Gary Langer's Oct. 2012 presentation to the National Science Foundation on the future of survey research. Discusses the limitations of emerging approaches to public opinion research (such as opt-in online panels and social media analysis).

1. Probability Sampling
and Alternative
Methodologies
“The Future of Survey Research”
National Science Foundation
Oct. 4, 2012
Gary Langer
Langer Research Associates
glanger@langerresearch.com
@Langer Research

3. Good Data
• Are powerful and compelling
• Rise above anecdote
• Sustain precision
• Expand our knowledge, enrich our
understanding and inform our judgment

4. Other Data
• May be manufactured to promote a product,
client or point of view
• Are easily manipulated
• Are increasingly prevalent (cheaply produced
via Internet, e-mail)
• Leave the house of inferential statistics
• Lack the validity and reliability we seek
• Often mis-ask and misanalyze
• Misinform, even disinform our judgment

5. The Challenge
• Subject established methods to rigorous and
ongoing evaluation
• Subject new methods to the same standards
• Value theoreticism as much as empiricism
• Consider fitness for purpose
• Go with the facts, not with the fashion

6. Survey Sampling
A brief history…

7. Sample Techniques
pre-1936
• Full population (census survey)
• Pros: High level of accuracy
• Cons: Prohibitively expensive and highly challenging
(unless the population is small)
• “Availability” sampling (e.g., straw polls)
• Pros: Quick and inexpensive
• Cons: No-scientific basis, no theoretical justification for
generalizing beyond the sample
• Justification: “Good enough” – Literary Digest correctly
predicted presidential election winners from1916 to 1932

8. The 1936 Election
Roosevelt (D) vs. Landon (R)
• Literary Digest poll
• Technique: An availability sample
• Sample frame: Combination of magazine subscribers, phone books,
and car registration lists
• Contacted 10 million, 2.4 million responded
• Found an easy win for Landon. Roosevelt won in a landslide.
• What happened?
• Coverage bias: The sampling frame differed in important ways from
the population of interest. Republicans were more likely to afford
magazine subscriptions, telephones and cars during the Great
Depression.
• Systematic nonresponse: Republicans were disproportionately likely
to return their ballot (perhaps to express discontent with the New
Deal).

9. On to quota sampling
• After 1936, availability sampling largely was replaced with
quota sampling (which had correctly predicted FDR‟s win).
• Quota sampling essentially attempts to build a “miniature”
version of the target population.
• Identifies supposed key demographic characteristics in the
population and attempts to mirror them in the sample.
• Matching the sample to the population on variables such as
gender, race, age, income and others was said to produce a
“representative” sample.

10. How‟d that work out?

11. What went wrong?
1. Timing: Pollsters stopped collecting data in mid-October,
assuming that there would be no late-breaking changes.
2. Faulty assumptions: Pollsters erroneously assumed that
undecided voters would vote in proportion with those who
had made up their minds. Likely voters also may have
been misidentified.
3. Sampling problems: Quota sampling may have allowed
interviewers to select more-educated and better-off
respondents within their assigned quotas – pro-Dewey
groups. This is a key potential flaw in purposive sampling.

12. Quota Sampling Issues
• Even if quota sampling were not entirely to blame, the
Dewey-Truman fiasco highlighted inherent limitations in
quota sampling
• It‟s impossible to anticipate, let alone match, every
potentially important demographic:
• e.g., if you match on sex (2), ethnicity (2), race (5), education (4), age (5),
region (4) and income (6) that yields 9,600 cells. If you want to interview
more than one person per cell…
• Respondent selection is left up to the interviewers, allowing
human bias to sneak in (e.g., picking easy-to-reach resps).
• As with availability sampling, there is no theoretical
justification for drawing inferences about the greater
population.

