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Index

Published online by Cambridge University Press:  01 November 2024

Clifford Young  and
Kathryn Ziemer
Clifford Young
Affiliation:
Ipsos Public Affairs
Kathryn Ziemer
Affiliation:
Ipsos Public Affairs

Information

Type
Chapter
Information
Polls, Pollsters, and Public Opinion
A Guide for Decision-Makers
 , pp. 223 - 231
Publisher: Cambridge University Press
Print publication year: 2024

Index

1936 US presidential election, 18
2008 US presidential election, 52
2010 Brazilian presidential election, 48
2015 Greek referendum, 9, 96–104, 106
context of, 96–98
coverage bias, 102–103
estimation error, 103–104
measurement error, 100–102
nonresponse bias, 102
polls versus actual results, 98
sampling error, 100
spread calculations, 98–100
2016 Brexit, 106
2016 US presidential election, 2, 9, 83–93, 106
aggregate polls, 94–95, 136–138
approval ratings, 129
author’s story, 53–54
availability bias, 121
change election model, 128
confirmation bias, 119–120
coverage bias, 61, 85–87
economy and, 130, 132
effective public opinion in, 18–19
estimation error, 88–89
expert predictions and, 114
fundamental prediction models, 139–140
likely voter models, 71
measurement error, 89–93
nonresponse bias, 87–88
as polling miss example, 136
probabilistic thinking, 123
salad bowl model, 141–142
sampling error, 83–85
as single input failure, 116–118
triangulation, 142–145
2018 Brazilian presidential election, 63
2018 Columbian referendum, 106
2018 US midterm elections, 219–220
2019 Argentine presidential election, 22–23, 106, 120–121
2020 US presidential election, 23, 106
2022 Brazilian presidential election, 11, 77
antiestablishment sentiment, 194–195
background, 190–191
behavioral intent polls, 206–208
benchmarks, creating, 190
as change election, 193–194
context (for messaging), 192–197
main problem heuristic, 195–197
message stickiness, 200–202
message testing, 202
messenger credibility, 197–200
packaging perspective of public opinion, 200–204, 208
second round messaging, 204–206
segmentation of electorate, 191–192
structural perspective of public opinion, 191–200, 208
summary table (for messaging), 202–204
a posteriori cut point approaches, 77–78
a priori cut point approaches, 76–77
Abramovitz, Alan, 117, 119
Adams, Eric, 174–175, 180
aggregate enlightenment of public opinion, 42–43
aggregate polls, 9, 94–96, 136
2015 Greek referendum poll assessment, 96–104
herding bias, 96, 105
“one person, one vote,” 17–18, 24
probabilities and, 136–138
total survey error, 96
weighted aggregation, 18–19, 24, 99–100
aggregate stability of public opinion, 8, 39–42
alternative data sources, 218–222
American Soldier, The (Stouffer), 18
analytical methods of pollsters, 188
antiestablishment sentiment
2022 Brazilian presidential election, 194–195
rise of, 163–166
antiquity, public opinion in, 15, 24
approval ratings, 10, 110, 147, 148–160
Biden example, 27–30, 154–160
convergent validity of social media data, 219
external events and, 46–48
as heuristic model, 128–129
policy factors, 149–150, 153–154
“rally around the flag” effect, 41, 152
salad bowl model, 141–142
structural factors, 149–153
Athens, public opinion in, 15, 24
attention (information processing), 36, 181–182
attitude formation, 13, 25, 37
dynamic model, 8, 13, 25, 30–37, 168
misinformation, 185
static model, 8, 13, 25, 26–30, 37, 168
attitudes
defined, 20, 24
measuring, 19–21, 24
non-attitudes, 44–45
public, 214
public opinion, relationship with, 8
rankings and ratings, 26–30, 179–181
in single polls (behavioral intent), 134
attributes (attitude formation), 26–30
Biden approval ratings example, 27–30
defined, 27
Auxílio Brasil, 194
availability bias, 121
