Calculation strategy for survey and population weighting of the data.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

Included here are weights to remove any differences between the composition of the sample and the estimated composition of the population. See the attached codebook for information regarding how these weights were calculated.

Demographic information for a Utah survey of mental health stigma used to generate different survey weights to match census data.

Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functional forms used in weighting. In the following paper, we propose two sensitivity analyses for the exclusion of important covariates: (1) a sensitivity analysis for partially observed confounders (i.e., variables measured across the survey sample, but not the target population), and (2) a sensitivity analysis for fully unobserved confounders (i.e., variables not measured in either the survey or the target population). We provide graphical and numerical summaries of the potential bias that arises from such confounders, and introduce a benchmarking approach that allows researchers to quantitatively reason about the sensitivity of their results. We demonstrate our proposed sensitivity analyses using state-level 2020 U.S. Presidential Election polls.

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.

Conventional survey tools such as weighting do not address non-ignorable nonresponse that occurs when nonresponse depends on the variable being measured. This paper describes non-ignorable nonresponse weighting and imputation models using randomized response instruments, which are variables that affect response but not the outcome of interest \citep{SunEtal2018}. The paper uses a doubly robust estimator that is valid if one, but not necessarily both, of the weighting and imputation models is correct. When applied to a national 2019 survey, these tools produce estimates that suggest there was non-trivial non-ignorable nonresponse related to turnout, and, for subgroups, Trump approval and policy questions. For example, the conventional MAR-based weighted estimates of Trump support in the Midwest were 10 percentage points lower than the MNAR-based estimates. Data to replicate estimation described in "Countering Non-Ignorable Nonresponse in Survey Models with Randomized Response Instruments and Doubly Robust Estimation"

In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias---and that these methods failed to adjust for important interactions between key variables like educational attainment, race, and geographic region. Finding calibration weights that account for important interactions remains challenging with traditional survey methods: raking typically balances the margins alone, while post-stratification, which exactly balances all interactions, is only feasible for a small number of variables. In this paper, we propose multilevel calibration weighting, which enforces tight balance constraints for marginal balance and looser constraints for higher-order interactions. This incorporates some of the benefits of post-stratification while retaining the guarantees of raking. We then correct for the bias due to the relaxed constraints via a flexible outcome model; we call this approach Double Regression with Post-stratification (DRP). We use these tools to to re-assess a large-scale survey of voter intention in the 2016 U.S. presidential election, finding meaningful gains from the proposed methods. The approach is available in the multical R package. Contains replication materials for "Multilevel calibration weighting for survey data", including raw data, scripts to clean the raw data, scripts to replicate the analysis, and scripts to replicate the simulation study.

Media use related to crime. Weighting of criminal offenses. Perception of safety.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The STEP target population is the urban population aged 15 to 64 (inclusive). Areas are classified as urban based on Armenia's official definition.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Armenia STEP survey comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc - Persons living outside the country at the time of data collection

In some countries, extremely remote villages or conflict-ridden regions could not be surveyed.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Armenia sample design is a 3 stage sample design. There was no explicit stratification but the sample is implicitly stratified by Region. Implicit stratification was achieved by sorting the PSUs by Region and selecting a systematic sample of PSUs.

First Stage Sample The primary sample unit (PSU) is a cluster of households that are users of Electricity Networks of Armenia (ENA). The first stage units were selected by the World Bank Survey Methodologist. Each PSU is uniquely defined by the sample frame variable 'Cluster_ID'. The sampling objective was to conduct interviews in 200 PSUs. In addition, 25 extra PSUs were selected for use in case it was impossible to conduct any interviews in one or more initially selected PSUs. (N.B. None of the 25 extra PSUs were required to be activated.)

Second Stage Sample The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. The households were selected in each PSU using a systematic random method. There was an expectation of high non-response for the Armenia STEP. Therefore, it was decided to select 60 households in each PSU; in each PSU, 2 replicates of 30 households each were selected. The sample of 60 households was divided randomly into an initial sample of 15 households and a reserve sample of 45 households which was activated as necessary in the order in which the sample was selected. During the data collection operation, one PSU (i.e., PSU #183) required additional sample due to exceptionally high non-response. A 3rd replicate of 30 households was selected to accommodate this requirement. Thus, a sample of 90 households was selected in this PSU.

Third Stage Sample The third stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - The background questionnaire developed by the WB STEP team - Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The WB STEP team and ETS collaborated closely with the Armenian survey firm during the process and reviewed the adaptation and translation to Armenian (using a back translation).

The survey instruments were both piloted as part of the survey pretest.

The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process:

1) Raw data is sent by the survey firm 2) The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

An overall response rate of 50.3% was achieved in the Armenia STEP Survey. Table 18 of the STEP Survey Weighting Procedures Summary provides the detailed percentage distribution by final status code.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. Please refer to the STEP Survey Weighting Procedures Summary provided as an external resource.

Abstract (en): The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Data collected in this study measured the amount of time that people spent doing various activities in 2005, such as paid work, child care, religious activities, volunteering, and socializing. Respondents were randomly selected from households that had completed their final month of the Current Population Survey (CPS), and were interviewed two to five months after their household's last CPS interview. Respondents were interviewed only once and reported their activities for the 24-hour period from 4 a.m. on the day before the interview until 4 a.m. on the day of the interview. Respondents indicated the total number of minutes spent on each activity, including where they were and whom they were with. Except for secondary child care, data on activities done simultaneously with primary activities were not collected. Part 1, Respondent and Activity Summary File, contains demographic information about respondents and a summary of the total amount of time they spent doing each activity that day. Part 2, Roster File, contains information about household members and nonhousehold children under the age of 18. Part 3, Activity File, includes additional information on activities in which respondents participated, including the location of each activity and the total time spent on secondary child care. Part 4, Who File, includes data on who was present during each activity. Part 5, ATUS-CPS 2005 File, contains data on respondents and members of their household collected two to five months prior to the ATUS interviews during their participation in the Current Population Survey (CPS). Parts 6-10 contain supplemental data files that can be used for further analysis of the data. Part 6, Case History File, contains information about the interview process, such as identifiers and interview outcome codes. Part 7, Call History File, gives information about each call attempt, including the call date and outcome. Part 8, Trips File, provides information about the number, duration, and purpose of overnight trips away from home for two or more nights in a row. Part 9, Replicate Weights File I, contains base weights, replicated base weights, and replicate final weights for each case that was selected to be interviewed for ATUS, while Part 10, Replicate Weights File II, contains replicate weights that were generated using the 2006 weighting method. Demographic variables include sex, age, race, ethnicity, education level, income, employment status, occupation, citizenship status, country of origin, relationship to household members, and the ages and number of children in the household. The data contain weight variables which should be used in analyzing the data. Unweighted data are not representative of the population due to differences between population groups in both sampling and nonresponse. ATUS weight variables include the ATUS final weight (TUFINLWGT), which indicates the number of person-days the respondent represents, the ATUS base weight (TUBWGT), and a ATUS final weight based on 2006 weighting methodology (TU06FWGT). ATUS weights were selected from the Current Population Survey (CPS), and CPS weights (after the first-stage adjustment) are the basis for the ATUS weights. These base weights were adjusted to account for the fact that less populous states were not oversampled in ATUS, as they were in the CPS. Further adjustments were made to account for the probability of selecting each household within the ATUS sampling strata and the probability of selecting each person from each sample household. Part 9 contains replicate weights for the variable TUFINLWGT, as well as base weights, while Part 10 contains replicate weights for the variable TU06FWGT. ATUS replicate weights were based on the replicate weights developed for the CPS. ATUS began with the CPS replicate weight after the first-stage ratio adjustment, and each replicate was processed through all of the stages of the ATUS weighting procedure. The CPS replicate weights were based on a modified balanced half-sample method of replication, developed in the 1980s by Robert Fay. For more information about the replicate weights, see the publication, Technical Paper 63RV: Current Population Survey -- Design and Methodology, available via the Bureau of Labor Statistics Web site. More information on the weighting variables used in this study can be found in t...

Abstract

The STEP (Skills Toward Employment and Productivity) Skills Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely designed modules in the Employer survey aim to assess the structure of the labor force; the skills (cognitive skills, behavior and personality traits, and job-relevant skills) currently being used; the skills that employers look for when hiring new workers; the propensity of firms to provide training (including satisfaction with education, training, and levels of specific skills) and the link between skills and compensation and promotion. The survey also captures background characteristics (size, legal form, industry, full time vs. non-standard employment, occupational breakdown), performance (revenues, wages and other costs, profits, scope of market), key labor market challenges and their ranking relative to other challenges, and job skill requirements of the firms being interviewed. An additional component of the survey conducted in Vietnam is a module on innovation designed to capture the characteristics of Research & Development (including factors related to product development and capacity building).

The questionnaire can be adapted to address a sample of firms in both informal and formal sectors, with varying sizes and industry classifications.

Geographic coverage

Capital Hanoi and other urban areas

Analysis unit

The units of analysis are establishments or workplaces – a single location at which one or more employees work. The larger legal entity may include multiple establishments.

Universe

The universe of the study are formal sector non-government enterprise workplaces included in the General Statistics Office Vietnam enterprise census 2009 and informal sector firms registered with provincial Departments of Planning and Investment (DPIs)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling objective of the survey was to obtain interviews from 400 non-government enterprise workplaces in the capital and urban regions of Vietnam.

Two-stage stratified random sampling was used in the survey. A list of businesses registered with the General Statistics Office Vietnam enterprise census 2009 served as the sampling frame for formal sector. Informal sector firms were drawn from a sample created using data from the Departments of Planning and Investment (DPIs).

Detailed information about the sampling is available in the Vietnam Survey Implementation and Findings Report and Vietnam Employer Survey Weighting Procedure, provided as an external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire for the Vietnam STEP Employer Survey consists of six modules: Section 1 – Work Force Section 2 – Skills Used Section 3 – Hiring Practices Section 4 – Training and Compensation Section 5 – Background Section 6 - Innovation

It has been provided as an external resource.

In the case of Vietnam, the questionnaire was adapted to the Vietnamese context and published in English and Vietnamese.

Cleaning operations

STEP Data Management Process:

1) Raw data is sent by the survey firm.

2) The World Bank (WB) STEP team runs data checks on the Questionnaire data. Comments and questions are sent back to the survey firm.

3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data.

4) The WB STEP team again check to make sure the data files are clean. This might require additional iterations with the survey firm.

5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies.

Response rate

An overall response rate of 63.8% was achieved in Vietnam STEP Survey. Detailed distribution of responses by stratum can be found in the Vietnam Employer Survey Weighting Procedure (Table 6), available as an external resource.

The impact on Labour Force Survey estimates of applying tenure weighting to mitigate for non-response bias during the coronavirus pandemic. The new methodology is applied from January to March 2020.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language. The STEP Skills Measurement Survey for Ukraine is integrated into the Ukrainian Longitudinal Monitoring Survey (ULMS) 2012.

Geographic coverage

The STEP survey was limited to the urban area of Ukraine.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population for the Ukraine STEP survey comprises all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of the country at the time of data collection. This includes all residents except foreign diplomats and non-nationals working for international organizations.

The sample excluded individuals permanently institutionalized in medical facilities, military quarters, and prisons; these exclusions totalled about 725,000 persons or about 2% of the population. Also excluded from STEP was a 30-km zone around the Chernobyl Nuclear Power Plant, an area with a high level of radiation contamination where public access is restricted and all population was evacuated.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Ukraine used a stratified sample design comprised of three components: 1) ULMS Panel Sample; 2) New ULMS-2012 subsample; 3) Step Urban Subsample.

In the ULMS Panel Sample and the Step Urban Subsample, the urban sample was selected within 26 strata consisting of the Autonomous Republic of Crimea, the city of Kiev, and 24 Oblasts, i.e., geographic administrative units. In the New ULMS-2012 subsample, there was no explicit stratification. The Survey Weighting Summary (see related materials) provides more information on the sampling procedure.

Sampling deviation

Some of the sampled households were ineligible for STEP for reasons such as vacant, not habitable, no eligible household member, etc.

Mode of data collection

Face-to-face [f2f]

Research instrument

The merged ULMS-STEP survey instrument consists of three questionnaires : (i) the merged household questionnaire\roster, including the first block of STEP; (ii) the standard individual questionnaire, including the fifth block of STEP and some parts of the second, third and fourth blocks of STEP; (iii) and the extended individual questionnaire, with an additional module on employment skills and a Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments from English, following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. In the Ukraine STEP survey, household and individual questionnaires were prepared in both Ukranian and Russian. However, the literacy assessment was done in Ukrainian only (due to budget constraints).

• The survey instruments were both piloted as part of the survey pretest.
• The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Country-specific questions on the Household Questionnaire result from a merge of STEP Ukraine survey with ULMS-2012 panel study.

Cleaning operations

STEP Data Management Process 1. Raw data is sent by the survey firm 2. The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6. ETS scales the Reading Literacy Assessment data. 7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

An overall response rate of 60.4% was achieved in the Ukraine STEP Survey.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

The City of Bloomington contracted with National Research Center, Inc. to conduct the 2019 Bloomington Community Survey. This was the second time a scientific citywide survey had been completed covering resident opinions on service delivery satisfaction by the City of Bloomington and quality of life issues. The first was in 2017. The survey captured the responses of 610 households from a representative sample of 3,000 residents of Bloomington who were randomly selected to complete the survey. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the City of Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The American Time Use Survey (ATUS) is the Nation's first federally administered, continuous survey on time use in the United States. This multi-year data collection contains information on the amount of time (in minutes) that people spent doing various activities on a given day, including the arts activities, in the years 2003 through 2023. Data collection for the ATUS began in January 2003. Sample cases for the survey are selected monthly, and interviews are conducted continuously throughout the year. In 2023, approximately 9,000 individuals were interviewed. Estimates are released annually. ATUS sample households are chosen from the households that completed their eighth (final) interview for the Current Population Survey (CPS), the nation's monthly household labor force survey. ATUS sample households are selected to ensure that estimates will be nationally representative. One individual age 15 or over is randomly chosen from each sampled household. This "designated person" is interviewed by telephone once about his or her activities on the day before the interview--the "diary day." The ATUS Activity Coding Lexicon is a 3-tiered classification system with 17 first-tier categories. Each of the first-tier categories has two additional levels of detail. Respondents' reported activities are assigned 6-digit activity codes based on this classification system. Additionally, the study provides demographic information--including sex, age, ethnicity, race, education, employment, and children in the household. IMPORTANT: The 2020 ATUS was greatly affected by the coronavirus (COVID-19) pandemic. Data collection was suspended in 2020 from mid-March to mid-May. ATUS data files for 2020 contain all ATUS data collected in 2020--both before and after data collection was suspended. For more information, please visit BLS's ATUS page. The weighting method was changed for 2020 to account for the suspension of data collection in early 2020 due to the COVID-19 pandemic. Respondents from 2020 will have missing values for the replicate weights on this data file. The Pandemic Replicate weights file for 2019-20 contains 160 replicate final weights for each ATUS final weight created using the 2020 weighting method. Chapter 7 of the ATUS User's Guide provides more information about the 2020 weighting method.

The proportions of childhood chronic undernutrition for 64X5 cross-classified domains of 64 districts and five children age groups (0-11, 12-23, 24-35, 36-47, & 48-59 months) are estimated from Bangladesh DHS surveys conducted during 1996-97 to 2017-18. Both survey design and sampling weights are considered for calculation.

Background
The Annual Population Survey (APS) Household datasets are produced annually and are available from 2004 (Secure Access) and 2006 (End User Licence). They allow production of family and household labour market statistics at local areas and for small sub-groups of the population across the UK. The data comprise key variables from the Labour Force Survey (LFS) (held at the UK Data Archive under GN 33246) and the APS (person) datasets (held at the Data Archive under GN 33357). The former is a quarterly survey of households living at private addresses in the UK. The latter is created by combining individuals in waves one and five from four consecutive LFS quarters with the English, Welsh and Scottish Local Labour Force Surveys (LLFS). The APS Household datasets therefore contain results from four different sources.

The APS Household datasets include all the variables on the LFS and APS person datasets except for the income variables. They also include key family and household level derived variables. These variables allow for an analysis of the combined economic activity status of the family or household. In addition they also include more detailed geographical, industry, occupation, health and age variables.

For information on the main (person) APS datasets, for which EUL and Secure Access versions are available, please see GNs 33357 and 33427, respectively.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published in 2021.

Secure Access APS Household data
Secure Access datasets for the APS Household survey include additional variables not included in the EUL versions (GN 33455). Extra variables that may be found in the Secure Access version but not in the EUL version relate to:

• geography (see 'Spatial Units' below)
• individual demographics, including age bands, day of birth, sex/marital status and detailed ethnicity
• main reason for coming to the UK
• number of bedrooms
• health problems, work-related health problems, sickness absence from work
• reasons why not in work, including health and other reasons, wage received when not in work, time away from job, and whether and when will work in the future
• type of benefit claimed
• education and training, including
  • vocational and work-related qualifications and training
  • class of first degree
  • qualifications from government schemes
  • number of O levels/GCSEs, etc held
  • qualifications held from UK and abroad
  • qualifications gained from school/home schooling
  • qualifications below highest level
  • other qualifications
  • time spent in taught courses
  • who paid for training
  • main place of education/training
  • length of training course
  • level of Welsh baccalaureate
• worst 30 local authorities based on Indices of Deprivation
• casual/holiday work
• disability, including learning difficulty/disability
• payment of own National Insurance and tax

Prospective users of the Secure Access version of an APS Household dataset will need to fulfil additional requirements, including completion of face-to-face training and agreement to Secure Access' User Agreement, in order to obtain permission to use that version (see 'Access' section below). The EUL version of the data, for which less stingent access conditions apply, may suffice for many users' research requirements. Further details and links to all APS studies can be found via the APS Key Data series webpage.

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

For the ninth edition (October 2023), the data file covering January - December 2021 has been revised.