1. Organizing national surveys
Sampling for EHES

2. 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:
These slides can be used freely, translated and adapted to national use (e.g. concerning national sampling frames and sample selection criteria).

3. 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

4. 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)

5. 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

6. 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

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

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

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

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

11. 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

12. 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

13. 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)

14. 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

15. 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, years)
A one-stage designs allows a reduction in sample size
Sub-national estimates will most probably require a larger sample size

16. 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

17. 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

18. 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 through SANCO/2008/C2/02-SI EHES and Grand Agreement number , and in through Grand Agreement number /BRIDGE Health

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