Disseminating research results to kids: practical tips from the Neighbourhoods for Active Kids study

ABSTRACT

Appropriately disseminating results to children are important because it respects the role they played in the research process. Clearly conveying complex messages to children, however, can be challenging and take a substantial amount of time for researchers. This paper reports on the results dissemination processes and accompanying critical reflection, that occurred during Neighbourhoods for Active Kids study – a community-based health research project with 1102 children aged 8–13 years residing in Auckland, Aotearoa/New Zealand. The results dissemination items included: an individual physical activity and food-purchasing behaviour summary, school physical activity and food-purchasing behaviour summary, colouring-in poster, a video, comic, results booklet, school summary report and school data. The results from critical reflection have been developed into five tips for disseminating results to children. The tips are: consult with children and incorporate their feedback throughout; allow space for change and adaptation; meet children where they are at in the digital world and in the physical world; it’s OK to ask for help; and be careful of the words you use. Findings can be used to inform results dissemination activities across a range of social science disciplines.     

Local and omnibus goodness-of-fit tests in classical measurement error models

We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.     

Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

Massively Parallel Signature Sequencing (MPSS) is a high-throughput counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression (SAGE) and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflated Poisson (ZIP) distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base-pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using non-parametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries.