1. Measurement 201:
Collecting quantitative information
Institute for Public Relations Summit on Measurement
David Geddes, Ph.D.
evolve24, a Maritz Research company
Saint Louis, Missouri
david.geddes@evolve24.com
October 6, 2010
Copyright © 2010 by The Institute for Public Relations 1

2. Today’s objectives
• Smart consumer
• Quantitative survey methods
1. Telephone
2. Web
3. Mail and multi-modal
4. Face-to-face
• Sampling
• Case studies
• Questions … as they come
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3. Great resources
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4. First steps
What are your objectives?
What do you want to explore,
discover, test, or document?
Who are the right people to talk
with?
What are appropriate data collection
methods?
What is the value of the information?
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5. Telephone surveys
• Types
– Random digit dial (RDD)
– List from sample provider or panel
– Company or client list (customers, employees,
industry analysts, donors, partners, etc.)
• Uses and advantages
• Limitations and weaknesses
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6. Telephone surveys: Current issues
Cell phone only households
– Same or different?
– What to do?
– Ethical and legislative issues
– A trend to follow
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7. Surveys in general: Current issues
• Non-response -.74%
bias
Behavior Risk Factors Survey Response Rate -1.5%
100
90 Maximum
Median All
80
States
Response Rate
70
60
50 Pennsylvania
40
Minimum
30
20
7
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
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8. Are more intensive methods helpful?
The Keeter et al study
• Standard Survey: 36% response rate
– Calling done over five days
– Selected respondent from people at home at time of call (no
random selection)
– Five call-backs, one call-back to refusals
• Rigorous Survey: 60.6% response rate
– Eight-week calling period
– Random selection of respondent from list
– Pre-notification letters with $2 incentive
– Multiple attempts (including letters to refusals)
– Multiple call-backs
Source: Scott Keeter et al, Consequences of Reducing Nonresponse in a National
Telephone Survey, Public Opinion Quarterly 64:125-148 (2000)
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9. Other design issues
• Oversampling
– Example: Attitudes to location-based apps
– Example: National survey on water conservation
• Weighting
– Example: Ethnicity
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10. Telephone omnibus polls
• National random sample of 1,000 households
• Offered by all major research firms
• Fast
• Low cost
– Cost per question
– Demographics included
– Costing parameters
• Deliverables
• Applications
• Limitations
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11. Internet surveys
• Advantages and uses
• Limitations
– http://www.aapor.org/Content/NavigationMenu/Home/Left/A
APOROnlinePanelsTFReportFinalRevised.pdf
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12. Mail surveys and multi-modal surveys
• Don Dillman et al, Internet, Mail, and Mixed-Mode
Surveys: The Tailored Design Method, (Wiley, 2008)
• TDM method:
1. Respondent-friendly questionnaire
2. Personalized correspondence
3. Token financial incentive ($1 or $2 prepaid)
4. Up to five phone contacts
5. Mail survey with stamped return envelopes
6. Phone again
• Other ways to improve response rates
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13. Sampling
– Examine different sampling techniques
– Strengths and shortcomings
– Cases and examples
– Tools to make decisions in practice
– Not a comprehensive textbook treatment
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14. Why care about sampling?
• Formal definition of our targets
– Example: Caregivers of Type II diabetes patients
• Generalize or project?
– Example: Poll of Kansas City voters about rental
car tax
• Understand and minimize sampling error
– How far off might our result be if we interviewed
another group of individuals?
• Make tradeoffs
– Budget, time, other factors given objectives
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15. What is sampling?
• Probability sampling
– “Sampling is the science of systematically
drawing a valid group of objects from a
population reliably.” (Stacks, p. 150)
• Non-probability sampling (informal
definition)
– Process of systematically drawing a group of
objects from a population sufficient to meet
information needs. (Adapted from Stacks)
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16. Some definitions
• Universe
– General concept of who or what will be sampled
• Population
– People or units to be sampled, formally defined
and described
• Sampling frame
– List of all people to be surveyed
– Example: List of all 90,000 registered
veterinarians under age 65
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17. Some definitions
• Sample
– Actual people chosen for inclusion in the research
– Example: Selection of 10,000 veterinarians from
the list
• Completed sample
– People who actually responded to the survey
– Example: 3,000 veterinarians completed the
survey
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18. Types of error
• Sampling error
– Issue: Potential error or uncertainty as a result of not
sampling from all members of sampling frame
– How far off would we be if we interviewed a different
500 people?
• Coverage error
– Issue: The sampling frame does not contain all
members of a population or contains a biased list
– Example: People without landlines in a telephone poll
– Example: People with invalid e-mail addresses in
membership
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19. Some definitions
• Measurement error
– Error when respondents misunderstand or
incorrectly respond to questions
• Nonresponse error
– Respondents unlike nonrespondents
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20. Problems and errors in sampling
• Understanding and reducing coverage
error
– Does the sampling frame (list) contain
everyone in the population?
