Chapter 11 Sampling Design Chapter Objectives define sampling, sample, population, element, subject...
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Transcript of Chapter 11 Sampling Design Chapter Objectives define sampling, sample, population, element, subject...
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Chapter 11Sampling Design
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Chapter Objectives
• define sampling, sample, population, element, subject and sampling frame
• describe and discuss the different probability and non-probability sampling designs
• identify the use of appropriate sampling designs for different research purposes
• discuss precision and confidence • estimate sample size • discuss efficiency in sampling • discuss generalisability in the context of sampling
designs
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The Principles of Sampling Design
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Population, Element, Sampling Frame, Sample and Subject
• Population (or target population)• entire group of people, events or things of interest that
the researcher wishes to investigate
• Element• a single member of the population
• Sampling Frame• a listing of all the elements in the population from which
the sample is drawn
• Sample• a subset of the population
• Subject• a single member of the sample
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Relationship between Population, Sampling Frame
and Sample
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Relationship between Sample Statistics
and Population Parameters
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Advantages of Sampling
• Less costs– cheaper than studying whole population
• Less errors due to less fatigue– better results
• Less time– quicker
• Destruction of elements avoided– eg bulbs
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Normal Distibution in a Population
As the sample size n increases, the means of the random samples taken from practically any population approach a normal distribution with mean μ and standard deviation
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Representativeness of Samples• If the sample mean is much > than the
population mean μ then the sample would overestimate the true population mean
• If the sample mean is much < than the population mean μ then the sample would underestimate the true population mean
• The more representative the sample is of the population, the more generalisable are the findings of the research.
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Preparing a Sampling Design
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Probability & Non-probability Sampling
• Probability Sampling– the elements in the population have some known
chance or probability of being selected as sample subjects
• Non-probability Sampling– the elements do not have a known or
predetermined chance of being selected as subjects
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Probability Sampling
• Simple random sampling– every element in the population has a known and
equal chance of being selected as a subject
• Complex (or restricted) probability sampling– procedures to ensure practical viable alternatives
to simple random sampling, at lower costs, and greater statistical efficiency
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Simple Random Sampling
• Is the most representative of the population for most purposes
• Disadvantages are:– Most cumbersome and tedious– The entire listing of elements in population
frequently unavailable– Very expensive– Not the most efficient design
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Complex Probability Sampling
• Systematic sampling
• Stratified random sampling
• Cluster sampling
• Area sampling
• Double sampling
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Systematic Sampling• Every nth element in the population starting
with a randomly chosen element
• Example:– Want a sample of 35 households from a total of 260
houses. Could sample every 7th house starting from a randomly chosen number from 1 to 10. If that random number is 7, sample 35 houses starting with 7th house (14th house, 21st house, etc)
– Possible problem is that there could be systematic bias. eg every 7th house could be a corner house, with different characteristics of both house and dwellers.
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Stratified Random Sampling• Comprises sampling from populations segregated
into a number of mutually exclusive sub-populations or strata. Eg– University students divided into juniors, seniors, etc– Employees stratified into clerks, supervisors, managers,
etc
• Homogeneity within stratum and heterogeneity between strata
• Statistical efficiency greater in stratified samples• Sub-groups can be analysed• Different methods of analysis can be used for
different sub-groups.
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Stratified Random Sampling Example
Stratum Motivation LevelClerks LowMiddle ManagersVery highTop Managers Medium
Combined X would not discrimate among groups
• Stratified Sampling– Proportionate sampling– Disproportionate sampling
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Proportionate & Disproportionate
Stratified Random Sampling
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Cluster Sampling• Take clusters or chunks of elements for study
– Eg, sample all students in MGMT 303 and MGMT 304 to study the characteristics of Management Science majors
• Advantage of cluster sampling is lower costs• Statistically it is less efficient than other
probability sampling procedures discussed so far
Area Sampling:• Cluster sampling confined to a particular area
– Eg, sampling residents of a particular locality, county, etc
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Double Sampling
• Collect preliminary data from a sample, and choose a sub-sample of that sample for more detailed investigation.