13. Probability Sampling

14. Not a new concept
A “criterion that a sample design should meet (at least if
one is to make important decisions on the basis of the
sample results) is that the reliability of the sample
results should be susceptible of measurement. An
essential feature of such sampling methods is that each
element of the population being sampled … has a
chance of being included in the sample and, moreover,
that that chance or probability is known.”
Hansen and Hauser, POQ, 1945

15. This is really not a new concept
“Diagoras, surnamed the Atheist, once paid a visit to
Samothrace, and a friend of his addressed him thus: „You
believe that the gods have no interest in human welfare.
Please observe these countless painted tablets; they show
how many persons have withstood the rage of the tempest
and safely reached the haven because they made vows to
the gods.‟
“„Quite so,‟ Diagoras answered, „but where are the tablets of
those who suffered shipwreck and perished in the deep?‟”
“On the Nature of the Gods,” Marcus Tullius Cicero, 45 B.C.
(cited by Kruskal and Mosteller, International Statistical Review, 1979)

16. A shared view
• “…the stratified random sample is recognized by mathematical statisticians
as the only practical device now available for securing a representative
sample from human populations…” Snedecor, Journal of Farm Economics,
1939
• “It is obvious that the sample can be representative of the population only if
all parts of the population have a chance of being sampled.” Tippett, The
Methods of Statistics, 1952
• “If the sample is to be representative of the population from which it is
drawn, the elements of the sample must have been chosen at random.”
Johnson and Jackson, Modern Statistical Methods, 1959
• “Probability sampling is important for three reasons: (1) Its measurability
leads to objective statistical inference, in contrast to the subjective
inference from judgment sampling. (2) Like any scientific method, it permits
cumulative improvement through the separation and objective appraisal of
its sources of errors. (3) When simple methods fail, researchers turn to
probability sampling…” Kish, Survey Sampling, 1965

17. Today: Internet Opt-Ins

18. Copyright 2003 Times Newspapers Limited
Sunday Times (London)
January 26, 2003, Sunday
SECTION: Features; News; 12
LENGTH: 312 words
HEADLINE: Blair fails to make a case for action that
convinces public
BODY:
Tony Blair has failed to convince people in Britain of
the need for war with Iraq, a poll for The Sunday
Times shows. Even among Labour supporters, the
prime minister has not yet made the case.
The poll of nearly 2,000 people by YouGov shows that
only 26% say Blair has convinced them that Saddam
Hussein is sufficiently dangerous to justify military
action, against 68% who say he has not done so.

21. -----Original Message-----
From: Students SM Team [mailto:alumteam@teams.com]
Sent: Wednesday, October 04, 2006 11:27 AM
Subject: New Job opening
Hi,
Going to school requires a serious commitment, but most students still need extra money for rent, food,
gas, books, tuition, clothes, pleasure and a whole list of other things.
So what do you do? "Find some sort of work", but the problem is that many jobs are boring, have low pay
and rigid/inflexible schedules. So you are in the middle of mid-terms and you need to study but you
have to be at work, so your grades and education suffer at the expense of your "College Job".
Now you can do flexible work that fits your schedule! Our company and several nationwide companies
want your help. We are looking to expand, by using independent workers we can do so without buying
additional buildings and equipment. You can START IMMEDIATELY!
This type of work is Great for College and University Students who are seriously looking for extra income!
We have compiled and researched hundreds of research companies that are willing to pay you
between $5 and $75 per hour simply to answer an online survey in the peacefulness of your
own home. That's all there is to it, and there's no catch or gimmicks! We've put together the most
reliable and reputable companies in the industry. Our list of research companies will allow you to earn
$5 to $75 filling out surveys on the internet from home. One hour focus groups will earn you $50 to
$150. It's as simple as that.
Our companies just want you to give them your opinion so that they can empower their own market
research. Since your time is valuable, they are willing to pay you for it.
If you want to apply for the job position, please email at:
job2@alum.com Students SM Team

22. -----Original Message-----
From: Ipsos News Alerts [mailto:newsalerts@ipsos-na.com]
Sent: Friday, March 27, 2009 5:12 PM
To: Langer, Gary
Subject: McLeansville Mother Wins a Car By Taking Surveys
McLeansville Mother Wins a Car By Taking Surveys
Toronto, ON- McLeansville, NC native, Jennifer Gattis beats the odds and
wins a car by answering online surveys. Gattis was one of over 105 300
North Americans eligible to win. Representatives from Ipsos i-Say, a
leading online market research panel will be in Greensboro on Tuesday,
March 31, 2009 to present Gattis with a 2009 Toyota Prius.
Access the full press release at:
http://www.ipsos-na.com/news/pressrelease.cfm?id=4331

23. Opt-in online panelist
32-year-old Spanish-speaking female
African-American physician
residing in Billings, MT

25. Professional Respondents?
Among 10 largest opt-in panels:
•10% of panel participants account for 81% of survey
responses;
• 1% of participants account for 34% of responses.
Gian Fulgoni, chairman, comScore, Council of American Survey
Research Organizations annual conference, Los Angeles, October 2006.

26. Questions
 Who joins the club, how and why?
 What verification and validation of respondent identities are
undertaken?
 What logical and QC checks (duration, patterning, data quality)
are applied?
 What weights are applied, and how? On what theoretical basis
and with what effect?
 What level of disclosure is provided?
 What claims are made about the data, and how are they
justified?

27. One Claim:
Google Consumer Surveys
• “Produces results that are as accurate as probability-based
panels.” http://www.google.com/insights/consumersurveys/how
• 1 or 2-question pop-ups delivered to search engine users of
“premium content.”
• Demographic data are imputed through analysis of users‟ IP
addresses and previous page views
• Among Langer Research staff, one woman was identified as
a 55-year-old male with an interest in beauty and fitness;
another as a 65+ woman (double her actual age) with an
interest in motorcycles and one man as a senior citizen in
the Pacific Northwest with an interest in space technology.
https://www.google.com/settings/ads/onweb/

28. Another claim:
“Bayesian Credibility Interval”
• Ipsos opt-in online panel – presdential tracking
poll
• Computation of “Credibility Internval” appears to
match how you‟d compute SRS MoE.
• Its basis is “the opinion of Ipsos experts.”
• (Headdesk)

29. Further claims:
Convenience Sample MoE
• Zogby Interactive: "The margin of error is +/- 0.6 percentage points.”
• Ipsos/Reuters: “The margin of error is plus or minus 3.1 percentage
points."
• Kelton Research: “The survey results indicate a margin of error of +/-
3.1 percent at a 95 percent confidence level.”
• Economist/YouGov/Polimetrix: “Margin of error: +/- 4%.”
• PNC/HNW/Harris Interactive: “Findings are significant at the 95 percent
confidence level with a margin of error of +/- 2.5 percent.”
• Radio One/Yankelovich: “Margin of error: +/-2 percentage points.”
• Citi Credit-ED/Synovate: “The margin of error is +/- 3.0 percentage
points.”
• Spectrem: “The data have a margin of error of plus or minus 6.2
percentage points.”
• Luntz: “+3.5% margin of error”

30. Online Survey
Traditional phone-based survey techniques suffer from deteriorating response rates and
escalating costs. YouGovPolimetrix combines technology infrastructure for data collection,
integration with large scale databases, and novel instrumentation to deliver new
capabilities for polling and survey research.
(read more)

31. Response Rates
• Surveys based in probability sampling cannot achieve pure
probability (e.g., 100% RR)
• Pew (5/15/12) reports decline in its RRs from 36% in 1997
to 9% now. (ABC/Post: 19%)
• Does this trend poison the well?

32. A Look at the Lit
2006: "…little to suggest that unit nonresponse within the range of
response rates obtained seriously threatens the quality of survey
estimates."
Keeter et al., POQ, 2006 – comp. RR 25 vs. 50%; see also Keeter et al., POQ, 2000, RR 36 vs. 61
2008: “In general population RDD telephone surveys, lower response
rates do not notably reduce the quality of survey demographic estimates.
… This evidence challenges the assumption that response rates are a
key indicator of survey data quality…”
Holbrook et al.: Demographic comparison, 81 RDD surveys, 1996-2005; AAPOR3 RRs from 5 to 54%
2012: “Overall, there are only modest differences in responses between
the standard and high-effort surveys. Similar to 1997 and 2003, the
additional time and effort to encourage cooperation in the high-effort
survey does not lead to significantly different estimates on most
questions.”
Pew 5/15/12 - Comp. RR 9 vs. 22%

33. See also O‟Neil, 1979; Smith, 1984; Merkle et al., 1993;
Curtin et al., 2000; Groves et al., 2004; Curtin et al., 2005.
And Groves, 2006:
“Hence, there is little empirical support for the notion that low
response rate surveys de facto produce estimates with high
nonresponse bias.”

