A Bayesian workflow for the analysis and reporting of international large-scale surveys: A case study using the OECD Teaching and Learning International Survey

This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular methodological framework for the analysis of educational data generally, and large-scale surveys more specifically. The report argues that Bayesian statistical methods can provide a more nuanced analysis of results of policy relevance compared to standard frequentist approaches commonly found in large-scale survey reports. The data utilised for this report comes from the OECD Teaching and Learning International Survey (TALIS). The report provides steps in implementing a Bayesian analysis and proposes a workflow that can be applied not only to TALIS but to large-scale surveys in general. The report closes with discussion of other Bayesian approaches to international large-scale survey data, in particularly for predictive modelling.