This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute of Statistics (RSIS), the Federal Institute of Statistics (FOS) and the Agency for Statistics of BiH (BHAS), carried out a Living Standards Measurement Survey (LSMS).

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, labor) 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 analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and provided funding for a further three years of data collection for a panel survey, known as the Household Survey Panel Series (HSPS) – and more popularly known as Living in BiH (LiBiH). Birks Sinclair & Associates Ltd. in cooperation with the Independent Bureau for Humanitarian Issues (IBHI) were responsible for the management of the HSPS with technical advice and support provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The panel survey provides longitudinal data through re-interviewing approximately half the LSMS respondents for three years following the LSMS, in the autumns of 2002 and 2003 and the winter of 2004. The LSMS constitutes Wave 1 of the panel survey so there are four years of panel data available for analysis. For the purposes of this documentation we are using the following convention to describe the different rounds of the panel survey: - Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel - Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 - Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003 - Wave 4 Fourth interview with sub-sample respondents in Winter 2004

The panel data allows the analysis of key transitions and events over this period such as labour market or geographical mobility and observations on the consequent outcomes for the well-being of individuals and households in the survey. The panel data provides information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty and movements in an out of poverty are experienced by different types of households and individuals over the four year period. Most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within BiH at a time of social reform and rapid change.

In order to develop base line (2004) data on poverty, incomes and socio-economic conditions, and to begin to monitor and evaluate the implementation of the BiH MTDS, EPPU commissioned this modified fourth round of the LiBiH Panel Survey.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation; Republic

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The Wave 4 sample comprised of 2882 households interviewed at Wave 3 (1309 in the RS and 1573 in FBiH). As at previous waves, sample households could not be replaced with any other households.

Panel design

Eligibility for inclusion

The household and household membership definitions assume the same standard definitions used at Wave 3. While the sample membership, status and eligibility for interview are as follows: i) All members of households interviewed at Wave 3 have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview, i.e. younger than 15 years. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules The panel design provides that sample members who move from their previous wave address must be traced and followed to their new address for interview. In some cases the whole household will move together but in other cases an individual member may move away from their previous wave household and form a new "split-off" household of their own. All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This method has the benefits of maintaining the maximum number of respondents within the panel and being relatively straightforward to implement in the field.

Definition of 'out-of-scope'

It is important to maintain movers within the sample to maintain sample sizes and reduce attrition and also for substantive research on patterns of geographical mobility and migration. The rules for determining when a respondent is 'out-of-scope' are:

i. Movers out of the country altogether i.e. outside BiH This category of mover is clear. Sample members moving to another country outside BiH will be out-of-scope for that year of the survey and ineligible for interview.

ii. Movers between entities Respondents moving between entities are followed for interview. Personal details of "movers" are passed between the statistical institutes and an interviewer assigned in that entity.

iii. Movers into institutions Although institutional addresses were not included in the original LSMS sample, Wave 4 individuals who have subsequently moved into some institutions are followed. The definitions for which institutions are included are found in the Supervisor Instructions.

iv. Movers into the district of Brcko Are followed for interview. When coding, Brcko is treated as the entity from which the household moved.

Feed-forward

Details of the address at which respondents were found in the previous wave, together with a listing of household members found in each household at the last wave were fed-forward as the starting point for Wave 4 fieldwork. This "feed-forward" data also includes key variables required for correctly identifying individual sample members and includes the following: - For each household: Household ID (IDD); Full address details and phone number - For each Original Sample Member: Name; Person number (ID); unique personal identifier (LID); Sex; Date of birth

The sample details are held in an Access database and in order to ensure the confidentiality of respondents, personal details, names and addresses are held separately from the survey data collected during fieldwork. The IDD, LID and ID are the key linking variables between the two databases i.e. the name and address database and the survey database.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Dat entry

As at previous waves, CSPro was the chosen data entry software. The CSPro program consists of two main features intended to reduce the number of keying errors and to reduce the editing required following data entry: - Data entry screens that included all skip patterns. - Range checks for each question (allowing three exceptions for inappropriate, don't know and missing codes).

The Wave 4 data entry program had similar checks to the Wave 3 program - and DE staff were instructed to clear all anomalies with SIG fieldwork members. The program was tested prior to the commencement of data entry. Twelve data entry staff were employed in each Field Office, as all had worked on previous waves training was not undertaken.

Editing

Instructions for editing were provided in the Supervisors Instructions. At Wave 4 supervisors were asked to take more time to edit every questionnaire returned by their interviewers. The SIG Fieldwork Managers examined every Control Form.

Response rate

The level of cases that were unable to be traced is extremely low as are the whole household refusal or non-contact rates. In total, 9128 individuals (including children) were enumerated within the sample households at Wave 4, 5019 individuals in the FBiH and 4109 in the RS. Within in the 2875 eligible households, 7603 individuals aged 15 or over were eligible for interview with 7116 (93.6%) being successfully interviewed. Within co-operating households (where there was at least one interview) the interview rate was higher (98.6%).

A very important measure in longitudinal surveys is the annual individual re-interview rate as a high attrition rate, where large numbers of respondents drop out of the survey over time, can call into question the quality of the data collected. In BiH the individual re-interview rates have been high for the survey. The individual re-interview rate is the proportion of people who gave an interview at time t-1 who also give an interview at t. Of those who gave a full interview at wave 3, 6654 also gave a full

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Malawi in 2009 and 2014, as part of Africa Enterprise Surveys roll-out, an initiative of the World Bank.

New Enterprise Surveys target a sample consisting of longitudinal (panel) observations and new cross-sectional data. Panel firms are prioritized in the sample selection, comprising up to 50% of the sample in the current wave. For all panel firms, regardless of the sample, current eligibility or operating status is determined and included in panel datasets.

Malawi ES 2014 was conducted between April 2014 and February 2015, Malawi ES 2009 was carried out in May - July 2009. The objective of the Enterprise Survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

Data from 673 establishments was analyzed: 436 businesses were from 2014 ES only, 63 - from 2009 ES only, and 174 firms were from both 2009 and 2014 panels.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively measure characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

For the Malawi ES, multiple sample frames were used: a sample frame was built using data compiled from local and municipal business registries. Due to the fact that the previous round of surveys utilized different stratification criteria in the 2009 survey sample, the presence of panel firms was limited to a maximum of 50% of the achieved interviews in each stratum. That sample is referred to as the panel.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments were used for Malawi ES 2009 and 2014: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth. There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The NHANES I Epidemiologic Followup Study (NHEFS) is a longitudinal study of adults originally examined, measured, and interviewed in 1971-1975 as part of the first National Health and Nutrition Examination Survey (NHANES I). The NHEFS was jointly initiated by the National Center for Health Statistics (NCHS), the National Institute on Aging, and other components of the National Institutes of Health and Public Health Service. The primary purpose of the followup study is to investigate longitudinal relationships between the extensive data on physiological, nutritional, behavioral, and demographic characteristics collected during NHANES I and subsequent morbidity or mortality from specific diseases and conditions. The 1982-1984 wave of data collection for NHEFS followed all medically examined respondents who had been 25 to 74 years in 1971-1975. The 1986 NHEFS wave focused on older members of the NHANES I NHEFS cohorts, those who had been 55-74 years of age at their baseline examinations in 1971-1975 and were not known to be deceased at the time of the 1982-1984 NHEFS. In the 1986 NHEFS, the surviving respondents were 65-89 years of age. Data were collected on changes in vital, health, and functional status and use of health care services that had occurred since the last contact, whether the contact was in 1982-1984 or 1971-1975. The vital and tracing status file documents efforts to trace all subjects who had been 55 years of age and over at NHANES I (N = 5,677) and ascertain their vital status and demographic data. Further data collection was aimed at the 3,980 subjects who were not known to be deceased by 1982-1984. Thirty-minute telephone interviews were conducted with either sample members (N = 2,558) or with proxies for the incapacitated (N = 469) and deceased (N = 581) subjects. Questions were asked on household composition, self-reports of physician-diagnosed medical conditions (with detail on reports of cancer, bone fractures, and non-hospital health facility stays), death if applicable, functional limitations, use of health care facilities, and interviewer observations about the respondent. Items on coronary bypass surgery, pacemaker procedures, and community services utilization were 1986 additions to the NHEFS questionnaire. For those respondents who had not been interviewed in 1982-1984, questions were included on smoking and alcohol use, vision and hearing, exercise and weight, and pregnancy and menstrual history. Health care facility records were abstracted to provide diagnostic and summary information on single or multiple overnight stays in hospitals and nursing homes for 2,021 subjects reporting such stays. Death certificate data, including International Classification of Diseases, 9th Revision codes for multiple causes of death, were added for 661 decedents reported since the 1982-1984 wave, for a total of 2,266 decedents.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study used secondary analysis of data from several different sources to examine the impact of increased oil development on domestic violence, dating violence, sexual assault, and stalking (DVDVSAS) in the Bakken region of Montana and North Dakota. Distributed here are the code used for the secondary analysis data; the data are not available through other public means. Please refer to the User Guide distributed with this study for a list of instructions on how to obtain all other data used in this study. This collection contains a secondary analysis of the Uniform Crime Reports (UCR). UCR data serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. Each year, participating law enforcement agencies contribute reports to the FBI either directly or through their state reporting programs. Distributed here are the codes used to create the datasets and preform the secondary analysis. Please refer to the User Guide, distributed with this study, for more information. This collection contains a secondary analysis of the National Incident Based Reporting System (NIBRS), a component part of the Uniform Crime Reporting Program (UCR) and an incident-based reporting system for crimes known to the police. For each crime incident coming to the attention of law enforcement, a variety of data were collected about the incident. These data included the nature and types of specific offenses in the incident, characteristics of the victim(s) and offender(s), types and value of property stolen and recovered, and characteristics of persons arrested in connection with a crime incident. NIBRS collects data on each single incident and arrest within 22 offense categories, made up of 46 specific crimes called Group A offenses. In addition, there are 11 Group B offense categories for which only arrest data were reported. NIBRS data on different aspects of crime incidents such as offenses, victims, offenders, arrestees, etc., can be examined as different units of analysis. Distributed here are the codes used to create the datasets and preform the secondary analysis. Please refer to the User Guide, distributed with this study, for more information. The collection includes 17 SPSS syntax files. Qualitative data collected for this study are not available as part of the data collection at this time.

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

Antenatal care (ANC) is the care given to pregnant by qualified medical experts in order to guarantee the optimal health conditions for the mother and the unborn child during pregnancy. Four or fewer antenatal care (ANC) visits are strongly linked to maternal and perinatal death. Because of this, the World Health Organization created a new model known as minimum of eight antenatal care (ANC8+) contact. This study aims to focus on the current antenatal care contact which not previously addressed. Therefore, the aim of this to investigate time to first antenatal care contact and its predictors among pregnant women at Bishoftu General Hospital 2023/24Methods: An institutional-based cross-sectional study design was conducted among 347 study participants which was selected by systematic random sampling method. The data was collected using pretested, structured questionnaires. Data was entered into Epi Data version 4.6 and analyzed using STATA 15. Descriptive summary statistics like median survival time, Kaplan Meier survival curve, and Log-rank test were computed. Bivariate and multivariable Weibull regresion models were fitted to identify the time to first antenatal care contact and predictors. A hazard ratio with a 95% confidence interval was calculated and p-values < 0.05 were considered statistically significantEthical approval and informed consentEthical clearance was obtained from an institutional Research Ethics Review Board (IRB) of the University of Arsi University (with Reference number, A/CHS/18/2023). In addition, a letter of ethical approval was sent to Bishoftu General Hospital to be obtained from the hospital’s administrators. Informed, voluntary, and verbal were obtained from the head of the hospital and mothers. There are no study participants under the age of 18 years. Before conducting the interviews, information was given to the participants, and were assured of voluntary participation, confidentiality, and freedom to withdraw from the study at any time. The nature and significance of the study were explained to the participantsData collection tool and proceduresTo ensure the quality of data at the beginning, a data collection questionnaire was pre-tested on 5% of the calculated sample size at Chelelaka Health Center and necessary modifications will be made based on gaps identified in the questionnaire. Any error found during the process of checking will be corrected and modifications will be made to the final version of the data abstraction format. Training will be given to data collectors and supervisors for 01 days before the actual data collection task on the already existing records, half-day theoretical and half-day practical training. Data quality will be controlled by designing the proper data collection materials, through continuous supervision. All completed data collection forms will be examined for completeness and consistency during data management, storage, cleaning, and analysis. The data will be entered and cleaned by the principal investigator before analysis. Midwives, who are working in the maternity ward, will collect the data. The principal investigator of the study will control the overall activity.

The Diary Study (also known as The T.E.A.M. Study or The Progress Principle Study) was carried out in the late 1990s to early 2000s in order to probe the everyday work experiences of professionals working on important innovation projects within their companies. Teresa Amabile was the principal investigator. The database contains quantitative data and detailed categorical coding of qualitative data (not the verbatim qualitative data itself). Data were collected daily from the 238 professionals in 26 project teams who participated in this study throughout the entire course of a project (or discrete project phase) that required creativity – novel, useful ideas – in order to be successful. Many of the projects involved new product development. To the extent possible, daily data collection with a given team began on the first day of the project and continued until the last day. A large body of additional data on the individuals and their performance was collected at various other points during the study. The 26 teams were recruited from seven different companies in three industries: high tech, chemicals, and consumer products. Five of the companies had four teams that participated; one company had five teams; and one company had one team. (Please see the Metadata tab, below, for full description.) The primary source of data is the Daily Questionnaire (DQ) diary form that each participant was emailed each workday, Monday through Friday, throughout the course of the project on which the participant’s team was working. Participants were asked to return the completed diary, which took most people 5-10 minutes to complete, shortly before the end of their workday. Most did complete the diary on the day that the diary referred to, but some habitually completed the diary early the next day. The overall response rate was 75%, yielding a total of 11,637 individual daily diary entries. The DQ, which was identical for each day, contained questions calling for Likert-scale responses to questions about psychological state that day: (a) emotions; (b) motivation; and (c) perceptions of the project supervisor, the project team, the work environment, and the work itself. In addition, participants completed an open-ended question asking them to describe one event that stood out in their minds from the day that was relevant to the work in some way – the “Event Description” (ED) – and then answered additional Likert-scale questions about the event. The DQ included a few additional quantitative items. Although the DQ forms collected both quantitative and qualitative data (the EDs), the raw qualitative data are not included in this database. All included data have been de-identified, and it was not possible to adequately disguise the qualitative data. However, this database contains codes from several different coding schemes that prior researchers using this database created to categorize the events (and attributes of events) that participants reported in their EDs. Of the two primary coding schemes, the Detailed Event Narrative Analysis (DENA) scheme is extremely detailed; the Broad Event Narrative Analysis (BENA) scheme is considerably less detailed. In addition, several LIWC (Linguistic Inquiry and Word Count) analyses of the EDs are included in this database. A great deal of additional quantitative data was collected from all participants at various points in the study of their teams, including: demographics; personality; job satisfaction; cognitive style; motivational orientation; broad perceptions of the work environment, the project team, and the project; and monthly assessments of the performance of themselves and each of their teammates. Data were also collected from multiple managers in the participant’s area of the organization, who were broadly familiar with projects in that area. These managers completed monthly surveys assessing each of the participating projects, as well as a set of comparable but non-participating projects, on several dimensions. The book, The Progress Principle (Amabile, T. & Kramer, S., 2011, Harvard Business Publishing), reports a number of findings derived from quantitative and qualitative analyses of this database. The Research Appendix of this book contains descriptions (written in non-technical terms) of the Diary Study companies, participants, procedure, data collection instruments, data, and primary analyses conducted by Amabile and her colleagues. The Dataverse record lists several papers that used this database. Like the book, they can be used for additional information about the data collection methods and instruments as well as findings. Approval is required for use of this data. To apply for access, fill out the Diary Study application for use; make sure first you already have a Dataverse account.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York (fig. 1, inset). During the storm, an overwash channel opened a breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. This breach is now referred to as the Wilderness Breach (fig. 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impacts, post-storm beach response, and recovery. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established to include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Hurricane Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water (MHW; 0.46 meters, North American Vertical Datum of 1988 [NAVD88]) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly data collection through April 2013 and repeat surveys every 2-3 months thereafter until October 2014. Bi-annual surveys have been collected through September 2016. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness Breach. See below for survey collection dates for all data types. For along shore shoreline data, the MHW shoreline (0.46 m [NAVD88]; Weber and others, 2005) is derived from the field data using an interpolation method that creates a series of equally-spaced cross-shore profiles between the two survey lines that flank the MHW contour. The foreshore slope is assumed to be uniform on each profile. Using this slope and the two surveyed positions on each cross-shore profile, a simple geometric calculation is done to find where each profile line intersects the MHW contour. This shapefile, FIIS_Shorelines_Oct2012_Oct2017.shp, consists of Fire Island, NY pre- and post-storm shoreline data collected from October 2012 to October 2017. This dataset contains 25 Mean High Water (MHW) shorelines for Fire Island, NY (A total of 23 full shorelines, where two shorelines were collected over multiple days). Prior to and following Hurricane Sandy in October 2012, continuous alongshore DGPS data were collected to assess the positional changes of the shoreline (MHW - 0.46 m NAVD88) and the upper portion of the beach. In the five years following Sandy, 24 surveys were conducted collecting data along shore-parallel tracks to capture the base of the dune, the mid-beach, and the upper and lower foreshore. The alongshore tracks extend from just west of Fire Island Lighthouse to the western flank of the storm-induced breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Sep 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 24 2017 (MHW shoreline/Cross-shore data/Breach shoreline) May 23 2017 (MHW shoreline/Cross-shore data/Breach shoreline) Oct 17 2017 (MHW shoreline/Cross-shore data/Breach shoreline)

Abstract

The Bosnia and Herzegovina MICS4 2011–2012 was conducted using a representative sample in order to provide estimates for a large number of indicators on the situation of children, women and men as well as household living conditions at the level of BiH, the Federation of BiH (FBiH), the Republic of Srpska (RS) and for urban and rural areas. The survey is based on a representative sample of 6,838 households in BiH (4,107 in FBiH, 2,408 in RS and 323 in Brcko District (BD) of BiH) with an overall response rate of 91 per cent (in total, 5,778 households were interviewed). The results reflect data collected during the period November 2011 and March 2012.

The survey was undertaken as part of the fourth global round of the MICS programme and implemented by the Federal Ministry of Health (FMH) and the Ministry of Health and Social Welfare of the Republic of Srpska (MHSW RS) in cooperation with the Institute for Public Health of the FBiH (IPH FBiH) and the Agency for Statistics of BiH (BHAS). Financial and technical support was provided by UNICEF with additional financial support provided by UN Women for preparing the master sample frame, as well as by UNFPA and UNHCR.

The primary aim of MICS is to provide indicators for monitoring the level of progress towards the Millennium Development Goals, the Plan of Action for A World Fit for Children as well as other international and national commitments undertaken by BiH. The survey findings are presented from the equity perspective by indicating disparities in accordance with administrative units, sex, area type, the level of education of the respondent or head of the household, household wealth and other characteristics.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household, and all men aged 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the BiH Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators at the BiH, FBiH and RS level and for urban and rural areas. A two stage stratified sampling approach was used for the selection of the cluster sample.

The official population estimate for BiH is 3.8 million inhabitants living in about one million households. However, some sampling frame exercises conducted due to the lack of an official Census since 1991 estimate this number at approximately 3.3 million.

As stated previously, BiH is composed of three administrative units: two entities, the FBiH and RS and a third administrative unit, BD. The FBiH covers approximately 51 per cent of the territory of BiH and 62 per cent of the population. RS covers approximately 49 per cent of the territory and about 36 per cent of the population and BD covers less than 1 per cent of the territory and approximately 2 per cent of the population.

The target sample size was 6,800 households, which was determined based on lessons learned through the previous round of MICS as well as by budgetary limitations. The standard sample design used in most of the countries participating in the MICS programme needed to be adapted for BiH due to the low birth rate; therefore, it was necessary to target (oversample) households with children under 5 and members aged 5-24.

Accordingly, the sample was stratified by households with children under 5 (type 1), households with children aged 5-24 (type 2) and all other households (type 3). In addition, the size of the three strata could not jeopardise the indicator estimates for the other target populations, such as the indicators that referred to fertile women.

As the sample size was defined as 6,800 households it was necessary to calculate the size of stratum 1 and stratum 2. The size of stratum 3 was obtained as the difference between the total sample size and the sum of the size of strata 1 and 2.

The sampling procedures are more fully described in "Bosnia and Herzegovina Multiple Indicator Cluster Survey 2011 - Final Report" pp.150-153.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child discipline and hand washing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, access to mass media and ICT, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The children's questionnaire includes child's age, early childhood development, breastfeeding, care of illness, immunisation and anthropometry.

The men's questionnaire includes man's background, access to mass media and ICT, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tobacco and alcohol use, life satisfaction and health care.

The questionnaires were based on the MICS4 model questionnaire.19 From the MICS4 model English version, the questionnaires were translated into local languages used in BiH. The questionnaires were pre-tested in the FBiH and RS in the City of Banja Luka and in Sarajevo Canton during September 2011. The pre-test plan provided for interviews to be conducted in 48 households in the FBiH and 24 households in RS. The households, of which 50 per cent were urban and rural households respectively, were randomly selected from the Master Sample template. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

Cleaning operations

Data entry and processing was conducted separately for the FBiH, RS and BD. The data was entered using CSPro software. Data was entered into a total of 11 microcomputers by 8 data entry operators in the FBiH and 6 persons in RS; the process was supervised by data entry supervisors.

Data entry commenced in the FBiH four weeks after the start of data collection (December 2011) and was concluded in April 2012. In RS data entry for the RS and BD started one week after data collection began (December 2011) and was concluded in May 2012.

The data was analysed using the SPSS (Statistical Package for Social Sciences) software programme (Version 18) and the model syntax and tabulation plans developed by UNICEF were also used for this purpose. In order to ensure quality control all of the questionnaires were double entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the BiH questionnaires were used throughout.

Response rate

Of the 6,838 households in the sample 6,334 were found to be occupied; of these, 5,778 households were successfully interviewed for a household response rate of 91 percent. In the interviewed households 4,645 women aged 15-49 were identified and 4,446 successfully interviewed, yielding a response rate of 96 percent. In addition, 4,718 men aged 15-49 were listed in the household questionnaire as being household members. Questionnaires were completed for 4,353 eligible men, which corresponds to a response rate of 92 percent within the interviewed households. There were 2,332 children under age five listed in the household questionnaire. Questionnaires were completed for 2,297 children, which corresponds to a response rate of 99 percent within the interviewed households. The overall response rate for the women’s, men’s and children’s questionnaires were 87 percent, 84 percent, and 90 percent, respectively.

Sampling error estimates

The sample of respondents selected for the BiH MICS was only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would have yielded results that differed somewhat from the results of the actual selected sample. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly but can be estimated statistically from the survey data.

The sampling error measures below are presented in this appendix for each of the selected indicators. - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor Linearization method was used for the estimation of standard errors. - Coefficient of variation (se/r): is the ratio of the standard error to the value of the indicator and is a measure of the relative sampling error. - Design effect (deff): is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of

This dataset represents land cover mapping, physical habitat measurements, continuous hydrology measurements, salt tracer measurements, and benthic macroinvertebrate sample scores from nine small streams in unincorporated King County within the Puget Sound region of Washington State. These data were collected during two periods, 2008 – 2012/2013 and 2018 – 2022, as part of a study to evaluate the performance of King County’s land use regulations at protecting stream ecosystems. Six of the streams drained watersheds that were developed or developable and were subject to King County’s land use regulations. Three of the streams drained watersheds that were largely protected from development and served as references for comparison. The initial data collection (2008 – 2012/2013) is described in a report titled, “Assessing Land Use Effects and Regulatory Effectiveness on Streams in Rural Watersheds of King County, Washington,” published in 2014. An analysis of the two combined datasets is described in a report titled, “The Effects of the Land Use Regulatory Framework on Stream Ecosystems in Unincorporated King County Watersheds,” published in 2025. See these reports for details about the sampling methods, study results, and what these data represent. Below we briefly describe the types of data included in this dataset. For questions about these data, please contact James Bower (james.bower@kingcounty.gov), Aaron David (adavid@kingcounty.gov), Ian Higgins (ihiggins@kingcounty.gov), or Rebekah Stiling (rstiling@kingcounty.gov). All data were collected by the King County Water and Land Resources Division, Science and Technical Support Section. Land cover mapping of the nine study watersheds was conducted once at the beginning and end of the first period (2007 and 2012) and once at the beginning and end of the second period (2017 and 2022). The land cover data are represented by ‘Land_cover.csv’. Physical habitat measurements were collected once a year within a defined and consistent section of each stream. Physical habitat measurements are represented by ‘Pools.csv’, ‘Reach_lengths.csv’, ‘Substrate.csv’, ‘Thalweg_depths.csv’, and ‘Wood.csv’. Continuous hydrology measurements of stream discharge, water temperature, and conductivity were collected in each stream throughout most years of the study. Continuous hydrology measurements were summarized into daily values and are represented by ‘Hydrology_daily.csv’. Samples of the benthic macroinvertebrate community were collected in each stream during late summer or early fall across all study years. These samples were used to calculate Puget Sound lowlands Benthic-Index of Biotic Integrity scores for each stream and year. Benthic macroinvertebrate sample scores are represented by ‘BIBI.csv’. Salt tracer measurements were conducted in each stream across multiple flows within each year. Salt tracer measurements are represented by ‘Tracer_measurements.csv’. The ‘Variable_names.csv’ file contains a list of each of the variable/field names within each data file, the variable type for each field, and a brief description of what each variable/field represents.

This dataset describes the Survey Data collected for the Planning Assistance to the States (PAS) study along Little Sugar Creek and selected tributaries, near Bella Vista, Arkansas, and Pineville, Missouri, December 2019. Little Sugar Creek is a tributary to the Elk River in Missouri that commences in Benton County, Arkansas and terminates in McDonald County, Missouri. The stream headwaters are located southeast of Garfield, Arkansas. Little Sugar Creek flows through Bella Vista, Arkansas, and runs north to its confluence with the Big Sugar Creek just south of Pineville, Missouri where it forms the Elk River. Browning Creek, Blowing Spring Creek, Spanker Creek and McKisic Creek are all tributaries to the Little Sugar Creek between Bella Vista, Arkansas, and Pineville, Missouri. These streams were selected for bathymetric and specified structure survey by the U.S. Army Corps of Engineers (USACE) PAS study. The survey consisted of channel cross-sections, bridge/culvert cross-sections, and high-water locations along Little Sugar Creek, Browning, Blowing Spring, Spanker Creek, and McKisic Creek in the town of Bella Vista, Benton County, Arkansas to the town of Pineville, McDonald County, Missouri. The surveys included 38 channel cross-sections, 35 bridge/culvert cross-sections, 1 dam outlet works, 1 dam spillway, 1 dam road, and 3 high-water locations. Topographic data and supplemental photographic data were collected for each survey section. These data were collected using a surveying total station, Trimble R10, and a Trimble R8. Trimble R10 and Trimble R8 are the real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receivers. The GNSS receivers were connected to the Arkansas Department of Transportation (ARDOT) or the Missouri Department of Transportation (MODOT) real-time network (RTN), which provided real-time survey grade horizontal and vertical positioning, and were used to obtain Northing, Easting, and the elevation location information for one control point in the survey area. Supplemental photographic data were collected using cellular telephone cameras. The survey was conducted by the U.S. Geological Survey during a two-week period in December 2019. Six items containing the survey data and the relevant information are available for download. They are LittleSugarCreekPAS_Culverts.xlsx, LittleSugarCreek_PAS_Bridges.xlsx, LittleSugarCreek_PAS_XS.xlsx, LittleSugarCreekPASmoshp.zip, LittleSugarPASarshp.zip, LittleSugarCreek_PAS_pictures_Summary.pptx, LittleSugarCreek_PAS_Field_Pictures.pdf, and LittleSugarCreek_PAS_Field_Sheets.pdf. The topographic points with centerline stationing are available in Microsoft Excel file format (LittleSugarCreekPAS_Culverts.xlsx, LittleSugarCreek_PAS_Bridges.xlsx, LittleSugarCreek_PAS_XS.xlsx). Field notes, which describe bridge/culvert details, are available in pdf format (LittleSugarCreek_PAS_Field_Sheets.pdf). Supplemental photographic data were compiled in a Microsoft PowerPoint file and .pdf as requested (LittleSugarPASpictures.pptx, LittleSugarCreek_PAS_Field_Pictures.pdf). Topographic points are also available in the Environmental Systems Research Institute (ESRI) ArcGIS Shapefile format (LittleSugarPASmoshp.zip & LittleSugarPASarshp.zip). All files in the shapefile group must be retrieved to be useable.

This data is the Saving young lives: Triage and treatment using the pediatric rapid sepsis trigger (PRST) tool study. Data collected for this study occurred from April 2020 to April 2022. Objective(s): This is a pre-post intervention study involving pediatric patients presenting to the study hospitals in seek of medical care for an acute illness. The purpose of this study was to develop a prediction model and to perform clinical validation of a digital triage tool to guide triage and treatment of children at health facilities in LMICs with severe infections/suspected sepsis. The study involved three phases: (I) Baseline Period, (II) Interphase Period, (III) Intervention Period. The study hospitals include 2 sites in Kenya (1 control site, 1 experimental site) and 2 in Uganda (1 control site, 1 experimental site). Data Description: Predictor variables were collected at the time of triage by trained study nurses using a custom-built mobile application. All data entered into the mobile application was stored an encrypted database. Data was uploaded directly from the mobile device to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Outcomes were obtained from facility records or telephone follow-up at 7-10 days and the data was collected electronically. Time-specific outcomes were tracked using an RFID tagging system with study personnel as backup. Limitations: There is missing data and some variables were not collected at all sites. Ethics Declaration: This study was approved by the Makerere University Higher Degrees research and Ethics Committee (No. 743), the Uganda National Institute of Science and Technology (HS 528ES), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H19-02398 & H20-00484). Associated datasets: Saving young lives: Triage and management of sepsis in children using the point-of care Paediatric Rapid Sepsis Trigger (PRST) tool NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Since the beginning of the 1960s, Statistics Sweden, in collaboration with various research institutions, has carried out follow-up surveys in the school system. These surveys have taken place within the framework of the IS project (Individual Statistics Project) at the University of Gothenburg and the UGU project (Evaluation through follow-up of students) at the University of Teacher Education in Stockholm, which since 1990 have been merged into a research project called 'Evaluation through Follow-up'. The follow-up surveys are part of the central evaluation of the school and are based on large nationally representative samples from different cohorts of students.

Evaluation through follow-up (UGU) is one of the country's largest research databases in the field of education. UGU is part of the central evaluation of the school and is based on large nationally representative samples from different cohorts of students. The longitudinal database contains information on nationally representative samples of school pupils from ten cohorts, born between 1948 and 2004. The sampling process was based on the student's birthday for the first two and on the school class for the other cohorts.

For each cohort, data of mainly two types are collected. School administrative data is collected annually by Statistics Sweden during the time that pupils are in the general school system (primary and secondary school), for most cohorts starting in compulsory school year 3. This information is provided by the school offices and, among other things, includes characteristics of school, class, special support, study choices and grades. Information obtained has varied somewhat, e.g. due to changes in curricula. A more detailed description of this data collection can be found in reports published by Statistics Sweden and linked to datasets for each cohort.

Survey data from the pupils is collected for the first time in compulsory school year 6 (for most cohorts). Questionnaire in survey in year 6 includes questions related to self-perception and interest in learning, attitudes to school, hobbies, school motivation and future plans. For some cohorts, questionnaire data are also collected in year 3 and year 9 in compulsory school and in upper secondary school.

Furthermore, results from various intelligence tests and standartized knowledge tests are included in the data collection year 6. The intelligence tests have been identical for all cohorts (except cohort born in 1987 from which questionnaire data were first collected in year 9). The intelligence test consists of a verbal, a spatial and an inductive test, each containing 40 tasks and specially designed for the UGU project. The verbal test is a vocabulary test of the opposite type. The spatial test is a so-called ‘sheet metal folding test’ and the inductive test are made up of series of numbers. The reliability of the test, intercorrelations and connection with school grades are reported by Svensson (1971).

For the first three cohorts (1948, 1953 and 1967), the standartized knowledge tests in year 6 consist of the standard tests in Swedish, mathematics and English that up to and including the beginning of the 1980s were offered to all pupils in compulsory school year 6. For the cohort 1972, specially prepared tests in reading and mathematics were used. The test in reading consists of 27 tasks and aimed to identify students with reading difficulties. The mathematics test, which was also offered for the fifth cohort, (1977) includes 19 assignments. After a changed version of the test, caused by the previously used test being judged to be somewhat too simple, has been used for the cohort born in 1982. Results on the mathematics test are not available for the 1987 cohort. The mathematics test was not offered to the students in the cohort in 1992, as the test did not seem to fully correspond with current curriculum intentions in mathematics. For further information, see the description of the dataset for each cohort.

For several of the samples, questionnaires were also collected from the students 'parents and teachers in year 6. The teacher questionnaire contains questions about the teacher, class size and composition, the teacher's assessments of the class' knowledge level, etc., school resources, working methods and parental involvement and questions about the existence of evaluations. The questionnaire for the guardians includes questions about the child's upbringing conditions, ambitions and wishes regarding the child's education, views on the school's objectives and the parents' own educational and professional situation.

The students are followed up even after they have left primary school. Among other things, data collection is done during the time they are in high school. Then school administrative data such as e.g. choice of upper secondary school line / program and grades after completing studies. For some of the cohorts, in addition to school administrative data, questionnaire data were also collected from the students.

he sample consisted of students born on the 5th, 15th and 25th of any month in 1953, a total of 10,723 students.

The data obtained in 1966 were: 1. School administrative data (school form, class type, year and grades). 2. Information about the parents' profession and education, number of siblings, the distance between home and school, etc.

This information was collected for 93% of all born on the current days. The reason for this is reduced resources for Statistics Sweden for follow-up work - reminders etc. Annual data for cohorts in 1953 were collected by Statistics Sweden up to and including academic year 1972/73.

1. Answers to certain questions that shed light on students' school motivation, leisure activities and study and career plans. Some of the questions changed significantly compared to the cohort in 1948 due to the fact that they did not function satisfactorily from a metrological point of view.
2. Results on three aptitude tests, one verbal, one spatial and one inductive.
3. Standard test results in reading, writing, mathematics and English, which were offered to the students who belonged to year 6.

Response rate for test and questionnaire data is 88% Standard test results were received for just over 85% of those who took the tests.

The sample included a total of 9955 students, for whom some form of information was obtained.

Part of the "Individual Statistics Project" together with cohort 1953.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The metropolitan, urban and rural population and all .administrative regional units. as defined in Official Europe Union Statistics (NUTS 2) covered proportionately the respective population aged 18 and above. The country was divided into an appropriate number of areas, grouping NUTS regions at whatever level appropriately. The NUTS covered in Sweden were the following; Stockholm/Södertäjle A-Region, Gothenburgs A-Region, Malmö/Lund/Trelleborgs A-region, Semi urban area, Rural area.

The basic sample design was a multi-stage, random probability sample. 100 sampling points were drawn with probability proportional to population size, for a total coverage of the country. The sampling points were drawn after stratification by NUTS 2 region and by degree of urbanisation. They represented the whole territory of the country surveyed and are selected proportionally to the distribution of the population in terms of metropolitan, urban and rural areas. In each of the selected sampling points, one address was drawn at random. This starting address forms the first address of a cluster of a maximum of 20 addresses. The remainder of the cluster was selected as every Nth address by standard random route procedure from the initial address. In theory, there is no maximum number of addresses issued per country. Procedures for random household selection and random respondent selection are independent of the interviewer.s decision and controlled by the institute responsible. They should be as identical as possible from to country, full functional equivalence being a must.

At every address up to 4 recalls were made to attempt to achieve an interview with the selected respondent. There was only one interview per household. The final sample size is 1,000 completed interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The random sample of 5000 respondents of the Lithuanian adult population, 18 years and older, was drawn by the National Statistical Department. The sampling frame was based on individual residents.

The frame was updated in 1989. The sample frame provides the following information about each respondent: name (first and family), address, data of birth and gender.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

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 labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the 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 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

----> Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

----> Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

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

----> Harmonized Data

• The STATA is used to clean and SPSS is used harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then 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 then converted to STATA, to be disseminated.

These occurrence data were encoded from the inventory works carried out by 5 students at the end of their training at the University of Abomey-Calavi, in the context of the accomplishment of their thesis