35. Next step
Empirical testing

36. Yeager, Krosnick, et al., 2011
(See also Malhotra and Krosnick, 2006)
• Paper compares seven opt-in online convenience-sample
surveys with two probability sample surveys
• Probability-sample surveys were “consistently highly accurate”
• Opt-in online surveys were “always less accurate… and less
consistent in their level of accuracy.”
• Weighted probability samples sig. diff. from benchmarks 31 or
46 percent of the time for probability samples, 62 to 77 percent
of the time for convenience samples.
• Opt-in data‟s highest single error vs. benchmarks, 19 points;
average error, 9.9.

37. Average Absolute Errors
primary (weighting) demographics, unweighted
20%
18%
16%
14% 12.0%
12%
10%
8% 6.4% 6.4%
6% 5.0% 5.3% 4.7%
4.1%
4% 3.3%
2.0%
2%
0%
Yeager, Krosnick, et al., 2011

38. Average Absolute Errors
secondary and non-demographic results
10% Unweighted Weighted
9%
8%
7%
5.9%
6% 5.2%
5%
3.8%
4%
3.2%
3%
2%
1%
0%
Probability samples (combined) Internet samples (combined)
Yeager, Krosnick, et al., 2011

39. Largest Absolute Error
all benchmarks, unweighted
40%
35.5%
35%
30%
25%
20% 18.0%
15.6% 15.3% 16.0%
15% 13.2% 13.7%
11.7%
9.6%
10%
5%
0%
Yeager, Krosnick, et al., 2011

40. Largest Absolute Error
secondary and non-demographic results
20% Unweighted Weighted
18%
16% 14.9%
14% 13.5%
12% 10.7%
10% 8.7%
8%
6%
4%
2%
0%
Probability samples (combined) Internet samples (combined)
Yeager, Krosnick, et al., 2011

41. % significantly different from benchmarks
secondary and non-demographic results
100% Unweighted Weighted
90%
80%
70.6% 71.4%
70%
60% 57.5%
50%
38.5%
40%
30%
20%
10%
0%
Probability samples (combined) Internet samples (combined)
Yeager, Krosnick, et al., 2011

42. Additional conclusions
Yeager, Krosnick, et al., 2011
• Little support for the claim that some non-probability internet
surveys are consistently more accurate than others.
• Weighting did not always improve the accuracy of the opt-in samples.
• No support for the idea that higher completion = greater
accuracy:
• There was a significant negative correlation between a survey‟s average
absolute error and its response rate.
• Probability samples were not only more accurate overall, but
also more consistently accurate.
• True both across surveys (using pre-existing probability samples as
comparison points) as well as within surveys.
• Meaning it‟s virtually impossible to anticipate whether an opt-in survey
will be just somewhat less accurate than a probability sample or
substantially less accurate; and knowing an opt-in is accurate on one
benchmark does not predict its accuracy on other measures.

43. Additional conclusions
Yeager, Krosnick, et al., 2011
• The fact that internet samples are less accurate, and less
consistent, on average, than probability samples should “come
as no surprise, because no theory provides a rationale whereby
samples generated by non-probability methods would yield
accurate results”
• It is “possible to cherry-pick such results to claim that non-
probability sampling can yield veridical measurements. But a
systematic look at a wide array of benchmarks documented that
such results are the exception rather than the rule.”

44. ARF FoQ via Reg Baker, 2009
• Reported data on estimates of smoking prevalence: similar
across three probability methods, but with as many as 14
points of variation across 17 opt-in online panels.
• “In the end, the results we get for any given study are
highly dependent (and mostly unpredictable) on the panel
we use. This is not good news.”

