Unweighted and weighted percentages for demographic and smoking-related variables.

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

In this regard, the National Institute of Statistics of Rwanda conducted the Seasonal Agriculture Survey (SAS) from September 2018 to august 2019 to gather up-to-date information for monitoring progress on agriculture programs and policies. This 2019 SAS covered Main agricultural seasons are Season A (which starts from September to February of the following year) and Season B (which starts from March to June). Season C is the small agricultural season mainly for vegetables and sweet potato grown in swamps and Irish potato grown in volcanic agro-ecological zone and provides data on farm characteristics (area, yield and production), agricultural practices, agricultural inputs and use of crop production

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

This seasonal agriculture survey focused on the following units of analysis: Small scale agricultural farms and large scale farms

Universe

The SAS 2019 targeted potential agricultural land and large scale farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

Out of 10 strata, only 4 are considered to represent the country land potential for agriculture, and they cover the total area of 1,787,571.2 hectares (ha). Those strata are: 1.0 (tea plantations), 1.1 (intensive agriculture land on hillsides), 2.0 (intensive agriculture land in marshlands) and 3.0 (rangelands). The remainder of land use strata represents all the non-agricultural land in Rwanda. Stratum 1.0, which represents tea plantations, is assumed to be well monitored through administrative records by the National Agriculture Export Board (NAEB), an institution whose main mission is to promote the agriculture export commodities. Thus, SAS is conducted on 3 strata (1.1; 2.0 & 3.0) to cover other major crops. Within district, the agriculture strata (1.1, 2.0 & 3.0) were divided into larger sampling units called first-step or primary sampling units (PSUs) (as shown in Figure 2). Strata 1.1 and 2.0 were divided into PSUs of around 100 ha while stratum 3.0 was divided into PSUs of around 500 ha. After sample size determination, a sample of PSUs was done by systematic sampling method with probability proportional to size, then a given number of PSUs to be selected for each stratum, was assigned in every district. In 2019, the 2018 SAS sample of 780 segments has been kept the same for SAS 2019 in Season A and B.

At first stage, 780 PSUs sampled countrywide were proportionally allocated in different levels of stratification (Hill side, marshland and rangeland strata) for 30 districts of Rwanda, to allow publication of results at district level. Sampled PSUs in each stratum were systematically selected from the frame with probability of selection proportional to the size of the PSU.

At the second stage 780 sampled PSUs were divided into secondary sampling units (SSUs) also called segments. Each segment is estimated to be around 10 ha for strata 1.1 and 2.0 and 50 ha for stratum 3.0 (as shown in Figure 3). For each PSU, only one SSU is selected by random sampling method without replacement. This is why for 2019 5 SAS season A and B, the same number of 780 SSUs was selected. In addition to this, a list frame of large-scale farmers (LSF), with at least 10 hectares of agricultural holdings, was done to complement the area frame just to cover crops mostly grown by large scale farmers and that cannot be easily covered in area frame

At the last sampling stage, in strata 1.1 and 2.0 each segment of an average size of 10 ha (100,000 Square meters) has been divided into around 1,000 grids squares of 100 Sq. meters each, while for stratum 3.0 around 5,000 grids squares of 100 Sq. meters each have been divided. A point was placed at the center of every grid square and named a grid point (A grid point is a geographical location at the center of every grid square). A random sample of 5% of the total grid points were selected in each segment of strata 1.1 and 2.0 whereas a random sample of 2% of total grid points was selected in each segment of stratum 3.0. Grids points are reporting units within a segment, where enumerators go to every grid point, locate and delineate the plots in which the grid falls, and collect records of land use and related information. The recorded information represents the characteristics of the whole segment which are extrapolated to the stratum level and hence the combination of strata within each district provides district area related statistics.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires used for this survey namely screening questionnaire and plot questionnaire. A Screening questionnaire was used to collect information that enabled identification of a plot and its land use using the plot questionnaire. For point-sampling, the plot questionnaire is concerned with the collection of data on characteristics of crop identification, crop production and use of production, inputs (seeds, fertilizers and pesticides), agricultural practices and land tenure. All the surveys questionnaires used were published in English

Cleaning operations

The CAPI method of data collection allows the enumerators in the field to collect and enter data with their tablets and then synchronize to the server at headquarters where data are received by NISR staff, checked for consistency at NISR and thereafter transmitted to analysts for tabulation using STATA software, and reporting using office Excel and word as well.

Response rate

Data collection was done in 780 segments and 222 large scale farmers holdings for Season A, whereas in Season C data was collected in 232 segments, response rate was 100% of the sample

Abstract

The National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation (MOSPI), Government of India, since its inception in 1950 has been conducting nationwide integrated large scale sample surveys, employing scientific sampling methods, to generate data and statistical indicators on diverse socio-economic aspects. In its 70th round of survey, conducted during the period 1st January, 2013 to 31st December, 2013, NSSO carried out a Situation Assessment Survey (SAS) of Agricultural Households.

The SAS 2013 (70th round) was conducted as a repeat of the SAS conducted in the 59th round, with the same aim of capturing the condition of agricultural households in the country in the context of policies and programme of the Government of India. The survey schedule was designed for collection of information on various aspects relating to farming and other socio-economic characteristics of agricultural households. Along with information on consumer expenditure, income and productive assets, their indebtedness, farming practices and preferences, resource availability, their awareness of technological developments and access to modern technology in the field of agriculture, information on crop loss, crop insurance and awareness about Minimum Support Price (MSP) was also collected during 70th round. The information was collected in two visits from the same set of sample households with a view to collect relevant information separately for the two major agricultural seasons in a year.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

The universe of the survey include all household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified multi-stage design has been adopted for the 70th round survey. The first stage units (FSU) are the census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units (USU) are households in both the sectors. In case of large FSUs, one intermediate stage of sampling is the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

For the rural sector, the list of 2001 census villages updated by excluding the villages urbanised and including the towns de-urbanised after 2001 census (henceforth the term 'village' would mean Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the latest updated list of UFS blocks (2007-12) is considered as the sampling frame.

