Neighbourhood disadvantage, individual-level socioeconomic position, and self-reported chronic arthritis : a cross-sectional multilevel study

Objective: To examine the association between individual- and neighborhood-level disadvantage and self-reported arthritis. Methods: We used data from a population-based cross-sectional study conducted in 2007 among 10,757 men and women ages 40–65 years, selected from 200 neighborhoods in Brisbane, Queensland, Australia using a stratified 2-stage cluster design. Data were collected using a mail survey (68.5% response). Neighborhood disadvantage was measured using a census-based composite index, and individual disadvantage was measured using self-reported education, household income, and occupation. Arthritis was indicated by self-report. Data were analyzed using multilevel modeling. Results: The overall rate of self-reported arthritis was 23% (95% confidence interval [95% CI] 22–24). After adjustment for sociodemographic factors, arthritis prevalence was greatest for women (odds ratio [OR] 1.5, 95% CI 1.4–1.7) and in those ages 60–65 years (OR 4.4, 95% CI 3.7–5.2), those with a diploma/associate diploma (OR 1.3, 95% CI 1.1–1.6), those who were permanently unable to work (OR 4.0, 95% CI 3.1–5.3), and those with a household income <$25,999 (OR 2.1, 95% CI 1.7–2.6). Independent of individual-level factors, residents of the most disadvantaged neighborhoods were 42% (OR 1.4, 95% CI 1.2–1.7) more likely than those in the least disadvantaged neighborhoods to self-report arthritis. Cross-level interactions between neighborhood disadvantage and education, occupation, and household income were not significant. Conclusion: Arthritis prevalence is greater in more socially disadvantaged neighborhoods. These are the first multilevel data to examine the relationship between individual- and neighborhood-level disadvantage upon arthritis and have important implications for policy, health promotion, and other intervention strategies designed to reduce the rates of arthritis, indicating that intervention efforts may need to focus on both people and places.

Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Ethnic diversity and its impact on community social cohesion and neighborly exchange

Putnam's “constrict theory” suggests that ethnic diversity creates challenges for developing and sustaining social capital in urban settings. He argues that diversity decreases social cohesion and reduces social interactions among community residents. While Putnam's thesis is the subject of much debate in North America, the United Kingdom, and Europe, there is a limited focus on how ethnic diversity impacts upon social cohesion and neighborly exchange behaviors in Australia. Employing multilevel modeling and utilizing administrative and survey data from 4,000 residents living in 148 Brisbane suburbs, we assess whether ethnic diversity lowers social cohesion and increases “hunkering.” Our findings indicate that social cohesion and neighborly exchange are attenuated in ethnically diverse suburbs. However, diversity is less consequential for neighborly exchange among immigrants when compared to the general population. Our results provide at least partial support for Putnam's thesis.

Prior storm experience moderates water surge perception and risk

Background How accurately do people perceive extreme water speeds and how does their perception affect perceived risk? Prior research has focused on the characteristics of moving water that can reduce human stability or balance. The current research presents the first experiment on people's perceptions of risk and moving water at different speeds and depths. Methods Using a randomized within-person 2 (water depth: 0.45, 0.90 m) ×3 (water speed: 0.4, 0.8, 1.2 m/s) experiment, we immersed 76 people in moving water and asked them to estimate water speed and the risk they felt. Results Multilevel modeling showed that people increasingly overestimated water speeds as actual water speeds increased or as water depth increased. Water speed perceptions mediated the direct positive relationship between actual water speeds and perceptions of risk; the faster the moving water, the greater the perceived risk. Participants' prior experience with rip currents and tropical cyclones moderated the strength of the actual–perceived water speed relationship; consequently, mediation was stronger for people who had experienced no rip currents or fewer storms. Conclusions These findings provide a clearer understanding of water speed and risk perception, which may help communicate the risks associated with anticipated floods and tropical cyclones.

Wind speed perception and risk

Background How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human–wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. Method We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. Results Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual–perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. Conclusion These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.

Job performance ratings: The relative importance of mental ability, conscientiousness, and career adaptability

According to career construction theory, continuous adaptation to the work environment is crucial to achieve work and career success. In this study, we examined the relative importance of career adaptability for job performance ratings using an experimental policy-capturing design. Employees (N = 135) from different vocational backgrounds rated the overall job performance of fictitious employees in 40 scenarios based on information about their career adaptability, mental ability, conscientiousness, and job complexity. We used multilevel modeling to investigate the relative importance of each factor. Consistent with expectations, career adaptability positively predicted job performance ratings, and this effect was relatively smaller than the effects of conscientiousness and mental ability. Job complexity did not moderate the effect of career adaptability on job performance ratings, suggesting that career adaptability predicts job performance ratings in high-, medium-, and low-complexity jobs. Consistent with previous research, the effect of mental ability on job performance ratings was stronger in high- compared to low-complexity jobs. Overall, our findings provide initial evidence for the predictive validity of employees' career adaptability with regard to other people's ratings of job performance.

Shared perceptions of supervisor conflict management style

- Purpose This study aims to investigate the extent to which employee outcomes (anxiety/depression, bullying and workers’ compensation claims thoughts) are affected by shared perceptions of supervisor conflict management style (CMS). Further, this study aims to assess cross-level moderating effects of supervisor CMS climate on the positive association between relationship conflict and these outcomes. - Design/methodology/approach Multilevel modeling was conducted using a sample of 401 employees nested in 69 workgroups. - Findings High collaborating, low yielding and low forcing climates (positive supervisor climates) were associated with lower anxiety/depression, bullying and claim thoughts. Unexpectedly, the direction of moderation showed that the positive association between relationship conflict and anxiety/depression and bullying was stronger for positive supervisor CMS climates than for negative supervisor CMS climates (low collaborating, high yielding and high forcing). Nevertheless, these interactions revealed that positive supervisor climates were the most effective at reducing anxiety/depression and bullying when relationship conflict was low. For claim thoughts, positive supervisor CMS climates had the predicted stress-buffering effects. - Research limitations/implications Employees benefit from supervisors creating positive CMS climates when dealing with conflict as a third party, and intervening when conflict is low, when their intervention is more likely to minimize anxiety/depression and bullying. - Originality/value By considering the unique perspective of employees’ shared perceptions of supervisor CMS, important implications for the span of influence of supervisor behavior on employee well-being have been indicated.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Understanding IT value : a multilevel, complex, and adaptive system perspective

One remaining difficulty in the Information Technology (IT) business value evaluation domain is the direct linkage between IT value and the underlying determinants of IT value or surrogates of IT value. This paper proposes a research that examines the interacting effects of the determinants of IT value, and their influences on IT value. The overarching research question is how those determinants interact with each other and affect the IT value at organizational value. To achieve this, this research embraces a multilevel, complex, and adaptive system view, where the IT value emerges from the interacting of underlying determinants. This research is theoretically grounded on three organizational theories – multilevel theory, complex adaptive systems theory, and adaptive structuration theory. By integrating those theoretical paradigms, this research proposes a conceptual model that focuses on the process where IT value is created from interactions of those determinants. To answer the research question, agent-based modeling technique is used in this research to build a computational representation based on the conceptual model. Computational experimentation will be conducted based on the computational representation. Validation procedures will be applied to consolidate the validity of this model. In the end, hypotheses will be tested using computational experimentation data.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.