SAMPLING SAMPLING Population Definition The term population refers

SAMPLING

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

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

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.

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.

• 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.

• 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

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

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

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

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

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

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

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

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

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 nonconfirming cases.