A Bayesian model for measurement and misclassification errors alongside missing data,with an application to higher education participation in Australia

In this paper we consider the impact of both missing data and measurement errors on a longitudinal analysis of participation in higher education in Australia. We develop a general method for handling both discrete and continuous measurement errors that also allows for the incorporation of missing values and random effects in both binary and continuous response multilevel models. Measurement errors are allowed to be mutually dependent and their distribution may depend on further covariates. We show that our methodology works via two simple simulation studies. We then consider the impact of our measurement error assumptions on the analysis of the real data set.     

Does the supplemental nutrition assistance program really increase obesity? The importance of accounting for misclassification errors

The prevalence of obesity among US citizens has grown rapidly over the last few decades, especially among low-income individuals. This has led to questions about the effectiveness of nutritional assistance programs such as the Supplemental Nutrition Assistance Program (SNAP). Previous results on the effect of SNAP participation on obesity are mixed. These findings are however based on the assumption that participation status can be accurately observed, despite significant misclassification errors reported in the literature. Using propensity score matching, we conclude that there seems to be a positive effect of SNAP participation on obesity rates for female participants and no such effect for males, a result that is consistent with several previous studies. However, an extensive sensitivity analysis reveals that the positive effect for females is sensitive to misclassification errors and to the conditional independence assumption. Thus analogous findings should also be used with caution unless examined under the prism of classification errors and of other assumptions used for the identification of causal parameters.     

A decision-theoretic approach to variable selection in discriminant analysis

In discriminant analysis it is often desirable to find a small subset of the variables that were measured on the individuals of known origin, to be used for classifying individuals of unknown origin. In this paper a Bayesian approach to variable selection is used that includes an additional subset of variables for future classification if the additional measurement costs for this subsst are lower than the resulting reduction in expected misclassification costs.     

Simple computation of a bivariate normal integral arising from a problem of misclassification with applications to the diagnosis of hypertension

The joint distribution of the true and observed values of a variable that is subject to measurement error is bivariate normal.An important special case occurs when we want the joint probability of the true value being below a cutoff point and the observed value above it.In that case the required integral can be simply evaluated using a Gaussian quadrature formula, which can easily be evaluated using a calculator.This formula is used to estimate the probabilities of misclassification of participants in screening programs for hypertension.It shows that basing a diagnosis on a single visit, at which a single measurement was made leads to a very high risk of misclassification.The probability of a subject having a blood pressure below the cutoff point, given that the observed pressure is above it, would be 0.45.Increasing the number of visits to three, and measuring the blood pressure twice at each visit, as advocated by Rosner and Polk (1979), would bring the probability down to 0.29.     

Item reliability based on a paucity of item failures

When item reliability is high, lot acceptance sampling is likely to uncover no defective units. Nevertheless, the reliability lower confidence limits are often disappointing. The use of a Bayesian procedure with certain beta priors produces tighter confidence limits. The case where one observed defective is permitted in accepting a lot is also examined. Numerical results for both cases are presented. In addition, the impact of item misclassification is addressed.     

New tech,old exploitation: Gig economy,algorithmic control and migrant labour

Digital platforms are the newest technological wave that is reshaping and reconfiguring the economic and labour landscape. Digital platforms often known as the gig economy are increasingly adopting app-based models to connect consumers with workers to complete their on-demand tasks. However, on-demand platforms continue to rely on the unequal division of labour and the precarious nature of the work to create labour markets that can respond accordingly to the increase in service provision. This review highlights two main themes that have emerged within the on-demand gig economy in the current literature—mythical autonomy and algorithmic control and misclassification of labour and the complexity of migrant workers in navigating this space. Finally, this review calls for further research into the inside/outside dichotomy of migrant labour within the gig economy and their experiences of labour exploitation through app-based digital platforms.