1. Chapter 24 Survey Methods and Sampling Techniques

2. 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.

3. 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

4. Steps to conduct a survey
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.
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

5. 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.

6. Determining Sample Size
Until the sample becomes a sizeable fraction, accuracy is determined by sample size alone:
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:
A general formula for determining sample size is:
Where : N= size of total number of cases
n=sample size
a = expected error
t=value taken from t distribution corresponding to confidence level
p=probability of event

7. Determining Sample Size
For determining sample size for z-test:
None of these solutions is exact, but are close.
A better approach for Lean Sigma practitioners:
Test some minimum, predetermined , number of subjects
Stop if the P value is <.01 or ≥ 0.36
Otherwise, increase the sample size

8. 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
Economical and fast, include pictures and sound, technological incompatibility to overcome.

9. 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

10. 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.

11. 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:
Poorly designed questionnaire
Use of an inadequate design
Recording and measurement errors
Nonresponse problems and related issues