A total number of 8042 FSUs were allocated for the central sample at all-India level. For the state sample, 8998 FSUs were allocated for all-India. A detailed description of the sampling strategy is given in the Note on Sample Design and Estimation Procedure of NSS 70th Round, attached in the documentation/external resource.

Mode of data collection

Face-to-face paper [f2f]

Weighted logistic regression results, stratified by experimental condition.

Abstract

The main objective of the new agricultural statistics program is to provide timely, accurate, credible and comprehensive agricultural statistics to describe the structure of agriculture in Rwanda in terms of land use, crop production and livestock; which can be used for food and agriculture policy formulation and planning, and for the compilation of national accounts statistics.

In this regard, the National Institute of Statistics of Rwanda (NISR) conducted the Seasonal Agriculture Survey (SAS) from November 2015 to October 2016 to gather up-to-date information for monitoring progress on agriculture programs and policies in Rwanda, including the Second Economic Development and Poverty Reduction Strategy (EDPRS II) and Vision 2020. This 2016 RSAS covered three agricultural seasons (A, B and C) and provides data on background characteristics of the agricultural operators, farm characteristics (area, yield and production), agricultural practices, agricultural equipments, use of crop production by agricultural operators and by large scale farmers.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

The 2016 RSAS targeted agricultural operators and large scale farmers operating in Rwanda.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Seasonal Agriculture Survey (SAS) sample is composed of two categories of respondents: agricultural operators1 and large-scale farmers (LSF).

For the 2016 SAS, NISR used as the sampling method a dual frame sampling design combining selected area frame sample3 segments and a list of large-scale farmers.

NISR used also imagery from RNRA with a very high resolution of 25 centimeters to divide the total land of the country into twelve strata. A total number of 540 segments were spread throughout the country as coverage of the survey with 25,346 and 23,286 agricultural operators in Season A and Season B respectively. From these numbers of agricultural operators, sub-samples were selected during the second phases of Seasons A and B.

It is important to note that in each of agricultural season A and B, data collection was undertaken in two phases. Phase I was mainly used to collect data on demographic and social characteristics of interviewees, area under crops, crops planted, rainfall, livestock, etc. Phase II was mainly devoted to the collection of data on yield and production of crops.

Phase I serves at collecting data on area under different types of crops in the screening process, whereas the Phase II is mainly devoted to the collection of data on demographic, social characteristics of interviewees, together with yields of the different crops produced. Enumerated large-scale farmers (LSF) were 558 in both 2015 Season A and B. The LSF were engaged in either crop farming activities only, livestock farming activities only, or both crop and livestock farming activities.

Agricultural operators are the small scale farmers within the sample segments. Every selected segment was firstly screened using the appropriate materials such as the segment maps, GIS devices and the screening form. Using these devices, the enumerators accounted for every plot inside the sample segments. All Tracts6 were classified as either agricultural (cultivated land, pasture, and fallow land) or non-agricultural land (water, forests, roads, rocky and bare soils, and buildings).

During Phase I, a complete enumeration of all farmers having agricultural land and operating within the 540 selected segments was undertaken and a total of 25,495 and 24,911 agricultural operators were enumerated respectively in Seasons A and B. Season C considered only 152 segments, involving 3,445 agricultural operators.

In phase II, 50% of the large-scale farmers were undertaking crop farming activities only and 50% of the large-scale farmers were undertaking both crop and livestock farming and were selected for interview. A sample of 199 and 194 large-scale farmers were interviewed in Seasons A and B, respectively, using a farm questionnaire.

From the agricultural operators enumerated in the sample segments during Phase I, a sample of the agricultural operators was designed for Phase II as follows: 5,502 for Season A, 5,337 for Season B and 644 for Season C. The method of probability proportional to size (PPS) sampling at the national level was used. Furthermore, the total number of enumerated large-scale farmers was 774 in 2016 Season A and 622 in Season B.

The Season C considered 152 segments counting 8,987 agricultural operators from which 963 agricultural operators were selected for survey interviews.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

There were two types of questionnaires used for this survey namely Screening questionnaire and farm questionnaires.

A Screening Questionnaire was used to collect information that enabled identification of an Agricultural Operator or Large Scale Farmer and his or her land use.
Farm questionnaires were of two types: a) Phase I Farm Questionnaire was used to collect data on characteristics of Agricultural Operators, crop identification and area, inputs (seeds, fertilizers, labor, …) for Agricultural Operators and large scale farmers. b) Phase 2 Farm questionnaire was used in the collection of data on crop production and use of production.

It is important to mention that all these Farm Questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. For season C, after screening, an interview was conducted for each selected tract/Agricultural Operator using one consolidated Farm questionnaire. All the surveys questionnaires used were published in both English and Kinyarwanda languages.

Cleaning operations

Data editing took place at different stage. Firstly, the filled questionnaires were repatriated at NISR for office editing and coding before data entry started. Data entry of the completed and checked questionnaires was undertaken at the NISR office by 20 staff trained in using the CSPro software. To ensure appropriate matching of data in the completed questionnaires and plot area measurements from the GIS unit, a LOOKUP file was integrated in the CSPro data entry program to confirm the identification of each agricultural operator or LSF before starting data entry. Thereafter, data were entered in computers, edited and summarized in tables using SPSS and Excel.

Response rate

The response rate for Seasonal Agriculture Survey is 98%.

Data appraisal

All Farm questionnaires were subjected to two/three rounds of data quality checking. The first round was conducted by the enumerator and the second round was conducted by the team leader to check if questionnaires had been well completed by enumerators. And in most cases, questionnaires completed by one enumerator were peer-reviewed by another enumerator before being checked by the Team leader.

Abstract

The 2017 Tajikistan Demographic and Health Survey (TjDHS) is the second Demographic and Health Survey conducted in Tajikistan. It was implemented by the Statistical Agency under the President of the Republic of Tajikistan (SA) in collaboration with the Ministry of Health and Social Protection of Population (MOHSP).

The primary objective of the 2017 TjDHS is to provide current and reliable information on population and health issues. Specifically, the TjDHS collected information on fertility and contraceptive use, maternal and child health and nutrition, childhood mortality, domestic violence against women, child discipline, awareness and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking and high blood pressure. The 2017 TjDHS follows the 2012 TjDHS survey and provides updated estimates of key demographic and health indicators.

The information collected through the TjDHS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents) and all women age 15-49 years resident in the sample household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017 TjDHS is the 2010 Tajikistan Population and Housing Census conducted by the SA. Administratively, Tajikistan is divided into five regions: Dushanbe, Districts of Republican Subordination (DRS), Sughd, Khatlon, and Gorno-Badakhshan Autonomous Oblast (GBAO). Each region is subdivided into urban and rural areas. The country is divided into districts distributed over the country’s regions. Each district is further divided into census divisions, which are subdivided into instruction areas. Each instruction area is divided into urban enumeration areas (EAs) or rural villages. The sampling frame of the 2017 TjDHS is a list of EAs and natural villages covering all urban and rural areas of the country, with the primary sampling units (PSUs) being EAs in urban areas and natural villages in rural areas. An EA is a geographical area, usually a city block, consisting of the minimum number of households required for efficient counting; each EA serves as a counting unit for the population census.

The sample was designed to yield representative results for the urban and rural areas separately, and for each of the four administrative regions and Dushanbe. In addition, as in the previous TjDHS survey, the sample was designed to allow certain indicators to be presented for the 12 districts in Khatlon covered under the Feed the Future program (FTF); these 12 districts have been combined as a single FTF domain. The sampling frame excluded institutional populations such as persons in hotels, barracks, and prisons.

The 2017 TjDHS followed a stratified two-stage sample design. The first stage involved selecting sample PSUs (clusters) with a probability proportional to their size within each sampling stratum. A total of 366 clusters were selected, 166 in urban areas and 200 in rural areas.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters, and a fixed number of 22 households was selected from each cluster with an equal probability systematic selection process, for a total sample of just over 8,000 households.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2017 TjDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Tajikistan. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire. Suggestions were solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Russian and Tajik.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system (IFSS) to the SA central office in Dushanbe, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by The DHS Program staff. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in August 2017 and completed in February 2018.

Response rate

All 8,064 households in the selected housing units were eligible for the survey, of which 7,915 were occupied. Of the occupied households, 7,843 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 10,799 women age 15-49 were identified for subsequent individual interviews; interviews were completed with 10,718 women, yielding a response rate of 99%, which is the same response rate achieved in the 2012 survey.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Tajikistan Demographic and Health Survey (TjDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 TjDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 TjDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children

See details of the data quality tables in Appendix C of the survey final report.

Résumé

The Service Delivery Indicators ("the Indicators") provide a set of metrics for benchmarking service delivery performance in education and health in Africa to track progress across and within countries over time. The Indicators seek to enhance active monitoring of service delivery by policymakers and citizens, as well as to increase accountability and good governance. The perspective adopted by the Indicators is that of citizens accessing services and facing shortcomings.

The Service Delivery Indicators were piloted in Tanzania and Senegal in the spring/summer of 2010. The main objective of the pilots was to test the survey instruments in the field and to verify that robust indicators of service delivery quality could be collected with a single facility-level instrument in different settings. To this end, it was decided that the pilots should include an Anglophone and Francophone country with different budget systems. The selection of Senegal and Tanzania was also influenced by the presence of strong local research institutes from the AERC network: Centre de Recherche Economique et Sociale (CRES) in Senegal and the Research on Poverty Alleviation (REPOA) in Tanzania. Both research institutes have extensive facility survey experience and are also grantees of the Hewlett-supported Think Tank Initiative.

Geographic coverage

National

Analysis unit

Shool facility, health facility

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to provide estimates for each of the key Indicators, broken down by urban and rural location. To achieve this purpose in a cost-effective manner, a stratified multi-stage random sampling design was employed. Given the overall resource envelope, it was decided that roughly 150 facilities would be surveyed in each sector in Senegal. The sample frame employed consisted of the most recent list of all public primary schools and public primary health facilities, including information on the size of the population they serve.

Sample size:

Health: Urban=102 Rural=49 Total=151 Education: Urban=92 Rural=59 Total=151

Research instrument

The survey used a sector-specific questionnaire with several modules, all of which were administered at the facility level. The questionnaires built on previous similar questionnaires based on international good practice for PETS, QSDS, SAS and observational surveys. A pre-test of the instruments was done by the technical team, in collaboration with the in-country research partners, in the early part of 2010. The questionnaires were translated into French. In collaboration with the in-country research partners, members of the technical team organized a one-week training session, which included three days of testing the instruments in the field. The enumerators and supervisors were university graduates, and in many cases were also trained health and education professionals (teachers, doctors, and health workers) with previous survey experience.

EDUCATION:

• Module 1: Administered to the principal, head teacher or most senior teacher in the school
• Module 2: Administered to (a maximum of) 10 teachers randomly selected from the list of all teachers
• Module 3: Administered to the same 10 teachers as in module 2
• Module 4: Classroom observations
• Module 5: Test of teachers
• Module 6: Test of grade 4 children

HEALTH: - Module 1: Administered to the in- charge or the most senior medical staff at the facility. - Module 2: Administered to (a maximum of) 10 medical staff randomly selected from the list of all medical staff - Module 3: Administered to the same 10 medical staff as in module 2 - Module 4: Health facility observations - Module 5: Test of health workers. Patient case simulations.

The data are from a descriptive study of the USDA National School Lunch Program (NSLP) and School Breakfast Program (SBP). The data were collected between April and August 2018 using web-based surveys given to a nationally representative sample of School Food Authorities (SFAs) and 53 of 55 State agencies that administer the NSLP and SBP (Puerto Rico and the Virgin Islands were not asked to participate due to hurricanes in the region at the time of data collection). The survey questions collected data about NSLP and SBP operations during school year (SY) 2017–18 and financial data from SY 2016–17. Study date(s) and duration Data collection occurred from April to August 2018. The survey questions collected data about NSLP and SBP operations during SY 2017–18 and financial data from SY 2016–17. Study spatial scale (size of replicates and spatial scale of study area) The study area is the United States and outlying Territories that operate the NSLP and SBP. Sampling precision (within-replicate sampling or pseudoreplication) There was no sampling for the State agency survey. There was sampling for the SFA survey. The target universe was all 14,854 SFAs operating in public school districts in the United States and outlying Territories that were required to submit FNS-742 to FNS in SY 2014–15. The sampling frame of 14,854 SFAs was stratified into 10 strata based on number of students in the SFA and percentage of students certified for free or reduced-price meals. The research team implicitly stratified the 10 strata by sorting SFAs within each stratum by FNS Region and by urbanicity status to ensure the sample selected was balanced on these additional factors. Precision calculations confirmed that a responding sample of 1,750 SFAs allocated among the strata would meet the statistical requirements of the study. Therefore, assuming an 80 percent response rate, a sample of 2,187 SFAs was needed each study year. Additional information about the sampling procedures is presented in the study report: https://www.fns.usda.gov/cn/program-operations-study-school-year-2017-18 Level of subsampling (number and repeat or within-replicate sampling) Study design (before–after, control–impacts, time series, before–after-control–impacts) Descriptive study Description of any data manipulation, modeling, or statistical analysis undertaken Calculating SY 2017-18 nationally representative estimates for the SFA survey required sample weights that account for the sample design, nonresponse, and school year. The weighting procedures are described in detail in the study report: https://www.fns.usda.gov/cn/program-operations-study-school-year-2017-18 The State agency and SFA survey data include variables collected from the survey as well as variables constructed for use in analyses. To preserve the confidentiality of the SFAs all variables that could be used to determine the precise size in terms of number of students, number of schools, and/or number of meals served were “masked” by setting the variable to its size. To avoid the exposure of personal identifiable information, some of the variables in the State agency data file were classified into categories. Open ended text responses and derived variables were dropped from the SA and SFA data files. Description of any gaps in the data or other limiting factors Two of the 55 State agencies that administer the NSLP and SBP were not asked to complete the State agency survey (Puerto Rico and the Virgin Islands were experiencing hurricanes at the time of data collection). The remaining 53 State agencies completed the survey. Of the 2,187 sampled SFAs, seven were given an initial exemption due to hurricane damage, requests for exemptions, and due to lack of contact information. Additionally, four SFAs were found to be either closed or no longer participating in USDA school meals programs. Of the remaining 2,176 SFAs, 1,653 provided valid responses, yielding a response rate of 76.1 percent. Outcome measurement methods and equipment used The surveys asked State agencies and SFAs about the following topics related to operating the NSLP and SBP: eligibility determination and verification, financial management, food and beverage marketing, meal counting, meal pattern requirements, meal prices, revenues and expenditures, school participation, student participation, and Buy American/local food purchasing. Resources in this dataset:

Resource Title: Child Nutrition Program Operations Study, School Year 2017-2018 - Datasets for SA and SFA Surveys File Name: CNOPS 2017-18_PUF_AG_DATA_COMMONS.zip Resource Description: Child Nutrition Program Operations Study, School Year 2017-2018 - Datasets for SA and SFA Surveys in SAS format. Includes Codebooks, Survey Instruments, SAS Datasets, SAS Formats and Data User Guide.

This SAS macro generates childhood mortality estimates (neonatal, post-neonatal, infant (1q0), child (4q1) and under-five (5q0) mortality) and standard errors based on birth histories reported by women during a household survey. We have made the SAS macro flexible enough to accommodate a range of calculation specifications including multi-stage sampling frames, and simple random samples or censuses. Childhood mortality rates are the component death probabilities of dying before a specific age. This SAS macro is based on a macro built by Keith Purvis at MeasureDHS. His method is described in Estimating Sampling Errors of Means, Total Fertility, and Childhood Mortality Rates Using SAS (www.measuredhs.com/pubs/pdf/OD17/OD17.pdf, section 4). More information about Childhood Mortality Estimation can also be found in the Guide to DHS Statistics (www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf, page 93). We allow the user to specify whether childhood mortality calculations should be based on 5 or 10 years of birth histories, when the birth history window ends, and how to handle age of death with it is reported in whole months (rather than days). The user can also calculate mortality rates within sub-populations, and take account of a complex survey design (unequal probability and cluster samples). Finally, this SAS program is designed to read data in a number of different formats.