45. Callegaro et al., 2012
• Reviewed 45 studies comparing the quality of data
collected via opt-in panels vs. another mode and/or
benchmarks. In summary:
• Panel estimates substantially deviated from benchmarks, and
to a far greater degree than probability samples
• High variability in data from different opt-in panels
• High levels of multiple-panel membership (between 19-45% of
respondents belong to 5+ panels)
• Substantial differences among low- and higher-membership
respondents
• Weighting did not correct variations in the data

46. Traugott, 2012
• Tested the accuracy of four low cost data collection
(LCDC) methods (Mechanical Turk, Pulse Opinion
Research, Qualtrics and Zoomerang)
• LCDC unweighted demographics substantially deviated from
ACS benchmarks to a far greater degree than RDD polls or
the ANES
• MTurk was substantially younger (45% under 30), female
(60%), and more educated (47% with a college degree) than
benchmarks.
• Pulse was disproportionately older (43% over 65), female
(61%), white (90%) and educated (46% with a college degree).
• Qualtrics and Zoomerang had better unweighted age, sex, and
race distributions, but still had far too few respondents without a
high school degree and too many with some college or more.

47. Traugott, 2012 (cont‟d)
100% Unweighted Party ID
90% Republican Democrat Independent
80%
70%
60% 57%
49%
50% 44%
41% 42%
40% 35% 37% 36%
31%
27% 28%
30%
20% 13% 13%
11%
10%
1%
0%
ANES Mturk Zoomerang Pulse Qualtrics

48. Traugott, 2012 (cont‟d)
100% Weighted Party ID
90% Republican Democrat Independent
80%
70%
58%
60%
50% 43% 44% 45% 46%
40% 37%
34% 34%
31%
30% 26% 26%
20% 12% 13% 13%
10% 2%
0%
ANES Mturk Zoomerang Pulse Qualtrics

49. AAPOR‟s “Report on Online Panels,” April 2010
• “Researchers should avoid nonprobability online panels when one of the
research objectives is to accurately estimate population values.”
• “The nonprobability character of volunteer online panels … violates the
underlying principles of probability theory.”
• “Empirical evaluations of online panels abroad and in the U.S. leave no
doubt that those who choose to join online panels differ in important and
nonignorable ways from those who do not.”
• “In sum, the existing body of evidence shows that online surveys with
nonprobability panels elicit systematically different results than probability
sample surveys in a wide variety of attitudes and behaviors.”
• “The reporting of a margin of sampling error associated with an opt-in
sample is misleading.”

50. AAPOR‟s conclusion
“There currently is no generally accepted theoretical basis
from which to claim that survey results using samples from
nonprobability online panels are projectable to the general
population. Thus, claims of „representativeness‟ should be
avoided when using these sample sources.”
AAPOR Report on Online Panels, April 2010

51. OK, but apart from population values, we
can still use convenience samples to
evaluate relationships among variables
and trends over time.
Right?

52. Pasek & Krosnick, 2010
• Comparison of opt-in online and RDD surveys sponsored by the U.S.
Census Bureau assessing intent to fill out the Census.
• “The telephone samples were more demographically representative of
the nation‟s population than were the Internet samples, even after
post-stratification.”
• “The distributions of opinions and behaviors were often significantly
and substantially different across the two data streams.
Thus, research conclusions would often be different.”
• Instances “where the two data streams told very different stories about
change over time … over-time trends in one line did not meaningfully
covary with over-time trends in the other line.”

