It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

HD Mini SAS Cable Market Outlook

The global HD Mini SAS Cable market size is expected to witness significant growth, with projections estimating a rise from USD 1.2 billion in 2023 to USD 2.5 billion by 2032, reflecting a CAGR of 8.1%. The growth of this market is driven by the increasing demand for high-speed data transfer solutions in various industries.

The surge in data consumption and the exponential growth of data centers worldwide are primary growth factors for the HD Mini SAS Cable market. As businesses increasingly rely on big data analytics, cloud computing, and IoT, the need for efficient and reliable data transfer becomes paramount. HD Mini SAS Cables, known for their high-speed data transfer capabilities and robust performance, are essential components in modern data infrastructure. Consequently, the proliferation of data centers and the continuous advancement of data-driven technologies significantly bolster market expansion.

Another crucial growth driver is the escalating demand for high-performance computing and real-time data processing in telecommunications and consumer electronics. Telecommunications companies are rapidly upgrading their network infrastructure to support the growing demand for bandwidth-intensive applications like video streaming and online gaming. Similarly, the consumer electronics sector is witnessing an upsurge in the adoption of devices that require high-speed data transfer, such as gaming consoles, high-definition televisions, and storage devices. These trends are expected to propel the demand for HD Mini SAS Cables in the coming years.

The automotive industry's shift towards advanced driver-assistance systems (ADAS) and autonomous vehicles also contributes to market growth. Modern vehicles are increasingly equipped with sophisticated electronics systems that necessitate high-speed data communication for functions such as navigation, infotainment, and safety features. HD Mini SAS Cables play a critical role in ensuring reliable and swift data transmission within these systems, thereby supporting the automotive segment's expansion within the market.

From a regional perspective, North America is anticipated to dominate the HD Mini SAS Cable market, owing to its early adoption of advanced technologies and the presence of major data centers and IT companies. Asia Pacific is expected to exhibit the highest growth rate, driven by rapid technological advancements, increasing investment in data infrastructure, and the booming consumer electronics market in countries like China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also poised to contribute to the market's growth, although their impact may be relatively lower compared to North America and Asia Pacific.

Product Type Analysis

The HD Mini SAS Cable market is segmented by product type into Internal HD Mini SAS Cables and External HD Mini SAS Cables. Internal HD Mini SAS Cables are designed for use within a computer or server, providing connectivity between internal components such as hard drives, SSDs, and motherboards. These cables are crucial in data centers and high-performance computing environments where efficient and reliable internal data transfer is essential. The demand for Internal HD Mini SAS Cables is expected to grow steadily, driven by the increasing deployment of high-density servers and storage solutions.

External HD Mini SAS Cables, on the other hand, are used for connections between external devices and systems. They are commonly employed in scenarios where data needs to be transferred between different hardware units, such as connecting external storage devices to a network or linking multiple servers. External HD Mini SAS Cables are particularly favored in data centers and enterprise environments due to their ability to support high-speed data transfer over longer distances. The growth of cloud computing and the increasing complexity of IT infrastructure are expected to fuel the demand for these cables.

Both internal and external HD Mini SAS Cables are designed to handle large volumes of data with minimal latency and high reliability. However, the choice between the two depends on the specific application requirements and the physical layout of the system. The market for both types of cables is expected to witness robust growth, driven by the ongoing expansion of data centers and the rising demand for high-speed data transfer solutions in various industries.

Manufacturers of HD Mini SAS Cables are continuously innovating to enha

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

Andalusian Public Healthcare System (SSPA)

In Spain, the competence for healthcare is the responsibility of the regions. The Andalusian Public Healthcare System (SSPA) is an ecosystem of public and universal healthcare provision, which is made up of a series of public agencies, managed by the Government of Andalusia.

The SSPA has a total of 32 hospitals in the region of Andalusia.

Within this ecosystem, the main healthcare provider agency is the Andalusian Health Service.

Andalusian Health Service (SAS)

The Andalusian Health Service, created in 1986 by Law 8/1986, of May 6, 1986, on the Andalusian Health Service, is attached to the Regional Ministry of Health and Consumer Affairs and performs the functions attributed to it under the supervision and control of the same.

Its mission is to provide healthcare to the citizens of Andalusia, offering quality public health services, ensuring accessibility, equity and user satisfaction, seeking efficiency and optimal use of resources.

The SAS guarantees free public health care to more than 8 million inhabitants, which represents about 17% of the Spanish population. The Andalusian Health Service has 28 hospitals, distributed throughout Andalusia. It is also functionally responsible for the centers belonging to the Public Health Business Agencies and the Aljarafe Public Health Consortium. In addition, there are 14 Health Management Areas.