Weighted logistic regression results, for the entire sample and current smokers only.

Abstract

Millions of farmers in India have made significant contributions in providing food and nutrition to the entire nation, while also providing livelihoods to millions of people in the country. During the past five decades of planned economic development, India has moved from food-shortage and imports to self-sufficiency and exports. Food security and well being of the farmer appears to be major areas of concern of the planners and policy makers of Indian agriculture. In order to have a comprehensive picture of the farming community at the commencement of the third millennium, and to analyze the impact of the transformation induced by public policy, investments and technological change on the farmers' access to resources and income, as well as well-being; the Ministry of Agriculture decided to collect information on Indian farmers through a Situation Assessment Survey (SAS) and entrusted the job of conducting the survey to the National Sample Survey Organisation (NSSO).

The SAS 2003 is the first of its kind to be conducted by NSSO. Though information on a majority of items to be collected through SAS have been collected in some round or other of NSS, an integrated schedule - Schedule 33, covering some basic characteristics of farming households and their access to basic and modern farming resources was canvassed for the first time in SAS. Moreover, information on consumption of various goods and services in an abridged form were also collected to have an idea about the pattern of consumption expenditure of the farming households.

Schedule 33 was designed for collecting information on aspects relating to farming and other socio-economic characteristics of farming households. The information was collected in two visits to the same set of sample households. The first visit was made during January to August 2003 and the second, during September to December 2003. The survey was conducted in rural areas only. It was canvassed in the Central Sample except for the States of Maharashtra and Meghalaya where it was canvassed in both State and Central samples.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified multi-stage sampling design was adopted for the SAS 2003, 59th round. The First Stage Unit (FSU), also known as the primary sampling unit, was the census village in the rural sector and UFS block in the urban sector. The Ultimate Stage Units (USUs) were households in both sectors. Hamlet-group / sub-block constitute the intermediate stage, if these are formed in the selected area.

The list of villages (panchayat wards for Kerala) based on the Population Census of 1991 constituted the sampling frame for FSUs in rural areas, while the latest UFS frame was the sampling frame used for urban areas. For stratification of towns by size class, provisional population of towns as per Census 2001 was used. A detailed description of the sampling strrategy can be found in the estimation procedure document attached in the documentation/external resource.

Mode of data collection

Face-to-face paper [f2f]

Abstract (en): This survey is the first broad-based, systematic examination of the nature of civil litigation in state general jurisdiction trial courts. Data collection was carried out by the National Center for State Courts with assistance from the National Association of Criminal Justice Planners and the United States Bureau of the Census. The data collection produced two datasets. Part 1, Tort, Contract, and Real Property Rights Data, is a merged sample of approximately 30,000 tort, contract, and real property rights cases disposed during the 12-month period ending June 30, 1992. Part 2, Civil Jury Cases Data, is a sample of about 6,500 jury trial cases disposed over the same time period. Data collected include information about litigants, case type, disposition type, processing time, case outcome, and award amounts for civil jury cases. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Forty-five jurisdictions chosen to represent the 75 most populous counties in the nation. The sample for this study was designed and selected by the United States Bureau of the Census. It was a two-stage stratified sample with 45 of the 75 most populous counties selected at the first stage. The top 75 counties account for about 37 percent of the United States population and about half of all civil filings. The 75 counties were divided into four strata based on aggregate civil disposition data for 1990 obtained through telephone interviews with court staffs in the general jurisdiction trial courts. The sample consisted of tort, contract, and real property rights cases disposed between July 1, 1991, and June 30, 1992. 2011-11-02 All parts are being moved to restricted access and will be available only using the restricted access procedures.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CB6587.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2004-06-01 The data have been updated by the principal investigator to include replicate weights and a few other variables. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data have been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements have been revised to reflect these changes.2001-03-26 The data had been updated by the principal investigator to include replicate weights. The codebook and SAS and SPSS data definition statements had been revised to reflect these changes.1997-07-29 The codebook had been revised to correct errors documenting both data files. Column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.4 for Part 1, Tort, Contract, and Real Property Data. It was accurately shown in the data definition statements as 9.4. Variables listed after WGHT were inaccurately reported one column off in the codebook. Similarly, column location (and width) of variable WGHT "TOTAL WEIGHT" was incorrectly shown as 10.2 for Part 2, Civil Jury Data. It was accurately shown in the data definition statements as 9.2. Variables listed after WGHT were inaccurately reported one column off in the codebook. Fundi...

