Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology). Topics: most important political problems of the country; political interest; party inclination; behavior at the polls in the Federal Parliament election 1972; political participation and willingness to participate in political protests. Demography: age; sex; marital status; religious denomination; school education; interest in politics; party preference. Übungsdatensatz zum SAS-Buch von Uehlinger. Auswahl einzelner Variablen und Fälle aus dem Datensatz der ZA-Studie 0757 (Politische Ideologie). Themen: Wichtigste politische Probleme des Landes; politisches Interesse; Parteineigung; Wahlverhalten bei der Bundestagswahl 1972; politische Partizipation und Teilnahmebereitschaft an politischen Protesten. Demographie: Alter; Geschlecht; Familienstand; Konfession; Schulbildung; Politikinteresse; Parteipräferenz. Random selection Zufallsauswahl Oral survey with standardized questionnaire

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.

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.

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

Stepwise selection summary table for female and male populations.

Abstract

In Croatia only one independent census of agricultural holdings, or farm structure surveys, was conducted before the year 2000 and it was in 1960. In 1969 a sample census of agricultural holdings was conducted, and in 1971, 1981 and 1991 data about agriculture were collected within population censuses. However, due to a limited number of questions related to agriculture, these data do not provide complete and comparable information on the structure of agricultural holdings in Croatia. In the year 2003 first EU comparable Agricultural Census (hereinafter AC) was carried out. A survey on the structure of agricultural holdings was conducted on the sample basis for the first time in 2005. The survey “conducted in 2005, was in fact a study of the structure of agricultural holdings. The 2005 survey included all sown areas, main categories of land use, labour force, supplementary activities, agricultural machinery, production for own use, or for sale. Since the said 2005 survey was the first ever conducted of the kind, the set of questions pertaining to the number of livestock was not included. In 2007, a survey on the structure of agricultural holdings was conducted on a new sample. Questions pertaining to livestock were included for the first time. The sample was also stratified for the first time. In 2010 the FSS and Survey on agricultural production methods is conducted.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the FSS 2010 was the agricultural holding (farm), defined as a production management unit engaged in agriculture, either as its primary or secondary activity, which jointly uses labour force and production means (machinery, buildings, or land, etc.). The FSS covered all holdings engaged in agricultural production: family farms, business entities and parts thereof.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Complete and/or sample enumeration methods All agricultural enterprises (business entities) were surveyed using complete enumeration. Private family farms were surveyed using the sampling method.

(b) Sample design (r f sampling was used) The sample design for family farms was stratified random sampling. The population of family farms was divided into two parts: (i) the first part consisted of family farms for which the farm's size in terms of European Size Units (ESUs) was calculated; and (ii) the second part consisted of family farms without a calculated ESU. For the population with a calculated ESU, the stratification criteria were the following: · variable size, a combination of ESUs and UAA - eight sizes; · specialization of the farm - nine types; · NUTS 2 regions - three regions defined.

The biggest farms with a large ESU and UAA (9 806 farms) were included exhaustively in the sample. The farms without a defined ESU were stratified according to their UAA, area under orchards and area under vineyard. Stratified random sampling with explicit regional (NUTS 2) stratification was used.

(c) Frame The census frame was provided by the Statistical Register of Agricultural Holdings (SRAH), and has been regularly updated since the AC 2003, when it was established. The sampling frame was a list of all active family farms from the SRAH.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

Two versions of the paper questionnaire were prepared: one for family farms and another for business entities. The two questionnaires were slightly different regarding the chapter related to labour force. The FSS 2010 covered all 16 core items recommended in the WCA 2010. Some of the characteristics were added to the questionnaire for national purposes only:

• holder's name and surname • areas under tricitale (included in other cereals) • areas under secondary crops • address of the holder • number of trees in extensive orchards and olive groves and number of vines in vineyards – needed for calculation of production • all spices of vegetables are added in open fields, in glasshouses and in kitchen gardens • machinery and equipment • energy consumption • average age of agricultural buildings

The characteristics surveyed only for national purposes are used in EAA, for updating farm register and for calculating standard output

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING FSS and SAPM data were entered at the CBS with optical readers for the intelligent character recognition and optical mark recognition. To draw the sample SAS programme, the PROC SURVEYSELECT procedure was used. The estimation method used was the Horvitz-Thompson estimator (regular design weight), multiplied with calculated response weights.

