About this dataset

Context

The dataset tabulates the Non-Hispanic population of Cape May Point by race. It includes the distribution of the Non-Hispanic population of Cape May Point across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Cape May Point across relevant racial categories.

Key observations

With a zero Hispanic population, Cape May Point is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 119 (76.77% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Cape May Point
• Population: The population of the racial category (for Non-Hispanic) in the Cape May Point is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape May Point total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape May Point Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Cape Vincent town by race. It includes the population of Cape Vincent town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape Vincent town across relevant racial categories.

Key observations

The percent distribution of Cape Vincent town population by race (across all racial categories recognized by the U.S. Census Bureau): 77.08% are white, 11.54% are Black or African American, 0.55% are American Indian and Alaska Native, 1.10% are Asian, 3.31% are some other race and 6.42% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape Vincent town
• Population: The population of the racial category (excluding ethnicity) in the Cape Vincent town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Vincent town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Vincent town Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Cape May County, NJ (B03002019E034009) from 2009 to 2023 about Cape May County, NJ; Ocean City; NJ; latino; hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset tabulates the population of Cape May Point by race. It includes the population of Cape May Point across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape May Point across relevant racial categories.

Key observations

The percent distribution of Cape May Point population by race (across all racial categories recognized by the U.S. Census Bureau): 76.77% are white, 17.42% are Black or African American, 3.87% are Asian and 1.94% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape May Point
• Population: The population of the racial category (excluding ethnicity) in the Cape May Point is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape May Point total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape May Point Population by Race & Ethnicity. You can refer the same here

As of 2024, South Africa's population increased, counting approximately 63 million inhabitants. Of these, roughly 27.5 million were aged 0-24, while 654,000 people were 80 years or older. Gauteng and Cape Town are the most populated South Africa’s yearly population growth has been fluctuating since 2013, with the growth rate dropping below the world average in 2024. The majority of people lived in the borders of Gauteng, the smallest of the nine provinces in terms of land area. The number of people residing there amounted to 16.6 million in 2023. Although the Western Cape was the third-largest province, the city of Cape Town had the highest number of inhabitants in the country, at 3.4 million. An underemployed younger population South Africa has a large population under 14, who will be looking for job opportunities in the future. However, the country's labor market has had difficulty integrating these youngsters. Specifically, as of the fourth quarter of 2024, the unemployment rate reached close to 60 percent and 384 percent among people aged 15-24 and 25–34 years, respectively. In the same period, some 27 percent of the individuals between 15 and 24 years were economically active, while the labor force participation rate was higher among people aged 25 to 34, at 74.3 percent.

South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Cape Girardeau County, MO (B03002010E029031) from 2009 to 2023 about Cape Girardeau County, MO; Cape Girardeau; MO; non-hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races (5-year estimate) in Bollinger County, MO (B03002009E029017) from 2009 to 2023 about Bollinger County, MO; Cape Girardeau; MO; non-hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Cape Vincent by race. It includes the distribution of the Non-Hispanic population of Cape Vincent across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Cape Vincent across relevant racial categories.

Key observations

Of the Non-Hispanic population in Cape Vincent, the largest racial group is White alone with a population of 468 (97.50% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Cape Vincent
• Population: The population of the racial category (for Non-Hispanic) in the Cape Vincent is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Vincent total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Vincent Population by Race & Ethnicity. You can refer the same here

Geographic coverage

The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

Analysis unit

The units of analysis under observation in the South African census 1985 are households and individuals

Universe

The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

Mode of data collection

Face-to-face [f2f]

Research instrument

The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

Data appraisal

UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
Whites 7.6%
Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Some Other Race Alone (5-year estimate) in Lee County, FL (B03002008E012071) from 2009 to 2023 about Lee County, FL; Cape Coral; FL; non-hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset tabulates the population of Cape Elizabeth town by race. It includes the population of Cape Elizabeth town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cape Elizabeth town across relevant racial categories.

