Unit 4: Sampling approaches. After completing this unit you should be able to:. Outline the purpose of sampling Understand key theoretical concepts in sampling Understand the need for more complex sampling designs Understand the main sampling issues and primary sampling options for BSS
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This requires a random/probability sample. Use the class as an example.
In summary:
Probability
Non

probability
sample
sample
Prone to selection bias
No
Yes
Can generalise results to survey population
Yes
No
Can estimate precision of survey estimates (i.e.,
Yes
No
use statistical techniques)
Results considered credible
No
Yes
Requires sample frame
Yes
No
Requires following fixed procedures that are
Yes
No
sometimes costly or unfeasible
Method replicable (important for measuring
Yes
No
trends)
A summary of probability and nonprobability samplingThis requires an adequate sample size.
In summary:
Solution:Use systematic sampling (i.e., sample people at regular intervals down the sample frame).
Problem 2: Sample frames for an entire target population rarely exist and are too impractical to construct.
Solution:Develop a sampling frame of larger units (clusters). Randomly select clusters and construct a sample frame of individuals in the selected clusters. Randomly sample individuals within those clusters.
1. All members of the target population must be included in one of the clusters on the sample frame in order to have a chance of being selected.
2. If clusters are unequal sizes, we need to take this into account to ensure that our sample is not biased by the fact that people in smaller clusters have a higher probability of being selected than those in larger clusters. We can do this by:
3. Cluster sampling results in less precise estimates of our indicators than simple random sampling. As respondents within clusters may be similar to each other, we need to compensate for this by increasing the sample size.
Solution: Use cluster sampling, as it concentrates fieldwork in specific clusters.
Problem 4:The population consists of distinct subgroups that we are interested in.
Solution:Make precise estimates for each subgroup (‘strata’) by using stratified sampling (i.e., take a sample of adequate size from each strata). If we want an estimate for the entire population, we can combine the estimates for the strata if we know the proportion of the population in each strata.
Possible cluster
Brothelbased sex workers
Brothels
Nonbrothelbased sex workers
Streets, bars, hotels, guesthouses
Men who have sex with men
Cruising sites
Intravenous drug users
Shooting galleries, injecting sites
Truckers
Loading/u
nloading/halting points
Migrants
Households, workplaces
Examples of possible clusters for highrisk groups3. Cluster sampling is difficult when clusters are not stable.
4. Members of highrisk groups may be difficult to identify and access.
5. Cluster sampling is impossible if group members do not congregate. Some groups do not congregate at all. In others, only some members of the population congregate and important sections of the group may be missed.
Appropriate for the general population, youth and a few highrisk groups, such as prisoners.
Cluster 1= Site 1 weekday afternoon
Cluster 2= Site 2 weekday evening
Cluster 3= Site 1 weekend
Cluster 4= Site 2 weekday afternoon
Cluster 5= Site 1 weekday evening
Cluster 6= Site 2 weekend
Criteria for choosing a sampling approach for behavioural
surveillance
Criterion
Sampling Approach
Is the sub

population of interest the
Yes
Cluster sampling
general population or youth?
No
Does the sub

populat
ion congregate in
No
RDS
identifiable and accessible locations
in high proportions?
Yes
Is creating a list of group members
No
TLS or RDS
associated with each site feasible?
Yes
Are a high proportion of
sub

No
TLS or RDS
population group members likely
to be accessible at data collection
sites on randomly chosen days/times?
Yes
Cluster sampling
The sample size can be based on the number of participants needed to detect a change in each round (or year) in the proportion of an indicator from one round to the next.
[Z1 2P (1P) + Z1 P1 (1 P1) + P2 (1P1)]2
(P2 – P1)2
Where:
Z1α = The z score for the desired confidence level
Z1β = The z score for the desired power
P1 = The proportion of the sample reporting indicator in year 1
P2 = The proportion of the sample reporting indicator in year 2
P = (P1 + P2)/2
n= D
Sample size calculation, cont.
Indicator level in wave 1 (P1)
Indicator level in wave 2 (P2)
Sample size needed each wave with a design effect of 1.25
Sample size needed each wave with a design effect of 2.0
.10
.20
.10
.25
247
395
.20
.30
123
197
.20
.35
363
581
.30
.40
171
274
.30
.45
441
706
.40
.50
201
322
.40
.55
480
768
.50
.60
214
343
.50
.65
480
768
.60
.70
210
336
.60
.75
441
706
.70
.80
188
301
.70
.85
363
581
.80
.90
149
239
.80
.95
247
93
395
149
Table 4.5. Precalculated sample size estimates
How many sex workers do you need to include each year?
Solution:
D = 2 (moderate)
Z1α =1.96 (95% confidence level)
Z1β = 0.83 (80% power)
P1 = 20% condom use in year 1
P2 = 30% condom use in year 2
P = (.20 + .30)/2 = .25
N = 2 {1.96 SQT[2x.25(1  .25)] + 0.83 SQT[.20(1.20) + .30(1.3))]}2/(.30  .20) 2
= 582 sex workers per survey wave
a. What sampling strategies have you had experience with?
b. What difficulties and successes did you have with the strategy?
a. Group 1: Youth
b. Group 2: MSM