Zachary Joseph Roman’s research while affiliated with University of Zurich and other places

We examined the association between changes in employee job and off-job crafting and their self-rated health during the COVID-19 pandemic. Further, we investigated how these associations differed across sample subgroups, contrasting differences in work location, living situation, and contractual changes (short-time work). We used four waves of a longitudinal dataset surveying NTotal = 783 German-speaking employees from Germany, Switzerland, and Austria from 2019 to 2021. We applied latent change score modeling and multigroup analyses to investigate our research questions. Results indicated that the mean job and off-job crafting and self-rated health trajectories remained relatively stable. However, we observed significant interindividual variance in job and off-job crafting changes. We found a consistent small positive relationship between crafting changes in both life domains over time, indicating that employees tended to change their crafting efforts similarly across domains. Additionally, job crafting increases between Waves 1 and 2 were linked to higher subsequent self-rated health at Wave 2, and similarly, off-job crafting increases between Waves 3 and 4 were linked to higher self-rated health at Wave 4. We observed only minor differences in this pattern across subgroups. Our results show how adaptive changes in crafting are linked to broader interindividual health differences and help identify groups who are not able to increase crafting during crises and thus could benefit from targeted support. Crafting can be an effective individual strategy for maintaining health, complementing organizational and public health measures. We encourage future research to incorporate temporal and contextual phenomena into crafting research.

Content Non-Response (CNR) is situation where participants respond to survey instruments without considering the item content. This phenomena adds noise to data leading to erroneous inference. There are multiple approaches to identifying and accounting for CNR in survey settings, of these approaches the best performing are model based classification techniques. Classic approaches to accounting for CNR treat it as a person level phenomena. They first use some method to identify participants who exhibit CNR, then use list-wise deletion to remove them prior to analysis. We argue that CNR is actually a state that participants may exhibit for a portion of a survey instrument. In other words, participants start a survey with the intention to follow instructions, but at some point transition to a CNR state and no longer respond in line with item contents. Accounting for CNR at the item level, as opposed to the person level preserves data resulting in increased power. In this article we present a Bayesian Dynamic Latent Class Structural Equation Modeling (DLCSEM) approach for simultaneously accounting for CNR at the item level and estimating a model of interest. We use a simulation study to establish the approaches performance under empirically relevant conditions and to compare it to other methods. We then conducted an experimental validation in which we induce CNR like responses from human subjects and investigate the approaches ability to identify the point in which participants transition to a CNR state. We also compare the model to existing approaches.In both the simulation and experimental validation the DLCSEM outperforms the alternative approaches. We conclude that the approach should be used by applied researchers for the pragmatic benefits of the method. Conclusions, limitations, and future directions are discussed.

Objective: Evidence on associations of lifestyle factors with Alzheimer's pathology and cognition are ambiguous, potentially because they rarely addressed interrelationships of factors and sex effects. While considering these aspects, we examined the relationships of lifestyle factors with brain amyloid burden and cognition. Methods: We studied 178 cognitively normal individuals (women, 49%; 65.0 [7.6] years) and 54 individuals with mild cognitive impairment (women, 35%; 71.3 [8.3] years) enrolled in a prospective study of volunteers who completed 18 F-Flutemetamol amyloid positron emission tomography. Using structural equation modeling, we examined associations between latent constructs representing metabolic/vascular risk, physical activity, and cognitive activity with global amyloid burden and cognitive performance. Furthermore, we investigated the influence of sex in this model. Results: Overall, higher cognitive activity was associated with better cognitive performance and higher physical activity was associated with lower amyloid burden. The latter association was weakened to a non-significant level after excluding multivariate outliers. Examination of the moderating effect of sex in the model revealed an inverse association of metabolic/vascular risk with cognition in men whereas in women metabolic/vascular risk trended towards increased amyloid burden. Furthermore, a significant inverse association between physical activity and amyloid burden was found only in men. Inheritance of an APOE4 allele was associated with higher amyloid burden only in women. Interpretation: Sex modifies effects of certain lifestyle-related factors on amyloid burden and cognition. Notably, our results suggest that the negative impact of metabolic/vascular risk influences the risk of cognitive decline and Alzheimer's disease through distinct paths in women and men. This article is protected by copyright. All rights reserved.

Behavioral scientists have become increasingly reliant on online survey platforms such as Amazon's Mechanical Turk (Mturk). These platforms have many advantages, for example it provides ease of access to difficult to sample populations, a large pool of participants, and an easy to use implementation. A major drawback is the existence of bots that are used to complete online surveys for financial gain. These bots contaminate data and need to be identified in order to draw valid conclusions from data obtained with these platforms. In this article, we will provide a Bayesian latent class joint modeling approach that can be routinely applied to identify bots and simultaneously estimate a model of interest. This method can be used to separate the bots' response patterns from real human responses that were provided in line with the item content. The model has the advantage that it is very flexible and is based on plausible assumptions that are met in most empirical settings. We will provide a simulation study that investigates the performance of the model under several relevant scenarios including sample size, proportion of bots, and model complexity. We will show that ignoring bots will lead to severe parameter bias whereas the Bayesian latent class model results in unbiased estimates and thus controls this source of bias. We will illustrate the model and its capabilities with data from an empirical political ideation survey with known bots. We will discuss the implications of the findings with regard to future data collection via online platforms.

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.