53. Top Predictors of Intent to
Complete the Census
In the probability RDD sample In the internet opt-in sample
Age: 18-24 -1.76 Age: 18-24 -1.81
Education: <H.S. -1.16 Age: 25-44 -1.29
Education: H.S. graduate -.97 Think Census can help them 1.19
Spanish speaking household .97 Think Census can harm them -.98
Counting everyone is important .92 Participation doesn‟t matter .95
Age: 25-44 -.76 (disagree)
Don‟t have time (disagree) .70 Counting everyone is important .71
Education: Some college -.67 Age: 45-64 -.62
Participation doesn‟t matter .67 Don‟t have time (disagree) .61
(disagree) Race: Hispanic .56
Counting everyone is -.62 Education: <H.S. -.47
important (disagree) Pasek & Krosnick, 2010

54. Biggest Differences in Predictors
of Intent to Complete the Census
RDD Opt-In Diff.
Spanish speaking household .97*** -.38 -1.34***
Importance of counting -.62*** .14 .76**
everyone (disagree)
Education: <H.S. -1.16*** -.47* .69**
Education: H.S. graduate -.97*** -.38 .59**
Think Census can help them .61*** 1.19*** .57***
Age: 25-44 -.76*** -1.29*** -.54**
Age: 45-64 -.21* -.62*** -.42*
Region: Northeast -.06 .27* .33*
Pasek & Krosnick, 2010

55. Differences in Time Trend
Pasek & Krosnick, 2010

56. Pasek/Krosnick conclusion
“This investigation revealed systematic and often sizable
differences between probability sample telephone data
and non-probability Internet data in terms of demographic
representativeness of the samples, the proportion of
respondents reporting various opinions and behaviors, the
predictors of intent to complete the Census form and
actual completion of the form, changes over time in
responses, and relations between variables.”
Pasek and Krosnick, 2010

57. Can non-probability samples
be „fixed‟?
• Bayesian analysis?
• What variables? How derived? Are we weighting to the DV
or its correlates?
• Sample balancing? (i.e., quota sampling revisited)
• “The microcosm idea will rarely work in a complicated
social problem because we always have additional
variables that may have important consequences for the
outcome.”
• Gilbert, Light and Mosteller, Statistics and Public Policy, 1977

58. Some say you can
“The survey was administered by YouGovPolimetrix during
July 16-July 26, 2008. YouGovPolimetrix employs sample
matching techniques to build representative web samples
through its pool of opt-in respondents (see Rivers 2008).
Studies that use representative samples yielded this way
find that their quality meets, and sometimes exceeds, the
quality of samples yielded through more traditional survey
techniques.”
Perez, Political Behavior, 2010

59. AAPOR‟s task force disagrees
“There currently is no generally accepted theoretical
basis from which to claim that survey results using
samples from nonprobability online panels are
projectable to the general population. Thus, claims of
„representativeness‟ should be avoided when using these
sample sources.”
AAPOR Report on Online Panels, 2010

60. It has company
• “Unfortunately, convenience samples are often used inappropriately
as the basis for inference to some larger population.”
• “…unlike random samples, purposive samples contain no
information on how close the sample estimate is to the true value of
the population parameter.”
• “Quota sampling suffers from essentially the same limitations as
convenience, judgment, and purposive sampling (i.e., it has no
probabilistic basis for statistical inference).”
• “Some variations of quota sampling contain elements of random
sampling but are still not statistically valid methods.”
Biemer and Lyberg, Introduction to Survey Quality, 2003

61. Social Media

62. Sampling Challenges
• Users are not limited to one tweet/post per day;
some may post incessantly; others rarely
• Users can have multiple Twitter accounts
• Accounts often do not belong to individuals, but
rather companies, organizations or associations
• Posts can be driven by a public relations campaign
(i.e., through volunteers, paid agents or bots) rather
than as an expression of individual attitudes

63. Sampling Challenges (cont.)
• Users‟ location is often not accurately captured (if
at all)
• Users are self-selected. There is no theoretical
basis on which to assume that their views are
representative of the views of non-users.
• 15 percent of online adults use Twitter, 8 percent daily (Pew)
• Fewer than 30 percent of Twitter users are Americans
(Semiocast)
• In 2011, 45% of U.S. Facebook users were 25 or younger
(Smith, 2012)

64. Sentiment Analysis
• Determining the meaning of posts or tweets requires
content analysis:
• Traditional approaches (using independent coders and a
codeback) typically is unrealistic given the volume of data
• Computerized sentiment analysis programs, while lower-cost
and faster, are fraught with problems:
• Parsing
slang, irony, sarcasm, abbreviations, acronyms, emoticons,
contextual meaning and hashtags is highly complex
• Ambiguity in determining target words (e.g., Obama vs.
prez)
• Regardless, contextual data (demographic, other attitudes) are
sparse or absent, severely limiting the research questions that
can be answered