average absolute difference (AAD), 98
ballot question. See behavioral intent polls
barriers (in behavioral outcomes), 134
behavioral intent polls, 133–136, 206–208
belief confirmation, 181–182
benchmarks, 190
between-person change, 45–46
bias
availability bias, 121
cognitive biases, 9–10, 105, 109
confirmation bias, 118–121, 125
coverage bias, 9, 60–64, 82, 85–87, 102–103
explained, 55–56
herding bias, 96, 105, 121–122
mathematical depictions of, 56–57
measurement error, 67–69, 82, 89–93, 100–102
nonresponse bias, 64–67, 82, 87–88, 102
in single polls (behavioral intent), 135–136
Biden, Joe. See also 2020 US presidential election
approval ratings example, 27–30, 154–160
infrastructure example, 33–35, 181–182, 184–185
targets (of messaging), 173
binary thinking, 122
Bishop, George, 44
Blanco, Jimena, 120
Bolduc, Don, 22
Bolsonaro, Jair, 63, 164. See also 2018 Brazilian presidential election; 2022 Brazilian presidential election
bottom-up attention, 36
bounded rationality, 45
Brazilian presidential election (2022). See 2022 Brazilian presidential election
Brazilian quality-of-life agenda, 160–163
“broken system” index, 163–165, 194–195
Bush, George H.W., 152
Bush, George W., 41, 46, 152, 154
California Proposition 8, 23
change elections, 127–128, 143, 193–194
change in public opinion, mechanisms of, 45–46
cherry-picking, 118–121
Clinton, Hillary, 2. See also 2016 US presidential election
cognitive biases, 9–10, 105, 109
availability bias, 121
binary thinking, 122
confirmation bias, 118–121
experts and prediction, 113–114
herding bias, 121–122
probabilistic thinking, 122–124
single input learning styles, 114–118
cognitive processing. See information processing
cognitive science, 30–37
combinatorial models, 73
communications
analytical methods of pollsters, 188
attention, grabbing, 181–182
behavioral intent polls (2022 Brazilian election), 206–208
benchmarks, creating, 190
context (for messaging), 192–197
evaluation (information processing stage), 187–188
literature on, 171
main problems questions, 195–197
message ecosystem, 171
message stickiness, 182–185, 200–202
message testing, 186–187, 202
messages, 174–177
messenger credibility, 177–178, 197–200
messenger familiarity, 178–179, 197–198
misinformation, 185
packaging perspective of public opinion, 181–188
priority linkages, 185–186
rankings and ratings, 179–181
second round messaging (2022 Brazilian election), 204–206
structural perspective of public opinion, 171–181
summary table example, 189, 202–204
targets (of messaging), 172–174, 191–192
conceptual frameworks for public opinion, 10–11
confirmation bias, 118–121, 125
Congress (US), founding of, 16–17
context
2015 Greek referendum, 96–98
2016 US presidential election poll assessment, 89
2022 Brazilian presidential election messaging, 192–197
antiestablishment sentiment, rise of, 163–166
for approval ratings, 153
Brazilian quality-of-life agenda, 160–163
in fundamental prediction models, 138–141
importance of, 3–4
measurement error and, 67–68
context-based analysis, 10, 110, 147–148, 160–166, 212
context-independent fundamental models, 141–142
continuity elections, 127–128, 193
convergence, 8, 21–23, 24
convergent validity, 80, 219
Converse, Philip, 44
country image, diplomacy and, 175–176
coverage bias, 9, 60–64, 82, 135
2015 Greek referendum poll assessment, 102–103
2016 US presidential election poll assessment, 85–87
COVID vaccine uptake, 80
credibility
of information, 31, 36, 37, 185, 187
linkage with familiarity, 178–179
of messengers, 177–178, 182–185, 197–200
of pollsters, 214–216
cues and clues, 183–185, 200–202
cut points in likely voter models, 76–78
data quality. See quality of data
data scientists, pollsters as, 7, 9, 51–52, 110, 168, 211
