sampling-120714090137-sampling probabality1.pptx

sampling

1. Research Methodology Sample Designs and Sampling Procedures

2. S a m p le Subset of a larger population

3. The process of using a small number of items or parts of larger population to make a conclusions about the whole population S a mp l i n g

4. Source: Saunders et al . (2009) Selecting samples Population, sample and individual cases

5. Sampling techniques Source: Saunders et al . (2009) Overview of sampling techniques

6. Overview of sampling techniques Types of Sampling Designs/Methods of Sampling Sampling A Probability Sampling B Non-Probability Sampling A 1 Random Sampling B-1 Incidental or Accidental Sampling A-2 Systematic Sampling B-2 Judgment Sampling A-3 Stratified Sampling B- 3 Purposive Sampling A-4 Multistage Sampling B-4 Quota Sampling A-5 Purposive Sampling A-6 Cluster Sampling A-7 Multiple Sampling or Double Sampling

7. Define the target population Select a sampling frame Conduct fieldwork Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Stages in the Selection of a Sample

8. Target Population The specific , complete group to research project

9. Random Sampling Error The difference between the sample results and the result of a census conducted using identical procedures Statistical fluctuation due to chance variations

10. Sampling Error A l though a sample is a part of the population it cannot be expected generally to supply to supply full information about population. So there may be in most cases difference between statistics and parameter. The discrepancy between a parameter and its estimates due to sampling process is known as sampling error.

11. Two Major Categories of Sampling Probability sampling Known, nonzero probability for every element Nonprobability sampling Probability of selecting any particular member is unknown

12. Nonprobability Sampling Convenience Judgment Quota Snowball

13. Probability Sampling Simple random sample Systematic sample Stratified sample Cluster sample Multistage area sample

14. Convenience Sampling Convenience samples are nonprobability samples where the element selection is based on ease of accessibility. They are the least reliable but cheapest and easiest to conduct. Examples include informal pools of friends and neighbors, people responding to an advertised invitation, and “on the street” interviews.

15. Judgment Sampling Also called purposive sampling An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member

16. Quota Sampling Ensures that the various subgroups in a population are represented on pertinent sample characteristics To the exact extent that the investigators desire It should not be confused with stratified sampling.

17. Snowball Sampling A variety of procedures Initial respondents are selected by probability methods Additional respondents are obtained from information provided by the initial respondents

18. Simple Random Sampling A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample

19. Simple Random Advantages Easy to implement with random dialing 14-25 Disadvantages Requires list of population elements Time consuming Larger sample needed Produces larger errors High cost

20. Systematic Sampling A simple process Every nth name from the list will be drawn

21. 14 - 2 7 Sy s t e m a t i c Advantages Simple to design Easier than simple random Easy to determine sampling distribution of mean or proportion Disadvantages Periodicity within population may skew sample and results Trends in list may bias results Moderate cost

22. Stratified Sampling Probability sample Subsamples are drawn within different strata Each stratum is more or less equal on some characteristic Do not confuse with quota sample

23. 14 - 2 9 Stratified Advantages Control of sample size in strata Increased statistical efficiency Provides data to represent and analyze subgroups Enables use of different methods in strata Disadvantages Increased error if subgroups are selected at different rates Especially expensive if strata on population must be created High cost

24. Cluster Sampling The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. The primary sampling unit is no longer the individual element in the population The primary sampling unit is a larger cluster of elements located in proximity to one another

25. 14 - 3 1 Clust e r Advantages Provides an unbiased estimate of population parameters if properly done Economically more efficient than simple random Lowest cost per sample Easy to do without list Disadvantages Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous Moderate cost

26. Population Element Indian adult population Possible Clusters in the United States States Metropolitan Statistical Area Districts Blocks H o use h o l ds Examples of Clusters

27. Possible Clusters in the United States A i r p or ts Planes Population Element Airline travelers Sports fans Football stadiums Basketball arenas Baseball parks Examples of Clusters