Work-Unit Absenteeism: Effects of Satisfaction, Commitment, Labor Market Conditions, and Time

Prior research is limited in explaining absenteeism at the unit level and over time. We developed and tested a model of unit-level absenteeism using five waves of data collected over six years from 115 work units in a large state agency. Unit-level job satisfaction, organizational commitment, and local unemployment were modeled as time-varying predictors of absenteeism. Shared satisfaction and commitment interacted in predicting absenteeism but were not related to the rate of change in absenteeism over time. Unit-level satisfaction and commitment were more strongly related to absenteeism when units were located in areas with plentiful job alternatives

Assessing the differences between numerical methods and real experiments for the evaluation of reach envelopes of the human body

The use of static human body dimensions to assess the human accessibility is an essential part of an ergonomic approach in user-centered design. Assessments of reach capability are commonly performed by exercising external anthropometry of human body parts, which may be found in anthropometric databases, to numerically define the reach area of an intended user population. The result is a reach envelope determined entirely by the segment lengths, without taking into account external variables, as the nature of the task or the physical capacities of the subject, which may influence the results. Considering the body as a simple assembly of static parts of different anthropometry is limiting. In this paper, the limit of validity of this approach is assessed by comparing the reach envelopes obtained by this method to those obtained with a simple two-dimensional experimental reaching task of a panel of subjects. Forty subjects experimentally evaluated the reach, first with the body constrained and second unconstrained. Results were recorded and compared with those obtained numerically with a model, based on their own anthropometric characteristics, previously measured. A statistical study of the results allowed the definition of the shape of a confidence bound containing the real reach envelope. The results indicated important differences between the experiment and the numerical evaluation of the reach envelope.Comment: Colloque National AIP-Prim\'eca, Mar 201

Adaptive sampling for linear state estimation

When a sensor has continuous measurements but sends occasional messages over a data network to a supervisor which estimates the state, the available packet rate fixes the achievable quality of state estimation. When such rate limits turn stringent, the sensor’s messaging policy should be designed anew. What are the good causal messaging policies ? What should message packets contain ? What is the lowest possible distortion in a causal estimate at the supervisor ? Is Delta sampling better than periodic sampling ? We answer these questions for a Markov state process under an idealized model of the network and the assumption of perfect state measurements at the sensor. If the state is a scalar, or a vector of low dimension, then we can ignore sample quantization. If in addition, we can ignore jitter in the transmission delays over the network, then our search for efficient messaging policies simplifies. Firstly, each message packet should contain the value of the state at that time. Thus a bound on the number of data packets becomes a bound on the number of state samples. Secondly, the remaining choice in messaging is entirely about the times when samples are taken. For a scalar, linear diffusion process, we study the problem of choosing the causal sampling times that will give the lowest aggregate squared error distortion. We stick to finite-horizons and impose a hard upper bound N on the number of allowed samples. We cast the design as a problem of choosing an optimal sequence of stopping times. We reduce this to a nested sequence of problems, each asking for a single optimal stopping time. Under an unproven but natural assumption about the least-square estimate at the supervisor, each of these single stopping problems are of standard form. The optimal stopping times are random times when the estimation error exceeds designed envelopes. For the case where the state is a Brownian motion, we give analytically: the shape of the optimal sampling envelopes, the shape of the envelopes under optimal Delta sampling, and their performances. Surprisingly, we find that Delta sampling performs badly. Hence, when the rate constraint is a hard limit on the number of samples over a finite horizon, we should should not use Delta sampling

Health State Values for the HUI 2 descriptive system: results from a UK survey

This paper reports the results of a study to estimate a statistical health state valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble health state preference data. A sample of 51 health states were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting health state valuations for all 8,000 states defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed health state values within the valuation sample and for a separate validation sample of health states. However, there are concerns over large prediction errors for two health states in the valuation sample. These problems must be balanced against concerns over the validity of using the VAS based health state valuation data of the original HUI2 valuation model

Health state values for the HUI 2 descriptive system: results from a UK survey

This paper reports the results of a study to estimate a statistical health state valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble health state preference data. A sample of 51 health states were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting health state valuations for all 8,000 states defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed health state values within the valuation sample and for a separate validation sample of health states. However, there are concerns over large prediction errors for two health states in the valuation sample. These problems must be balanced against concerns over the validity of using the VAS based health state valuation data of the original HUI2 valuation model.HUI2