2015 Greek referendum poll assessment, 96–104
2016 US presidential election poll assessment, 83–93
analyst assessment of likely voter models, 78
author’s story, 52–54
bias, explained, 55–56
coverage bias, 9, 60–64
cut points in likely voter models, 76–78
derived intention model, 75–76
election miss examples, 106
error, explained, 55–56
estimation error, 9, 69–70
likely voter models, necessity of, 70–72
mathematical depictions of bias/error, 56–57
measurement error, 67–69
nonresponse bias, 64–67
non-sampling error, 9, 60
reliability, explained, 80–81
sampling error, 9, 58–60
sensitivity analysis of 2022 Brazilian presidential election, 78–79
total survey error, 9, 57–58
traditional Gallup model, 74–76
validity, explained, 80–81
Davis, James, 40
decision inputs
approval ratings, 147, 148–160
context-based analysis, 147–148, 160–166
public opinion as, 8, 16, 23–24, 167–169, 214. See also communications
decision makers, 214
convergence with public opinion, 21–23, 24
public opinion usage, 5, 8
DEFF (sample design effect), 59–60
democracy, public opinion in formation of, 16–17
demographics, stability of public opinion, 40
derived intention model, 73, 75–76
descriptive aspect of pollsters, 212
design decisions
mode of survey administration, 61–63, 66, 85–86
post-survey weighting, 63–64, 66, 86–87
detractors, 172–173, 191–192
differential name recognition, 48, 178, 197–198
differential nonresponse, 66
diplomacy, country image and, 175–176
direction (attitudes), 20
discrete outcome polling, 145–146
divergent validity, 80
“don’t know” (DK) options
2016 US presidential election poll assessment, 89–93
measurement error and, 68–69
Doxiadis, Aristos, 97
dynamic model (attitude formation), 8, 13, 25, 30–37, 168
credibility of information, 31, 36
emotions and, 32–33
infrastructure example, 33–35
memory as networks, 33–35
online processing model, 35–37
short-term and long-term memory, 32
economy
in 2016 US presidential election, 132
approval ratings and, 47, 150–151, 155–158
in heuristic models, 129–131
effective public opinion, 18–19, 24, 172
elaboration (information processing), 182–185
election prediction, 10, 106, 109–110, 211–212
2016 US presidential election triangulation, 142–145
approval ratings, 147, 148–160
author’s story, 111–112
discrete outcome polling, 145–146
fundamental models, 138–142
heuristic models, 126–133
poll-based models, 133–138
social media data sources, 219–220
triangulation, 126
election turnout rates, 71
emotions, information processing and, 32–33, 182–185
enhancement (with non-survey data), 221
Enlightenment, public opinion during, 15–16, 24
enlightenment of public opinion, 42–43
error, 9
estimation error, 9, 69–70, 82, 88–89, 103–104
explained, 55–56
mathematical depictions of, 56–57
measurement error, 67–69, 82, 89–93, 100–102
non-sampling error, 9, 60, 82
sampling error, 9, 58–60, 81, 83–85, 100
in single polls (behavioral intent), 135–136
total survey error, 9, 57–58, 83, 96, 98–100, 211
estimation error, 9, 69–70, 82, 135
2015 Greek referendum poll assessment, 103–104
2016 US presidential election poll assessment, 88–89
evaluation (information processing), 36, 187–188
Expert Political Judgment: How Good Is It? How Can We Know? (Tetlock), 113
experts
prediction and, 113–114
single input learning styles, 114–118
expressions of public opinion, 214
extension (with non-survey data), 221
external events, impact on public opinion, 46–48
familiarity of messengers, 178–179, 197–198
favorability. See credibility
Federalist Papers, The: No. 63, 17
Fernández, Alberto, 22–23, 120–121
five-variable segmentation scheme, 173
forecasts. See election prediction; prediction
fortune tellers, pollsters as, 7, 9–10, 52, 109–111, 168, 211–212
