SAMPLING

1. SAMPLING

2. SAMPLING Population Definition: The term population refers to the aggregate or totality of all the objects, subjects, or members that conform to a set of specifications. The Accessible Population The aggregate of cases Conform to the designated criteria Accessible to the researcher

3. The Target Population • The aggregate of cases • The researcher would like to make generalizations Criteria • Eligibility criteria or inclusion criteria • Exclusion criteria

4. Sample and Sampling Sample Definition: Sample is a subgroup of the population. It is defined as a collection of individual observations from the population about which inferences are to be made, and is obtained by a specific method. Sampling: It refers to the process of selecting a portion of the population to represent the entire population.

5. Aim of sampling: • To draw valid inferences about the population parameters using the sample statistics Theory of sampling This is based on • The law of statistics regularity • The law of inertia of large numbers Some Terminology • Element – The most basic unit of a population from which a sample will be drawn. • Representative sample-A sample whose characteristics are highly similar to those of the population from which it is drawn.

6. Strata -Subdivisions of the population according to some characteristic. • Sampling bias-Refers to the systematic over representation or under representation of some segment of the population in terms of a characteristic relevant to the research question. • Sampling distribution-A theoretical distribution of a statistic using the valves of the statistic computed from an infinite number of samples as the data points in the distribution.

7. Sampling error -Refers to differences between populations values and sample values • Sampling frame -A list of all the elements in the population, from which the sample is drawn • Sampling frame-A list of all the elements in the population, from which the sample is drawn

8. Sampling designs • Probability sampling • Non probability sampling Non probability sampling • It is less likely to produce accurate and representative samples than probability sampling.

9. Methods • Convenience sampling. • Snowball sampling or network sampling. • Quota sampling. • Purposive sampling or judgmental sampling.

10. Probability sampling Methods • Simple random sampling • Stratified random sampling • Cluster sampling or multistage sampling • Systematic sampling Sample size • Estimated using a procedure known as power analysis

11. Factors that Affect Sample Size Decisions • Homogeneity of the population • Effect size • Attrition • Number of variables • Subgroup analyses • Sensitivity of the measures

12. Steps in sampling • Identify the target population • Identify the accessible population • Specify the eligibility criteria • Specify the sampling plan • Recruit the sample

13. Factors that Influence the Rate of Co-operation • Method of recruitment • Pleasantness of the recruiters • Persistence • Payment of an incentive • Explanation of research benefits • Offers of a research summary • Making participation convenient • Endorsements • Assurances of research integrity

14. Tips for Sampling • Identify important extraneous variables • Select study participants from two or more sites • Understand and document who the participants are • As you recruit, document thoroughly • Develop contingency plans for recruiting more subjects.

15. Sampling in qualitative research Types of qualitative sampling • Convenience sampling • Snow ball sampling • Theoretical sampling

16. Sample size - Data saturation Sampling process • Selection on the basis of convenience or snow-balling or both methods. • Sample selection serially ratter then up-front • Informants are often used to facilitate the selection • The sample is adjusted in an ongoing fashion • Sampling continues until saturation is achieved • Final sampling includes a search for confirming and non-confirming cases.