This repository includes all companion files (syntax, data files, etc.) for the open textbook, Big Data for Epidemiology: Applied Data Analysis Using National Health Surveys. This textbook was developed based on a need to develop open education resources to train future public health professionals how to conduct applied data analysis using secondary national health surveys. A recent study of local health departments demonstrates that MPH graduates may not be prepared for analytical/assessment competencies, including a lack of knowledge, skills and abilities for data collection, database management, data cleaning, quantitative data analysis/statistics, and data analysis using SAS statistical software. A lack of MPH program course offerings and structured curriculum materials available for developing applied data analysis courses may contribute to the limited analytical/assessment competencies among recent graduates. The open textbook includes details on how to analyze public-use data from five common national health surveys, including the National Health Interview Survey (NHIS), Medical Expenditure Panel Survey (MEPS), Health Information National Trends Survey (HINTS), Behavior Risk Factor Surveillance Survey (BRFSS) and National Health and Nutrition and Examination Survey (NHANES). This data repository includes accompanying SAS syntax and data files for:Chapter 4 Basic Data AnalysisChapter 5 Complex Survey Design FeaturesChapter 6 National Health Interview Survey (NHIS)Chapter 7 Medical Expenditure Panel Survey (MEPS)Chapter 8 Health Information National Trends Survey (HINTS)Chapter 9 Behavioral Risk Factor Surveillance System (BRFSS)Chapter 10 National Health and Nutrition Examination Survey (NHANES)

Abstract

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities. The AgSS is composed of four components: Crop Production Forecast Survey, Meher Season Post Harvest Survey (Area and production, land use, farm management and crop utilization), Livestock Survey and Belg Season Survey.

The specific objectives of Meher Season Post Harvest Survey are to estimate the total crop area, volume of crop production and yield of crops for Meher Season agriculture in Ethiopia. The report is based on private peasant holdings in rural sedentary areas of the country and part of companion reports on the performance of agriculture in the country. The report is compiled at regional and zonal level.

Geographic coverage

The 2010/11 (2003 E.C.) Annual Agricultural Sample Survey (Meher season) covered the entire rural parts of the country except the non-sedentary population of three zones of Afar & six zones of Somali regions.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME
The list containing EAs of all regions and their respective households obtained from the 1999 E.C cartographic census frame was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA at the beginning of the survey.

SAMPLE DESIGN In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. The sample size for the 2010/11 agricultural sample survey was determined by taking into account of both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors, manageability of the survey in terms of quality and operational control was also considered.
All regions were taken to be the domain of estimation for which major findings of the survey are reported.

SELECTION SCHEME Enumeration areas from each stratum were selected systematically using probability proportional to size sampling technique; size being number of agricultural households. The sizes for EAs were obtained from the 2007 E.C Population and Housing census frame. From the fresh list of households prepared at the beginning of the survey 20 agricultural households within each sample EA were selected systematically.

Distribution of sampling units (sampled and covered EAs and households) by stratum is presented in Appendix-III of the final report.

Sampling deviation

A total of 2,280 Enumeration Areas (EAs) were selected. However, due to various reasons that are beyond control, in 25 EAs the survey could not be successful and hence interrupted.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010-2011 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households.

List of forms in the questionnaires: - AgSS Form 2003/0: It contains forms that used to list all households in the sample areas. - AgSS Form 2003/1: It contains forms that used to list selected agricultural households and holders in the sample areas. - AgSS Form 2003/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 2003/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 2003/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 2003/5: It contains forms that used to collect information about list of temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office.

An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 66 editors-coders and verifiers were trained for two days in editing, coding and verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100 % basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires took 18 days.

Data Entry, Cleaning and Tabulation Before data entry, the Agriculture, Natural Resources and Environment Statistics Directorate of the CSA prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 70 data encoders, 10 data encoder supervisors, 12 data cleaning operators and 55 personal computers. The data entered into the computers using the entry module of the CSPRO (Census and Survey Processing System) software, which is a software package developed by the United States Bureau of the Census. Following the data entry operations, the data was further reviewed for data inconsistencies, missing data … etc. by the regular professional staff from Agriculture, Natural Resources and Environment Statistics Directorate. The final stage of the data processing was to summarizing the cleaned data and produce statistical tables that present the results of the survey using the tabulation component of the PC based CSPRO software produced by professional staff from Agriculture, Natural Resources and Environment Statistics Directorate.

