File List ACS.zip -- .zip file containing SAS macro and example code, and example Aletris bracteata data sets. acs.sas chekika_ACS_estimation.sas chekika_1.csv chekika_2.csv philippi.3.1.zip

Description "acs.sas" is a SAS macro for computing Horvitz-Thompson and Hansen-Horwitz estimates of population size for adaptive cluster sampling with random initial sampling. This version uses ugly base SAS code and does not require SQL or SAS products other than Base SAS, and should work with versions 8.2 onward (tested with versions 9.0 and 9.1). "chekika_ACS_estimation.sas" is example SAS code calling the acs macro to analyze the Chekika Aletris bracteata example data sets. "chekika_1.csv" is an example data set in ASCII comma-delimited format from adaptive cluster sampling of A. bracteata at Chekika, Everglades National Park, with 1-m2 quadrats. "chekika_2.csv" is an example data set in ASCII comma-delimited format from adaptive cluster sampling of A. bracteata at Chekika, Everglades National Park, with 4-m2 quadrats. "philippi.3.1.zip" metadata file generated by morpho, including both xml and css.

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

File List Supplement_Annotated_SAS_code_Dzialak_et_al.sas -- (MD5: 8ac1ab31f6592777a2dde8c0a3b1352d) Description Annotated SAS code for random-effects resource selection models described in this paper.

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.

This dataset consists of a series of computer programs in the PASCAL language that implement a simple model of animal movement. Results of model use were published in: Porter, J.H. and J.L. Dooley, Jr. 1993. Animal dispersal patterns: a reassessment of simple mathematical models. Ecology 74:2436-2443. MOVESIM.PAS, MOVESV1.PAS and MOVESV21.PAS are the model. Input is a set of coordinates for sampling points. The model randomly selects a starting point, then selects a random distance and direction. The distance to the nearest sampling point is then calculated and the process is repeated. There are a number of associated programs. MOVESTAT.SAS - SAS program to list frequency histogram for MOVESIM output. RANDGEN.PAS - Generate a set of random sampling stations within a given area. FITIT.PAS - Estimate fit of different movement models to output. ADJUST1.PAS - Estimate the number of individuals that would have been caught at a given distance if sample points had been evenly distributed.

Abstract (en): The Urban Household Sample of the 1860 United States Census was designed to supplement the Bateman-Foust rural sample with observations from urban areas. The sample covers both northern and southern towns and cities and permits examination of female occupations and labor force participation rates. Information on individuals includes occupation, city of residence, age, sex, race, dollar value of real and personal property owned, whether American or foreign born, and literacy. The second release of this collection adds nine constructed variables, including several weight variables, collapsed occupation, ICPSR state code, region, and unique internal family and household identifier numbers. 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 variable labels and/or value labels.. All individuals living in towns with populations of 3,000 or more who were enumerated in the 1860 Census of Population Manuscript Schedules. Stratified random sample. 2009-07-24 SAS, SPSS, and Stata setups have been added to this data collection. Funding insitution(s): University of Chicago. Booth School of Business. Center for Population Economics. Nathanial T. Wilcox of the University of Chicago collaborated with Jon Moen for the second release of the data collection.

The MAP methodology employs the Lot Quality Assurance Sampling (LQAS) technique to draw a random sample of 19 hotzones from each of the four supervision areas (SAs) in Myanmar: Yangon city (Supervision area 1), Mandalay city (Supervision area 2), cities in the lower Myanmar where Drop-in-center (DIC) exists except Yangon city (Supervision area 3) and cities in the upper Myanmar where Drop-in-center (DIC) exists except Mandalay city (Supervision area 4). The LQAS assessment of coverage estimates the proportion of hotzones where PSI-Myanmar's Aphaw condoms are available in each of four supervision areas. In addition, the LQAS assessment also determines quality of coverage , i.e. the proportion of hotzones in the above cities, in which the product delivery points conform to additional minimum standards.

Number of observations and percent (bracket) correct classified for female and male sample population using discriminant analysis.

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.

Stepwise selection summary table for female and male populations.

Number of observations and percent-classified (in brackets) into the site using a non-parametric discriminant for both male and female sample chicken populations.

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.

Raw data (quantitative traits) used in chicken morpho-biometric characterization.

Traits used in discriminating the chicken population from different sites in stepwise discriminant analysis.

Agro-ecological description, number of chickens, and major feed resources for chickens in northwest Ethiopia.

Summary of canonical correlations in female and male chickens.

The squared Mahalanobis distance between sites for the female (below the diagonal) and male (above the diagonal) sample chickens.

Number of observations and percent-classified (in bracket) into site using nonparametric discriminant for both male and female sample populations.

Squared Mahalanobis’ distance between locations for male (above diagonal) and female (below diagonal) sample populations.

Class means on canonical variables of female and male chickens.