Abstract

The Indonesia Demographic and Health Survey (IDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health. The 2002-2003 IDHS follows a sequence of several previous surveys: the 1987 National Indonesia Contraceptive Prevalence Survey (NICPS), the 1991 IDHS, the 1994 IDHS, and the 1997 IDHS. The 2002-2003 IDHS is expanded from the 1997 IDHS by including a collection of information on the participation of currently married men and their wives and children in the health care.

The main objective of the 2002-2003 IDHS is to provide policymakers and program managers in population and health with detailed information on population, family planning, and health. In particular, the 2002-2003 IDHS collected information on the female respondents’ socioeconomic background, fertility levels, marriage and sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, childhood and adult mortality including maternal mortality, maternal and child health, and awareness and behavior regarding AIDS and other sexually transmitted infections in Indonesia.

The 2002-2003 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality, and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs - Measure trends in fertility and contraceptive prevalence rates, analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception - Evaluate achievement of goals previously set by the national health programs, with special focus on maternal and child health - Assess men’s participation and utilization of health services, as well as of their families - Assist in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the area of family planning, fertility, and health in general.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN AND IMPLEMENTATION

Administratively, Indonesia is divided into 30 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The primary objective of the 2002-2003 IDHS is to provide estimates with acceptable precision for the following domains: · Indonesia as a whole; · Each of 26 provinces covered in the survey. The four provinces excluded due to political instability are Nanggroe Aceh Darussalam, Maluku, North Maluku and Papua. These provinces cover 4 percent of the total population. · Urban and rural areas of Indonesia; · Each of the five districts in Central Java and the five districts in East Java covered in the Safe Motherhood Project (SMP), to provide information for the monitoring and evaluation of the project. These districts are: - in Central Java: Cilacap, Rembang, Jepara, Pemalang, and Brebes. - in East Java: Trenggalek, Jombang, Ngawi, Sampang and Pamekasan.

The census blocks (CBs) are the primary sampling unit for the 2002-2003 IDHS. CBs were formed during the preparation of the 2000 Population Census. Each CB includes approximately 80 households. In the master sample frame, the CBs are grouped by province, by regency/municipality within a province, and by subdistricts within a regency/municipality. In rural areas, the CBs in each district are listed by their geographical location. In urban areas, the CBs are distinguished by the urban classification (large, medium and small cities) in each subdistrict.

Note: See detailed description of sample design in APPENDIX B of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 2002-2003 IDHS used three questionnaires: the Household Questionnaire, the Women’s Questionnaire for ever-married women 15-49 years old, and the Men’s Questionnaire for currently married men 15-54 years old. The Household Questionnaire and the Women’s Questionnaire were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. In consultation with the NFPCB and MOH, BPS modified these questionnaires to reflect relevant issues in family planning and health in Indonesia. Inputs were also solicited from potential data users to optimize the IDHS in meeting the country’s needs for population and health data. The questionnaires were translated from English into the national language, Bahasa Indonesia.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Basic information collected for each person listed includes the following: age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, the Household Questionnaire also identifies unmarried women and men age 15-24 who are eligible for the individual interview in the Indonesia Young Adult Reproductive Health Survey (IYARHS). Information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, construction materials used for the floor and outer walls of the house, and ownership of various durable goods were also recorded in the Household Questionnaire. These items reflect the household’s socioeconomic status.

The Women’s Questionnaire was used to collect information from all ever-married women age 15-49. These women were asked questions on the following topics: • Background characteristics, such as age, marital status, education, and media exposure • Knowledge and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Marriage and sexual activity • Woman’s work and husband’s background characteristics • Childhood mortality • Awareness and behavior regarding AIDS and other sexually transmitted infections (STIs) • Sibling mortality, including maternal mortality.

The Men’s Questionnaire was administered to all currently married men age 15-54 in every third household in the IDHS sample. The Men’s Questionnaire collected much of the same information included in the Women’s Questionnaire, but was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, and maternal mortality. Instead, men were asked about their knowledge and participation in the health-seeking practices for their children.

Cleaning operations

All completed questionnaires for IDHS, accompanied by their control forms, were returned to the BPS central office in Jakarta for data processing. This process consisted of office editing, coding of open-ended questions, data entry, verification, and editing computer-identified errors. A team of about 40 data entry clerks, data editors, and two data entry supervisors processed the data. Data entry and editing started on November 4, 2002 using a computer package program called CSPro, which was specifically designed to process DHS-type survey data. To prepare the data entry programs, two BPS staff spent three weeks in ORC Macro offices in Calverton, Maryland in April 2002.

Response rate

A total of 34,738 households were selected for the survey, of which 33,419 were found. Of the encountered households, 33,088 (99 percent) were successfully interviewed. In these households, 29,996 ever-married women 15-49 were identified, and complete interviews were obtained from 29,483 of them (98 percent). From the households selected for interviews with men, 8,740 currently married men 15-54 were identified, and complete interviews were obtained from 8,310 men, or 95 percent of all eligible men. The generally high response rates for both household and individual interviews (for eligible women and men) were due mainly to the strict enforcement of the rule to revisit the originally selected household if no one was at home initially. No substitution for the originally selected households was allowed. Interviewers were instructed to make at least three visits in an effort to contact the household, eligible women, and eligible men.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2002-2003 Indonesia Demographic and Health Survey (IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents

BackgroundThe objective of this research was to conduct a qualitative study among a diverse group of providers to identify their clinical needs, barriers, and adverse safety events in the peripartum care of people with a body mass index (BMI) ≥ 50 kg/m2.MethodsObstetricians, anesthesiologists, certified nurse midwives, nurse practitioners, and nurses were invited to participate in focus group discussions if they were employed at the hospital for >6 months. Key concepts in the focus group guide included: (1) Discussion of challenging situations, (2) Current peripartum management approaches, (3) Patient and family knowledge and counseling, (4) Design and implementation of a guideline (e.g., checklist or toolkit) for peripartum care. The audiotaped focus groups were transcribed verbatim, uploaded to a qualitative analysis software program, and analyzed using inductive and constant comparative approaches. Emerging themes were summarized along with representative quotes.ResultsFive focus groups of 27 providers were completed in 2023. The themes included staffing (level of experience, nursing-patient ratios, safety concerns), equipment (limitations of transfer mats, need for larger sizes, location for blood pressure cuff, patient embarrassment), titrating oxytocin (lack of guidelines, range of uses), monitoring fetal heart rate and contractions, patient positioning, and communication (lack of patient feedback, need for bias training, need for interdisciplinary relationships). Providers gave examples of items to include in a “BMI cart” and suggestions for a guideline including designated rooms for patients with a BMI ≥ 50 kg/m2, defining nursing ratios and oxytocin titration plans, postpartum incentive spirometer, and touch points with providers (nursing, physicians) at every shift change.ConclusionsProviders discussed a range of challenges and described how current approaches to care may negatively affect the peripartum experience and pose threats to safety for patients with a BMI ≥ 50 kg/m2 and their providers. We gathered information on improving equipment and communication among providers.

Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall

National Health and Nutrition Examination Survey (NHANES) Data

Health Indicators for Different Locations

By Centers for Disease Control and Prevention [source]

About this dataset

This dataset offers an in-depth look into the National Health and Nutrition Examination Survey (NHANES), which provides valuable insights on various health indicators throughout the United States. It includes important information such as the year when data was collected, location of the survey, data source and value, priority areas of focus, category and topic related to the survey, break out categories of data values, geographic location coordinates and other key indicators.Discover patterns in mortality rates from cardiovascular disease or analyze if pregnant women are more likely to report poor health than those who are not expecting with this NHANES dataset — a powerful collection for understanding personal health behaviors

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For more datasets, click here.

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How to use the dataset

Step 1: Understand the Data Format - Before beginning to work with NHANES data, you should become familiar with the different columns in the dataset. Each column contains a specific type of information about the data such as year collected, geographic location abbreviations and descriptions, sources used for collecting data, priority areas assigned by researchers or institutions associated with understanding health trends in a given area or population group as well as indicator values related to nutrition/health.

Step 2: Choose an Indicator - Once you understand what is included in each column and what type of values correspond to each field it is time to select which indicator(s) you would like plots or visualizations against demographic/geographical characteristics represented by NHANES data. Selecting an appropriate indicator helps narrow down your search criteria when conducting analyses of health/nutrition trends over time in different locations or amongst different demographic groups.