2016 US presidential election triangulation, 142–145
approval ratings, 147, 148–160
author’s story, 111–112
availability bias, 121
binary thinking, 122
confirmation bias, 118–121
context-based analysis, 147–148, 160–166
discrete outcome polling, 145–146
experts and prediction, 113–114
fundamental prediction models, 138–142
herding bias, 121–122
heuristic prediction models, 126–133
poll-based prediction models, 133–138
probabilistic thinking, 122–124
single input learning styles, 114–118
triangulation, 124–125
foxes, 115–116, 117–118
framing, 176, 180, 200
fundamental prediction models, 138–142, 143
future of pollsters, 222
Gallup, George, 18
Gallup model, 73, 74–76
General Social Survey (GSS), 40
general will, 16, 24
government popularity. See approval ratings
Greek referendum. See 2015 Greek referendum
Haddad, Fernando, 63, 191
Hamilton, Alexander, 17
hedgehogs, 115, 116–117
herding bias, 96, 105, 121–122
heuristic prediction models, 126–133, 143
approval ratings, 128–129
change election, 127–128
economy in, 129–131
main problem heuristic, 131–133
heuristic-based reasoning, 45
hot cognition, 33, 183
house effects, 136
House of Representatives (US), founding of, 16–17
Hurricane Katrina, 46
ill-informed citizenry
instability of public opinion and, 43–45
low information rationality, 45
incomplete sample frame, 60
incumbency, 141–142
inflation (Biden approval ratings example), 155–158
information processing, 30–37
attention, grabbing, 181–182
credibility of information, 31, 36, 187
emotions and, 32–33, 182–185
evaluation stage, 187–188
infrastructure example, 33–35
memory as networks, 33–35
misinformation, 185
online processing model, 35–37
priority linkages, 185–186
short-term and long-term memory, 32
information sources, 25
infrastructure example
attention, grabbing, 181–182
dynamic model (attitude formation), 33–35
message stickiness, 184–185
priority linkages, 186
instability of public opinion, 38, 43–45, 46–49
intensity (attitudes), 20
intent–behavior link, 134–135
intention (in behavioral outcomes), 134
interpretation (information processing), 36
issue salience, impact on public opinion, 48–49
Jay, John, 43
law of large numbers, 42
likely voter models
analyst assessment of, 78
constructing, 72–73
cut points in, 76–78
derived intention model, 75–76
estimation error, 88–89, 103–104
necessity of, 70–72
sensitivity analysis, 78–79, 88
traditional Gallup model, 74–76
Lippman, Walter, 43
long-term memory, 32
low information rationality, 45
Lula da Silva, Luiz Inácio, 111–112, 161, 163, 169–170. See also 2022 Brazilian presidential election
Macri, Mauricio, 22–23, 120–121
Madison, James, 17
main problem heuristic, 131–133
2016 US presidential election triangulation, 143
2022 Brazilian presidential election, 195–197
Biden approval ratings example, 156–158
Brazilian quality-of-life agenda, 160–163
policy factors in approval ratings, 153–154
majoritarian aggregation, 17–18
margin of error (MOE), 9, 58–60, 81, 83–85
Markov Chain Monte Carlo Simulation (MCMC), 136
McCain, John, 52
measurement error, 67–69, 82, 135
2015 Greek referendum poll assessment, 100–102
2016 US presidential election poll assessment, 89–93
median voter model, 21
memory
emotions and, 32–33
as networks, 33–35
short-term and long-term, 32
Menoume Europi (Stay in Europe) movement, 97
message stickiness, 181, 182–185, 200–202
message testing, 186–187, 202
message-messenger fit, 179, 198–200
messages (in political communications), 174–177
messaging. See communications
messengers
credibility, 177–178, 182–185, 197–200
familiarity, 178–179, 197–198
methodological statements, 87
microtargeting, 173
middling elections, 127–128
Milbank, Dana, 116
misinformation, 44, 185
