The relationships of change in work climate with changes in burnout and depression: a 2-year longitudinal study of Chinese mental health care workers

Mental health care workers face heavy emotional demand and are prone to work burnout. Work burnout has been associated with poor mental health and work climate, which refers to individual perceptions about work setting. The purpose of this study was to examine whether intra-individual changes in work climate were associated with intra-individual changes in burnout and depression over two years. The present sample included Chinese mental health care workers (N = 312; mean age = 38.6, SD = 9.9) working in a psychosocial rehabilitation institution. The participants completed questionnaires on work climate, work burnout and depression at seven time points across two years. Parallel process latent growth modeling was used to analyze the associations of change between work climate and burnout and depression. Work climate displayed a logarithmic decreasing trend while burnout and depression displayed logarithmic increasing trends over two years. Baseline levels of work climate were negatively and moderately associated with baseline levels of burnout and depression (r = ?.44 to ?.60, p < .01). Changes in work climate were negatively and moderately associated with change in burnout (r = ?.43, p < .01) and change in depression (r = ?.31, p < .05). Change in burnout was positively and strongly associated (r = .58, p < .01) with change in depression. The current results support temporal relationships among changes in work climate, burnout and depression across time. Practical implications for future preventive work in burnout interventions were discussed within this population.     

Examining Spirituality Over Time: Latent Growth Curve and Individual Growth Curve Analyses

SUMMARY

Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques.     

A within-person theoretical perspective in sales research: outlining recommendations for adoption and consideration of boundary conditions

Abstract

In the sales literature it is standard practice for researchers to collect cross-sectional data from multiple salespeople, and to compare those salespeople on the data obtained. This between-person approach is suitable for research aiming to draw conclusions between salespeople. However, many salesperson processes are dynamic and vary within salespeople over time, requiring datasets with repeated-measures. This article highlights the need to adopt a within-person theoretical perspective in sales research. Critically, the article shows how our present understanding of boundary conditions may change depending on whether a between-person or within-person level of analysis is adopted. Using examples from the sales literature, we show how the practical implications from between-persons research designs do not necessarily generalize to the within-person level. Further, we explain the methodological and analytical considerations that researchers must account for when undertaking within-person research. Furthermore, the article provides decision criteria that help to identify when within-person analysis should be conducted, outlining analysis tools that are capable of correctly estimating within-person effects without bias. Examples of how within-person research can enhance theory within future sales research, and how within-person research may influence management implications are also discussed. Finally, potential remedies to within-person research barriers are given.     

Individual,Community, and National Resilience in Peace Time and in the Face of Terror: A Longitudinal Study

The present paper is based on thrice-repeated measures. The sample constituted 561 Jewish Israeli adults who experienced these terror attacks. The study examined individual, community and national resilience and their associations with resilience-promoting factors (sense of coherence, social support, and self-efficacy); as well as resilience-suppressing factors (distress symptoms, sense of danger, and exposure). Results indicated that resilience scores were quite stable across the three repeated measures, whereas sense of coherence, distress symptoms, sense of danger, and exposure significantly changed across the three repeated measures. Sense of coherence was the best predictor for individual, community, and national resilience.     

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.