Response rate

The survey succeeded to cover 2,236 EAs (98.5 %) throughout the regions

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix-I and II of the final report.

This folder contains the material of an experiment that compares MDD and CDD with respect to Bug Localization tasks.

The material in this folder* is organized in three subfolders: 01_EXPERIMENT, which contains the information used to run the experiment; 02_RESULTS, which contains the information collected in the experiment; and 03_STATISTICAL ANALYSIS, which contains the data set and the results of the statistical analysis performed.

The documentation contained in each of these folders is described below:

01_EXPERIMENT

11_ CdEUSJ_FavorableReport: pdf file of the Favorable report from the ethical and scientific committee regarding the execution of the experiment.

12_Forms&Tables: This folder contains the Taks Sheet for each group and the scenarios of each task and each group.

13_SupportMaterial: This folder contains the support material that the subjects could use during the performance of the different tasks of the experiment.

14_SessionMaterial: This folder contains the material used by the instructors during the session, including the video tutorial.

15_CorrecctionMaterial: This folder contains the solution to the tasks and the correction template.

02_RESULTS

21_RESULTS: Excel file with the data extracted from the Forms and the pre-processed results before the statistical analysis.

22_SubjectComments&FocusGroup. Pdf file with the analysis of the subjects' comments on the forms, and the transcription of the comments during the sessions and in the focus group.

03_STATISTICAL ANALYSIS

301_DATASET: Data files containing the values of variables and factors necessary for conducting the statistical analysis proposed in the study. They are included in both IBM SPSS Statistics (.sav) and Microsoft Excel (.xlsx) formats.

302_DEMOGRAPHIC: Excel file with the demographic results.

303_DESCRIPTIVES: Files that contain the values of the main descriptive statistics and the results of the normality tests that correspond to all the variables measured in the study: the response variables and the independent variables or factors. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

304_LMM: Files that contain the execution and results of the LMM Type III test of fixed effects with unstructured repeated covariance for all the variables in the study with different statistical models. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

305_DATASET_BugLocalization.xlsx_lmmRes: Files that contain the dataset that includes the residuals of LMM executions. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

306_NORMALITYTEST: Files that contain the dataset that includes the normality test of the residuals of LMM executions. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

307_EFFECTSIZE-Cohend: Files that include the computations executed to determine the effect size of the factors in all the dependent variables. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

308_DATASET_BugLocalization.xlsx_lmm_student_Res: Files that contain the dataset that includes the residuals of LMM executions for non-experienced subjects (students) in the study. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

309_LMM_Students: Files that contain the execution and results of the LMM tests (Type III test of fixed effects with unstructured repeated covariance) for non-experienced subjects (students) in the study for all the variables in the study with different statistical models, the normality analysis of the residuals and the computations executed to determine the effect size of the factors considered in each dependent variable. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

310_DATASET_BugLocalization.xlsx_lmm_professionals_Res.xlsx: Files that contain the dataset that includes the residuals of LMM executions for experienced subjects (professionals) in the study. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

311_LMM_Profesionales: Files that contain the execution and results of the LMM tests (Type III test of fixed effects with unstructured repeated covariance) for the experienced subjects (professionals) in the study for all the variables in the study with different statistical models, the normality analysis of the residuals and the computations executed to determine the effect size of the factors considered in each dependent variable. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

312_Boxplots: Files that contain the execution and results of the boxplots by method (MDD/CDD) and by Experience (students/professionals). They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

*Note: The documentation that appears in this folder contains texts in Spanish, since this is the language in which the experiment is executed.

As of 2025, JavaScript and HTML/CSS are the most commonly used programming languages among software developers around the world, with more than 66 percent of respondents stating that they used JavaScript and just around 61.9 percent using HTML/CSS. Python, SQL, and Bash/Shell rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

Abstract

The sound performance of agriculture warrants the availability of food crops. This accomplishment in agriculture does not only signify the adequate acquisition of food crops to attain food security, but also heralds a positive aspect of the economy. In regard to this, collective efforts are being geared to securing agricultural outputs of the desired level so that self reliance in food supply can be achieved and disaster caused food shortages be contained in the shortest possible time in Ethiopia. The prime role that agriculture plays in a country's political, economic and social stability makes measures of agricultural productions extremely sensitive. Statistics collected on agricultural productions are, therefore, fraught with questions of reliability by data users. To tackle these questions convincingly and dissipate the misgivings of users, information on agriculture has to be collected using standard procedures of data collection. Upholding this principle, the Central Statistical Agency (CSA) has been furnishing statistical information on the country's agriculture since 1980/81 to alert policy interventionists on the changes taking place in the agricultural sector. As part of this task the 2007-08 (2000E.C) Agricultural Sample Survey (AgSS) was conducted to provide data on crop area and production of crops within the private peasant holdings for Main (“Meher”) Season of the specified year.

