In U.S. presidential elections since 1964, voter turnout among male and female voters has changed gradually but significantly, with women consistently voting at a higher rate than men since the 1980 election. 67 percent of eligible female voters took part in the 1964 election, compared to 72 percent of male voters. This difference has been reversed in recent elections, where the share of women who voted has been larger than the share of men by around four percent since 2004.

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

This data set consists of all Fulton County Election results from April 2012 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

This is the dataset I used to figure out which sociodemographic factor including the current pandemic status of each state has the most significan impace on the result of the US Presidential election last year. I also included sentiment scores of tweets created from 2020-10-15 to 2020-11-02 as well, in order to figure out the effect of positive/negative emotion for each candidate - Donald Trump and Joe Biden - on the result of the election.

Details for each variable are as below: - state: name of each state in the United States, including District of Columbia - elec16, elec20: dummy variable indicating whether Trump gained the electoral votes of each state or not. If the electors casted their votes for Trump, the value is 1; otherwise the value is 0 - elecchange: dummy variable indicating whether each party flipped the result in 2020 compared to that of the 2016 - demvote16: the rate of votes that the Democrats, i.e. Hillary Clinton earned in the 2016 Presidential election - repvote16: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2016 Presidential election - demvote20: the rate of votes that the Democrats, i.e. Joe Biden earned in the 2020 Presidential election - repvote20: the rate of votes that the Republicans , i.e. Donald Trump earned in the 2020 Presidential election - demvotedif: the difference between demvote20 and demvote16 - repvotedif: the difference between repvote20 and repvote16 - pop: the population of each state - cumulcases: the cumulative COVID-19 cases on the Election day - caseMar ~ caseOct: the cumulative COVID-19 cases during each month - Marper10k ~ Octper10k: the cumulative COVID-19 cases during each month per 10 thousands - unemp20: the unemployment rate of each state this year before the election - unempdif: the difference between the unemployment rate of the last year and that of this year - jan20unemp ~ oct20unemp: the unemployment rate of each month - cumulper10k: the cumulative COVID-19 cases on the Election day per 10 thousands - b_str_poscount_total: the total number of positive tweets on Biden measured by the SentiStrength - b_str_negcount_total: the total number of negative tweets on Biden measured by the SentiStrength - t_str_poscount_total: the total number of positive tweets on Trump measured by the SentiStrength - t_str_poscount_total: the total number of negative tweets on Trump measured by the SentiStrength - b_str_posprop_total: the proportion of positive tweets on Biden measured by the SentiStrength - b_str_negprop_total: the proportion of negative tweets on Biden measured by the SentiStrength - t_str_posprop_total: the proportion of positive tweets on Trump measured by the SentiStrength - t_str_negprop_total: the proportion of negative tweets on Trump measured by the SentiStrength - white: the proportion of white people - colored: the proportion of colored people - secondary: the proportion of people who has attained the secondary education - tertiary: the proportion of people who has attained the tertiary education - q3gdp20: GDP of the 3rd quarter 2020 - q3gdprate: the growth rate of the 3rd quarter 2020, compared to that of the same quarter last year - 3qsgdp20: GDP of 3 quarters 2020 - 3qsrate20: the growth rate of GDP compared to that of the 3 quarters last year - q3gdpdif: the difference in the level of GDP of the 3rd quarter compared to the last quarter - q3rate: the growth rate of the 3rd quarter compared to the last quarter - access: the proportion of households having the Internet access

Analysis of ‘🗳 VEP Turnout’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vep-turnoute on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Files:

National level

• U.S. VEP Turnout 1789-Present-Statistics - The complete time series of national presidential and midterm general election turnout rates from 1787-present.

National and state level

• 1980-2014 November General Election - Turnout Rates
• 2016 November General Election - Turnout Rates
• 2018 November General Election - Turnout Rates
• 2020 November General Election - Turnout Rates

Turnout rates by demographic breakdown, 1986-2018, from the Census Bureau's Current Population Survey, November Voting and Registration Supplement (or CPS for short). These tables are corrected for vote overreporting bias. For uncorrected weights see the source link.

