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 and document who the participants are As you recruit, document thoroughly Develop contingency plans for recruiting more subjects.

Sampling in qualitative research Convenience sampling 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 non-confirming cases.