Effective Rotation Patterns Under Non Response in Two-Occasion Successive Sampling

This article intends to develop some effective rotation patterns with the aid of attractive imputation methods when the problems of non response occur in two-occasion successive sampling. Utilizing the information on p (p ??1) auxiliary variables regression methods of imputation have been considered and subsequently multiple linear regression type estimators are proposed to estimate the current population mean in two-occasion successive sampling. Proposed estimators are compared with the estimator for same situations but in the absence of non-response. Optimum replacement strategies of the respective estimators have been discussed and results are interpreted with the help of empirical studies. Conclusions and suitable recommendations are made.     

Some estimation procedures using a linear model in successive sampling

ABSTRACT

In successive sampling some recent works depict the use of super-population models where information on stable auxiliary variable over occasions has been utilized. Stability character of auxiliary variable may not sustain, if the duration between occasions is large. To cope with such situations, the present work is an attempt to develop some estimation procedures by utilizing the information on two independent auxiliary variables through a linear super-population model. Some estimators are proposed to estimate the current population mean in two occasions successive (rotation) sampling. Optimum replacement strategies are formulated and performances of the proposed estimators have been discussed. Results are interpreted through empirical studies.     

The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies

The often paradoxical relationship between investment in information technology and gains in productivity has recently been attributed to a lack of user acceptance of information technology innovations. Diverse streams of research have attempted to explain and predict user acceptance of new information technologies. A common theme underlying these various research streams is the inclusion of the perceived characteristics of an innovation as key independent variables. Furthermore, prior research has utilized different outcomes to represent user acceptance behavior. In this paper we focus on individual's perceptions about the characteristics of the target technology as explanatory and predictive variables for acceptance behavior, and present an empirical study examining the effects of these perceptions on two frequently used outcomes in the context of the innovation represented by the World Wide Web. The two outcomes examined are initial use of an innovation and intentions to continue such use in the future, that is, to routinize technology use. Two research questions motivated and guided the study. First, are the perceptions that predict initial use the same as those that predict future use intentions? Our results confirm, as hypothesized by prior research, that innovation characteristics do explain acceptance behavior. The results further reveal that the specific characteristics that are relevant for each acceptance outcome are different. The second research question asks if perceived voluntariness plays a role in technology acceptance. Results show that external pressure has an impact on adopters' acceptance behavior. Theoretical and practical implications that follow are presented.     

An efficient and fast algorithm for estimating the parameters of two-dimensional sinusoidal signals

In this paper we propose a computationally efficient algorithm to estimate the parameters of a 2-D sinusoidal model in the presence of stationary noise. The estimators obtained by the proposed algorithm are consistent and asymptotically equivalent to the least squares estimators. Monte Carlo simulations are performed for different sample sizes and it is observed that the performances of the proposed method are quite satisfactory and they are equivalent to the least squares estimators. The main advantage of the proposed method is that the estimators can be obtained using only finite number of iterations. In fact it is shown that starting from the average of periodogram estimators, the proposed algorithm converges in three steps only. One synthesized texture data and one original texture data have been analyzed using the proposed algorithm for illustrative purpose.     

Supplies from developing countries: Optimal order quantities under loss risks

When raw material suppliers of a global supply chain are situated in developing countries, not only will there be long lead times but there could also be a possibility of material losses in transit. The magnitude of the losses will be uncertain and can be significant. We consider the optimization of order quantity decisions in such situations. The long lead times imply that we need to take into account the uncertainty in finished goods demands for which the raw materials are to be ordered. The order quantities have to be optimized carefully as they determine the “real options” that become available later in production plans. Using data from the plywood industry and using stochastic programming we demonstrate a method for solving such problems. The method combines simulation and optimization. An interesting observation we make is that the optimal order quantity of a material need not be monotonic in expected loss of that material. In addition, we offer explanations as to why the expected loss need not be monotonic.     

Additive risks regression model for middle censored exponentiated-exponential lifetime data

Jammalamadaka and Mangalam introduced middle censoring which refers to data arising in situations, where the exact lifetime becomes unobservable if it falls within a random censoring interval. In the present article, we propose an additive risks regression model for a lifetime data subject to middle censoring, where the lifetimes are assumed to follow exponentiated exponential distribution. The regression parameters are estimated using the Expectation-Maximization algorithm. Asymptotic normality of the estimator is proposed. We report a simulation study to assess the finite sample properties of the estimator. We then analyze a real-life data on survival times of larynx cancer patients studied by Karduan.     

Race,Gender, and Social Support: A Study of Networks in a Financial Services Organization

A wealth of research indicates that social support improves employees' well-being and job performance. What is not well understood is how employees' race and gender influence the receipt of this type of support. I analyze qualitative and quantitative data to understand how race and gender influence the social support that workers receive. The results suggest that neither structural nor relational factors explain why blacks receive social support from fewer network members than whites. There is some evidence that relational factors contribute to gender differences in the receipt of social support, however. Interviews with workers suggest that gender schemata, the sex-typing of networks, reactions to racial discrimination, and differences in the value placed on social support contribute to race and gender differences in social support.     

Enhancing Wellbeing in Social Work Students: Building Resilience in the Next Generation

The need for social workers to be resilient is widely emphasised. Although enhancing resilience in social work trainees presents a challenge to educators, they are nonetheless responsible for developing professionals who are able to cope with the emotional demands of the job. This paper argues that building resilience in the future workforce should be a key element of social work education. However, as little is known about the competencies and support structures that underpin resilience or the extent to which resilience protects the wellbeing of trainees, an evidence-based approach is required to inform curriculum development.

Recent research conducted by the authors of this paper has highlighted the protective nature of resilience in social work trainees. Emotional intelligence and associated competencies, such as reflective ability, aspects of empathy and social confidence, were found to be key predictors of this important quality. The important role played by social support from various sources was also emphasised. The present paper summarises this research, and presents interventions based on the findings that have the potential to promote resilience and wellbeing in social work trainees. Also considered are ways in which the curriculum might be further enhanced to provide trainees with an internal ‘tool-box’ of strategies that will help them manage their wellbeing more effectively in their future career.     

Prevalence of job strain among Indian foundry shop floor workers

Global competition in manufacturing sector demand higher productivity levels. In this context, workers in this sector are set with high output targets, leading to job strain. In addition to the strain, hazardous conditions also prevail in some of the manufacturing processes like foundry activities. This paper attempts to appraise the prevalence of job strain among foundry shop floor workers in India with the help of Demands-Control model [8]. In this study, data was collected through a survey using 49-item Job Content Questionnaire (JCQ) [9], a widely used and well-validated test for job strain. Then the data was subjected to statistical analysis after ascertaining the reliability. This survey has revealed that 25% of workers in foundry were experiencing high job strain. Hazardous working conditions, limited decision making authority, etc. appear to be the main contributing factors for the higher levels of strain.