• Turnout Rate 1986-2018 by Age
• Turnout Rate 1986-2018 by Education
• Turnout Rate 1986-2018 by Race and Ethnicity

For more information on these files see the source link below.

Source: Data prepared and maintained by Dr. Michael P. McDonald at the University of Florida, at electproject.org

Updated: synced from source weekly

License: CC-BY

This dataset was created by Government and contains around 100 samples along with Unnamed: 7, Denominators, technical information and other features such as: - Unnamed: 4 - Unnamed: 5 - and more.

How to use this dataset

• Analyze Unnamed: 16 in relation to Unnamed: 14
• Study the influence of Unnamed: 12 on Unnamed: 9
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Government

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Between 1964 and 2020, turnout among black voters in U.S. presidential elections fluctuated between 48 and 62 percent, with the highest turnouts coming in 2008 and 2012, when Barack Obama (the first African American candidate from a major party) was the Democratic candidate. Voter turnout has always been lowest among those under 25 years of age, although younger black voters did participate in high numbers in the 1960s, during the civil rights movement, and again in 2008, during Obama's first election campaign; young black voters also participated in higher numbers than white voters of the same age between 2000 and 2012.

In 1964, black voters over the age of 65 voted at a similar rate to those in the 18 to 24 bracket, however they have consistently had the highest turnout rates among black voters in recent years, overtaking voters in the 45 to 64 years bracket (whose voting rate has consistently been between 60 and 70 percent) in the 1996 election.

General

The League of Women Voters conducts surveys of Texas County voting websites. The data and further reading is available here (under County Website Reports). Any mistakes or errors found here are mine and the data on the LWV website is the authoritative data - I have no affiliation with the LWV but wanted to make the datasets more accessible.

Data Changes

I cleaned some of the data (split numeric and text ratings from one column to two columns) and made a few edits to values that appeared to be typos based on context - these will be noted in the description of each set. Column names were shortened in some cases and "NA" was added to empty cells. Each survey used slightly different questions, thought both 2016 sets appear to use the same ones and the 2017 is very similar.

Commonalities

Abbreviations used include SOS for the Texas Secretary of State website and 203 refers to Section 203 of the federal Voting Rights Act (for information, see this 2016 report).

Each dataset has at least these columns: county name, fips, date, total points, overall evaluation, perc calc na, and perc calc num.

• The county name was changed to match the name listed in the FIPS set, there were some typos and variations with hyphens.
• The FIPS (Federal Information Processing Standards) code is from here.
• The date is the month and year associated with the survey.
• The total number of points is the sum of all points a county received.
• The overall evaluation is the category associated with the number of points - these varied between sets and for 2020 the categories from the report were added to the dataset.
• I added two columns to the end of each set, perc_calc_na and perc_calc_num that represent the percent of total points for that county out of the possible points for that dataset - the first has "NA" for any county without a website and the second has "-1" for those counties. Some of the surveys included bonus points - these were included in the total possible points for the calculation.

This repository builds a compute environment, and executes code against data in support of the publication Email Mobilization Messages Suppress Turnout Among Black and Latino Voters: Experimental Evidence From the 2016 General Election. The paper was published in the Journal of Experimental Political Science in 2020.

The dataset includes the number of eligible voters and voter turnout as a percentage of the large-scale elections in Thurgau 2008, 2012, 2016 and 2020 by political municipalities. (Note: New District Regulations from 2010)Note to the year 2020: Data as published in Official Journal No. 12/2020 of 20 March 2020 (Districts of Arbon, Kreuzlingen, Münchwilen and Weinfelden) and in Official Journal No 27/2020 of 3 July 2020 (Frauenfeld District)

Swiss national parliamentary elections are frequently considered "low salience" elections. On the one hand, the emphasis on direct democratic elements in the Swiss constitution provides citizens with extensive opportunities to exert institutionalized political influence beyond the parliamentary channel. On the other hand, shifts in political parties' electoral fortunes had not had any consequences for government composition between 1959 and 2003, due to an informal agreement called the "Zauberformel" (magic formula). The interest in national elections has thus been rather limited for a long time – not only on the part of the Swiss electorate (turnout between 1971 and 2019 has mostly been under 50%), but also on the part of academic electoral research: No single election survey had been conducted until the early 1970s.

