Multiple Approaches to Absenteeism Analysis

Absenteeism research has often been criticized for using inappropriate analysis. Characteristics of absence data, notably that it is usually truncated and skewed, violate assumptions of OLS regression; however, OLS and correlation analysis remain the dominant models of absenteeism research. This piece compares eight models that may be appropriate for analyzing absence data. Specifically, this piece discusses and uses OLS regression, OLS regression with a transformed dependent variable, the Tobit model, Poisson regression, Overdispersed Poisson regression, the Negative Binomial model, Ordinal Logistic regression, and the Ordinal Probit model. A simulation methodology is employed to determine the extent to which each model is likely to produce false positives. Simulations vary with respect to the shape of the dependent variable\u27s distribution, sample size, and the shape of the independent variables\u27 distributions. Actual data,based on a sample of 195 manufacturing employees, is used to illustrate how these models might be used to analyze a real data set. Results from the simulation suggest that, despite methodological expectations, OLS regression does not produce significantly more false positives than expected at various alpha levels. However, the Tobit and Poisson models are often shown to yield too many false positives. A number of other models yield less than the expected number of false positives, thus suggesting that they may serve well as conservative hypothesis tests

Understanding the Role of Relationship Satisfaction in Social Support Provision for Youth in the Child Welfare System

The objective of this study was to examine kin social support and relationship satisfaction, as well as the interaction between these two variables, in terms of their association with placement stability, externalizing behaviors, and internalizing symptoms for youth in the child welfare system. Ordinary Least Squares Regression methods were used in conjunction with Poisson and Negative Binomial Regression methods. the study also examined two different methods for calculating the interaction term to determine relationship satisfaction\u27s moderating effect on the relationship between social support and the outcomes. Results suggested that relationship satisfaction does act as a moderator when externalizing behaviors and internalizing symptoms are the outcomes of interest, but it may not moderate the relationship between social support and placement stability. This study introduced a novel way to calculate interactions between individuals when network-based models are used, and it demonstrated that relationship satisfaction may play a role in the way that social support promotes fewer symptoms for youth in the child welfare system. the results of this study suggest that future studies that continue to explore this relationship are warranted

Brief mindfulness training reduces salivary IL-6 and TNF-α in young women with depressive symptomatology.

Pro-inflammatory cytokines have been implicated in the pathophysiology and maintenance of depression. This study investigated the effects of a brief mindfulness intervention on salivary pro-inflammatory correlates of depression (IL-6, TNF-α) and self-reported symptoms of depression in college women

Human hair shaft proteomic profiling: individual differences, site specificity and cuticle analysis

Hair from different individuals can be distinguished by physical properties. Although some data exist on other species, examination of the individual molecular differences within the human hair shaft has not been thoroughly investigated. Shotgun proteomic analysis revealed considerable variation in profile among samples from Caucasian, African–American, Kenyan and Korean subjects. Within these ethnic groups, prominent keratin proteins served to distinguish individual profiles. Differences between ethnic groups, less marked, relied to a large extent on levels of keratin associated proteins. In samples from Caucasian subjects, hair shafts from axillary, beard, pubic and scalp regions exhibited distinguishable profiles, with the last being most different from the others. Finally, the profile of isolated hair cuticle cells was distinguished from that of total hair shaft by levels of more than 20 proteins, the majority of which were prominent keratins. The cuticle also exhibited relatively high levels of epidermal transglutaminase (TGM3), accounting for its observed low degree of protein extraction by denaturants. In addition to providing insight into hair structure, present findings may lead to improvements in differentiating hair from various ethnic origins and offer an approach to extending use of hair in crime scene evidence for distinguishing among individuals

Conditional Heteroskedasticity in Count Data Regression: Self-Feeding Activity in Fish

The paper introduces a new approach to incorporating time dependent overdispersion for Poisson related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data some wellknown estimators are adapted and a GMM type estimator is suggested. The estimators are applied to a time series of self-feeding activity in Arctic charr. There is strong support for both a dynamic conditional mean function and a dynamic model for the overdispersion.Poisson; Overdispersion; ARCH; Estimation; Self-Feeding; Arctic Charr

THE VALUE OF THE GULF OF MEXICO RECREATIONAL RED SNAPPER FISHERY

This study estimates the value of recreational red snapper fishing in the Gulf of Mexico. Additionally, the study shows how to decompose the estimated red snapper recreation demand function into changes: (i) due to recreationists who were not taking recreational red snapper fishing trips but were induced to take a trip in response to changes in catch rates and (ii) due to recreationists already taking trips and responding to changes in catch rates. The decomposition allows us to also decompose the estimated elasticities and consumer surplus. The results indicate that an improvement in expected fishing quality will increase consumer surplus and that most of the increase is contributed by recreationists who initially do not take recreational red snapper fishing trips, but later take a positive number of trips. This finding has important policy implications for managing the red snapper fishery in the Gulf of Mexico.Resource /Energy Economics and Policy,

Hierarchical Bivariate Time Series Models: A Combined Analysis of the Effects of Particulate Matter on Morbidity and Mortality

In this paper we develop a hierarchical bivariate time series model to characterize the relationship between particulate matter less than 10 microns in aerodynamic diameter (PM10) and both mortality and hospital admissions for cardiovascular diseases. The model is applied to time series data on mortality and morbidity for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series should be related through a shared relationship with PM10. At the first stage of the hierarchy, we fit two seemingly unrelated Poisson regression models to produce city-specific estimates of the log relative rates of mortality and morbidity associated with exposure to PM10 within each location. The sample covariance matrix of the estimated log relative rates is obtained using a novel generalized estimating equation approach that takes into account the correlation between the mortality and morbidity time series. At the second stage, we combine information across locations to estimate overall log relative rates of mortality and morbidity and variation of the rates across cities. Using the combined information across the 10 locations we find that a 10 mu g/m3 increase in average PM10 at the current day and previous day is associated with a 0:26% increase in mortality (95% posterior interval -0:37; 0:65), and a 0:71% increase in hospital admissions (95% posterior interval 0:35; 0:99). The log relative rates of mortality and morbidity have a similar degree of heterogeneity across cities: the posterior means of the between-city standard deviations of the mortality and morbidity air pollution effects are 0:42 (95% interval 0:05; 1:18), and 0:31 (95% interval 0:10; 0:89), respectively. The city-specific log relative rates of mortality and morbidity are estimated to have very low correlation, but the uncertainty in the correlation is very substantial (posterior mean = 0:20; 95% interval -0:89; 0:98). With the parameter estimates from the model, we can predict the hospitalization log relative rate for a new city for which hospitalization data are unavailable, using that city\u27s estimated mortality relative rate. We illustrate this prediction using New York as an example