What Mediates the Relationship Between Religious Service Attendance and Aspects of Well-Being?

Religious service attendance predicts increased well-being across a number of studies. It is not clear, however, whether this relationship is due to religious factors such as intrinsic religiosity or due to nonreligious factors such as social support or socially desirable responding. The purpose of the present study was to examine the relationship between religious service attendance and well-being while simultaneously examining intrinsic religiosity, social support, and socially desirable responding as potential mediators of the relationship. A sample of 855 participants (71 % female, average age 19.5) completed questionnaires assessing religiosity, social support, socially desirable responding, and well-being. Path models were estimated using maximum likelihood estimation to analyze the data. Intrinsic religiosity was the strongest mediator of the relationship between religious service attendance and depressive and anxiety symptoms. This suggests that the mental health benefits of religious service attendance are not simply the result of increased social support or a certain response style on questionnaires; rather, it appears that the relationship is at least partly the result of people trying to live their religion in their daily lives.     

Modelling inter-individual differences in latent within-person variation: The confirmatory factor level variability model

Psychological theories often produce hypotheses that pertain to individual differences in within-person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between-person differences in the mean level of a certain variable and the residual within-person variance. Currently, these approaches can be applied only when the data stem from a single variable. However, it is common practice in psychology to assess not just a single measure but rather several measures of a construct. In this paper we describe a model in which we combine the single-indicator model with confirmatory factor analysis. The new model allows individual differences in latent mean-level factors and latent within-person variability factors to be estimated. Furthermore, we show how the model's parameters can be estimated with a maximum likelihood estimator, and we illustrate the approach using an example that involves intensive longitudinal data.     

A motivated information processing perspective on the antecedents of empowering leadership

Empowering leadership is an important influence on team effectiveness. This makes the question of what the antecedents of team empowering leadership are an important issue to consider. To address this question, we propose a motivated information processing perspective that holds that engaging in empowering leadership is based on social information processing, and that there are individual differences in how elaborate that information processing is. We argue that a key consideration in shifting control from the leader to the team is the extent to which empowering leadership is driven by leaders’ consideration of their trust in the team (i.e., an instance of social information processing), and that leader need for closure (a trait capturing the disposition to carefully consider decisions and actions) moderates the relationship between leader trust in team and empowering leadership. A survey of N = 156 work teams supported these hypotheses.     

A Monte Carlo study comparing PIV,ULS and DWLS in the estimation of dichotomous confirmatory factor analysis

We conducted a Monte Carlo study to investigate the performance of the polychoric instrumental variable estimator (PIV) in comparison to unweighted least squares (ULS) and diagonally weighted least squares (DWLS) in the estimation of a confirmatory factor analysis model with dichotomous indicators. The simulation involved 144 conditions (1,000 replications per condition) that were defined by a combination of (a) two types of latent factor models, (b) four sample sizes (100, 250, 500, 1,000), (c) three factor loadings (low, moderate, strong), (d) three levels of non‐normality (normal, moderately, and extremely non‐normal), and (e) whether the factor model was correctly specified or misspecified. The results showed that when the model was correctly specified, PIV produced estimates that were as accurate as ULS and DWLS. Furthermore, the simulation showed that PIV was more robust to structural misspecifications than ULS and DWLS.     

The influence of between and within-person hope among emergency responders on daily affect in a stress and coping model

This study reports on daily hope, coping, stress, and affect among 84 fire service members who completed a 21-day diary. Multilevel modeling showed that between-person hope was related to higher positive affect. Three-way interactions were found for within-person hope, stress, and daily coping in the prediction of daily affect. When under high stress and if low in hope, more emotion–expression coping predicted higher next day positive affect whereas if high in hope, more problem-focused coping predicted more positive affect. Within-person hope, stress, and emotion–expression coping interacted to predict negative affect. Findings for the prediction of negative affect necessitate further research to determine if the nature of coping under low hope and low stress is qualitatively distinct from high hope states.     

J.S. Mill's Canons of Induction: From True Causes to Provisional Ones

In this essay, my aim is twofold: to clarify how the late Mill conceived of the certainty of inductive generalisations and to offer a systematic clarification of the limited domain of application of Mill's Canons of Induction. I shall argue that Mill's views on the certainty of knowledge changed over time and that this change was accompanied by a new view on the certainty of the inductive results yielded by the Canons of Induction. The key message of the later editions of The System of Logic as conceived by the late Mill was no longer that by the Canons of Induction we can establish scientific certainty and true causes, but rather that the Canons are useful in establishing causal laws in a provisional way.

Deduction is a game and induction a grievance. (Bagehot 1913, vol. 3, p. 37)     

Examining antecedents and outcomes of part‐time working nurses' motives to search and not to search for a full‐time position

Grounded in self‐determination theory, this study sought to examine the antecedents and outcomes of part‐time working nurses' (n = 404) motives for searching and not searching for full‐time employment. After controlling for various background variables, autonomous motivation and economic motivation to search for a full‐time job related positively to job search intensity, controlled motivation to search related negatively to experienced positive experiences of part‐time work, whereas autonomous motivation not to search related negatively to job search intensity and positively to positive experiences from part‐time work. Finally, experienced managerial autonomy support toward part‐time work and negative feedback from colleagues regarding part‐time work were established as contextual antecedents of part‐time nurses' autonomous and controlled motives to search and not to search.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.