A theoretical and empirical investigation of nutritional label use

Due in part to increasing diet-related health problems caused, among others, by obesity, nutritional labelling has been considered important, mainly because it can provide consumers with information that can be used to make informed and healthier food choices. Several studies have focused on the empirical perspective of nutritional label use. None of these studies, however, have focused on developing a theoretical economic model that would adequately describe nutritional label use based on a utility theoretic framework. We attempt to fill this void by developing a simple theoretical model of nutritional label use, incorporating the time a consumer spends reading labels as part of the food choice process. The demand equations of the model are then empirically tested. Results suggest the significant role of several variables that flow directly from the model which, to our knowledge, have not been used in any previous empirical work

Multi-source statistics:Basic situations and methods

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems

Modelling measurement errors by object-oriented Bayesian Networks: an application to 2008 SHIW

In this paper we propose to use the object-oriented Bayesian network architecture to model measurement errors. We then apply our model to the Italian Survey on Household Income and Wealth (SHIW) 2008. Attention is focused on errors caused by the respondents. The parameters of the error model are estimated using a validation sample. The network is used to stochastically impute micro data for households. In particular imputation is performed also using an auxiliary variable. Indices are calculated to evaluate the performance of the correction procedure and show that accounting for auxiliary information improves the results. Finally, potentialities and possible extensions of the Bayesian network approach both to the measurement error context and to official statistics problems in general are discussed

Interpreting the results of studies using latent variable models to assess data quality: an empirical example using confirmatory factor analysis

Data quality, Latent variable models, Differential item functioning, Confirmatory factor analysis, Survey methods,

Children as respondents in survey research: Cognitive development and response quality

Although children are no longer a neglected minority in official statistics and surveys, methodological knowledge on how to survey children is still scarce. Researchers have to rely mainly on ad-hoc knowledge from such diverse fields as child psychiatry and educational testing, or extrapolate from methodological knowledge on how to survey adults. In this article, we review the available literature on children as respondents and present the first results of a secondary analysis of the influence of cognitive development on response quality. We end with recommendations for surveying childre