Wide area X-ray and far-infrared surveys are a fundamental tool to investigate the link between AGN growth and star formation, especially in the low-redshift universe (z < 1). The Herschel-Astrophysical Terahertz Large Area Survey (H-ATLAS) has covered 550 deg² in five far-infrared and sub-mm bands, 16 deg² of which have been presented in the Science Demonstration Phase (SDP) catalogue. The reference paper cited below introduces the XMM-Newton observations in the H-ATLAS SDP area, covering 7.1 deg² with flux limits of 2 x 10^-15, 6 x 10^-15, and 9 x 10^-15 erg/s/cm² in the 0.5-2, 0.5-8 and 2-8keV bands, respectively. The paper presents the source detection techniques and the "main" catalog, which includes 1700, 1582 and 814 sources detected by EMLDetect in the 0.5-8, 0.5-2 and 2-8keV bands, respectively; the number of unique sources is 1816. The authors extract spectra and derive fluxes from power-law fits for 398 sources with more than 40 counts in the 0.5-8 keV band. They compare the best-fit fluxes with those in the catalog, which were obtained assuming a common photon index Gamma of 1.7; the authors find no bulk difference between the fluxes, and a moderate dispersion s of 0.33 dex. Using wherever possible the fluxes from the spectral fits, the authors derive the 2-10 keV Log N-Log S distribution, which is consistent with a Euclidean distribution. Finally, they release the computer code for the tools which they developed for this project. Sources were detected with a two-stage process. With the first pass at low significance, the authors got a list of candidate detections; and on the second pass they raised the significance threshold and derived accurate source parameters. Between the two passes, and because the second pass needs an input catalog, they identified the sources detected in more than one band. In the first pass, the SAS wavelet detection program ewavelet was run separately on each of the 0.5-2, 2-8 and 0.5-8 keV images of the entire mosaic, with a significance threshold of 4 sigma and the default wavelet scales (minimum 2 pixels, maximum 8 pixels, with a pixel size of 4). All parameters in this catalog which were derived from ewavelet have been given a prefix of 'wav' in this HEASARC representation so as to distinguish them from the parameters derived using EMLDetect. In the second pass, the authors used the SAS EMLDetect program to validate the detections, refine the coordinates and obtain maximum-likelihood estimates of the source counts, count rates and fluxes. The EMLDetect minimum likelihood was set at L = 4.6, as in Ranalli et al. (2013, A&A, 555, A42), which corresponds to a false-detection probability of 1.01 x 10^-2. Together with the 4-sigma threshold for ewavelet, for the final catalog this yields a joint significance between 4 sigma and 5 sigma, but which cannot be further constrained without simulations. This table contains the X-ray sources which were detected in the 7.1 deg² XMM-Newton observations of the H-ATLAS field. The 1816 sources which were detected by both programs were presented in the main table in the reference paper (and are included in this HEASARC table where they are indicated by a value of the source_sample parameter of 'main'), while the 234 sources which were only detected by ewavelet were presented in the supplementary table in the reference paper (and are included in this HEASARC table where they are indicated by a value of the source_sample parameter of 'supp'). The same parameters were present in both the main and supplementary tables in the reference paper, but those parameters which came from EMLDetect are empty for the sources in the supplementary table. The parameters obtained using ewavelet (those parameters with the 'wav' prefix in their names) containing the source properties (counts, count rates, fluxes, exposure times, background, wavelet detection scale and source extent), while reported in this table for all sources, are actually only interesting for supplementary sources, according to the authors. This table was created by the HEASARC in May 2015 based on the union of CDS Catalog J/A+A/577/A121 files main.dat (which contain 1816 sources detected by both detection algorithms) and suppl.dat (which contains 234 'supplementary' sources detected only by the wavelet detection algorithm ewavelet). It thus contains a total of 2050 sources. This is a service provided by NASA HEASARC .

I-CVI, item-level content validity index.S-CVI/UA, scale-level content validity index, universal agreement calculation method.

Abstract (en): The National Hospital Discharge Survey (NHDS) collects medical and demographic information annually from a sample of hospital discharge records. Variables include patients' demographic characteristics (sex, age, race, marital status), dates of admission and discharge, status at discharge, final diagnoses, surgical and nonsurgical procedures, dates of surgeries, and sources of payment. Information on hospital characteristics such as bedsize, ownership, and region of the country is also included. The medical information is coded using the INTERNATIONAL CLASSIFICATION OF DISEASES, 9TH REVISION, CLINICAL MODIFICATION (ICD-9-CM). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.. Patient discharges from nonfederal short-stay hospitals located in the 50 states and the District of Columbia. The redesigned (as of 1988) NHDS sample includes with certainty all hospitals with 1,000 or more beds or 40,000 or more discharges annually. The remaining sample of hospitals is based on a stratified three-stage design. The first stage consists of selection of 112 primary sampling units (PSUs) that comprise a probability subsample of PSUs used in the 1985-1994 National Health Interview Surveys. The second stage consists of selection of noncertainty hospitals from the sample PSUs. At the third stage, a sample of discharges was selected by a systematic random sampling technique. For 2000, the sample consisted of 509 hospitals. Of these, 28 were found to be ineligible. Of the 481 eligible hospitals, 434 hospitals responded to the survey. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. (1) Per agreement with NCHS, ICPSR distributes the data file and text of the technical documentation in this collection in their original form as prepared by NCHS. (2) The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. In addition to core items, new content includes questions on values, political knowledge, and attitudes on racial policy, as well as more general attitudes conceptualized as antecedent to these opinions on racial issues. The Main Data File also contains vote validation data that were expanded to include information from the appropriate election office and were attached to the records of each of the respondents in the post-election survey. The expanded data consist of the respondent's post case ID, vote validation ID, and two variables to clarify the distinction between the office of registration and the office associated with the respondent's sample address. The second data file, Bias Nonresponse Data File, contains respondent-level field administration variables. Of 3,833 lines of sample that were originally issued for the 1990 Study, 2,176 resulted in completed interviews, others were nonsample, and others were noninterviews for a variety of reasons. For each line of sample, the Bias Nonresponse Data File includes sampling data, result codes, control variables, and interviewer variables. Detailed geocode data are blanked but available under conditions of confidential access (contact the American National Election Studies at the Center for Political Studies, University of Michigan, for further details). This is a specialized file, of particular interest to those who are interested in survey nonresponse. Demographic variables include age, party affiliation, marital status, education, employment status, occupation, religious preference, and ethnicity. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Response Rates: The response rate for this study is 67.7 percent. The study was in the field until January 31, although 67 percent of the interviews were taken by November 25, 80 percent by December 7, and 93 percent by December 31. All United States households in the 50 states. National multistage area probability sample. 2015-11-10 The study metadata was updated.2009-01-09 YYYY-MM-DD Part 1, the Main Data File, incorporates errata that were posted separately under the Fourth ICPSR Edition. Part 2, the Bias Nonresponse Data File, has been added to the data collection, along with corresponding SAS, SPSS, and Stata setup files and documentation. The codebook has been updated by adding a technical memorandum on the sampling design of the study previously missing from the codebook. The nonresponse file contains respondent-level field administration variables for those interested in survey nonresponse. The collection now includes files in ASCII, SPSS portable, SAS transport (CPORT), and Stata system formats.2000-02-21 The data for this study are now available in SAS transport and SPSS export formats in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. Additionally, the Voter Validation Office Administration Interview File (Expanded Version) has been merged with the main data file, and the codebook and SPSS setup files have been replaced. Also, SAS setup files have been added to the collection, and the data collection instrument is now provided as a PDF file. Two files are no longer being released with this collection: the Voter Validation Office Administration Interview File (Unexpanded Version) and the Results of First Contact With Respondent file. Funding insitution(s): National Science Foundation (SOC77-08885 and SES-8341310). face-to-face interviewThere was significantly more content in this post-election survey than ...

