Oriol J. Bosch

Oriol J. Bosch

A Survey Methodologist in the era of Big Data

Postdoctoral Researcher at the University of Oxford, Leverhulme Centre for Demographic Science

Teaching

How do people feel about AI? A nationally representative survey of public attitudes to artificial intelligence in Britain

Ada Lovelace Institute and The Alan Turing Institute, Ada Lovelace Institute and The Alan Turing Institute, 2023

In late 2022 The Alan Turing Institute and the Ada Lovelace Institute conducted a nationally representative survey of over 4,000 members of the British public, to understand their awareness, experience and attitudes towards different uses of artificial intelligence (AI), including views and expectations on how these technologies should be regulated and governed. Read more

Recommended citation: Ada Lovelace Institute and The Alan Turing Institute, How do people feel about AI? A nationally representative survey of public attitudes to artificial intelligence in Britain (2023) . Available at: https://adalovelaceinstitute.org/report/public-attitudes-ai https://www.adalovelaceinstitute.org/report/public-attitudes-ai/

Methodological Lessons from the Pilot Longitudinal Survey on Debt Advice

MoneyHelper, ISER Working Paper Series, 2021

This reports evaluates and analyses a pilot longitudinal survey of people in debt in the UK (2,025 participants). We report methodological lessons learned, aimed at identifying the best procedures to use on the new survey, and provide estimates of the sample size that would be needed. Read more

Recommended citation: Bosch, Oriol & Lynn, Peter, 2021. "Methodological lessons from the pilot longitudinal survey on debt advice," ISER Working Paper Series 2021-03, Institute for Social and Economic Research. https://finchley.essex.ac.uk/research/publications/working-papers/iser/2021-03.pdf

When survey science met online tracking: presenting an error framework for metered data

RECSM Working Papers Series, 62, 2021

In this paper we present a framework of all errors that can occur when using metered data. To do so, we adapt the Total Survey Error framework to accommodate it to the specific error generating processes and error causes of metered data Read more

Recommended citation: Bosch, O.J., and M. Revilla (2021) When survey science met online tracking : presenting an error framework for metered data. http://hdl.handle.net/10230/46482 https://www.upf.edu/documents/3966940/6839730/WP62.pdf/16aaf443-c545-2f5a-faac-a2bb55dec4d6

Improving web panel respondent behaviour: The effect of encouragement messages throughout the course of the survey

CRONOS, Work Package 7: A survey future online, 2018

In this reports we establish the impact of motivational messages in web surveys on data quality, using an experiment conducted in waves 2, 4 and 6 of the CROss-National Online Survey (CRONOS) panel Read more

Recommended citation: Bosch, O.J., Weber, W., and M. Revilla (2018) Improving web panel respondent behaviour: The effect of encouragement messages throughout the course of the survey. Deliverable 7.12of the SERISS project funded under the European Union’s Horizon 2020 research and innovation programme GA No: 654221. Available at: https://seriss.eu/wp-content/uploads/2018/10/SERISS-Deliverable-7.12-Strategies-to-improve-panelist-responding-behaviour.pdf