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
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 Convenience sampling
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.