Organizing national surveys Sampling for EHES

Available at:http://urn.fi/URN:ISBN:978-952-302-700-8 Based on EHES Manual, Part A. Planning and preparation of the survey, 2nd edition (2016) Available at:http://urn.fi/URN:ISBN:978-952-302-700-8 These slides can be used freely, translated and adapted to national use (e.g. concerning national sampling frames and sample selection criteria).

Ideal target population The core target population for EHES is all adults aged 25 to 64 who reside in the country The age range can be extended by the individual countries Institutionalized persons should be included Temporary visitors are not included

Main sampling frame The main sampling frame is the list of people/addresses to take a sample from. An ideal list is: Updated regularly Includes everyone in the target population Contains contact information In reality, add-on lists may be necessary (especially for those in institutions)

Sampling designs A sample is taken to represent the population as a whole as we do not have the resources to survey everybody We recommend (for most countries) a multi-stage design to reduce costs/resources through clustering participants into manageable areas known as Primary Sampling Units (PSUs) An example country with random sampling Clustering participants reduces costs

What is a multi-stage sample? The country is divided into Primary Sampling Units (PSUs) A number of these are selected randomly With probability proportional to their size . An example country

What is a multi-stage sample? Within each selected PSU, people from the population register are selected randomly An example country

What is a multi-stage sample? Within each selected PSU, households (dwellings) from a household (address) list are selected randomly An example country

What is a multi-stage sample? Within each selected household we select all household members An example country

What is a multi-stage sample? Within each selected household we select 1 person An example country

What is random selection? Selecting a person randomly means that they are selected entirely by chance We can calculate how likely someone is to be selected. We cannot calculate if they actually will be selected – this is the random part

Why random selection? To estimate the health of the population we need to know everyone’s chances of being selected/invited This is only possible with random selection (believe it or not) Replacing someone who does not want to/cannot participate with somebody else means we no longer have a random sample and cannot estimate health figures accurately from the data

Stratification Grouping similar PSUs or individuals during the sampling stage is called stratification Stratification generally improves the accuracy of the estimates 2 PSU selected in each PSU (shown as white) An example country with stratification of PSUs (shown by separate colours)

Biased samples A sample is biased if it does not reflect the population and will tend to give wrong results Biased samples can result from: Samples that are not randomly taken from the population Low response rates among certain groups of the sample (eg. people who are not well) Population Biased sample Sample Population Sample Representative sample

Sample size A minimum sample size of 4000 is required in countries implementing a multi-stage design for EHES This is based on the accuracy required with response rates of 70% Based on a minimum of 500 in each of the 8 sex/age groups (25-34, 35-44, 45-54, 55-64 years) A one-stage designs allows a reduction in sample size Sub-national estimates will most probably require a larger sample size

Sample allocation How to allocate the sample among the Primary Sampling Units is a balance between resources and accuracy We recommend using the EHES program in R and/or a specialist survey statistician No clusters Very good accuracy of estimates High cost Many small clusters Medium accuracy of estimates Medium cost Few large clusters Low accuracy of estimates Low cost

General sampling tips Sampling using multi-stage designs can be complicated, however, can reduce overall costs while maintaining control over the accuracy of estimates An add-on package for the statistical software ”R” has been developed as a tool for sampling in EHES and is freely available

Acknowledgements Slides prepared by: Susie Jentoft, Johan Heldal and Kari Kuulasmaa Experiences and feedback from the EHES network have been utilized in the preparation of these slides Funding: Preparation of the slides is part of the activities of the EHES Coordinating Centre which has received funding from the EC/DG SANTÉ in 2009-2012 through SANCO/2008/C2/02-SI2.538318 EHES and Grand Agreement number 2009-23-01, and in 2015-2017 through Grand Agreement number 664691/BRIDGE Health

Disclaimer The views expressed here are those of the authors and they do not represent the Commission’s official position.