After two initial surveys in the wake of the 1971 (Sidjanski et al. 1975) and 1975 federal elections (Barnes and Kaase 1979), the 1979 election witnessed the launching of the first VOX survey realized by the Swiss Society for Applied Social Research (GfS) and the University of Bern (Hertig 1980). Thereafter, VOX surveys have accompanied the subsequent federal elections of 1983, 1987 and 1991, and a booklet has been published on each of them (Longchamp 1984, 1988; Longchamp and Hardmeier 1992). Although the VOX surveys could have laid the foundation of a Swiss national election study, these data collection efforts did not trigger many follow-up secondary analyses. Scholars interested in voting behaviour still focused much more on referendums and initiatives than on parliamentary elections – as did the VOX surveys.

It was probably the growing polarization of Swiss politics and the rise of the populist right in the early 1990s that generated a new surge of interest in federal elections. The 1995 election constituted, in Peter Farago's (1995) words, a "new start" in this respect, with the formation of the Swiss Election Study (Selects) project, initially an association of the political science departments of the universities of Bern, Geneva and Zurich. Since then, large-scale surveys have been carried out within the framework of the Selects project for the federal elections of 1995 (Farago 1996; Kriesi et al. 1998), 1999 (Hirter 2000; Sciarini et al. 2003), 2003 (Selb and Lachat 2004; Bühlmann et al. 2006), 2007 (Lutz 2008), 2011 (Lutz 2012), 2015 (Lutz 2016) and 2019 (Tresch et al. 2020), finally resulting in not only a consolidation but also in a massive expansion of electoral research in Switzerland.

One of the primary aims of Selects has been to systematically combine the new survey data with data collected by its precursor research projects. The fact that two complete data collections – those of the 1979 and 1983 VOX election surveys – were lost illustrates the importance of this task. In doing so, Selects has intended to provide a database that facilitates otherwise troublesome longitudinal studies of Swiss elections and voting behaviour (Lachat 2004; Trechsel 1995). The product of these efforts is presented here: A pooled set of Swiss election survey data which covers the period between 1971 and 2019 and includes most of the variables that have been included at least twice in the data collections.


Election Surveys Used:

All the available Swiss election surveys were used to build up this database. Each of them is separately archived and documented at DARISS:

Political Attitudes and Behaviour in Switzerland 1971/72, conducted by the Department of Political Science, University of Geneva (see https://forsbase.unil.ch/project/study-public-overview/13036/0/)
Political Attitudes in Switzerland 1975 (Political Action), conducted by the Department of Political Science, University of Geneva (see https://forsbase.unil.ch/project/study-public-overview/10710/0/)
National and Federal Council Elections 1979, conducted by the Department of Psychology, University of Zurich, on behalf of the Tages Anzeiger (see https://forsbase.unil.ch/project/study-public-overview/2551/0/)
National Elections 1987 (VOX), conducted by the Research Center for Swiss Politics, University of Bern, and GfS
National Elections 1991 (VOX), conducted by the Research Center for Swiss Politics, University of Bern, and GfS
Swiss Electoral Studies (Selects) 1995: Post-Election Survey, conducted by the Departments of Political Science, Universities of Bern, Geneva, and Zurich (see https://forsbase.unil.ch/project/study-public-overview/1993/0/)
Swiss Electoral Studies (Selects) 1999: Post-Election Survey, conducted by the Departments of Political Science, Universities of Bern, Geneva, and Zurich (see https://forsbase.unil.ch/project/study-public-overview/5937/0/)
Swiss Electoral Studies (Selects) 2003: Post-Election Survey, conducted by the Departments of Political Science, Universities of Bern, Geneva, St. Gall, and Zurich, DARIS and OVP/USTAT (see https://forsbase.unil.ch/project/study-public-overview/11328/0/)
Swiss Electoral Studies (Selects) 2007: Post-Election Survey, conducted by the Departments of Political Science, Universities of Bern, Geneva, Lausanne, St. Gall, and Zurich, FORS, OVP/USTAT, FSO and the Federal Chancellery (see https://forsbase.unil.ch/project/study-public-overview/8436/0/)
Swiss Electoral Studies (Selects) 2011: Post-Election Survey, conducted by FORS (see https://forsbase.unil.ch/project/study-public-overview/12631/0/)
Swiss Electoral Studies (Selects) 2015: Post-Election Survey, conducted by FORS (see https://forsbase.unil.ch/project/study-public-overview/13882/0/)
Swiss Election Study (Selects) 2019: Post-Election Survey, conducted by FORS (see https://forsbase.unil.ch/project/study-public-overview/16968/0/)