– Does the list contain people who are not in
the sampling frame?
– How is the list maintained and updated?
– Does the list contain other information that
can be used to improve sampling?
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21. Three approaches
1. Census
2. Probability sample
3. Nonprobability sample
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22. Census sampling
• Interview or measure all members of a
population
– Example: Wal-Mart annual employee survey
• No error due to sampling
– Other types of error
• Rare in practice
• Is it worth the effort?
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23. Probability sampling
• Every individual in a population has an
equal chance of being chosen
– In theory
– In practice
• Allows generalization or projection to the
population
• Known sampling error parameters
• What other sources of error?
• How much to invest, given objectives?
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24. Probability sampling
Key types of probability sampling
• Simple random sampling
• Systematic sampling
• Stratified random sampling
• Cluster sampling
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25. Nonprobability sampling
• Interview or measure without access to every
individual in a population
– Examples
• Situations where it is difficult to fully specify
the population or sampling frame
– Examples
• Cannot generalize
– How far off might our result be if we interviewed
another group of individuals?
• Key: Understand limitations … justify choice
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26. Nonprobability sampling
• Convenience sampling
– Selecting based on availability
– Example: Hospital survey of nurses leaving a shift
• Quota sampling
– Selecting based on availability but weight based
on predetermined characteristics
– Example: Mall intercept sampling
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27. Nonprobability sampling
• Purposive sampling
– Selecting participants based on knowledge of the
population and focus or objectives of the research
– Example: Survey of most influential journalists
covering the air transport industry
• Volunteer sampling
– Select based on agreement to participate
• Snowball sampling
– Selecting participants based on recommendations
of other participants
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28. Sample size in probability sampling
• Key questions:
– How much might our results differ had we
interviewed another 100 American voters?
– How much more would we learn, given our
objectives, had we interviewed another 100
customers?
– More technically, how much sampling and
measurement error can we tolerate?
• To reduce sampling error and measurement
error, you must increase sample size
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29. Sample size
• “Normal” curve
– Mean, standard deviation, 68%
we can calculate confidence 95%
intervals
99%
– See an interactive demo at
http://geographyfieldwork.com/StandardDeviation1.htm
– Sample size calculators on Web
• Maritz Stats (download)
• National Statistical Service
http://www.nss.gov.au/nss/home.NSF/pages/
Sample+Size+Calculator+Description?OpenD
ocument
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30. Sample size
– Sample size of 385 is
necessary for a
confidence level of plus
or minus 5 percentage
points at the 95%
confidence level.
– Is this the biggest
source of error?
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31. Case: National omnibus poll
• National random digit dialing completing surveys
with 1,000 adults
• Conducted Friday through Sunday
• Balanced post-survey to census figures for age,
gender, HHI, ethnicity (results only differ slightly)
• Evaluation
• Universe and population
• Sampling frame
• Sample and completed sample
• Sources of error or bias
• Final assessment – when is this appropriate? 31
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32. Case: Online survey
– National online panel survey with 1,000 adults
– Balanced post-survey to census figures for age,
gender, HHI, ethnicity (results only differ slightly)
– Evaluation
• Universe and population
• Sampling frame
• Sample and completed sample
• Sources of error or bias
• Final assessment
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33. Case: Employee survey at a multi-
division corporation
– Four divisions
– Management vs. non-management
– Results by age, gender, tenure at company
– Which survey methods?
– Develop a sampling plan:
• Universe and population
• Sampling frame
• Sample and completed sample
• Sources of error or bias
• Final assessment
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34. Case: Veterinarian survey
– American Veterinary Medicine Association
• 90,000 veterinarians under age 65
• 50,000 valid email addresses
• Goal: Low-cost survey
– Evaluation
• Which methods?
• Universe and population
• Sampling frame
• Sample and completed sample
• Sources of error or bias
• Can we work around the limits?
• Final assessment and recommendation
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35. Case: Journalist survey
– Client: Financial services company
– Respondents: List of 1,000 journalists who cover
personal finance, the economy, and lifestyle.
– Which survey methods?
– Evaluation
• Universe and population
• Sampling frame
• Sample and completed sample
• Sources of error or bias
• Final assessment and recommendation
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36. Some resources
Public relations research
• Don W. Stacks and David Michaelson. 2010. A Practitioner's
Guide to Public Relations Research, Measurement and
Evaluation. Businessexpert Press.
• Don W. Stacks. 2002. Primer of Public Relations Research.
New York: Guilford Press.
Market research (leading business school texts)
• Gilbert A. Churchill and Dawn Iacobucci. 2004. Marketing
Research: Methodological Foundations. Mason, OH: South-
Western Cengage Learning.
• Naresh K. Malhotra. 2007. Marketing Research: An Applied
Orientation. Upper Saddle River, NJ: Prentice Hall.
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