(b) CENSUS DATA QUALITY The response rate was 97.4 percent. Comprehensive data-checking procedures were put in place. Before corrections were accepted and entered, field supervisors or farmers were contacted by telephone, if necessary. Once the processing was complete, the results were checked at the macro level and compared with the results from other surveys. The data on labour force were compared with the results of the input of labour force in economic accounts for agriculture (EEA).

Data appraisal

The report, with preliminary results, was published in November 2010. The final results were disseminated in September 2012. A database with FSS and SAPM 2010 data is available on the CBS website.

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

Abstract

In order to have a comprehensive picture of the farming community 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 of the farmer households it was decided to collect information on Indian farmers through “Situation Assessment Survey” (SAS). The areas of interest for conducting SAS would include economic well-being of farmer households as measured by consumer expenditure, income and productive assets, and indebtedness; their farming practices and preferences, resource availability, and their awareness of technological developments and access to modern technology in the field of agriculture. In this survey, detailed information would be collected on receipts and expenses of households' farm and non-farm businesses, to arrive at their income from these sources. Income from other sources would also be ascertained, and so would be the consumption expenditure of the households.

Geographic coverage

The survey will cover the whole of the Indian Union.

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. Sample Design

3.1 Outline of sample design: 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.

3.2 Sampling Frame for First Stage Units: 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.

3.3 Stratification:

(a) Stratum has been formed at district level. Within each district of a State/ UT, generally speaking, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising all the urban areas of the district. However, within the urban areas of a district, if there were one or more towns with population 10 lakhs or more as per population census 2011 in a district, each of them formed a separate basic stratum and the remaining urban areas of the district was considered as another basic stratum.

(b) However, a special stratum in the rural sector only was formed at State/UT level before district- strata were formed in case of each of the following 20 States/UTs: Andaman & Nicobar Islands, Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Jharkhand, Karnataka, Lakshadweep, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. This stratum will comprise all the villages of the State with population less than 50 as per census 2001.

(c) In case of rural sectors of Nagaland one special stratum has been formed within the State consisting of all the interior and inaccessible villages. Similarly, for Andaman & Nicobar Islands, one more special stratum has been formed within the UT consisting of all inaccessible villages. Thus for Andaman & Nicobar Islands, two special strata have been formed at the UT level:

(i) special stratum 1 comprising all the interior and inaccessible villages (ii) special stratum 2 containing all the villages, other than those in special stratum 1, having population less than 50 as per census 2001.

3.4 Sub-stratification:

Rural sector: Different sub-stratifications are done for 'hilly' States and other States. Ten (10) States are considered as hilly States. They are: Jammu & Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Meghalaya, Tripura, Mizoram, Manipur, Nagaland and Arunachal Pradesh.

(a) sub-stratification for hilly States: If 'r' be the sample size allocated for a rural stratum, the number of sub-strata formed was 'r/2'. The villages within a district as per frame have been first arranged in ascending order of population. Then sub-strata 1 to 'r/2' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal population.

(b) sub-stratification for other States (non-hilly States except Kerala): The villages within a district as per frame were first arranged in ascending order of proportion of irrigated area in the cultivated area of the village. Then sub-strata 1 to 'r/2' have been demarcated in such a way that each sub-stratum comprised a group of villages of the arranged frame and have more or less equal cultivated area. The information on irrigated area and cultivated area was obtained from the village directory of census 2001.

(c) sub-stratification for Kerala: Although Kerala is a non-hilly State but because of non-availability of information on irrigation at FSU (Panchayat Ward) level, sub-stratification by proportion of irrigated area was not possible. Hence the procedure for sub-stratification was same as that of hilly States in case of Kerala.

Urban sector: There was no sub-stratification for the strata of million plus cities. For other strata, each district was divided into 2 sub-strata as follows:

 sub-stratum 1: all towns of the district with population less than 50000 as per census 2011 
 sub-stratum 2: remaining non-million plus towns of the district

3.5 Total sample size (FSUs): 8042 FSUs have been allocated for the central sample at all-India level. For the state sample, there are 8998 FSUs allocated for all-India. State wise allocation of sample FSUs is given in Table 1.

3.6 Allocation of total sample to States and UTs: The total number of sample FSUs have been allocated to the States and UTs in proportion to population as per census 2011 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators as well as comparability with previous round of survey on the same subjects has been kept in view.

3.7 Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample size has been allocated between two sectors in proportion to population as per census 2011 with double weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 16 FSUs (minimum 8 each for rural and urban sector separately) is allocated to each state/ UT.

3.8 Allocation to strata: Within each sector of a State/ UT, the respective sample size has been allocated to the different strata in proportion to the population as per census 2011. Allocations at stratum level are adjusted to multiples of 2 with a minimum sample size of 2.