65. Manual vs. Automated Coding
Manual Coding
Positive Neutral Negative Uncodable
(n=100) (n=285) (n=81) (n=34)
Positive 8% 5% 2% 0%
radian6
Neutral 86% 90% 83% 94%
55%
Negative 0% 5% 15% 6%
Positive 42% 25% 6% 21%
STAS
Neutral 45% 57% 71% 68%
45%
Negative 13% 18% 23% 12%
Positive 30% 20% 2% 9%
clarabridge
Neutral 60% 61% 85% 79%
43%
Negative 10% 19% 12% 12%
Kim et al., 2012

66. Data Mining
• Rather than attempting to parse the content of online
posts, some researchers instead focus on the sheer quantity
of posts about a certain topic.
• Tumasjan et al. (2010) analyzed the German national election and
found that “the mere number of tweets mentioning a political
party” was quite good at reflecting their vote share and that its
predictive power was similar to that of traditional polls.
• At the same time, they found that only 4% of users accounted for
more than 40% of the messages.
• When such a small number of people are accounting for a large
portion of the results, it opens the door for politically motivated
actors to distort results intentionally – especially as the use of such
methods becomes more popular.

67. Data Mining
• Similar analyses in the U.S. have differed
• Lui et al. (2011) showed that “Google trends” was worse at
predicting 2008 and 2010 election outcomes than the New
York Times (probability-sample) polls and even chance.
• Gayo-Avello et al. (2011) looked at the 2010 election and
found the mean average error for Twitter volume was 17.1%
(it was 7.6% for sentiment analysis), far higher than the 2-
3% that is typical of RDD polls.
• Olson & Bunnett (2011) found that Facebook “likes” in 2010
explained 13% of the variance in Senate races, but were
very weak (or slightly negative) predictors of gubernatorial
and house races

68. Twitter Index?
The Twitter Political Index
Aug. 6 – Obama‟s index # is three times greater than Romney‟s (“61” v. “24”)
Aug. 7 – “35” v. “19”

69. Facebook sampling?
• Bhutta, 2012: Snowball sampling to reach Catholics
• faster and cheaper than traditional methods, but:
Facebook GSS
70% 67% 66%
53%
33%
24% 27%
6%
% female % Latino % college grad. % attend mass
weekly+

70. “…the Facebook respondents cannot possibly
serve as a representative sample of the general
Catholic population. (Pollsters should view
Facebook findings with extreme caution.)”
Bhutta, 2012

71. Twitter bots
•Recent estimate that as many as 30% of Obama‟s
Twitter followers and 22% of Romney‟s were
fabricated. (Calzolari, 2012)
•News report: Individuals can purchase 25,000
fabricated Twitter followers for $247; these “bots” will
automatically tweet snippets of text. (Feifer, 2012)

72. And the future?
In probability sampling:
• Continued study of response-rate effects
• Concerted efforts to maintain response rates
• Renewed focus on other data-quality issues in
probability samples, e.g. coverage
• Development of probability-based
alternatives, e.g. mixed-mode, ABS

73. The Future, cont.
In convenience sampling:
• Further study of appropriate uses (as well as
inappropriate misuses) of convenience-sample
data
• Further evaluation of well-disclosed, emerging
techniques in convenience sampling
• Enhanced disclosure!
• The quest for an online sampling frame

74. The future, cont.
In social media:
• Vast and growing source of data
• Further evaluation of its appropriate use is needed
• It‟s important to study how social media influences the formation
and change of public opinion. But that does not mean it can
supplant probability-sample polling.
• Information gleaned from social media may be useful as a
compliment to public opinion polls (akin to focus groups); for
trend-spotting, qualitative analysis, perhaps modeling
• The key: establish the research question; evaluate
the limitations of the tools available to answer it.

75. Thank you!
Gary Langer
Langer Research Associates
glanger@langerresearch.com