A further examination of antecedents of correctional staff life satisfaction

Working in corrections is not only a demanding job, but a socially important one. While a growing number of studies have examined how the work environment impacts the job stress, job satisfaction, and organizational commitment of staff, very few studies have examined how working in corrections impacts the life satisfaction of workers. The current study utilized OLS regression to examine the antecedents of life satisfaction among staff at a Midwestern private prison. Job satisfaction had a positive relationship with life satisfaction, while age, work on family conflict, family on work conflict, and job involvement all had statistically significant negative effects. Finally, perceptions of the level of financial rewards, job stress, organizational commitment, gender, race, educational level, tenure, supervisory status, position, marital status, and having children, all had non-significant associations with overall satisfaction with life.     

Materials receiving capacity and inventory management

This paper analyses the optimal level of materials receiving capacity for a manufacturer that receives deliveries from many suppliers. Inventory levels and inventory carrying costs depend on the frequency of deliveries and thus, on the materials receiving capacity. An analytic model that captures the tradeoff between inventory costs and materials receiving costs is presented and discussed. The receiving cost is modeled as increasing in discrete jumps of varying sizes whenever materials receiving resources are added. Practical issues in implementing the model are highlighted and methods to reduce the marginal materials receiving cost are discussed. The paper also discusses connections to the JIT approach for production environments where materials receiving is heavily automated.     

Attachment Insecurities and Women's Sexual Function and Satisfaction: The Mediating Roles of Sexual Self-Esteem,Sexual Anxiety,and Sexual Assertiveness

We examined the potential role of three mediators—sexual self-esteem, sexual anxiety, and sexual assertiveness—of the association between romantic attachment insecurities (anxiety and avoidance) and two aspects of women's sexual functioning: sexual function and sexual satisfaction. A sample of 556 women aged 18 to 30 agreed to complete an online series of validated questionnaires assessing attachment insecurities and several aspects of sexual functioning. Lower sexual self-esteem and higher sexual anxiety mediated the associations between attachment anxiety and lower sexual function and satisfaction. Lower sexual self-esteem and higher sexual anxiety also partially mediated the links between attachment-related avoidance and the two sexual functioning variables. Sexual assertiveness, however, did not mediate these associations. A significant interaction between attachment anxiety and avoidance was also found to predict sexual satisfaction, with women high in avoidance and low in anxiety being the least satisfied. Results are discussed in terms of theoretical and clinical implications.     

Non parametric estimation of the structural expectation of a stochastic increasing function

This article introduces a non parametric warping model for functional data. When the outcome of an experiment is a sample of curves, data can be seen as realizations of a stochastic process, which takes into account the variations between the different observed curves. The aim of this work is to define a mean pattern which represents the main behaviour of the set of all the realizations. So, we define the structural expectation of the underlying stochastic function. Then, we provide empirical estimators of this structural expectation and of each individual warping function. Consistency and asymptotic normality for such estimators are proved.     

Inference in a generalized endpoint-inflated binomial regression model

Generalized endpoint-inflated binomial regression was recently proposed to model count data with large frequencies of both zeros and right-endpoints. Maximum likelihood estimation (MLE) was developed for this model and simulations suggest that the resulting estimates behave well. However, large-sample properties of the MLE have not yet been rigorously established. Such results are however essential for ensuring reliable statistical inference and decision-making. This paper addresses this issue. Identifiability of the generalized endpoint-inflated binomial regression model is first proved. Then, consistency and asymptotic normality of the MLE are established. A simulation study is conducted to assess finite-sample behaviour of the estimator.     

A study of regression calibration in a partially observed stratified Cox model

Regression calibration is a simple method for estimating regression models when covariate data are missing for some study subjects. It consists in replacing an unobserved covariate by an estimator of its conditional expectation given available covariates. Regression calibration has recently been investigated in various regression models such as the linear, generalized linear, and proportional hazards models. The aim of this paper is to investigate the appropriateness of this method for estimating the stratified Cox regression model with missing values of the covariate defining the strata. Despite its practical relevance, this problem has not yet been discussed in the literature. Asymptotic distribution theory is developed for the regression calibration estimator in this setting. A simulation study is also conducted to investigate the properties of this estimator.     

Stratified Logrank Test of No Randomized Treatment Effect with Missing Stratum Information

A major objective in many clinical trials is to compare several competing treatments in a randomized experiment. In such studies, it is often necessary to adjust for some other important factor that affects the event rates in the treatment groups. When this factor is discrete, one usual approach uses a stratified version of the logrank test. In this article, we consider the problem that arises when the factor giving rise to the strata is missing at random for some of the study subjects. This article proposes a modified version of the stratified logrank test, in which the unobserved stratum indicators are replaced by an estimate of their conditional expectation given available auxiliary covariate measurements. The null asymptotic distribution of the proposed test statistic is investigated. Simulation experiments are also conducted to examine the finite-sample behavior of this test under both null and alternative hypotheses. Simulations indicate that the proposed test performs well, even under some moderate deviations to the at-random missingness assumption.