sampling nursing research ppt
TRANSCRIPT
7/27/2019 Sampling Nursing Research Ppt
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SAMPLING
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Population
Definition:
The term population refers to theaggregate or totality of all the objects,
subjects, or members that conform to aset of specifications.
The Accessible Population
• The aggregate of cases• Conform to the designated criteria
• Accessible to the researcher
SAMPLING
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The Target Population
• The aggregate of cases
• The researcher would like to make
generalizationsCriteria
• Eligibility criteria or inclusion criteria
• Exclusion criteria
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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.
Sample and Sampling
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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 asample will be drawn.
•Representative sample-A sample whose characteristics arehighly similar to those of the population from which it is drawn.
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• Strata -Subdivisions of the population according to some
characteristic.
• Sampling bias- Refers to the systematic over representation or under representation of some segmentof the population in terms of a characteristic relevant tothe research question.
• Sampling distribution -A theoretical distribution of astatistic using the valves of the statistic computed from
an infinite number of samples as the data points in thedistribution.
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Sampling error -Refers to differences between populations valuesand 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
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Sampling designs • Probability sampling
• Non probability sampling
Non probability sampling
• It is less likely to produce accurate and
representative samples than probability
sampling.
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Methods
• Convenience sampling.
• Snowball sampling or network sampling.
• Quota sampling.
• Purposive sampling or judgmental
sampling.
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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
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Factors that Affect Sample Size
Decisions
• Homogeneity of the population
• Effect size
•
Attrition• Number of variables
• Subgroup analyses
• Sensitivity of the measures
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Steps in sampling
• Identify the target population
• Identify the accessible population
• Specify the eligibility criteria
• Specify the sampling plan
• Recruit the sample
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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
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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.
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Sampling in qualitative researchTypes of qualitative sampling
• Convenience sampling
• Snow ball sampling
• Theoretical sampling
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Sample size - Data saturation
Sampling process
• Selection on the basis of convenience or snow-balling or bothmethods.
•
• 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.