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Chapter 24
Survey Methods and Sampling
Techniques
Sample Statistics
• Mean, Median, Mode and standard deviation
– When calculated from sample data are called sample
statistics
– When calculated from the entire population, they are
called population parameters.
• For practical reasons we usually analyze a portion
(sample).
• Each sample statistic posses a probability distribution know
as its sampling distribution.
• A sample survey gathers information from a portion of the
population.
Planning a questionnaire
• Designers job:
– Define the purpose of the survey
– Choose the questions to include
– Determine appropriate future actions based
on survey results.
• Voice of Customer (VOC) – Six Sigma’s
approach to listening to the customer
Steps to conduct a survey
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Clearly define project goals –
what do you need to know about
the customer? (VOC)
Determine population – whom should be
surveyed
Select sample of respondents
Systematic sampling is a common
sampling method
Consider using random cluster sampling
when each member of the population
belongs to a subgroup
Consider the need for precise results
when choosing sample size and
confidence interval
Select survey method.
Create the questionnaire – what should be
asked?
Pilot testing – test questions in a
controlled environment.
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Conduct interviews and collect data –ask
the questions
Analyze the data
Prepare statistical tables and figures
Consider using the mean to measure for
centrality for equal-interval data
If the median has been selected to
measure centrality, use the interquartile
range as the measure of variability
Remember that the standard deviation
has a special relationship to the normal
curve
For moderately asymmetric distributions,
the mode, median and mean satisfy the
formula: mode = 3x median -2 X mean
Estimate error margins
Report results
Target population and
Sample size
• We must identify the correct target population, and
choose an appropriate sample size.
• Sample size can be calculated statistically, but factors
such as cost, time and confidence level must play a part.
• Sample size:
– Census – includes every member of the target population
– Sample survey – a portion of the target population.
Determining Sample Size
• Until the sample becomes a sizeable fraction, accuracy is
determined by sample size alone:
SD  p(1 p)
Where :
SD = Standard deviation
`
•
p = proportion where score is 1
n=sample size.
The standard error of estimate (SE); the standard deviation of the possible p
values based on the sample estimate is given by:
•
SE 
SD
n
A general formula for determining sample size is:
Where :
N= size of total number of cases
t 2Np(1  p)
n 2
n=sample size
t p(1  p)  a 2 (N  1)
a = expected error
t=value taken from t distribution corresponding to confidence level
p=probability of event
Determining Sample Size
2( Za  Z ) 2 a 2
n
D2
• None of these solutions is exact, but are close.
• For determining sample size for z-test:
• A better approach for Lean Sigma practitioners:
1. Test some minimum, predetermined , number of subjects
2. Stop if the P value is <.01 or ≥ 0.36
3. Otherwise, increase the sample size
How to conduct survey?
The purpose and target audience will help determine the method
• Personal Interview
– Costly, but targeted and extensive
• Telephone survey
– Quick, but intrusive, interviewer bias
• Mail
– Cost effective, long time to complete, no probing
• Computer Direct Interview
– Quick, targeted, respondents must have computer access
• Email
– Economical and fast, include pictures and sound, technological
incompatibility to overcome.
Random selection
• Simple random sampling – purest form of probability
sampling
– Each member of the population has an equal and known chance of
being selected.
– Could draw names or use a table of random numbers
• EXAMPLE 24.3
– Uses Minitab to generate a random number table
– Calc>Random Data>Integer
– Simulate random selection of 100 companies
Distributions
• Probability Distribution is the probability distribution
derived from the information on all elements of a
population.
• Sampling Distribution of X-bar is the probability
distribution of X-bar calculated from all possible samples
of the same size selected from a population.
Sampling and
Nonsampling errors
• Sampling error is the difference between the
value of a sample statistic and the value of the
corresponding population parameter:
– MEAN
Sampling error =X – m
• Most common error sources are:
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Poorly designed questionnaire
Use of an inadequate design
Recording and measurement errors
Nonresponse problems and related issues