Central to many influential theories in the occupational health and stress literature is that job resources reduce the negative effects of job demands on workers' well-being. However, empirical ...investigations testing this supposition have produced inconsistent findings. This study evaluates the interaction between job demands and job control on workers' well-being through a systematic literature search and using a Bayesian meta-analytic approach. Both aggregated study findings and raw participant-level data were included in the study, resulting in 104 effect sizes of aggregate-level data and 14 participant-level data sets. Overall, the data provided strong evidence for the absence of an interaction between job demands and job control. Longitudinal and nonlinear research designs were also examined but did not alter this overall conclusion. Contrary to the postulations of widespread theories, job control does not reduce the negative impact of job demands on workers' well-being. Alternative theoretical approaches and the need for more consistent and rigorous research standards, like open science practices, are discussed.
Age-related challenges and transitions can have considerable social, psychological, and physical consequences that may lead to significant changes in quality of life (QoL). As such, maintaining high ...levels of QoL in later life may crucially depend on the ability to demonstrate resilience (i.e., successful adaptation to late-life challenges). The current study set out to explore the interplay between several resilience factors, and how these contribute to the realization and maintenance of (different facets of) QoL. Based on the previous work, we identified behavioral coping, positive appraisal, self-management ability, and physical activity as key resilience factors. Their interplay with (various facets of) QoL, as measured with the WHOQOL-OLD, was established through network analysis. In a sample of community-dwelling older adults (55+;
N
= 1,392), we found that QoL was most strongly (and directly) related to positive appraisal style and self-management ability. Among those, self-efficacy seemed to be crucial. It connected directly to “satisfaction with past, present, and future activities,” a key facet of QoL with strong interconnections to other QoL facets. Our analysis also identified resilience factor(s) with the potential to promote QoL when targeted by training, intervention, or other experimental manipulation. The appropriate set of resilience factors to manipulate may depend on the goal and/or facet of QoL that one aims to improve.
No robust relation between larger cities and depression Huth, Karoline B S; Finnemann, Adam; van den Ende, Maarten W J ...
Proceedings of the National Academy of Sciences - PNAS,
01/2022, Letnik:
119, Številka:
2
Journal Article
Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as ...nodes, and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model and its parameters. Here, we outline a Bayesian approach to network analysis that offers three main benefits. In particular, applied researchers can use Bayesian methods to (1) determine structure uncertainty, (2) obtain evidence for edge inclusion and exclusion (i.e., distinguish conditional dependence or independence between variables), and (3) quantify parameter precision. In this article, we provide a conceptual introduction to Bayesian inference, describe how researchers can facilitate the three benefits for networks, and review the available R packages. In addition, we present two user-friendly software solutions: a new R package, easybgm, for fitting, extracting, and visualizing the Bayesian analysis of networks and a graphical user interface implementation in JASP. The methodology is illustrated with a worked-out example of a network of personality traits and mental health.
Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains, it is crucial to be able to quantify evidence for the absence of ...a treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit‐transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (ie, very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit‐transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default.
Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the ...network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.
Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains it is crucial to be able to quantify evidence for the absence of a ...treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit-transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (i.e., very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit-transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default.
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected \(350{,}757\) coin flips to test the ...counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (DHM; 2007). The model asserts that when people flip an ordinary coin, it tends to land on the same side it started -- DHM estimated the probability of a same-side outcome to be about 51%. Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, \(\text{Pr}(\text{same side}) = 0.508\), 95% credible interval (CI) \(0.506\), \(0.509\), \(\text{BF}_{\text{same-side bias}} = 2359\). Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias. Our data also confirmed the generic prediction that when people flip an ordinary coin -- with the initial side-up randomly determined -- it is equally likely to land heads or tails: \(\text{Pr}(\text{heads}) = 0.500\), 95% CI \(0.498\), \(0.502\), \(\text{BF}_{\text{heads-tails bias}} = 0.182\). Furthermore, this lack of heads-tails bias does not appear to vary across coins. Additional exploratory analyses revealed that the within-people same-side bias decreased as more coins were flipped, an effect that is consistent with the possibility that practice makes people flip coins in a less wobbly fashion. Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started. Our data provide compelling statistical support for the DHM physics model of coin tossing.
Abstract Objectives To evaluate the association of posttraumatic stress disorder (PTSD) with type 2 diabetes (T2D) or prediabetes in a large population-based sample. Methods In 2970 subjects (aged ...32–81 years) drawn from the population-based cross-sectional study KORA F4 from the Augsburg region (Southern Germany) a PTSD screening was performed employing the Posttraumatic Diagnostic Scale, the Impact of Event Scale, and interview data. The exposure variable PTSD was sub-classified into partial and full PTSD and additionally in subjects with traumatic event but no PTSD” to “The exposure variable PTSD was classified into (1) no traumatic event (2) traumatic event, but no PTSD, (3) partial PTSD, (4) full PTSD. A total of 50 (1.7%) subjects qualified for full PTSD, whereas 261 (8.8%) qualified for partial PTSD. A total of 333 subjects (11.2%) suffered from T2D and 498 (16.8%) from prediabetes as assessed by an oral glucose tolerance test and physicians’ validation. The associations of PTSD with T2D and prediabetes were estimated by multinomial logistic regression analyses with adjustments for sociodemographic characteristics, metabolic risk factors or psychopathological conditions. Results In the model adjusted for sociodemographic characteristics and metabolic risk factors, full PTSD was significantly associated with T2D (OR: 3.90, 95% CI: 1.61–9.45, p = 0.003) compared to subjects with no traumatic event. Significance remained after additional adjustment for other psychopathological conditions (OR: 3.56, 95% CI: 1.43–8.85, p = 0.006). Regarding prediabetes, no significant associations were observed. Conclusions Suffering from PTSD might activate chronic stress symptoms and trigger physiological mechanisms leading to T2D. Prospective studies are needed to investigate temporal and causal relationships between PTSD and T2D.
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