Understanding panel conditioning: an examination of social desirability bias in self-reported height and weight in panel surveys using experimental data

Typically reliant on self-reports from panel data, a growing body of literature suggests that relative body weight can have negative effects on labour market outcomes. Given the interest in the effects of relative weight in the social sciences, this paper addresses the question of whether repeated interviewing affects the quality of these data. A theory that focuses on the sensitivity of the questions rather than the survey context is proposed. Examining experimental panel data from Understanding Society using quantile-regression, the findings for women are consistent with the argument that conditioning reduces social desirability effects. The ameliorative effects of panel conditioning on social desirability bias in self-reported height and bodyweight appear to strengthen the association between relative weight and employment for men, but not women, however

Online Matrix Completion Through Nuclear Norm Regularisation

It is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommender systems to sensor network localization through seismic data reconstruction, we consider the matrix completion problem when entries of the matrix of interest are observed gradually. Precisely, we place ourselves in the situation where the predictive rule should be refined incrementally, rather than recomputed from scratch each time the sample of observed entries increases. The extension of existing matrix completion methods to the sequential prediction context is indeed a major issue in the Big Data era, and yet little addressed in the literature. The algorithm promoted in this article builds upon the Soft Impute approach introduced in Mazumder et al. (2010). The major novelty essentially arises from the use of a randomised technique for both computing and updating the Singular Value Decomposition (SVD) involved in the algorithm. Though of disarming simplicity, the method proposed turns out to be very efficient, while requiring reduced computations. Several numerical experiments based on real datasets illustrating its performance are displayed, together with preliminary results giving it a theoretical basis.Comment: Corrected a typo in the affiliatio

A Trinomial Test for Paired Data When There are Many Ties

This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statistic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties.Sign test; trinomial test; non-parametric test; ties; test statistics; hypothesis testing