1. Research Methods and Statistics
Lecture 4-I Sampling
Research Methods and Statistics

2. Sampling Why is sampling important?
- The quality of a research study stands or falls in part by the appropriateness and suitability of the sampling strategy used.
Purpose of sampling
- Reduced costs
- Speed
- Can provide more highly accurate measures than including an entire population in a study.

3. Guidelines for Making Decisions about Sampling
Your Sampling Scheme
- Should allow for a potentially valid means by which to answer your research question.
- Should be feasible
- Should be large enough to allow for the possibility of providing credible explanations and/or drawing clear inferences.
- Must be ethical.

4. Sampling: Defining a Population
Defining the population of interest
- In what group exactly are you interested?
- To whom do you want the result of the study to apply?
Characteristics of a population
- Can be any size
- Includes all of the individuals who possess a certain characteristic or set of characteristics

5. Determining Sample Size
How large should a sample be for a study?
- No clear-cut answers, except that it should be “large enough.”
- An adequate sample size depends on the purpose of the study and the nature of the population under investigation.
Rules of thumb for determining a sample size
- A sample of 30 as the minimum needed for statistical analyses
- The more heterogeneous the population, the larger the sample should be.
- For smaller populations, include the entire population.

6. Sample Size
Desired sample size/Estimated percentage of people likely to participate = Number of people to include in your original sample
Some tips for sample size: Response rate
- The final sample size < the number people who participate in your study (response rate).
- Always should over-sample to control for possible lower- than-expected response rates.

7. Probability Sampling: Simple Random SamplingC D E F G H I J K L M N O P Q R S T U V W X Y Z
Population
Sample
(Random Selection)
Advantage
- Good for obtaining a sample that is representative of individuals in a population.
Disadvantages
- May be difficult to reach all individuals selected in the sample, particularly if they are dispersed geographically.
- May not get good representation of subgroups in a population.

8. Probability Sampling: Systematic Random Sampling
Population
Sample T O J E Y
A B C D E F
G H I J K L
M N O P Q R
S T U V W X
Y Z
Every kth unit
Random starting point
Advantage
- Sample selection method is simple, and is especially useful if the population is very large.
Disadvantages
- Periodicity(pattern): If individuals on sampling frame are not randomly ordered, sample you draw may not be representative of population.

9. Probability Sampling: Stratified Random Sampling
Population
Sample A B C D E P Q R S T F G H I J K L M N O
(25%)
(50%)
F M J O
P S
B D
F M O
(33%)
P S T
B D E
Proportional
Disproportional
(Random Selection)
Advantages
- Increases likelihood of representation of key variable(s) for a particular study
Useful for examining group differences
Disadvantage
- Need to have information on variable of interest for each individual in the population

10. Probability Sampling: Cluster Sampling
Population
Sample HI
A B
C D
L M
J K
S T U
E F G
Q R
(Random Selection)
Advantages
- Efficient sampling method when population is large or individuals are dispersed over wide geographic area.
- Doesn’t require detailed information on individuals in a population
Disadvantage
- Reduction in the representativeness of the population, as similar people tend to naturally “cluster” together.

11. Probability Sampling: Two-Stage Cluster Sampling
Population
Sample
Q R
C D
E F G
A B
L M
J K
S T U HI D F Q
(Random Selection)
Advantages
- Smaller sample size than single- stage cluster sampling
Disadvantage
- If cluster are not approximately the same size, must use probability proportional to size (PPS) method to increase representativeness of the sample.