs’ impact on recidivism, a distal outcome. Through the use of semi-structured, in-depth interviews, this qualitative study focuses on more proximal outcomes, exploring how reentering individuals who are receiving volunteer mentoring through a transitional housing program define successful reentry and perceive the value of different types of support they received from their mentors. Participants identified several indicators of successful reentry and discussed the types of support that were helpful, harmful, or absent. Implications for practice and areas for future research are discussed.

Age varying polygenic effects on alcohol use in African Americans and European Americans from adolescence to adulthood

Genetic effects on alcohol use can vary over time but are often examined using longitudinal models that predict a distal outcome at a single time point. The vast majority of these studies predominately examine effects using White, European American (EA) samples or examine the etiology of genetic variants identified from EA samples in other racial/ethnic populations, leading to inconclusive findings about genetic effects on alcohol use. The current study examined how genetic influences on alcohol use varied by age across a 15 year period within a diverse ethnic/racial sample of adolescents. Using a multi-ethnic approach, polygenic risk scores were created for African American (AA, n = 192) and EA samples (n = 271) based on racially/ethnically aligned genome wide association studies. Age-varying associations between polygenic scores and alcohol use were examined from age 16 to 30 using time-varying effect models separately for AA and EA samples. Polygenic risk for alcohol use was found to be associated with alcohol use from age 22–27 in the AA sample and from age 24.50 to 29 in the EA sample. Results are discussed relative to the intersection of alcohol use and developmental genetic effects in diverse populations.

Joint Growth Trajectories of Trait Emotional Intelligence Subdomains Among L2 Language Learners: Estimating a Second-Order Factor-of-Curves Model With Emotion Perception

The present study assessed the developmental dynamics of trait emotional intelligence (TEI) and its subdomains during English as a foreign language (EFL) learning in a longitudinal study. A sample of 309 EFL learners (217 females, 92 males) was used to assess the trajectories of the global factor of TEI and the parallel development of the TEI subdomains over 1 year in the context of the EFL classroom using parallel process modeling (PPM) and factor of curve modeling (FCM). Additionally, emotion perception (EP) was used as a distal outcome to investigate how growth parameters, including intercept and slope factors in a TEI-FCM, influence the distal outcome of EP. The results revealed that there was sufficient inter-individual variation and intra-individual trends within each subdomain and a significant increase over time across the four subdomains. Additionally, concerning the covariances within and among the subdomains of TEI, the PPM results revealed moderate to high associations between the intercept and slope growth factors within and between these subdomains. Finally, regarding the direct association of the global growth factors (intercept and slope) of TEI on EP, the results indicated that the intercept and slope of global TEI were associated with EP (γ0 = 1.127, p < 0.001; γ1 = 0.321, p < 0.001). Specifically, the intercepts and slopes of emotionality and sociability turned out to be significantly linked to EP (γ03 = 1.311, p < 0.001; γ13 = 0.684, p < 0.001; γ04 = 0.497, p < 0.001; γ14 = 0.127, p < 0.001). These results suggest the dynamicity of TEI during learning a foreign language are discussed in this study in light of the potential variables associated with TEI and its related literature.

Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.

Predictive performance models in the South African Business Process Services industry

Orientation: An earlier systematic literature review study (Jacobs Roodt, 2011) conducted on research in Business Process Services (BPS) industry sector companies identified a number of variables that could be empirically linked to turnover intention and individual performance. The literature pointed to a potential health promotion process, as well as an individual performance process in the BPS environment.Research purpose: The purpose of this study is to test two different predictive models that may explain two distal outcomes, namely turnover intention and individual employee performance, in the South African (SA) BPS industry.Motivation for the study: There is little, if any, peer-reviewed, empirical research available on the BPS industry that links variables to either proximate or distal outcome variables, such as turnover intention and individual employee performance.Research approach/design and method: A two-stage, census-based sampling approach was followed that initially targeted 40 organisations within the industry that employ about 13000 employees. Sixteen of these organisations (employing about 6800 individuals) indicated that they wish to voluntarily participate in the study; 821 individuals were targeted to participate in the cross-sectional survey and 487 usable responses were obtained (a 59% response rate). Multivariate data analyses were conducted from an exploratory perspective to retrospectively explain relationships in the structural models.Main findings: An overall health promotion process model that predicted the distal outcome, turnover intention, was confirmed within the context of this exploratory study, where human resource management (HRM) practices, job demands (JDs) and job resources (JRs) were related to burnout as the only proximate outcome. On the other hand, an individual performance enhancing process model was also confirmed within the context of this exploratory study by using HRM practices, JRs and JDs, together with proximate variables, such as employee competence and engagement, to explain the distal outcome, individual performance.Practical/managerial implications: The study has implications for executive (strategic) management, human resource (HR) professionals and work unit team leaders in the BPS industry. This study shows which JRs contribute towards the reduction of burnout and turnover intention in the BPS context. On the other hand, it explains how HRM practices, as well as JRs and JDs, in combination with employee competence and engagement, can be used to promote individual performance.Contribution/value-add: This is the first SA study that uses a range of variables in a multivariate analysis to predict turnover intention and individual performance in the SA BPS industry.

A Comparison of Different Nonnormal Distributions in Growth Mixture Models

The purpose of the present study is to compare nonnormal distributions (i.e., t, skew-normal, skew- t with equal skew and skew- t with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts. To carry out this research, two simulation studies were conducted with two different models: an unconditional GMM and a GMM with a continuous distal outcome variable. For the simulation, data were generated under the conditions of a different number of time points (4, 8), sample size (300, 800, 1,500), and skewness for intercept (1.2, 2, 4). Results demonstrate that it is not appropriate to fit nonnormal data to normal, t, or skew-normal distributions other than the skew- t distribution. It was also found that if there is skewness over time, it is necessary to model skewness in the slope as well.