mobocracy, 16–17
Moore, Michael, 117
multi-attribute model (attitude formation). See static model (attitude formation)
naïve model, 73, 76
name recognition. See differential name recognition; familiarity of messengers
“Nature of Belief Systems in Mass Publics, The” (Converse), 44
“neither” options. See “don’t know” (DK) options
Nelson, Jim, 119
networks, memory as, 33–35
neutrality (attitudes), 20
no cut point approaches, 76
non-attitudes, 44–45
noneconomic factors in approval ratings, 151–153
nonresponse bias, 64–67, 82, 135
2015 Greek referendum poll assessment, 102
2016 US presidential election poll assessment, 87–88
non-sampling error, 9, 60, 82, 135
coverage bias, 9, 60–64, 85–87, 102–103
estimation error, 9, 69–70, 88–89, 103–104
measurement error, 67–69, 89–93, 100–102
nonresponse bias, 64–67, 87–88, 102
non-survey data, 218–222
Norpoth, Helmut, 117
Obama, Barack, 52
Obergefell v. Hodges (2015), 23
“one person, one vote,” 17–18, 24
online processing model, 35–37
“other” options. See “don’t know” (DK) options
overlapping spreads, 83–85
packaging perspective of public opinion, 10, 168, 181–188, 212
2022 Brazilian presidential election, 200–204, 208
attention, grabbing, 181–182
elaboration, 182–185
evaluation (information processing), 187–188
message testing, 186–187
misinformation, 185
priority linkages, 185–186
Page, Benjamin, 39
partisanship (infrastructure example), 33–35
party identification nonresponse problem, 66
personal relevance, 181–182
persuadables, 172–173, 191–192
Phantom Public, The (Lippman), 43
philosophical concept, public opinion as, 15–16, 24
polarization, 3
policy factors in approval ratings, 149–150, 153–154, 158
policy makers. See decision makers
political communications. See communications
political pollsters, 216–217
politico-graphic model, 73, 76
poll-based prediction models, 133–138, 143
aggregate polls and probabilities, 136–138
single polls (behavioral intent), 133–136
polling industry
organizations and associations, 213–214
origin of, 17–18
polling results
context, importance of, 3–4
public opinion, relationship with, 3
polls (surveys), 2
2015 Greek referendum, versus actual results, 98
aggregation. See aggregate polls
attitude measurement, 19–21, 24
election miss examples, 106
error in. See error
history of, 6
mode of administration, 61–63, 66, 85–86
non-attitudes and question wording, 44–45
non-survey data as proxy, 220–222
post-survey weighting, 63–64, 66, 86–87
public opinion, as synonymous, 17–18, 24
question wording, impact of, 49
as single input failure, 114–118
pollsters. See also data scientists, pollsters as; fortune tellers, pollsters as; spin doctors, pollsters as
author’s story, 3–4, 52–54, 111–112, 169–170
challenges for, 2–3, 4
credibility of, 214–216
defined, 6, 212–213
descriptive aspect, 212
future of, 222
polling organizations and associations, 213–214
purpose of, 212–213, 217–218
three-hatted, 7, 8, 52, 110, 168, 211–212
types of, 216–217
popularity of government. See approval ratings
populist politics, rise of, 163–166
post-survey weighting, 63–64, 66, 86–87
prediction, 109–111, 211–212. See also election prediction
availability bias, 121
binary thinking, 122
cognitive biases, 9–10, 109
confirmation bias, 118–121
context-based analysis, 147–148, 160–166
experts and, 113–114
herding bias, 121–122
probabilistic thinking, 122–124
single input learning styles, 114–118
triangulation, 124–125
predictive validity, 80, 219–220
presidential approval ratings. See approval ratings
presidential elections. See also 2016 US presidential election; 2022 Brazilian presidential election
1936 (US), 18
2008 (US), 52
2010 (Brazil), 48
2018 (Brazil), 63
2019 (Argentina), 22–23, 106, 120–121
2020 (US), 23, 106