For special stratum formed in the rural areas of 20 States/UTs, as discussed in para 3.3 (b), 2 FSUs were allocated to each.

For special stratum 1 in the rural areas of Nagaland and Andaman & Nicobar Islands, 4 and 2 FSUs were allocated respectively.

3.9 Allocation to sub-strata:

3.9.1 Rural: Allocation is 2 for each sub-stratum in rural.

3.9.2 Urban: Stratum allocations have been distributed among the two sub-strata in proportion to the number of FSUs in the sub-strata. Minimum allocation for each sub-stratum is 2.

3.10 Selection of FSUs:

For the rural sector, from each stratum x sub-stratum, required number of sample villages has been selected by Simple Random Sampling Without Replacement (SRSWOR).

For the urban sector, FSUs have been selected by using Simple Random Sampling Without Replacement (SRSWOR) from each stratum x sub-stratum.

Both rural and urban samples were drawn in the form of two independent sub-samples and equal number of samples has been allocated among the two sub rounds.

3.11 Selection of hamlet-groups/ sub-blocks - important steps

3.11.1 Criterion for hamlet-group/ sub-block formation: After identification of the boundaries of the FSU, it is first determined whether listing is to be done in the whole sample FSU or not. In case the approximate present population of the selected FSU is found to be 1200 or more, it is divided into a suitable number (say, D) of 'hamlet-groups' in the rural sector and 'sub-blocks' in the urban sector by more or less equalising the population as stated below.

approximate present population of the sample FSU no. of hg's/sb's to be formed

less than 1200 (no hamlet-groups/sub-blocks) 1
1200 to 1799 3
1800 to 2399 4
2400 to 2999 5
3000 to 3599 6
…………..and so on .

For rural areas of Himachal Pradesh, Sikkim, Uttarakhand (except four districts Dehradun, Nainital, Hardwar and Udham Singh Nagar), Poonch, Rajouri, Udhampur, Reasi, Doda, Kistwar, Ramgarh, Leh (Ladakh), Kargil districts of Jammu and Kashmir and Idukki district of Kerala, the number of hamlet-groups are formed as follows:

approximate present population of the sample village no. of hg's to be formed

less than 600 (no hamlet-groups) 1
600 to 899 3
900 to 1199 4
1200 to 1499 5
1500 to 1799 6
.………..and so on .

3.11.2 Formation and selection of hamlet-groups/ sub-blocks: In case hamlet-groups/ sub-blocks are to be formed in the sample

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

Number of observations and percentage classified (in bracket) in different locations for female and male sample population using discriminant analysis.

Abstract

The millions of farmers of India have made significant contributions in providing food and nutrition to the entire nation and provided livelihood to millions of people of the country. During the 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 of Indian agriculture. In order to have a snapshot 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 of the farmer households at the end of five decades of planned economic development, Ministry of Agriculture have decided to collect information on Indian farmers through “Situation Assessment Survey” (SAS) on Indian farmers and entrusted the job of conducting the survey to National Sample Survey Organisation (NSSO).

The Situation Assessment Survey of Farmers 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, viz., Schedule 33, covering some basic characteristics of farmer households and their access to basic and modern farming resources will be canvassed for the first time in SAS. Moreover, information on consumption of various goods and services in an abridged form are also to be collected to have an idea about the pattern of consumption expenditure of the farmer households.

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

Geographic coverage

The survey covered rural sector of Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Household (farmer)

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

Outline of sample design: A stratified multi-stage design has been adopted for the 59th round survey. The first stage unit (FSU) is the census village in the rural sector and UFS block in the urban sector. The ultimate stage units (USUs) will be households in both the sectors. Hamlet-group / sub-block will constitute the intermediate stage if these are formed in the selected area.

Sampling Frame for First Stage Units: For rural areas, the list of villages (panchayat wards for Kerala) as per Population Census 1991 and for urban areas the latest UFS frame, will be used as sampling frame. For stratification of towns by size class, provisional population of towns as per Census 2001 will be used.

Stratification

Rural sector: Two special strata will be formed at the State/ UT level, viz.

• Stratum 1: all FSUs with population between 0 to 50 and
• Stratum 2: FSUs with population more than 15,000.

Special stratum 1 will be formed if at least 50 such FSU's are found in a State/UT. Similarly, special stratum 2 will be formed if at least 4 such FSUs are found in a State/UT. Otherwise, such FSUs will be merged with the general strata.

