1. SAMPLING

2. Sampling Terminology
Population: the relevant target group for the study
Census: sample entire population
Sample: a subset of the target population selected to represent the population
Sample unit: individuals, households, stores, co.’s, or other target population “elements” available for selection during sampling process
Sample frame: a list or other way of identifying units from which sample is to be drawn (e.g., time/place frame)

3. Sampling Terminology
Sample representativeness: degree to which sample is similar to the target population in terms of key characteristics
Incidence rate: percentage of people in the general population or on a list that fits the qualifications of those the researcher wishes to describe

4. Sampling Terminology Sampling error Nonsampling error
Discrepancies between data generated from a sample and the actual population data as result of sampling instead of census
Nonsampling error
All other biases at any stage, including inaccurate population definition, sample frame error, etc., that can occur regardless of whether sample or census used

5. Steps in Developing a Sample Plan
Step 1: Define the target population
Step 2: Define the data collection method
Step 3: Obtain or designate a sample frame
Step 4: Determine sampling method
Time/Area sampling
Nonprobability methods
Probability methods
Step 5: Determine sample size
Step 6: Develop operational procedures

6. Step 1:Define the Target Population
Specify target population described in overall study objective(s)
Demographic characteristics (age, gender, income, geographic location, household)
Lifestyle characteristics (fun-loving, health-conscious, blue-collar)
Product Usage (customers who spend over $50/month, current customers, 1st-time users)

7. Step 2: Define Data Collection Method
Considerations:
Representativeness
Sources of Sample Bias
Incidence Rate
Response Rate

8. Step 3: Obtain/Designate Sample Frame
Directories, company records, mailing lists, public records, event or specified location and time frame
Screening questions (disqualifiers)
Sample frame error: coverage problems
Omission
Ineligibility
Duplication
Underrepresentation/Overrepresentation

9. Step 4: Determine Sampling Method
Time/Area Sampling
Different blocks of time
Different days
Different locations

10. Appendix C: Time/Area Matrix
Appendix C: Time/Area Matrix *filled blocks represent times surveyors were present at Duck Pond
M 10/15
T 10/16
W 10/17
R 10/18
F 10/19
12:00-12:30
12:30–1:00
1:00-1:30
1:30-2:00
2:00-2:30
2:30-3:00
3:00-3:30

11. Nonprobability Methods
Representativeness, not statistical chances of inclusion
Convenience samples: “catch as can,” self-selected volunteers
Judgment samples: hand-picked by researcher or other expert
Referrals (“snowballing”): additional respondents referred by previous respondents
Quota Sampling: set goals for sampling subgroup members (e.g., 5 men, 5 women each sampling session)

12. Probability Methods
Statistical probability, every population unit has known chance of being picked
Simple random sampling (SRS)
Every unit has equal probability of being selected from the sample frame (n/N)
Drawings
Random number tables, RDD

13. Probability Methods Systematic sampling (SYMRS)
Procedure that samples every ith unit after a random start point
Example:
List of 2,000 names and n = 250
2000/250 = skip interval of 8, every 8th name
Select a random # for start point (e.g., 5)
Selected names are 5th, 13th, 21st, etc.
Drop-down substitution for ineligibles, refusals

14. Probability Methods Stratified sampling (STRS)
Population is classified into subpopulations or “strata,” based on some known and available surrogate information. Then, apply sampling method to select units from within each strata.
Types:
Proportionate
Disproportionate

15. STRATIFIED SAMPLES (n = 400)
Proportionate Disproportionate
Income Population % Sample % n Sample % n
< 20, % 30% % 60
20 – 29,
30 – 49,
> 50,
100% 100% % 400

16. Probability Methods Cluster sampling
Population divided into subgroups, each of which are representative of the population. Then apply sampling method to select clusters (1-step), units within clusters (2-step).
Examples: neighborhoods, university classes, store locations

17. Step 5: Determine Sample Size
NONSTATISTICAL APPROACHES
Arbitrary: % of population
Conventional: past studies, industry standards
Cost Basis: budget or value of information

18. The Confidence Interval Statistical Approach
3 factors in determining sample size:
Confidence intervals (confidence in estimate)
Z value 90% z = % = 1.96
98% z = % = 2.58
Sampling error: precision, or tolerance for error around estimate stated in percentage points
Estimated standard deviation: estimate of variability of population characteristic based on prior information

19. Sample size formula for means (interval data)
Z2 * s2
n = e2
where
n = required sample size
Z = the Z value for your desired confidence level
s = estimated standard deviation for the population mean
e = desired accuracy range

20. Example – sample size for mean
You plan on doing a survey to estimate the average amount spent on a “special occasion” restaurant meal for two. How large should the sample be?
Desired confidence level is 95% ( z = 1.96)
Desired accuracy level is + or - $3.00
Estimated standard deviation is $15.70

21. Sample size formula for percentages (nominal or ordinal)
Z2 * ([P * Q])
n = e2
Where
n = required sample size
Z = the Z value for your desired confidence level
P = estimation of the population %
Q = (100 – P)
e = desired accuracy range

22. Example: Sample size for percentages
A perfume maker wants to know, within 2%, the percentage of women in the U.S. who have tried its new cologne. Trial rates for similar new products typically run about 10%. How large a sample would be needed to make the estimate with 90% confidence?

23. Effects of the 3 factors on statistical sample sizes
What happens to sample size when confidence level is increased?
What happens to sample size when allowable error is increased?
What happens to sample size when population variability is high?

24. Oversampling: Sample Size vs. Number of Contacts Needed
Calculate needed sample size (n)
Divide by estimates of ECR, RR, OIR (multiply all applicable terms)
Example: n = 1000, ECR = 50%, OIR = 65%
Number contacts = 1000/[.50 * .65] = 3077

25. Step 6: Develop Operational Procedures
Coping with refusals, increasing response rates
Tracking procedures: call-back logs, track # of refusals, breakoffs, & ineligibles
Schedules, time/area matrix
Training, practice (field pretests), interviewer guidelines & procedures
Materials (clipboards, response cards, pencils)
Keeping track of collected surveys, follow-up