Abstract

The main objective of the Seasonal Agricultural Survey is to provide timely, accurate, reliable and comprehensive agricultural statistics that describe the structure of agriculture in Rwanda mainly in terms of land use, crop area, yield and crop production to monitor current agricultural and food supply conditions and to facilitate evidence-based decision making for the development of the agricultural sector.

The National Institute of Statistics of Rwanda (NISR) has been conducting seasonal agricultural survey since 2012 for the estimation of the national agricultural crop area and production estimates. In 2022/2023 agricultural year, the NISR conducted Seasonal Agricultural Survey (SAS) covering the three agricultural seasons. The SAS provides information used as a tool to assist in addressing key agricultural issues and information needs that will inform policymakers and other stakeholders and allow more effective identification of priority intervention needs.

Geographic coverage

National coverage allowing district-level estimation of key indicators

Analysis unit

Small scale agricultural farms and large scale farms

Universe

The SAS 2023 targeted potential agricultural land and large-scale farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

The total country land was classified into five strata, of which four are agricultural, while the remaining stratum is designated for land not suitable for agriculture. The four agricultural strata are: dominant hill crop land, dominant wetland crops, dominant rangeland, and mixed stratum, all considered suitable for agriculture. The fifth stratum comprises non-agricultural land, including areas occupied by water bodies, forestry plantations, settlements, parks, and protected marshland not utilized for agriculture. The sampling frame excludes land areas covered by tea plantation farms. In 2023 agricultural year, the total sample used was 1200 segments. At first stage,1200 segments were selected and allocated at district level based on the power allocation approach (Bankier, 1988). Sampled segments inside each district were distributed among strata with a proportional-to-size criterion.

At the second stage, 25 sample points were systematically selected, following a special distance of 60 meters between points. For every sample point, a corresponding farm or plot is identified, and the operator is interviewed. The farms therefore constitute the sampling units within each segment. Enumerators locate every sample point, delineate plots in which the sample points fall using high accurate GPS devices and then collect information on land use and other related information. Sampling weights are calculated and applied to the sample data to obtain stratum-level estimates. District estimates are then derived by aggregating the estimates from all strata within the district.

Data collection was done in 1200 segments and 345 large scale farmers holdings for Season A and B, whereas in Season C data was collected in 1769 sites potential to grow season C crops in addition to 513 segments, response rate was 100% of the sample.

During the SAS 2023 exercise, data collection covered three main agricultural seasons A, B and C and was conducted into two separate phases in each season: A. The first phase, known as screening activity (post-planting phase), consists of visiting all sampled segments and demarcating all plots with sampled points with the aim of covering the information related to land area, planted crops and land use.

B. The second phase involves capturing of production data by visiting sampled agricultural plots identified from screening activity as well as all large-scale farmers. To ensure the smooth completion of the SAS workload, NISR employed 137 Enumerators and 23 Team Leaders. All fieldwork staff hold a degree in agriculture sciences and were consistently trained by NISR headquarter staff before starting data collection in each season. Moreover, higher-level supervision was organized and done by staff from NISR who frequently visited the field teams during each phase of data collection to ensure the quality of collected data. For Season A, data collection started on 4th December 2022 and ended on 16th February 2023. For Season B, data collection started on 2nd May 2023 and ended on 30th June 2023. For Season C, data collection started on 10th September 2023 and ended on 30th September 2023.