The general objective of CSA's Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, monitoring and evaluation of mainly food security and other agricultural activities.

The specific objectives of Main (“Meher”) Season Post Harvest Survey are: - To estimate the total crop area, volume of crop production and yield of crops for Main (“Meher”) Season agriculture in Ethiopia. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2007-08 (2000 E.C) annual Agricultural Sample Survey (Meher season) covered the entire rural parts of the country except the non-sedentary population of three zones of Afar and six zones of Somali regions. Accordingly, the survey took into account all parts of Harari, Dire Dawa, and 68 additional Zones / Special weredas (that are treated as zones) of other regions.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2006/07 (1999 E.C) cartographic census frame was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the 4 survey. The second stage sampling units, households, were selected from a fresh list of households that were prepared for each EA at the beginning of the survey.

Sample Design: In order to select the sample a stratified two-stage cluster sample design was implemented. Enumeration areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. The sample size for the 2007/08 (2000 E.C) agricultural sample survey was determined by taking into account both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non-sampling errors, manageability of the survey in terms of quality and operational control was also considered. Except Harari and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported.

Selection Scheme: Enumeration areas from each stratum were selected systematically using probability proportional to size sampling technique; size being number of agricultural households. The sizes for EAs were obtained from the 2006/07 (1999 E.C) cartographic census frame. From the fresh list of households prepared at the beginning of the survey 20 agricultural households within each sample EA were selected systematically. Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II respectively.

Note: Distribution of sampling units (sampled and covered EAs) by stratum is also presented in Appendix III of 2007-2008 Agricultural Sample Survey, Volume I report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2007-2008 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 2000/0: It contains forms that used to list all households in the sample areas. - AgSS Form 2000/1: It contains forms that used to list selected agricultural households and holders in the sample areas. - AgSS Form 2000/2A: It contains forms that used to collect information about crops, results of area measurements covered by crops and other land uses. - AgSS Form 2000/2B: It contains forms that used to collect information about miscellaneous questions for the holders. - AgSS Form 2000/4: It contains forms that used to collect information about list of temporary crop fields for selecting crop cutting plots. - AgSS Form 2000/5: It contains forms that used to collect information about list of temporary crop cutting results.

Note: The questionnaires are presented in the Appendix IV of the 2007-2008 Agricultural Sample Survey report Volume I.

Cleaning operations

a) Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field, hence the need for data editing, coding and verification. Although coding and editing are done by the enumerators and supervisors in the field, respectively, verification of this task is done at the Head Office. An editing, coding and verification instruction manual was prepared and reproduced for this purpose. Then 34 editors-coders and verifiers were trained for two days in editing, coding and verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100 % basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires took 35 days.

b) Data Entry, Cleaning and Tabulation: Before data entry, the Natural Resources and Agricultural Statistics Department of the CSA prepared edit specification for the survey for use on personal computers for data consistency checking purposes. The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 97 data encoders, 4 data encoder supervisors, 8 data cleaning operators and 57 personal computers. The data entered into the computers using the entry module of the CSPRO (Census and Survey Processing System) software, which is a software package developed by the United States Bureau of the Census. Following the data entry operations, the data was further reviewed for data inconsistencies, missing data … etc. by the regular professional staff from Natural Resources and Agricultural Statistics Department. The final stage of the data processing was to summarizing the cleaned data and produce statistical tables that present the results of the survey using the tabulation component of the PC based CSPRO software produced by professional staff from Data processing Department.

Response rate

To be covered by the survey, a total of 2,200 enumeration areas (EAs) were selected. However, due to various reasons that are beyond control, in 75 EAs the survey could not be successful and hence interrupted. Thus, all in all the survey succeeded to cover 2,125 EAs (96.59%) throughout the regions. The Annual Agricultural Sample survey (Meher season) was conducted on the basis of 20 agricultural households selected from each EA. Regarding the ultimate sampling units, it was intended to cover a total of 44,200 agricultural households, however, 42,523 (96.21%) were actually covered by the survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II respectively of 2007-2008 Agricultural Sample Survey, Volume I report.

