Sampling techniques refreshers

1. Sampling Techniques
By:
Weam Banjar. DDS., MS in clinical research

2. In a statistical study, sampling methods refer to how we select
members from the population to be in the study.

3. Population and Sample
Population Target population Accessible
population
Study
sample

4. Population and Sample
• Population: a complete set of people with a specified set of
characteristics
• Target population: a subset of population defined by clinical and
demographic characteristics
• Accessible population: a geographically and temporally defined
subset of the target population that available for study
• Study sample: a subset of accessible population that participate
in the study

5. Why sampling is important
• Resource Constraints
• Drawing Inferences About the Population

6. 3 Features to Keep in Mind While Constructing a
Sample
Consistency
DiversityTransparency

7. Sampling Techniques
Non-probability sampling Probability Sampling
• Does not involve selection of
elements at random
• Rarely representative of the
population
• has an equal, independent
chance of being selected.
• Allows researchers to
estimate the magnitude of
sampling error (difference
between population values
and sample values)

8. Types of non-probability sampling techniques:
• Convenience sampling: selecting the most conveniently available people
as participants
• Quota sampling: identifying population strata and figuring out how many
people are needed from each stratum
• Consecutive (Snowball) sampling: recruiting all people from an
accessible population over a specific time interval
• Purposive sampling: handpicking sample members

9. Types of probability sampling techniques:
• Simple random sampling: Completely random method for selecting
subjects
• Stratified random sampling: Involves splitting subjects into mutually
exclusive groups then using use simple random method for selection
• Systematic sampling: To choose every (nth) participant from a complete list
• Cluster random sampling: To randomly select participants from a list that is
too large for simple random sampling
• Multistage random sampling: Combination of techniques

10. Bias in sampling:
• Any pre-agreed sampling rules are deviated from
• People in hard-to-reach groups are omitted
• Selected individuals are replaced with others, for example if they are difficult
to contact
• There are low response rates
• An out-of-date list is used as the sample frame (for example, if it excludes
people who have recently moved to an area)

11. Special considerations:
• Dropouts
• Categorical variables
• Survival analysis
• Clustered samples
• Matching
• Equivalence studies

12. Strategies to minimize sample size and
maximize power:
• Use continuous variables
• Used paired measures
• Use more precise variables
• Use unequal group sizes
• Use more common outcome

13. How to estimate sample size where there is
insufficient information:
• Extensive search
• Pilot assessment
• Clinical meaningfully detectable effect size
• Educated guess