From FSUs other than those covered under special strata 1 & 2, general strata will be formed and its numbering will start from 3. Each district of a State/UT will be normally treated as a separate stratum. However, if the census rural population of the district is greater than or equal to 2 million as per population census 1991 or 2.5 million as per population census 2001, the district will be split into two or more strata, by grouping contiguous tehsils to form strata. However, in Gujarat, some districts are not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region will constitute a separate stratum.

Urban sector: In the urban sector, strata will be formed within each NSS region on the basis of size class of towns as per Population Census 2001. The stratum numbers and their composition (within each region) are given below. - stratum 1: all towns with population less than 50,000 - stratum 2: all towns with population 50,000 or more but less than 2 lakhs - stratum 3: all towns with population 2 lakhs or more but less than 10 lakhs - stratum 4, 5, 6, ...: each city with population 10 lakhs or more The stratum numbers will remain as above even if, in some regions, some of the strata are not formed.

Total sample size (FSUs): 10736 FSUs have been allocated at all-India level on the basis of investigator strength in different States/UTs for central sample and 11624 for state sample.

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample is allocated between two sectors in proportion to provisional population as per Census 2001 with 1.5 weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. Earlier practice of giving double weightage to urban sector has been modified considering the fact that two main topics (sch. 18.1 and sch 33) are rural based and there has been considerable growth in urban population. More samples have been allocated to rural sector of Meghalaya state sample at the request of the DES, Meghalaya. The sample sizes by sector and State/UT are given in Table 1 at the end of this Chapter.

Allocation to strata: Within each sector of a State/UT, the respective sample size will be allocated to the different strata in proportion to the stratum population as per census 2001. Allocations at stratum level will be adjusted to a multiple of 2 with a minimum sample size of 2. However, attempt will be made to allocate a multiple of 4 FSUs to a stratum as far as possible. Selection of FSUs: FSUs will be selected with Probability Proportional to Size with replacement (PPSWR), size being the population as per population census 1991 in all the strata for rural sector except for stratum 1. In stratum 1 of rural sector and in all the strata of urban sector, selection will be done using Simple Random Sampling without replacement (SRSWOR). Samples will be drawn in the form of two independent sub-samples.

Note: Detail sampling procedure is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 33 (Situation Assessment Survey) has been split into several blocks to obtain detailed information on various aspects of farmer households.

Block 0- Descriptive identification of sample household: This block is meant for recording descriptive identification particulars of a sample household.

Block 1- Identification of sample household: items 1 to 12: The identification particulars for items 1, 6 - 11 will be copied from the corresponding items of block 1 of listing schedule (Sch.0.0). The particulars to be recorded in items 2, 3, 4 and 5 have already been printed in the schedule.

Block 2- Particulars of field operation: The identity of the Investigator, Assistant Superintendent and Superintendent associated, date of survey/inspection/scrutiny of schedules, despatch, etc., will be recorded in this block against the appropriate items in the relevant columns.

Block 3- Household characteristics: Characteristics which are mainly intended to be used to classify the households for tabulation will be recorded in this block.

Block 4- Demographic and other particulars of household members: All members of the sample household will be listed in this block. Demographic particulars (viz., relation to head, sex, age, marital status and general education), nature of work, current weekly status, wage and salary earnings etc. will be recorded for each member using one line for one member.

Block 5- Perception of household regarding sufficiency of food: This block will record information about perception of households regarding sufficiency of food.

Block 6- Perceptions regarding some general aspects of farming: In this block some information regarding perception of the farmer household about some general aspects of farming are to be recorded.

Block 7- Particulars of land possessed during Kharif/Rabi: This block is designed to record information regarding the land on which farming activities are carried out by the farmer household during Kharif/Rabi.

Block 8- Area under irrigation during Kharif/Rabi: In this block information regarding the area under irrigation during last 365 days for different crops will be recorded according to the source of irrigation.

Block 9- Some particulars of farming resources used for cultivation during Kharif / Rabi: Information regarding farming resources used for cultivation during the last 365 days will be ascertained from the farmer households and will be recorded in this block.

Block 10- Use of energy during last 365 days: This block will be

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

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

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

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

Comparing the Shenoy et al [21] algorithm for low-value urinalysis and important diagnosis codes in the HSR Definition Builder application.

Class means on canonical variables of female and male chickens.

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

Least squares mean (± SE) body weight (kg) and other linear body measurements (cm) by agro-ecology, sex and location of indigenous chicken ecotypes in north-western Ethiopia.

Top 21 of 132 diagnosis codes for carrier claims with a knee arthroscopy procedure (CPT 29877), ordered by relative importance from the classification model.