1. Meeting-6 SAMPLING DESIGN

2. The Nature of Sampling Population:
total collection of elements about which we wish to make some inferences
Population element:
the individual participant or object on which the measurement is taken
Sampling:
some of the elements of population
Sample frame:
the listing of all population elements from which the sample will be drawn

3. Why Sample? Lower cost Greater accuracy of results
Greater speed of data collection
Availability of population elements

4. What is a Good Sample? Accurate: absence of bias
Precise estimate: small standard error of estimate

5. Steps in Sampling Design
What is the target population?
Should be defined clearly
What are the parameters of interest?
Summary descriptors of variables of interest in the population
What is the sampling frame?
Should complete and correct
What is the appropriate sampling method?
Probability or nonprobability sampling
What size sample is needed?

6. Parameters of Interest

7. What Size Sample Is Needed?
Some principles :
The greater the dispersion or variance , the larger the sample
The greater precision of the estimate, the larger the sample
The narrower or smaller the error range, the larger the sample
The higher the confidence level, the larger the sample
The greater the number of subgroups of interest within sample, the larger the sample
The lower cost/respondent, the larger the sample

8. Types of Sampling Design
Probability Sampling  based on the concept of random
selection
Simple random
Systematic
Stratified
Cluster
Double
Nonprobability Sampling  arbitrary and subjective
Convenience (unrestricted of element selection)
Purposive: Judgment, Quota
Snowball

9. Probability Sampling Designs
1. Simple random sampling:
Special case in case which each population element has a known and equal chance of selection
Easy to be implemented by random number or using computer (SPSS)

10. How to Choose a Random Sample

11. How to Choose a Random Sample

12. Probability Sampling Designs
Systematic Sampling
Every kth element in the population is sampled, beginning with a random start of an element in the range of 1 to kth
k = Skip interval = Population size
Sample size

13. Probability Sampling Designs
3. Stratified Random Sampling
Population is devided into strata, then a simple random sample can be taken within each stratum
Process for drawing sample:
Determine the variable to use for stratification
Determine the proportions of the stratification variables
Select proportionate or disproportionate stratification
Devide the sampling frame into separate frame for each stratum
Randomize the elements within each stratum
Follow random or systematic procedure to draw the sample from each stratum

14. Probability Sampling Designs
4. Cluster Sampling
Population is devided into subgroups based on area or cluster. Few clusters (subgroups) then are selected based on some criterion and finally elements within each cluster is chosen randomly
The reason of using this method:
Efficiency
Unavailability sampling frame for individual elements

15. Probability Sampling Designs

16. Probability Sampling Designs
Section 1
Section 2
Section 3
Section 5
Section 4

17. Probability Sampling Designs

18. Probability Sampling Designs
Double Sampling
Selecting subsample from sample which taken before for further study
Called as sequential sampling or multiphase sampling

19. Probability Sampling Designs

20. Nonprobability Sampling
Reasons to use
Procedure satisfactorily meets the sampling objectives
Lower Cost
Limited Time
Not as much human error as selecting a completely random sample
The population elements is not available

21. Nonprobability Sampling
1. Convenience Sampling
The sampling procedure used to obtain those units or people most conveniently available
Researchers have the freedom to choose whomever they find
2. Purposive Sampling
The sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose
3. Snowball Sampling
The sampling procedure in which the initial respondents are chosen by probability or non-probability methods, and then additional respondents are obtained by information provided by the initial respondents

22. VIDEO
Using random numbers
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling