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 USACE SACS Population and Infrastructure Exposure Index combines the normalized score of each Infrastructure and Population Index, and then re-normalizes these values across the study area.The SACS Infrastructure Exposure Index was created by spatially joining 52 infrastructure datasets from DHS’s HIFLD database and Military Installation Ranges and Training Areas from the DOD OSD to census tracts across the study area. The infrastructure elements were assigned a weighting value consistent with the NACCS Tier 1 Methodology (URL). The weighted infrastructure element values were then aggregated by count within each census tract, and then divided by the census tract area to generate an infrastructure density value. This infrastructure density value was then enumerated and ranked across all census tracts, and normalized to 0 to 1 using these rankings. The census tract feature dataset was then converted to a grid using these normalized values for aggregation with other SACS datasets.The SACS Population Exposure Index depicts the 2015 Census – American Community Survey data as population density. This population density is calculated as persons per square mile, per census tract. The census tracts depicted are within the USACE South Atlantic Coastal Study boundaries with an inland extent of NOAA’s Category 5 Maximum of Maximum Storm Surge Hazard Layer. The index ranks all census tracts within the SACS study area on a percentile index by population density. These data are represented on a normalized scale of 0 to 1, with 1 being the most densely populated census tract in the study area, and zero being the least densely populated census tract. The census tract feature dataset was then converted to a grid using these normalized values for aggregation with other SACS datasets.References:DHS HIFLDhttps://gii.dhs.gov/hifld/DOD OSDhttps://www.acq.osd.mil/dodsc/fast41_gisdatasets.htmlUS Censushttps://www.census.gov/NACCS (appendix C, page 107)https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdfThis Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

Data on the population of Czech Republic municipalities published by the Czech Statistical Office.

I've combined the datasets they published since 2011 into a time series.

I also added region names and codes together with a flag if a municipality is military or not. Military municipalities typically have no population, but it can change over time.

More details can be found here: https://www.czso.cz/csu/czso/population-of-municipalities-1-january-2023

The Sample Characteristics dataset includes information on the participants who are included in the studies. This information is provided at the study level—meaning, data are not broken down into the specific treatment arms such as the intervention or control groups. Use this dataset if you want to learn more about the number of participants in the study; inclusion and exclusion criteria related to substance use and suicidality; baseline clinical characteristics such as PTSD severity, trauma type, military status, and comorbidities; and, basic demographic information such as age, gender or race for the sample as a whole (not individual participants). Visualizations made from this dataset will be based on the 496 RCTs included in the PTSD-Repository. Values reported as "NA" or not reported "NR" by the study are null values (empty cells). Data is at the study level.

Special research wave of the research series "How are you, Slovakia?" was a reaction to the attack of the Russian Federation troops on Ukraine and was fielded between 25 February and 1 March 2022. The war in Ukraine began in the early hours of 24 February with the invasion of Russian troops into Ukraine. Since the beginning of the invasion, attacks on military and non-military targets and civilian casualties have been reported. Most of the Western world, as well as the European Union, has condemned the Russian aggression and imposed tough economic sanctions. Thousands of war refugees began to arrive in Slovakia from the territory of Ukraine. The survey monitors respondents' fears of war, willingness to help refugees, and preferred response to the attack on Ukraine. It also ascertains respondents' geopolitical orientation, views on responsibility for the war conflict, and attitudes toward NATO. The survey is the first special research wave within “How Are You, Slovakia?” and was conducted in parallel with the thirteenth regular research wave of the “How Are You, Slovakia?” survey series. How are you, Slovakia? Online interviews - CAWI Adult inhabitants of Slovakia (18+) with access to the internet The survey used a quota sample from the MNFORCE online panel. The sample was designed as representative for the following socio-demographic variables: gender, age, county (kraj), size of settlement and education of respondent. Only population with access to the internet is covered by the survey. This means that mostly older persons without internet access are missing from the sample.

