Mathematics and Statistics Seminar Series - Natalie Shlomo (The University of Manchester) | Dipartimento di Scienze Economiche

Mathematics and Statistics Seminar Series - Natalie Shlomo (The University of Manchester)

21 febbraio 2023 13:30 - 14:30
Luogo: 
Aula 16, via dei Caniana | On Microsoft Teams
Seminari di dipartimento
Persona di riferimento: 
Tommaso Lando (tommaso.lando@unibg.it)
Strutture interne organizzatrici: 
Dipartimento di Scienze Economiche

"Sampling Minoritized Populations: Experiences from the Evidence for Equality National Survey on the Impact of COVID-19 on Ethnic and Religious Minority Groups in Britain" 

(Authors: Natalie Shlomo, James Nazroo, Nissa Finney, Angelo Moretti, Harry Taylor, Andrea Aparcio-Castro, Daniel Ellingworth)    

 

The rapid improvements in our ability to conduct fast and cost-effective online surveys, together with advances in statistical theory and methods to adjust for selection and coverage biases in nonprobability sampling, has led to opportunities to consider alternative sampling approaches for minoritized populations where no sample frames exist and are less represented in standard probability-based surveys.

We present an application of a carefully designed non-probability online web survey to capture the experiences of ethnic and religious minority groups in Britain of the Covid-19 pandemic: the 2021 Evidence for Equality National Survey (EVENS). To conduct the survey, we formed partnerships with community organizations and used innovative recruitment strategies, including digital and social media. Daily monitoring of the data collection against desired sample sizes (quotas) and R-indicator calculations allowed the team to focus attention on  the recruitment of specific groups under a responsive data collection design. We also supplemented the sample with existing members in both established non-probability and probability-based panels in the UK. We describe the measures applied to improve the quality of the collected data and the statistical adjustments to correct for selection and coverage biases based on estimating the probability of participation in the non-probability sample using a probability-based reference sample followed by calibration.   

 

The link Teams is available here.