Sampling 1
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Transcript of Sampling 1
Why Sample?
Why not everyone?
Sampling
A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population
The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
Key sampling concepts
Key ideas
Sampling frame
Classification of Sampling Techniques
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple
Random
Sampling
Convenience Sampling
Subjects are selected because they are easily accessible.
This is one of the weakest sampling procedures.
An example might be surveying students in one's class.
Generalization to a population can seldom be made with this procedure.. ………..
Judgmental Sampling
Test markets
Purchase engineers selected in industrial marketing research
Bellwether precincts selected in voting behavior research (Exit poll?)
A form of convenience sampling in which the population elements are selected based on the judgment of the researcher.
Quota sampling
A pre-defined number (or quota) of people who meet certain criteria are surveyed.
• On a weekday morning,
For example, an interviewer
may be given the task of
interviewing 25 women with toddlers in a town centre
• Seven of these women should be aged under 30 years,
• Ten should be aged between 30 and 45 years,
• Eight should be aged over 45 years.
the instructions may specify
that
Snowball Sampling
In snowball sampling, an initial group of respondents is selected, usually at random.
After being interviewed, these respondents are asked to identify others who belong to the target population of interest.
Subsequent respondents are selected based on the referrals.
Probability samples
How?
Similarly………..
Cluster sampling is appropriate when it
is very time consuming or expensive to
choose the individuals one at a time
Multistage Sampling
Choosing Nonprobability vs. Probability Sampling
Conditions Favoring the Use of
Factors
Nonprobability sampling
Probability sampling
Nature of research
Exploratory
Conclusive
Relative magnitude of sampling and nonsampling errors
Nonsampling errors are larger
Sampling errors are larger
Variability in the population
Homogeneous (low)
Heterogeneous (high)
Statistical considerations
Unfavorable Favorable
Operational considerations Favorable Unfavorable
Table 11.4 cont.