Exposure to morally injurious events may have a severe, prolonged negative impact on psychosocial functioning, known as moral injury (MI). Research into the prevalence of MI has mostly focused on event exposure rather than on psychosocial impact. Also, the relationship between MI and post-traumatic stress disorder (PTSD) remains a matter of interest. The aim of this study was to identify MI and PTSD symptom profiles among trauma-exposed, treatment-seeking police officers and military veterans, and to explore demographic and clinical differences between symptom profiles. Latent class and multinomial regression analyses were conducted in a sample of 1,703 participants, using the Clinician-Administered PTSD Scale for DSM-5 and the Brief Symptom Inventory. Four classes of participants were identified, labeled as a MI class (n = 192; 11.27%), a MI-PTSD class (n = 565; 33.18%), a PTSD class (n = 644; 37.82%), and a Neither MI-nor PTSD class (n = 302; 17.73%), resulting in 44.45% (n = 757) of participants who met an MI symptom profile with or without PTSD. There were significant differences between the classes in terms of gender as well as PTSD and comorbid psychopathology symptom severity, the latter of which was highest in the MI-PTSD class. In conclusion, a substantial subgroup of trauma-exposed, treatment-seeking police officers and military veterans could be classified as suffering from MI. Routinely screening for MI in treatment-seeking police officers and military veterans is recommended, and interventions aimed at relieving MI in these populations may be indicated.

This layer contains a Vermont-only subset of block group level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.*VCGI exported a Vermont-only subset of the nation-wide layer to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator County_Name: County Name State_Name: State Name P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division COUNTY: County (FIPS) COUNTYNS: County (NS) TRACT: Census Tract BLKGRP: Block Group AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLAT: Internal Point (Latitude) INTPTLON: Internal Point (Longitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

The purpose of this study was to examine whether the Police Cadet Corps program in New York City had achieved its goal of improving the police force through additional training of applicants with higher education. The evaluation of the program was designed to answer questions such as (1) How was the program recruitment implemented, and with what success? (2) What were the role-related perceptions and attitudes of the cadets and how did they differ, if at all, among different types of cadets and from those of the members of the latest recruit class? (3) How, if at all, did the program experience affect the cadets' perceptions and attitudes? and (4) How did the attitudes and perceptions of cadets compare to non-cadet recruits with and without some college education in the same academy class? Four cohorts of cadets were asked to complete several different questionnaires throughout the course of the program, which culminated in graduation from the police academy. Two sets of non-cadet recruits from the academy were also included in the research. Major variables in the data collection detail reasons for entry into the police department, opinions regarding police, and perceptions and attitudes toward the police cadet program. Some questionnaires also provided information on demographic characteristics of the cadets (race, sex, marital status, military service and branch, highest level of education, family income, and year of birth). The unit of observation is the New York City police cadet.

Police-reported hate crime, number of incidents and rate per 100,000 population, Census Metropolitan Areas and Canadian Forces Military Police, 2014 to 2024.

The Population and Dwellings data from the 2021 Federal Census covers population in private households by age and gender. For questions, please contact socialresearch@calgary.ca. Please visit Data about Calgary's population for more information.

Population in private households refers to all persons or group of persons who occupy the same dwelling and do not have a usual place of residence elsewhere in Canada or abroad. For census purposes, households are classified into three groups: private households, collective households, and households outside Canada. Unless otherwise specified, all data in census products are for private households only. Population in private households includes Canadian citizens and landed immigrants whose usual place of residence is Canada. Also includes refugee claimants, holders of work and study permits, Canadian citizens and landed immigrants at sea or in port aboard merchant or government vessels, and Canadian citizens away from Canada on military or diplomatic business. Excludes government representatives and military members of other countries and residents of other countries visiting Canada.

Age refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well‑defined reference date).

Gender refers to an individual's personal and social identity as a man, woman, or non‑binary person (a person who is not exclusively a man or a woman). A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport, or driver's licence. A person's gender may change over time. Statistics Canada collected data about transgender and non-binary populations for the first time on the 2021 Census. The category "Men+" includes men (and/or boys), as well as some non-binary persons. The category "Women+" also includes women (and/or girls), as well as some non-binary persons.

This is a one-time load of Statistics Canada federal census data from 2021 applied to the Communities, Wards, and City geographical boundaries current as of 2022 (so they will likely not match the current year's boundaries). Update frequency is every 5 years. Data Steward: Business Unit Community Strategies (Demographics and Evaluation). This dataset is for general public and internal City business groups.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), Canada, provinces, territories, Census Metropolitan Areas and Canadian Forces Military Police, 1998 to 2024.