Outcomes & Impact Better data. Better decisions Wei Zhang Ph.D. Research Statistician Texas Children's Hospital Outcomes & Impact Service (TCHOIS) Assistant Professor Congenital Heart Surgery, Baylor College of Medicine Introduction to Sampling Methods
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Outcomes & Impact Better data. Better decisions
Wei Zhang Ph.D.
Research Statistician
Texas Children's Hospital Outcomes & Impact
Service (TCHOIS)
Assistant Professor
Congenital Heart Surgery, Baylor College of
Medicine
Introduction to Sampling Methods
Overview
• Purpose of Sampling
• Some Definitions
• Sample Designing Process
• Importance of Probability Sampling
• Four Commonly Used Probability Sampling Techniques
• Sample Size Determination
Purpose of Sampling
• Who = Target Population
• Bronchiolitis or Sepsis
• What = Parameter
• Characteristics of population
• Problem: Cannot study whole
• Solution: Sample
• Subset of “who”
• Calculate a statistics for “what”
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Some Definitions
• Observation Unit
• Target Population
• Study Population or Sampling Population
• Sampling Frame
• Sample
• Sampling Unit
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Advantages of Sampling
• Less Resource
• More Accuracy
• Reduced Inspection Fatigue
Disadvantages of Sampling
• May not be representative
• Chance of over or under estimation
• Associated with both sampling and non-sampling errors
Causes of Sample Failed to be Representative
Sampling Population not
Reflecting Target Population
Not Enough Sample
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Sampling Techniques
Non-probability Sampling – unequal chance of being selected
• Convenience sampling
• Judgement sampling
• Snowball sampling
• Quota sampling
Probability Sampling – equal chance of being selected
• Simple random sampling
• Systematic sampling
• Stratified sampling
• Clustered sampling
Sampling Techniques
Non-probability sampling – unequal chance of being selected
• Convenience sampling
• Judgement sampling
• Snowball sampling
• Quota sampling
Probability Sampling – equal chance of being selected
• Simple random sampling
• Systematic sampling
• Stratified sampling
• Clustered sampling
Why Probability Sampling?
• Avoid selection bias
• Be able to assess representativity based on sample size
Simple Random Sampling (SRS)
• Similar to draw numbered balls from
a bag.
• Each unit in the target population is
equally likely to be selected.
Simple Random Sampling – How to Do it
• List all units in the sampling population
• Generate a random number per unit
• Use Excel: E.g. “=randbetween(1,100)” if
100 patients in the target population
• If sample 10, then take patients with the
10 smallest numbers.
Simple Random Sampling - Example
Retrospective Chart Review on 30 Day Readmission Rate
• Sampling Population: A disease group defined by certain
ICD codes
• Sampling Frame: A list of MRNs pulled by these ICD
codes from the EMR system
• N patients were selected randomly
• m out N were readmitted within 30 days
• Rate = m/N*100%
Simple Random Sampling - Pros and Cons
Pros:
• Very simple technique
• Based on probability law
• No personal bias
Cons:
• Does not work well when population is heterogeneous
• Less efficient
• Need to get the whole list before sampling
Systematic Random Sampling
• Used when no list or the list is in roughly random order