The Andalusian Health Service is an administrative agency of those provided for in Article 65 of Law 9/2007, of October 22, is attached to the Ministry of Health and Consumer Affairs, depending specifically on the Vice-Ministry, according to Decree 156/2022, of August 9, which establishes the organisational structure of the Ministry of Health and Consumer Affairs. The Andalusian Health Service exercises the functions specified in this Decree, subject to the guidelines and general criteria of health policy in Andalusia and, in particular, the following:

- The management of the set of health services in the field of health promotion and protection, disease prevention, healthcare and rehabilitation that corresponds to it in the territory of the Autonomous Community of Andalusia.

- The administration and management of the health institutions, centers and services that act under its organic and functional dependence.

- The management of the human, material and financial resources assigned to it for the development of its functions.

Diraya: system used in the Andalusian Health Service to support the Electronic Health Record in Andalusia

Diraya is the system used in the Andalusian Health Service to support electronic health records. It integrates all the health information of each person treated in the healthcare centers in Andalusia, so that it is available wherever and whenever it is necessary to treat them, and it is also used for the management of the healthcare system.

Diraya's conceptual model and technological architecture have aroused significant interest in other healthcare administrations thanks to, among others, cutting-edge services such as the electronic prescription or the centralised appointment system.

A description of the Diraya system (objectives, basic components, modules, functional and technological architecture, impact assessment of its implementation, etc.) can be found in the following document: Health Care Information and Management Integrated System.

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]

Sequential designs and competing risks methodology are both well established. Their combined use has recently received some attention from a theoretical perspective, but their joint application in practice has been discussed less. The aim of this paper is to provide the applied statistician with a basic understanding of both sequential design theory and competing risks methodology and how to combine them in practice. Relevant references to more detailed theoretical discussions are provided and all discussions are illustrated using a real case study. Extensive R and SAS code is provided in the online supplementary material.

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

National, State, Rural, Urban

Analysis unit

Houdeholds

Universe

All Households of the type : 1-self-employed in agriculture 2-self-employed in non-agriculture 3-regular wage/salary earning 4-casual labour in agriculture 5-casual labour in non-agriculture 6-others

Kind of data

Sample survey data [ssd]

Sampling procedure

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. 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.

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.

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.

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

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.
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. Allocation to sub-strata:

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

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. 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.

For details reexternal refer to external resouce "Note on Sample Design and Estimation Procedure of NSS 70th Round" Page no.2

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are 17 blocks in visit 1. In Visits 1 & 2, Each sample FSU will be visited twice during this round. Since the workload of the first visit (i.e. visit 1) will be more, the first visit will continue till the end of July 2013. Thus, period of the first visit will be January - July 2013 and that of the second visit (i.e. visit 2) will be August - December 2013.

It is an SAS file with all the syntax used for statistical analysis. (SAS)

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

Results from analyses of the unbalanced two-factorial experiment using the lm, anova and Anova functions of R and the glm procedure of SAS: F-statistics and p-values for the test of sex.

It is an SAS file with all the syntax used for statistical analysis of breeding practices of donkey farmers’ data. (SAS)

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

It is an SAS file with all the syntax used for statistical analysis of socio-economic characteristics of donkey farmers’ data. (SAS)

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

The rising trend of scientific researches have led more people to pay their attention towards scientific researches, but simply the word "scientific research" does not explain the whole nature of itself, like any other things in reality, it is divided into many realms. The various fields of scientific research have already been discussed by many scholarly articles and have been evaluated by previous census and researches. However, the ultimate question remains unanswered, namely, what is the most popular field of scientific research and which one will become the focus in the future. Although the number of specific fields that can be derived is too vast to be counted, numerous major fields can be identified to categorize the various fields, such as astronomy, engineering, computer science, medicine, biology and chemistry. Several main factors are related to the popularity, such as the number of articles relating to respective fields, number of posts on social media and the number of views on professional sites. A program was developed to analyze the relationship between the subjects for scientific research and the future trend of them based on the number of mentions for each field of research, scholarly articles and quotations about them. The program uses the data from Altmetric data, an authoritative data source. SAS is used to analyze the data and put the data on several graphs that represent the value for each factor. Finally, suggestions for future scientific researches can be summarized and inferred from the result of this research, which is aimed to provide enlightenment for future research directions.Fig 1 - The functions used in this research.Fig 2 - The main Python program used in this research.Fig 3 - The structure of output.Fig 4 - Factor 1: Number of articles relating to each field.Fig 5 - Factor 2: Number of views on Mendeley, Connotea, and Citeulike.Fig 6 - Factor 3: Number of posts on Facebook and Twitter.Fig 7 - The correlation between individual factors.