priming, 183
priority linkages, 176–177, 185–186, 196
private pollsters, 216–217
probabilistic thinking, 122–124
probabilities from aggregate polls, 136–138
proof points, 176
Proposition 8 (California), 23
public attitudes, 214
public opinion, 1–2
alternative assessment methods, 6
in ancient Rome and Athens, 15, 24
approval ratings, 10, 110
attitude measurement, 19–21, 24
attitudes, relationship with, 8
benchmarks, creating, 190
conceptual frameworks, 10–11
context-based analysis, 10
convergence with decision makers, 21–23, 24
as decision input, 8, 16, 23–24, 167–169, 214. See also communications
decision-maker usage of, 5, 8
effective public opinion, 18–19, 24, 172
enlightenment of, 42–43
expressions of, 214
external events and, 46–48
in founding of United States, 16–17
instability of, 38, 43–45, 46–49
issue salience and, 48–49
mechanisms of change, 45–46
non-survey data, 218–222
packaging perspective, 181–188
as philosophical concept, 15–16, 24
polarization in, 3
polling results, relationship with, 3
polls, as synonymous, 17–18, 24
as predictor. See election prediction
quality of data. See quality of data
question wording and, 49
sociopolitical outcomes, assessing, 5, 8
stability of, 8, 38, 39–43
structural perspective, 10, 168, 171–181
public pollsters, 216–217
public will, 214
qualitative likelihood, 123
quality of data, 51–52
2015 Greek referendum poll assessment, 96–104
2016 US presidential election poll assessment, 83–93
analyst assessment of likely voter models, 78
bias, explained, 55–56
coverage bias, 9, 60–64
cut points in likely voter models, 76–78
derived intention model, 75–76
election miss examples, 106
error, explained, 55–56
estimation error, 9, 69–70
likely voter models, necessity of, 70–72
mathematical depictions of bias/error, 56–57
measurement error, 67–69
nonresponse bias, 64–67
non-sampling error, 9, 60
reliability, explained, 80–81
sampling error, 9, 58–60
sensitivity analysis of 2022 Brazilian presidential election, 78–79
total survey error, 9, 57–58
traditional Gallup model, 74–76
validity, explained, 80–81
quality-of-life agenda (Brazil), 160–163
question order
2016 US presidential election poll assessment, 89
measurement error and, 67–68
question wording
2015 Greek referendum poll assessment, 100–102, 105
2016 US presidential election poll assessment, 89–93
impact on public opinion, 49
measurement error and, 68
non-attitudes and, 44–45
“rally around the flag” effect, 41, 152
rankings (attitude formation), 26–30
Biden approval ratings example, 27–30
defined, 27
in political communications, 179–181
stability of, 28
ratings (attitude formation), 26–30
Biden approval ratings example, 27–30
defined, 27
in political communications, 179–181
Rational Public, The: Fifty Years of Treands in Americans’ Policy Preferences (Page and Shapiro), 39
referendum. See 2015 Greek referendum
relative optimism, 193
reliability, 80–81, 82
response order
2016 US presidential election poll assessment, 90
measurement error and, 68
Rome, public opinion in, 15, 24
Rousseau, Jean-Jacques, 16, 24
Rousseff, Dilma, 48, 161
rules of thumb, 4
Sabato, Larry, 116
salad bowl model, 117, 141–142
same-sex marriage legalization, 23
sample design effect (DEFF), 59–60
sample frames, 60
sample mean, 55–56
sample survey, 19
sampling error, 9, 58–60, 81, 135
2015 Greek referendum poll assessment, 100
2016 US presidential election poll assessment, 83–85
second round messaging (2022 Brazilian presidential election), 204–206
segmentation, 172–174, 191–192
Senate (US), founding of, 16–17
sensitivity analysis
2016 US presidential election, 88, 144
2022 Brazilian presidential election, 78–79
triangulation, 125
Serra, José, 48, 111–112, 169–170
Shapiro, Robert, 39