• Example:– Conduct unstructured interviews with a
sample of 50. – Repeat a structured interview with 30 from
the 50 originally sampled.
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Non-probability Sampling
• Convenience sampling– Survey whoever is easily available– Used for quick diagnosis of situations
• Simplest and cheapest• Least reliable
• Purposive sampling– Judgement sampling– Snowball sampling– Quota sampling
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Judgement Sampling
• Involves the choice of subjects who are in the best position to provide the information required
• Experts’ opinions could be sought– Eg, Doctors surveyed for cancer causes
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Snowball Sampling
• Used when elements in population have specific characteristics or knowledge, but are very difficult to locate and contact.
• Initial sample group can be selected by probability or non-probability methods, but new subjects are selected based on information provided by initial subjects. – Eg, used to locate members of different
stakeholder groups regarding their opinions of a new public works project.
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Quota Sampling
• Quotas for numbers or proportion of people to be sampled, established.
• Examples:1) survey for research on dual career
families: 50% working men and 50% working women surveyed.
2) Women in management survey: 70% women surveyed and 30% men surveyed.
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Choice Points in Sampling Design
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Precision and Confidence• Precision
– refers to how close the sample estimate eg X is to the true population characteristic( ) depends on the variablity in the sampling distribution of the mean, ie the standard error ( S X )
– indicates the confidence interval within which the population mean can be estimated (= X + KS X )
• Confidence– reflects the level of certainty that the sample
estimates will actually hold true for the population– bias is absent from the data– accuracy is reflected by the confidence level ( K )
XS
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Standard Error
S Sn
X
S X
S = standard deviation of the samplen
= sample size
= standard error or standard deviation of the sample mean
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Characteristics of the Standard Error
• The smaller the standard deviation of the population, the smaller the standard error and the greater the precision
• The standard error varies inversely with the square root of the sample size. Hence the larger the n, the smaller the standard error, and the greater the precision.
S Sn
X
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Confidence Interval for the Mean
X KS XX
S X
K
= population mean
= sample mean
= standard error
= z statistic for large samples ≥ 30
= t statistic for small samples < 30
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Confidence Levels
• For large samples, K = z score= 1.65 for 90% confidence level= 1.96 for 95% confidence level= 2.58 for 99% confidence level
• Example: a 95% confidence interval for mean purchases (μ) by customers based on a sample mean of $105 with a standard error of $1.43 is:
μ = 105 ± 1.96*1.43 = 105 ± 2.80 Hence μ would fall between $102.20 and
$107.80
X KS X
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Trade-off between Precision and Confidence
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Determining the Sample Size
X KS X
Example: Suppose a manager wants to be 95% confident that withdrawals from a bank will be within a confidence level of ± $500. From a sample of customers the standard deviation S was calculated as $3500. What sample size is needed?
The expression is equivalent to the precision or admissible margin of error. Let this be E.
KS X
E KS Xor E K S
n *
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Determining the Sample Size (cont’d)
Rearranging these terms, a formula for the sample size n is:
nK S
E
* 2
Substituting K=1.96 (95% confidence), S=3500, and E=500 into this equation, provides the sample size n:
n
n
n
1 3500
500
2
13
188
2
.96*
.72
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Roscoe’s Rules of Thumb for Determining Sample Size
• Sample sizes larger than 30 and smaller than 500 are appropriate for most research
• Minimum sample size of 30 for each sub-category is usually necessary
• In multivariate research, the sample size should be several times as large as the number of variables in the study
• For simple experimental research, successful research is possible with samples as small as 10 to 20
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Efficiency in Sampling
If n is constant, you should get a smaller
or
For the same , you should use a
smaller n
S X
S X
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Review of Sample Size Decisions
• How much precision is wanted in estimating the population characteristics, ie what is the margin of admissible error or confidence interval?
• How much confidence is really needed. How much risk can we take of making errors in estimating the population parameters (ie confidence level)?
• How much variability is in the population? The greater the variability, the larger the sample size needed.
• Cost and time constraints• The size of the population (N) itself
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