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract (en): Youth data for the third wave of the National Youth Survey are contained in this data collection, which includes data for youth interviewed in 1979 about events and behavior of the preceding year. The first wave of this study was conducted in 1976 (ICPSR 8375) and the second wave in 1977 (ICPSR 8424). Data were collected on the demographic and socioeconomic status of respondents, disruptive events in the home, youth aspirations, expectations for future goals, social isolation, normlessness, labeling, perceived disapproval, attitudes toward deviance, exposure and commitment to delinquent peers, sex roles, attitudes toward sexual assault, interpersonal violence, pressure for substance abuse by peers, exposure to substance abuse by parents, self-reported delinquency, and drug and alcohol use. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Youth in the United States. National sample of the American youth population selected by area probability sampling. 2008-09-10 New files were added. These files included one or more of the following: Stata setup, SAS transport (CPORT), SPSS system, Stata system, SAS supplemental syntax, and Stata supplemental syntax files, and a tab-delimited ASCII data file. Some other minor edits were made to improve the data and documentation. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. National Institute of Mental Health (MH27552). United States Department of Justice. Office of Justice Programs. Office of Juvenile Justice and Delinquency Prevention ( 78-JN-AX-0003). Two different schedules were used with two separate subsamples. These schedules are similar except for the sections on drug use, and data from both these schedules have been integrated into a single coding frame. Question numbers with an asterisk (*) were used only in the National Institute of Mental Health interviews, and question numbers with a numeric sign (#) were used only in the Office of Juvenile Justice and Delinquency Prevention interview schedule.Produced by the University of Colorado, Behavioral Research Institute at Boulder, CO.

Abstract (en): This series measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, tobacco, and nonmedical use of prescription drugs among members of United States households aged 12 and older. Questions include age at first use, as well as lifetime, annual, and past-month usage for the following drug classes: cannabis, cocaine, hallucinogens, heroin, alcohol, tobacco, and nonmedical use of prescription drugs, including psychotherapeutics. Respondents were also asked about problems resulting from their use of drugs, alcohol, and tobacco, their perceptions of the risks involved, and personal and family income sources and amounts. Half of the respondents were asked questions regarding substance use by close friends. Demographic data include gender, race, age, ethnicity, educational level, job status, income level, veteran status, household composition, and population density. Youth respondents were also asked about time spent on homework and leisure activities. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Response Rates: The interview completion rates for the three age groups were: 84 percent for youth, 81 percent for young adults, and 77 percent for older adults. The civilian, noninstitutionalized population of the coterminous United States (Alaska and Hawaii excluded) aged 12 and older. Multistage area probability sample design involving five selection stages: (a) primary areas (e.g., counties), (b) subareas within primary areas (geographic area of approximately 2,500 population in 1970), (c) housing units within subareas, (d) age group domains within listed units, and (e) members of households within sampled age groups. The two race classifications were: White, and Black/other. The three age groups were: youth (age 12 to 17), young adult (age 18 to 34), and older adult (age 35 and older). Each age group was sampled separately, and the probability of selection decreased with the prospective respondent's age. One youth and/or one adult could be chosen per household. The basic national sample was supplemented by a sample of residents of rural areas. The overall interview completion rate was 81 percent. 2015-11-23 Covers for the PDF documentation were revised.2015-02-03 Created a separate Questionnaire PDF that was extracted from the Codebook PDF.2013-06-19 Updated variable-level ddi files released.2008-06-18 New files were added. These files included one or more of the following: Stata setup, SAS transport (CPORT), SPSS system, Stata system, SAS supplemental syntax, and Stata supplemental syntax files, and tab-delimited ASCII data file. Also added variable CASEID to the dataset.1999-05-12 SAS and SPSS data definition statements have been updated to include value labels and missing values sections. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. National Institute on Drug Abuse. personal interviews and self-enumerated answer sheets (drug use)Data were collected by Response Analysis Corporation, Princeton, NJ, under contract with the National Institute on Drug Abuse. The data and codebook were prepared for release by Research Triangle Institute, Research Triangle Park, NC, and the codebook was initially distributed by National Opinion Research Center, Chicago, IL, under contracts with the Substance Abuse and Mental Health Services Administration.For selected variables, statistical imputation was done following logical imputation to replace missing responses. These variables are identified by the designation "IMPUTATION-REVISED" in the variable label, and the names of these variables begin with the letters "IR". For each imputation-revised variable there is a corresponding imputation indicator variable that indicates whether a case's value on the variable resulted from an interview response, logical imputation, or statistical imputation. The names of ...

Abstract (en): To ensure an accurate sampling frame for its Law Enforcement Management and Administrative Statistics (LEMAS) survey, the Bureau of Justice Statistics periodically sponsors a census of the nation's state and local law enforcement agencies. This census, known as the Directory Survey, includes all state and local law enforcement agencies that are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers. As in previous years, the 2000 Directory Survey collected data on the number of sworn and nonsworn personnel employed by each agency, including both full-time and part-time employees. The pay period that included June 30, 2000, was the reference date for all personnel data. A 97.4 percent response rate was obtained from the 17,784 state and local law enforcement agencies operating in the United States. This data collection contains June 2000 data from the fourth Directory Survey. Previous directory censuses were conducted in 1986 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1986: [UNITED STATES] [ICPSR 8696]), 1992 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1992: [UNITED STATES] [ICPSR 2266]), and 1996 (DIRECTORY OF LAW ENFORCEMENT AGENCIES, 1996: [UNITED STATES] [ICPSR 2260]). Variables include personnel totals, type of government, type of agency, and whether the agency had the legal authority to hold a person beyond arraignment for 48 or more hours. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Checked for undocumented or out-of-range codes.. All police and sheriffs' departments that were publicly funded and employed at least one full-time or part-time sworn officer with general arrest powers. inap. 2009-07-08 Personal names, email addresses, IP or web addresses, and odd entries in V17 and V19 were masked. Telephone and fax numbers in V31 and V33 were also masked.2003-05-09 The data file has been replaced to correct errors in the previous release. The SAS and SPSS data definition statements were also updated.2003-04-18 The data file has been replaced to correct errors that appeared in the original file as submitted by the principal investigator. The variable "V1 State" had the data for Oklahoma and Oregon switched and the data for Michigan and Minnesota combined. The SAS and SPSS data definition statements were updated as well. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics.