Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Household/families
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

Harmonized Data - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets - The harmonization process started with cleaning all raw data files received from the Statistical Office - Cleaned data files were then all merged to produce one data file on the individual level containing all variables subject to harmonization - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables - A post-harmonization cleaning process was run on the data - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

BackgroundUnderstanding and addressing the concerns of vaccine-hesitant individuals, including those with chronic diseases, is key to increasing vaccine acceptance and uptake. However, in Ethiopia, there is limited evidence on the COVID-19 vaccine hesitancy and predictor variables among diabetic patients. Hence, the study aimed to assess Covid-19 Vaccine Hesitancy and Predictor variables among Diabetic Patients on Follow-Up at Public Hospitals in Nekemte Town, Western Ethiopia.MethodFacility based cross sectional study was conducted among 422 diabetic patients attending public hospitals at Nekemte Town, Western Ethiopia between January, to February, 2023. Study participants were recruited by systematic random sampling. The data were collected interviewee administered pre-tested structured survey questioner. The collected data were entered and cleaned using Epi-Data software 4.6 version. The cleaned data were analyzed using SPSS. 25.0 Statical software. Descriptive statistics like frequency, mean and percentage, and binary logistic regression was applied to identify independent predictors of Covid-19 vaccine hesitancy and association between variables were declared at p-value of 0.05.ResultThe overall magnitude of COVID-19 vaccine hesitancy was 15.2% (95% CI: 11.6–18.7). The top three listed reasons for the COVID-19 vaccine hesitancy were: negative information about the vaccine (32.90%), lack of enough information (21.80%), and vaccine safety concern (19.40%). The hesitancy of the COVID-19 vaccination uptake among diabetes patients was independently influenced by age between 40–49 (Adjusted Odd Ratio [AOR] = 4.52(1.04–19.66)), having vaccine awareness (AOR = 0.029(0.001–0.86)), having a great deal of trust on vaccine development (AOR = 0.028(0.002–0.52)), and a fear amount trust (AOR = 0.05(0.003–0.79)) on the vaccine preparation, vaccinated for COVID-19 (AOR = 0.13(0.04–0.51)), perceived exposure to COVID-19 infection after having the vaccine as strongly agree/agree (AOR = 0.03(0.01–0.17))and neither agree nor disagree (AOR = 0.07(0.02–0.30)).ConclusionCOVID-19 vaccine hesitancy among diabetic patients was relatively low. The identified independent predictors were age, vaccine awareness, COVID-19 vaccination history, awareness on vaccine preparation and exposure status to COVID-19 infection. The relevant agency should focus on efforts to translating these high levels of vaccine acceptance into actual uptake, through targeting identifying predictor variables and vaccine availability for a high-risk diabetes patient.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

In light of the rapid socio-economic development in this era, it is necessary to make data on household expenditure and income available, as well as the relationship between those statistics and various variables with direct or indirect impact. Therefore, most of the countries are nowadays keen to periodically carry-out Household Expenditure and Income surveys. Given the continuous changes in spending patterns, income levels and prices, as well as in population both internal and external migration, it was now mandatory to update data for household income and expenditure over time. The main objective of the survey is to obtain detailed data on HH income and expenditure, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, it was well considered that the sample should be representative on the sub-district level. Hence, the data collected through the survey would also enable to achieve the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 2- Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns. 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators. 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it. 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps.. 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

This survey was carried-out for a sample of 12678 households distributed on urban and rural areas in all the Kingdom governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size has been uniformly selected, and in the second stage, a systematic approach guaranteing a representative sample of all sub-districts (Qada) has been applied.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires:

(1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data

The design and implementation of this survey procedures are: 1. Sample design and selection. 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals. 3. Design the tables template to be used for the dissemination of the survey results. 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks. 5. Selection and training of survey staff to collect data and run required data checkings. 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results. 7. Data collection. 8. Data checking and coding. 9. Data entry. 10. Data cleaning using data validation programs. 11. Data accuracy and consistency checks. 12. Data tabulation and preliminary results. 13. Preparation of the final report and dissemination of final results.

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Association between willingness to take daily oral PrEP and selected baseline characteristics among the MSM (n = 55).

ObjectivesTo assess the incidence and predictors of time to Tuberculosis (TB) development among Human Immunodeficiency Virus (HIV) positive patients attending follow-up care in health facilities of Hawassa, Ethiopia.MethodsWe conducted a retrospective cohort study from April 1–30, 2023. A total of 422 participants were selected using a simple random sampling method. Data was collected from the medical records of patients enrolled between January 1, 2018 –December 31, 2022, using the Kobo toolbox. We used Statistical Package for Social Studies (SPSS) version 26.0 for data analysis. To estimate the duration of TB-free survival, we applied the Kaplan-Meier survival function and fitted Cox proportional hazard models to identify the predictors of time to TB development. Adjusted hazard ratios (AHR) with 95% confidence intervals were calculated and statistical significance was declared at a P-value of 0.05.ResultsThe overall incidence rate of TB among HIV-positive patients was 6.26 (95% CI: 4.79–8.17) per 100 person-years (PYs). Patients who did not complete TB Preventive Therapy (TPT) were more likely to have TB than those who did (AHR = 6.2, 95% CI: 2.34–16.34). In comparison to those who began antiretroviral therapy (ART) within a week, those who began after a week of linkage had a lower risk of TB development (AHR = 0.44, 95% CI: 0.21–0.89). Patients who received ART for six to twelve months (AHR = 0.18, 95% CI: 0.05–0.61) and for twelve months or longer (AHR = 0.004, 95% CI: 0.001–0.02) exhibited a decreased risk of TB development in comparison to those who had ART for less than six months.ConclusionThe incidence of TB among HIV-positive patients is still high. To alleviate this burden, special attention should be given to regimen optimization and provision of adherence support for better completion of TPT, sufficient patient preparation, thorough clinical evaluation for major (Opportunistic Infections) OIs prior to starting ART, and ensuring retention on ART.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The HIECS 2008/2009 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2008/2009, among a long series of similar surveys that started back in 1955.

Survey Objectives: 1- To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials. 2- To estimate the quantities and values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is an important input for national planning. Current and past demand estimates are utilized to predict future demands 3- To measure mean household and per-capita expenditure for various expenditure items along with socio-economic correlates. 4- To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation 5- To define mean household and per-capita income from different sources. 6- To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependant on the results of this survey. 7- To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against. the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. 8- To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. 9- To study the relationships between demographic, geographical and housing characteristics of households and their income and expenditure for commodities and services. 10- To provide data necessary for national accounts especially in compiling inputs and outputs tables. 11- To identify consumers behavior changes among socio-economic groups in urban and rural areas. 12- To identify per capita food consumption and its main components of calories, proteins and fats according to its sources and the levels of expenditure in both urban and rural areas. 13- To identify the value of expenditure for food according to sources, either from household production or not, in addition to household expenditure for non food commodities and services. 14- To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles …) in urban and rural areas.

Geographic coverage

National

Analysis unit

• Househoolds
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS, 2008-2009 is a two-stage stratified cluster sample, approximately self-weighted, of nearly 48000 households. The main elements of the sampling design are described in the following.

Sample Size It has been deemed important to retain the same sample size of the previous two HIECS rounds. Thus, a sample of about 48000 households has been considered. The justification of maintaining the sample size at this level is to have estimates with levels of precision similar to those of the previous two rounds: therefore trend analysis with the previous two surveys will not be distorted by substantial changes in sampling errors from round to another. In addition, this relatively large national sample implies proportional samples of reasonable sizes for smaller governorates. Nonetheless, over-sampling has been introduced to raise the sample size of small governorates to about 1000 households As a result, reasonably precise estimates could be extracted for those governorates. The over-sampling has resulted in a slight increase in the national sample to 48658 households.

Cluster size An important lesson learned from the previous two HIECS rounds is that the cluster size applied in both surveys is found to be too large to yield an accepted design effect estimates. The cluster size was 40 households in the 2004-2005 round, descending from 80 households in the 1999-2000 round. The estimates of the design effect (deft) for most survey measures of the latest round were extraordinary large. As a result, it has been decided to decrease the cluster size to only 19 households (20 households in urban governorates to account for anticipated non-response in those governorates: in view of past experience non-response is almost nil in rural governorates).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Three different questionnaires have been designed as following: 1- Expenditure and consumption questionnaire. 2- Diary questionnaire for expenditure and consumption. 3- Income questionnaire.

Cleaning operations

Office Editing: It is one of the main stages of the survey. It started as soon as the questionnaires were received from the field and accomplished by selected work groups. It includes: a- Editing of coverage and completeness b- Editing of consistency c- Arithmetic editing of quantities and values.

Data Coding: Specialized staff has coded the data of industry, occupation and geographical identification.

Data Processing and preparing final results It included machine data entry, data validation and tabulation and preparing final survey volumes

Harmonized Data: - The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets. - The harmonization process starts with cleaning all raw data files received from the Statistical Office. - Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process is run on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

For the total sample, the response rate was 96.3% (93.95% in urban areas and 98.4% in rural areas). Response rates on the governorate level at each sampling stage are presented in the methodology document attached to the external resources in both Arabic and English.

Sampling error estimates

The sampling error of major survey estimates has been derived using the Ultimate Cluster Method as applied in the CENVAR Module of the Integrated Microcomputer Processing System (IMPS) Package. In addition to the estimate of sampling error, the output includes estimates of coefficient of variation, design effect (deff) and 95% confidence intervals.

Data appraisal

Quality Control Procedures:

The precision of survey results depends to a large extent on how the survey has been prepared for. As such, it was deemed crucial to exert much effort and to take necessary actions towards rigorous preparation for the present survey. The preparatory activities, extended over 3 months, included forming Technical Committee. The Committee has set up the general framework of survey implementation such as:

1- Applying the recent international recommendations of different concepts and definitions of income and expenditure considering maintaining the consistency with the previous surveys in order to compare and study the changes in pertinent indicators.

2- Evaluating the quality of data in all different Implementation stages to avoid or minimize errors to the lowest extent possible through: - Implementing field editing after finishing data collection for households in governorates to avoid any errors in suitable time. - Setting up a program for the Survey Technical Committee Members and survey staff for visiting field work in all governorates (each 15 days) to solve any problem in the proper time. - Re-interviewing a sample of households by Quality Control Department and examining the differences with the original responses. - For the purpose of quality assurance, tables were generated for each survey round where internal consistency checks were performed to study the plausibility of mean household expenditure on major expenditure commodity groups and its variability over major geographic regions.

Reported condom use among HIV negative participants (n = 55).

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria. The survey has been a joint endeavor of the Government of Mongolia and UNICEF to make an in-depth analysis of Mongolia's child and women health, education, livelihood status and right exercises and to assess the progress of implementation of a National Programme for Child Development and Protection (2002-2010). The data will furnish the preparation process of the national reporting to be presented by the Government of Mongolia at the special session of UN regarding the country's implementation of Declaration of the A World Fit for Children.

Survey Objectives The primary objectives of “Multiple Indicator Cluster Survey: Child Development 2005-2006” are the following: - To update the data for assessing the situation of child and women and their right exercises - To furnish the data needed for monitoring progress towards the goals of Millennium Declaration and the WorldFit for Children as a basis for future action planning - To contribute to the improvement of data and monitoring systems in Mongolia and strengthen the expertise in the design, implementation and analytical of these systems.

Survey plans The Mongolia Multiple Indicator Cluster Survey was conducted by the National Statistical Office of Mongolia with the support of the Government of Mongolia and UNICEF. Technical assistance and training for the surveys was provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

Household members (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV/AIDS. The population covered by the MICS - 3 is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child.

The MICS - 3 collected data from a nationally representative sample of households, women and children. The primary focus of the MICS - 3 was to provide estimates of key population and health, education, child protection and HIV related indicators for Mongolia as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 5 regions for key indicators. Mongolia is divided into 5 regions. Each region is subdivided into provinces (aimags) and a capital city, and each province into soums, a capital city into districts, each soum into bags and each districts into khoroos. As bag and khoroo household and population listing is annually updated, these were taken as primary sampling units. Bags and khoroos with a large population were divided into 2-3 primary sampling units in order to keep the similar number of households for sampling units. Bag and khoroos (primary sampling unit) were selected with probability proportional to size and 25 households within each of these selected units were sampled using the systematic method. The primary sampling unit variable is the cluster (HH1).

The survey estimates the indicators on the child and women situation by national level, rural, urban areas and regions. Five regions (Western, Khangai, Central, Eastern and Ulaanbaatar) were the main sampling domains and a two stage sampling design was used. Within each region households were selected with probability proportional to size.

A total of 6325 households in 253 primary sampling units were selected to represent 21 aimags and Ulaanbaatar city. Sample weights were used for estimating the data collected from each of the sampled households.

No replacement of households was permitted in case of non-response or non-contactable households. Adjustments were made to the sampling weights to correct for non-response, according to MICS standard procedures.

Sampling deviation

No major deviations from the original sample design were made. All primary sampling units were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the MICS were structured questionnaires based on the MICS - 3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household's characteristics, household listing, education, water and sanitation, child labour, child discipline, child disability, and salt iodization.

To reflect the country specific characteristics, module “Salt Iodization” of household questionnaire was enlarged by the question about the vitamin enriched flour and module “child discipline” was added with sub-module child behaviour. These additions were made based on the decisions made by work group members and Steering Committee.

In the meantime, the salt used for household cooking was on site tested to measure the iodine content.

Household questionnaire was administered to an adult household member who can best represent other members, women questionnaire to women themselves and under-five questionnaire to mothers or caretakers of children under 5 years. Child weights and heights were measured during the interviews.

The women's questionnaire includes women's characteristics, women listing, child mortality, maternal and infant health, marriage, contraception, attitudes towards family violence, and HIV/AIDS knowledge.

The children's questionnaire includes children's characteristics, child listing, birth registration and pre-schooling, child development , “A” vitamin supplement, breastfeeding, care of illness, immunization, and anthropometry.

The questionnaires were developed in Mongolian from the MICS3 Model Questionnaires, and were translated into English.

In order to check the clarity and logical sequence of questions and determine the interview duration per household, the pretest of questionnaires was made in September 2005 covering the selected households in Erdene soum of Tuv aimag. Based on the findings of the pretest, wording and logical sequence of the questions were improved.

Cleaning operations

Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: 1) Questionnaire reception 2) Office editing and coding 3) Data entry 4) Structure and completeness checking 5) Verification entry 6) Comparison of verification data 7) Back up of raw data 8) Secondary editing 9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: 10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) 11) Recoding of variables needed for analysis 12) Adding of sample weights 13) Calculation of wealth quintiles and merging into data 14) Structural checking of SPSS files 15) Data quality tabulations 16) Production of analysis tabulations

Details of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines in the MICS manual http://www.childinfo.org/mics/mics3/manual.php

Data entry was conducted by 8 data entry operators in tow shifts, supervised by 1 data entry supervisors, using a total of 9 computers (8 data entry computers plus one supervisor's computer). All data entry was conducted at the NSO using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach that controlled entry of each variable. All range checks and skips were controlled

Bivariate and multivariate logistic regression of heavy khat (Catha edulis) chewing and dyslipidemia as modifiable risk factors among patients in Southwest, Ethiopia, 2020.

Please note: This is a Synthetic data file, also known as a Dummy File - it is NOT real data. This synthetic data file should not be used for purposes other than to develop and test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical packing 'code' (e.g. SPSS syntax, SAS programs, etc.) in preparation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Research Data Centres. The Canadian Community Health Survey (CCHS) is a cross-sectional survey that collects information related to health status, health care utilization and health determinants for the Canadian population. Starting in 2007, the CCHS now operates using continuous collection. It is a large sample, general population health survey, designed to provide reliable estimates at the health region level. In order to provide researchers with a means to access the master file(s), a remote access facility has been implemented. Remote access provides researchers with the possibility to submit computer programs via e-mail to a dedicated address (cchs-escc@statcan.ca), and to receive the results by return e-mail. To obtain remote access privileges, it is necessary that researchers obtain advance approval from the Health Statistics Division. Requests must be submitted to the aforementioned e-mail address and must provide the following, clearly itemized information: •the researcher’s affiliation, • the name of all researchers involved in the project, • the title of the research project, • an abstract of the project, • the goals of the research, • the data to which access is required (survey, cycle), • why the project requires access to the master data rather than the PUMF, • why Remote Access service is chosen rather the on-site access in a Research Data Centre (RDC), • the expected results, and • the project’s expected completion date. Further information is available by contacting the CCHS team at the above e-mail address or by phone at (613) 951-1653. Once the request for remote access has been approved, the researcher can submit his/her computer programs to the CCHS team for processing on the master file(s). The computer output is reviewed by the team for confidentiality concerns and returned to the researcher. However, the correctness and accuracy of each program submission remains, at all times, the sole responsibility of the researcher.

Supplementary materials: 1. IIEQ, 2. LAS, 3. Descriptive statistics, 4. Correlations, 5. LogaritmicRegression_Results in Tables, 6. InteractionEffects_Figures, 7. Mediation models_Results, 8. Data file (SPSS)

Socio-demographic characteristics of study participants, Southwest Ethiopia, 2019.

This SPSS (v25) data file contains variables and scale scores for a repeated measures pre-post RCT experiment conducted in Hong Kong by Dr Tanja Sobko using the intervention Play&Grow. The data are reported in a manuscript under preparation: Linking Connectedness to Nature, caregivers’ feeding and children’s eating behaviours following the Play&Grow early environmental intervention program in Hong Kong.

Authors:

T. Sobko,1 G. T. L. Brown,2 W. H. G. Cheng1

Authors’ institutional affiliation:

1&3 School of Biological Sciences, Faculty of Science, The University of Hong Kong, Hong Kong

2 Faculty of Education & Social Work, The University of Auckland, Auckland, New Zealand.

Corresponding author: Dr. Tanja Sobko

6F, Kadoorie Biological Sciences Building, Pokfulam, Hong Kong. Office: (852) 22990611; Fax: (852) 2559 9114. Email: tsobko@hku.hk

Awareness, source of information and practice on COVID-19 vaccine hesitancy among diabetic patients attending public hospitals in Nekemte Town, East Wollega Zone, Western Ethiopia, 2023.

Socio-demography characteristics of the diabetic patients attending public hospitals in Nekemte Town, East Wollega Zone, Western Ethiopia, 2023.

Classification of dietary errors as per hospital’s criteria before the consumption of meals.

Abstract

The 2003 Agriculture Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmer organisations, etc. As a result the dataset is both more numerous in its sample and detailed in its scope compared to previous censuses and surveys. To date this is the most detailed Agricultural Census carried out in Africa.

The census was carried out in order to: · Identify structural changes if any, in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in rural infrastructure and in the level of agriculture household living conditions; · Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stake holders. · Establish baseline data for the measurement of the impact of high level objectives of the Agriculture Sector Development Programme (ASDP), National Strategy for Growth and Reduction of Poverty (NSGRP) and other rural development programs and projects. · Obtain benchmark data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc.

Geographic coverage

Tanzania Mainland and Zanzibar

Analysis unit

• Households
• Individuals

Universe

Large scale, small scale and community farms.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Mainland sample consisted of 3,221 villages. These villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the 2002 Population and Housing Census. The total Mainland sample was 48,315 agricultural households. In Zanzibar a total of 317 enumeration areas (EAs) were selected and 4,755 agriculture households were covered. Nationwide, all regions and districts were sampled with the exception of three urban districts (two from Mainland and one from Zanzibar).

In both Mainland and Zanzibar, a stratified two stage sample was used. The number of villages/EAs selected for the first stage was based on a probability proportional to the number of villages in each district. In the second stage, 15 households were selected from a list of farming households in each selected Village/EA, using systematic random sampling, with the village chairpersons assisting to locate the selected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census covered agriculture in detail as well as many other aspects of rural development and was conducted using three different questionnaires: • Small scale questionnaire • Community level questionnaire • Large scale farm questionnaire

The small scale farm questionnaire was the main census instrument and it includes questions related to crop and livestock production and practices; population demographics; access to services, resources and infrastructure; and issues on poverty, gender and subsistence versus profit making production unit.

The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices.

The large scale farm questionnaire was administered to large farms either privately or corporately managed.

Questionnaire Design The questionnaires were designed following user meetings to ensure that the questions asked were in line with users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data: • Where feasible all variables were extensively coded to reduce post enumeration coding error. • The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the farmer. • The responses to all questions were placed in boxes printed on the questionnaire, with one box per character. This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data entry. • Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent. • Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSPro, SPSS and the dissemination applications.

Cleaning operations

Data processing consisted of the following processes: · Data entry · Data structure formatting · Batch validation · Tabulation

Data Entry Scanning and ICR data capture technology for the small holder questionnaire were used on the Mainland. This not only increased the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to track errors during the verification process. The scanning operation was so successful that it is highly recommended for adoption in future censuses/surveys. In Zanzibar all data was entered manually using CSPro.

Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification and good handwriting. A score was given to each questionnaire based on the legibility and the completeness of enumeration. This score will be used to assess the quality of enumeration and supervision in order to select the best field staff for future censuses/surveys.

CSPro was used for data entry of all Large Scale Farm and community based questionnaires due to the relatively small number of questionnaires. It was also used to enter data from the 2,880 small holder questionnaires that were rejected by the ICR extraction application.

Data Structure Formatting A program was developed in visual basic to automatically alter the structure of the output from the scanning/extraction process in order to harmonise it with the manually entered data. The program automatically checked and changed the number of digits for each variable, the record type code, the number of questionnaires in the village, the consistency of the Village ID Code and saved the data of one village in a file named after the village code.

Batch Validation A batch validation program was developed in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to the more complex checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaires. After the long process of data cleaning, tabulations were prepared based on a pre-designed tabulation plan.

Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while ArcView and Freehand were used for the maps.

Analysis and Report Preparation The analysis in this report focuses on regional comparisons, time series and national production estimates. Microsoft Excel was used to produce charts; ArcView and Freehand were used for maps, whereas Microsoft Word was used to compile the report.

Data Quality A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of this, it is believed that the census is highly accurate and representative of what was experienced at field level during the Census year. With very few exceptions, the variables in the questionnaire are within the norms for Tanzania and they follow expected time series trends when compared to historical data. Standard Errors and Coefficients of Variation for the main variables are presented in the Technical Report (Volume I).

Sampling error estimates

The Sampling Error found on page (21) up to page (22) in the Technical Report for Agriculture Sample Census Survey 2002-2003

The data in this file are reaction times and accuracy percentages for the cued go/no go task, and also the trail making, digit span and grooved pegboard tests. Number of participants: 54 (20 males, mean age 21.43 +/- 2.72 years). The subjects were healthy right-handed young adults with no history of epilepsy, heart disease and were not pregnant. Two 2.5 hour sessions split by a 3-week. SPSS version 22 was used to analyse the data.