short-term memory, 32
Silver, Nate, 116, 118
simple aggregation, 17–18
simple random sampling (SRS), 59
single input learning styles, 114–118
single polls (behavioral intent), 133–136
Social Contract, The (Rousseau), 16
social media data sources, 219–220
sociopolitical outcomes
defined, 5
public opinion and, 8
sovereign debt crisis. See 2015 Greek referendum
spin doctors, pollsters as, 7, 10–11, 52, 110, 167–169, 212
analytical methods used by, 188
author’s story, 169–170
behavioral intent polls (2022 Brazilian election), 206–208
packaging perspective of public opinion, 181–188, 200–204, 208
second round messaging (2022 Brazilian election), 204–206
structural perspective of public opinion, 171–181, 191–200, 208
summary table example, 189, 202–204
spreads
2015 Greek referendum calculations, 98–100
overlapping, 83–85
stability
of issue rankings, 28
of public opinion, 8, 38, 39–43
static model (attitude formation), 8, 13, 25, 26–30, 37, 168
Stouffer, George, 18
structural factors in approval ratings, 149–153, 155–158
structural perspective of public opinion, 10, 168, 171–181, 212
2022 Brazilian presidential election, 191–200, 208
messages (in political communications), 174–177
messenger credibility, 177–178
messenger familiarity, 178–179
rankings and ratings, 179–181
targets (of messaging), 172–174
substitution (with non-survey data), 221
supporters, 172–173, 191–192
surveys. See polls (surveys)
targets (of messaging), 172–174, 191–192
testing messages, 186–187, 202
Tetlock, Philip, 113
three-hatted pollsters, 7, 8, 52, 110, 168, 211–212. See also data scientists, pollsters as; fortune tellers, pollsters as; spin doctors, pollsters as
three-variable political segmentation, 172–173, 191–192
time-for-change model, 117
timing effects
Biden approval ratings example, 155–156
in single polls (behavioral intent), 134–135
total survey error, 9, 57–58, 83, 96, 98–100, 211
traditional Gallup model, 73, 74–76
triangulation, 212
2016 US presidential election, 142–145
Biden approval ratings example, 158–160
cognitive biases, 109
election prediction, 124–125
fundamental prediction models, 139
single input learning styles, 115
as validation, 80
true population value, 55–56
Trump, Donald, 2, 49, 163, 180, 219. See also 2016 US presidential election; 2020 US presidential election
trust. See credibility
Tsipras, Alexis, 97
turnout rates for elections, 71
uncertainty, 123
United States, public opinion in founding, 16–17
vaccine uptake, 80
validity, 80–81, 82, 219–220
voting-age population, percentage by country, 70. See also likely voter models
Wang, Sam, 116
weighted aggregation, 18–19, 24, 99–100
weighted data
post-survey design, 63–64, 66, 86–87
sensitivity analysis of 2022 Brazilian presidential election, 78–79
triangulation, 125
will of all, 16
within-person change, 45–46

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  • Index
  • Clifford Young, Ipsos Public Affairs, Kathryn Ziemer, Ipsos Public Affairs
  • Book: Polls, Pollsters, and Public Opinion
  • Online publication: 01 November 2024
  • Chapter DOI: https://doi.org/10.1017/9781108855310.018
Available formats
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  • Index
  • Clifford Young, Ipsos Public Affairs, Kathryn Ziemer, Ipsos Public Affairs
  • Book: Polls, Pollsters, and Public Opinion
  • Online publication: 01 November 2024
  • Chapter DOI: https://doi.org/10.1017/9781108855310.018
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Index
  • Clifford Young, Ipsos Public Affairs, Kathryn Ziemer, Ipsos Public Affairs
  • Book: Polls, Pollsters, and Public Opinion
  • Online publication: 01 November 2024
  • Chapter DOI: https://doi.org/10.1017/9781108855310.018
Available formats
×