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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
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
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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
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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
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.
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• 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.