Measurement of the subjective work environment

Abstract

This paper considers the various issues that frame the development and use of measures of the subjective work environment. It begins by questioning the role of the work environment in determining occupational health, and explores the possible mechanisms by which that environment might exert its influence. It concludes that one of the important final common pathways is psycho-physiological in nature, and is rooted in individual perception and cognition and the experience of stress. Important for this model are the concepts of mediation and moderation. The measurement of the subjective work environment has often been idiosyncratic to the study in hand, and there are few well-established measures in common use. It is argued that researchers should be careful when deciding not to use established measures and effectively ‘invent’ their own. In developing new measures, decisions have to be made concerning the nature of the measure along with its ‘granularity’ and complexity. It is also argued that it is important that researchers should follow good psychometric practice in the development of those measures. Suggestions for ‘good practice’ are discussed. Attention is drawn to the issues of reliability and validity, and this paper discusses the role of triangulation in the planning and execution of data collection and analysis. The paper ends by reviewing the recommendations made towards the development and use of measures of the subjective work environment.     

Modal non‐linear regression in the presence of Laplace measurement error

In this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error. Starting with the modal regression procedure developed for the measurement error‐free case, a non‐trivial modification is made so that the modified version can effectively correct the potential bias caused by measurement error. Large sample properties of the proposed estimate, such as the convergence rate and the asymptotic normality, are thoroughly investigated. A simulation study and real data application are conducted to illustrate the satisfying finite sample performance of the proposed estimation procedure.     

An improved alternative estimation procedure for current population mean in presence of positively and negatively correlated auxiliary variables in two-occasion rotation patterns

Abstract

The present study confirms the influential role of a positively and a negatively correlated auxiliary variables in enhancing the precision of estimates of current population mean in two occasion rotation (successive) sampling. Exponential-type estimators of current population mean have been proposed for three different situations: (i) the information on a positively correlated auxiliary variable is readily available on both occasions (ii) the information on a negatively correlated auxiliary variable is readily available on both occasions and (iii) the information on both positively and negatively correlated auxiliary variables are readily available on both the occasions. The characteristics of the proposed estimators have been explored and their efficacious performances are compared with the natural and recent contemporary estimators. Optimum replacement strategies of the proposed estimation procedures have been formulated. Simulation and empirical studies are carried out to justify the proposition of the proposed estimators and appropriate recommendations have been put forward to the survey practitioners.     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Distribution of the two‐sample t‐test statistic following blinded sample size re‐estimation

We consider the blinded sample size re‐estimation based on the simple one‐sample variance estimator at an interim analysis. We characterize the exact distribution of the standard two‐sample t‐test statistic at the final analysis. We describe a simulation algorithm for the evaluation of the probability of rejecting the null hypothesis at given treatment effect. We compare the blinded sample size re‐estimation method with two unblinded methods with respect to the empirical type I error, the empirical power, and the empirical distribution of the standard deviation estimator and final sample size. We characterize the type I error inflation across the range of standardized non‐inferiority margin for non‐inferiority trials, and derive the adjusted significance level to ensure type I error control for given sample size of the internal pilot study. We show that the adjusted significance level increases as the sample size of the internal pilot study increases. Copyright © 2016 John Wiley & Sons, Ltd.     

基于灰色系统的小型精密机床热误差模型

为减少热误差对数控机床加工精度的影响,文章利用灰色系统研究热误差建模。初步分析机床热源后,应用灰 色关联度分析结合模糊分类方法优化得到13组关键温度测点,结合小型精密五轴机床处于实际加工状态时的实验数 据,应用GM(I,N)灰色结构进行建模。将实际数据与拟合数据进行对比分析,结果表明此灰色系统模型具有计算方法 成熟、鲁棒性强的特点,适用于机床热误差研究。     

Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Statistical inference procedures based on transforms such as characteristic function and probability generating function have been examined by many researchers because they are much simpler than probability density functions. Here, a probability generating function based Jeffrey's divergence measure is proposed for parameter estimation and goodness-of-fit test. Being a member of the M-estimators, the proposed estimator is consistent. Also, the proposed goodness-of-fit test has good statistical power. The proposed divergence measure shows improved performance over existing probability generating function based measures. Real data examples are given to illustrate the proposed parameter estimation method and goodness-of-fit test.     

Methods to assess measurement error in questionnaires of sedentary behavior

Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days. The first method fits a reduced model by assuming the accelerometer is without error, while the second method fits a more complete model that allows both measures to have error. Because accelerometers tend to be highly accurate, we show that ignoring the accelerometer's measurement error, can result in more accurate estimates of measurement error in some scenarios. In this article, we derive asymptotic approximations for the mean-squared error of the estimated parameters from both methods, evaluate their dependence on study design and behavior characteristics, and offer an R package so investigators can make an informed choice between the two methods. We demonstrate the difference between the two methods in a recent validation study comparing previous day recalls to an accelerometer-based ActivPal.     

武汉市固定资产投资与经济增长关系的实证分析

固定资产投资是经济增长的一个重要因素,对经济增长具有直接的拉动作用,同时对投资的调节也成为国家宏观调控的一个重要手段。以武汉市为例,选取了年固定资产投资与国内生产总值的数据进行协整分析,并建立了误差修正模型,得出了二者之间存在长期均衡关系。     

Measuring longevity achievements under welfare interdependencies: a case for joint life expectancy indicators

Whereas period life expectancy constitutes an intuitive indicator of the survival conditions prevailing at a particular period, this paper argues that, given the existence of welfare interdependencies, that widespread indicator is nonetheless an incomplete measure of the longevity achievements relevant for human well-being. The central importance of coexistence for human-beings implies that usual life expectancy measures should be complemented by joint life expectancy indicators, which measure the average coexistence time under particular survival conditions. After a study of the theoretical foundations of ‘single’ and ‘joint’ life expectancy indicators, it is shown that joint life expectancy measures tend to enrich significantly the comparison of longevity achievements across countries and periods. Moreover, the introduction of joint life expectancy indicators—as a complement to conventional life expectancy measures—into multi-variable indexes such as the United Nations’ HDI is also shown to affect international rankings of standards of living to a non negligible extent.