In the enterprise software market in India in 2024, the customer relationship management software segment generated the highest revenue, reaching approximately 1.23 billion U.S. dollars. The enterprise resource planning software segment ranked second with around 728.72 million U.S. dollars, while other enterprise software followed with about 651.22 million U.S. dollars.

Data for publication 'A 3D perspective on sediment turnover and feeding selectivity in blennies' (Marine Pollution Bulletin, 2022) which explored the extent to which the blenny, Ecsenius stictus, can shape sediment dynamics on coral reefs by quantifying their sediment ingestion and space use.

‘Transect data for abundance.csv’ contains the transect data used to quantify blenny abundance and determine the most abundant species (Figure S4). The data used in the justification of the 120 second time limit can be found in the ‘Timed observations.csv’ file. ‘Blenny Ingested Sediment Data.csv’ file contains the amount of inorganic sediment mass in the digestive tracts of blennies used for sediment turnover calculations as well as the gutted body mass used for the comparison of sediment transporting species. This file also contains the standard length of these dissected E. stictus individuals, used in statistical analysis (Table S1). The ‘E. stictus artemia.csv’ file contains the number of artemia cysts found in each of the 5 digestive tract sections (from start to end of the gut) following the pre-determined exposure time to artemia. ‘Data for figure 2a.csv’ contains raw data of the inorganic sediment weight per square meter collected via vacuum sampling (Site) and inorganic sediment weight per square meter accumulated on TurfPods (Pod). ‘Data for figure 3 and Table S2.csv’ contains the minimum convex polygon (MCP) areas of occupancy, feeding and 50% KUD of feeding locations of each E. stictus over 6 days as well as the standard length, used in statistical analysis (Figure S5, Table S2). The raw data for the elevations of the random locations of the study site (n = 800) and within each site (n = 300 per site) used to compare the elevation of blenny habitats to the available elevations of the reef (Table S3) are found in the ‘Site and reef random.csv’ file. The elevations of the random locations within each site and the elevations of activity locations (standardised by the lowest point of the site) used to identify small scale elevation use of E. stictus (Figure 4) can be found in the ‘Data for figure 4.csv’ file. This data was used in statistical analysis (Table S3, S4).

Software/equipment used to create/collect the data: Nikon Coolpix W300 digital camera

Software/equipment used to manipulate/analyse the data: Agisoft Metashape Professional (version 1.5.1), ArcGIS (ArcMap version 10.7.1, ESRI) and R statistics software

Purpose: This study aims to provide baseline data for welfare of optometrists and enhancement of working environment by grasping the effects of all dimensions of employee empowerment on professional job satisfaction through moderation of person-organization fit. The research model is supported with a theory called JCM (Job characteristic model). Research Design: This study used a structured questionnaire to collect data from optometrists, employed on any level in Pakistan. Respondents were asked to answer 5-point Likert scale questions regarding person-organization fit, employee empowerment, and job satisfaction. The collected data is analyzed through advanced statistical software SPSS & Smart PLS. Findings: The statistics showed that more than half of the optometrists (51.2%) in Pakistan are not satisfied with their job. The greater mean scores of all dimensions of employee empowerment showed that Optometrists are generally empowered at their job. They also have an impact over the workplace and work they do. Despite of all this it was found that there is low level of job satisfaction among optometrists. Furthermore the role of P-O Fit as moderator was also not established. The control variables showed the significant association of pay package with job satisfaction. We can conclude from this study that despite of being psychological empowered, Optometrists are not satisfied with their job due to low pay packages. Implications: The findings of this study can be helpful for monitoring bodies to enhance their employee’s productivity and job satisfaction. They are recommended to take measures to empower their employees and develop policies regarding optometric practices and their growing needs. Strategic planning and effective management of human resources for eye-health in Pakistan are essential in the development of quality eye-health systems and the provision of high-quality eye care services.

Abstract

In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Alongside these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs. In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH -BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labour Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.

The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made. The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank. The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Management Team, made up of two professionals from each of the three statistical organizations. The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labour) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analysed data.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE SIZE A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war. At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected. Master Sample [This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.] The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample. The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated.

(b) SAMPLE DESIGN For reasons of funding, the smaller option proposed by the team was used, or Option B. Stratification of Municipalities The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame. Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed, and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure. However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue).

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) DATA ENTRY

An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry. Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:

• Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output
• The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10)
• Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews)
• Understanding of Round 2 Data entry screen (Modules 11-13)
• Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
• Control Procedures; Copying

Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

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