Key observations

The percent distribution of Cape Elizabeth town population by race (across all racial categories recognized by the U.S. Census Bureau): 96.16% are white, 0.13% are Black or African American, 0.07% are American Indian and Alaska Native, 0.53% are Asian, 0.23% are some other race and 2.88% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Cape Elizabeth town
• Population: The population of the racial category (excluding ethnicity) in the Cape Elizabeth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Cape Elizabeth town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape Elizabeth town Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Some Other Race Alone (5-year estimate) in Alexander County, IL (B03002018E017003) from 2009 to 2023 about Alexander County, IL; Cape Girardeau; IL; latino; hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

This list ranks the 16 cities in the Cape May County, NJ by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of cities in the Cape May County, NJ by their Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Cape May County Other Race Population: This tells us how much of the entire Cape May County, NJ Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The purpose of this study was to explore the relationship between truth acceptance and reconciliation among South Africans during and since the political transition from Apartheid to democracy. The study investigated the extent to which South Africans participated in the truth as promulgated by the Truth and Reconciliation Commission and the degree to which they were "reconciled." The Truth and Reconciliation Commission (TRC) was based on the Promotion of National Unity and Reconciliation Act of 1995. The TRC investigated past gross human rights violations and granted amnesty to individuals in exchange for full and public disclosure of information related to these crimes. The hypothesis that truth acceptance leads to reconciliation was tested in this research. Data were collected through a rigorous and systematic survey of South Africans. Nearly all relevant segments of the South African population were included in the sample, as well as representative subsamples of at least 250 respondents of most major racial/ethnic/linguistic groups. Questions about the TRC investigated respondent awareness, knowledge, and approval of the activities of the TRC. Respondents were asked for their opinions on the effectiveness of the TRC in its efforts to provide a true and unbiased account of South Africa's history and in awarding compensation to those who suffered abuses under the Apartheid regime. Other questions about the TRC asked respondents how important it was to find out the truth about the past and achieve racial reconciliation. Demographic variables include age, marital status, education level, and employment status.Response Rates: A total of 3,727 interviews were completed. In the primary sample, 3,139 interviews were completed. The boost sample included 588 completed interviews. The overall response rate for the survey was approximately 87 percent.(1) This study was conducted in collaboration with Amanda Gouws (Stellenbosch University, South Africa), Charles Villa-Vicencio (Institute for Justice and Reconciliation, Cape Town, South Africa), and Helen Macdonald (Institute for Justice and Reconciliation, Cape Town, South Africa).(2) Two weight variables are included in the dataset. One weight variable (NATWT) should be used when analysis is not conducted by race, and the other (RACEWT) should be used when conducting analyses comparing respondent race. (3) Users must cite the original NSF grant number in all materials produced from this project.South African population, aged 18 and over.The area probability sample included a primary sample of South Africans of all races and a boost sample of white South Africans. Representative subsamples of at least 250 respondents of most major racial, ethnic, and linguistic groups were also included.

Total household population Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Cape May County, New Jersey by age, education, race, gender, work experience and more.

Population Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Cape Girardeau, Missouri by age, education, race, gender, work experience and more.

How population size influences quantitative genetic variation and differentiation among natural, fragmented populations remains unresolved. Small, isolated populations might occupy poor quality habitats and lose genetic variation more rapidly due to genetic drift than large populations. Genetic drift might furthermore overcome selection as population size decreases. Collectively, this might result in directional changes in additive genetic variation (VA) and trait differentiation (QST) from small to large population size. Alternatively, small populations might exhibit larger variation in VA and QST if habitat fragmentation increases variability in habitat types. We explored these alternatives by investigating VA and QST using nine fragmented populations of brook trout varying 50-fold in census size N (179-8416) and 10-fold in effective number of breeders, Nb (18-135). Across 15 traits, no evidence was found for consistent differences in VA and QST with population size and almost no evid...

About this dataset

Context

The dataset tabulates the Cape Vincent town median household income by race. The dataset can be utilized to understand the racial distribution of Cape Vincent town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Cape Vincent Town, New York median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Cape Vincent Town, New York (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Cape Vincent town median household income by race. You can refer the same here

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 Sierra Leone 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.

Sierra Leone - Sample size: 1,200 - Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample - Stratification: Region and rural-urban 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: 99% - Cooperation rate: 96% - Refusal rate: 2% - Response rate: 96%

Sampling error estimates

+/- 3% at 95% confidence level