The VOX 1979 and 1983 survey data were lost. While the department of psychology of the University of Zurich did a parallel election study in 1979 which we used to substitute the former, there is absolutely no replacement for the latter. Thus we are left with 12 national elections over a period of 48 years so far.

Analysis of ‘Canton Thurgau (level municipalities) ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/wahlen-gr_3-2020_stat-kanton-thurgau on 18 January 2022.

--- Dataset description provided by original source is as follows ---

The data sets include results of the Thurgauer Grand Council elections in 2008, 2012, 2016 and 2020 at municipal level: Party strength, party votes, turnout and voters. Note on the year 2020: Data as published in Official Gazette No 12/2020 of 20 March 2020 (Arbon, Kreuzlingen, Münchwilen and Weinfelden districts) and in Official Gazette No 27/2020 of 3 July 2020 (Bezirk Frauenfeld) Reference to the source: State Chancellery Canton of Thurgau

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Popular Initiative of 10 October 2016 “For Responsible Business — for the Protection of People and the Environment” ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/vote-volksinitiative-vom-10-oktober-2016-fuer-verantwortungsvolle-unternehmen-zum-schutz-von-mensch-und-umwelt-staatskanzlei-zug on 17 January 2022.

--- Dataset description provided by original source is as follows ---

Final results of the federal vote "People’s Initiative of 10 October 2016 "For responsible companies — for the protection of people and the environment", 29 November 2020, canton of Zug, broken down by municipalities.

--- Original source retains full ownership of the source dataset ---

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, Round 6 (2014-2015) 36 countries, and Round 7 (2016-2018) 34 countries. The survey covered 34 countries in Round 8 (2019-2021).

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens of Zambia who are 18 years and older

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Zambia - Sample size: 1,200 - Sampling Frame: 2020 population projections based on the 2016 Bureau of Statistics Population Census - Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample - Stratification: District and urban/peri-urban/rural location - Stages: PSUs (from strata), start points, households, respondents - PSU selection: Probability Proportionate to Population Size (PPPS) - Cluster size: 8 households per PSU - Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval - Respondent selection: Gender quota filled by alternating interviews between men and women; respondents of appropriate gender listed, after which computer randomly selects individual

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 8 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data.

The questionnaire consists of three parts: 1. Part 1 captures the steps for selecting households and respondents, and includes the introduction to the respondent and (pp.1-4). This section should be filled in by the Fieldworker. 2. Part 2 covers the core attitudinal and demographic questions that are asked by the Fieldworker and answered by the Respondent (Q1 – Q100). 3. Part 3 includes contextual questions about the setting and atmosphere of the interview, and collects information on the Fieldworker. This section is completed by the Fieldworker (Q101 – Q123).

Response rate

Outcome rates: - Contact rate: 93% - Cooperation rate: 74% - Refusal rate: 9% - Response rate: 69%

Sampling error estimates

The sample size yields country-level results with a margin of error of +/-3 percentage points at a 95% confidence level.

This graph shows the results of the 2016 presidential elections in the United States. Donald Trump has won the election with 306 votes in the electoral college. In the contested state of Florida he captured 49 percent of the vote.