Step 3: Utilizing Subsets - When narrowing down your search criteria it may be beneficial to break up large datasets into smaller subsets that focus on a single area or topic for study (i.e., looking at nutrition trends among rural communities). This allows users to zoom into certain datasets if needed within their larger studies so they can further drill down on particular topics that are relevant for their research objectives without losing greater context from more general analysis results when viewing overall datasets containing all available fields for all locations examined by NHANES over many years of records collected at specific geographical areas requested within the parameters set forth by those wanting insights from external research teams utilizing this dataset remotely via Kaggle access granted through user accounts giving them authorized access controls solely limited by base administration permissions set forth where required prior granting needs authorization process has been met prior downloading/extraction activities successful completion finalized allowed beyond initial site signup page make sure rules followed while also ensuring positive experience interactive engagement processes fluid flow signature one-time registration entry after exit page exits once completed neutralize logout button pops finish downloading extract image files transfer end destination requires hard drive storage efficient manner duplicate second backup remain resilient mitigate file corruption concerns start working properly formatted smooth transition between systems be seamless reflective channel dynamic organization approach complement function beneficial effort allow comprehensive review completed quality control standards align desires outcomes desired critical path

Research Ideas

• Creating a health calculator to help people measure their health risk. The indicator and data value fields can be used to create an algorithm that will generate a personalized label for each user's health status.
• Developing a visual representation of the nutritional habits of different populations based on the DataSource, LocationAbbr, and PriorityArea fields from this dataset.
• Employing machine learning to discern patterns in the data or predict potential health risks in different regions or populations by using the GeoLocation field as inputs for geographic analysis.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**Unknown License - Please check the dataset description for more information....

US Tobacco Use Prevalence

US Tobacco Use Prevalence by Year, State, Type, and Age

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information on tobacco use in the United States from 2011 to 2016. The data is sourced from the CDC Behavioral Risk Factor Survey, a reliable and extensive survey that captures important data about tobacco use behaviors across different states in the United States.

The dataset includes various key variables such as the year of data collection, state abbreviation indicating where the data was collected, and specific tobacco types explored in the survey. It also provides valuable insight into the prevalence of tobacco use through quantitative measures represented by numeric values. The unit of measurement for these values, such as percentages or numbers, is included as well.

Moreover, this dataset offers an understanding of how different age groups are affected by tobacco use, with age being categorized into distinct groups. This ensures that researchers and analysts can assess variations in tobacco consumption and its associated health implications across different age demographics.

With all these informative attributes arranged in a convenient tabular format, this dataset serves as a valuable resource for investigating patterns and trends related to tobacco use within varying contexts over a six-year period

How to use the dataset

Introduction:

Step 1: Familiarize Yourself with the Columns

Before diving into any analysis, it is important to understand the structure of the dataset by familiarizing yourself with its columns. Here are the key columns in this dataset:

• Year: The year in which the data was collected (Numeric)
• State Abbreviation: The abbreviation of the state where the data was collected (String)
• Tobacco Type: The type of tobacco product used (String)
• Data Value: The percentage or number representing prevalence of tobacco use (Numeric)
• Data Value Unit: The unit of measurement for data value (e.g., percentage, number) (String)
• Age: The age group to which the data value corresponds (String)

Step 2: Determine Your Research Questions or Objectives

To make effective use of this dataset, it is essential to clearly define your research questions or objectives. Some potential research questions related to this dataset could be:

• How has tobacco use prevalence changed over time?
• Which states have the highest and lowest rates of tobacco use?
• What are the most commonly used types of tobacco products?
• Is there a correlation between age group and tobacco use?

By defining your research questions or objectives upfront, you can focus your analysis accordingly.

Step 3: Analyzing Trends Over Time

To analyze trends over time using this dataset: - Group and aggregate relevant columns such as Year and Data Value. - Plot the data using line graphs or bar charts to visualize the changes in tobacco use prevalence over time. - Interpret the trends and draw conclusions from your analysis.

Step 4: Comparing States

To compare states and their tobacco use prevalence: - Group and aggregate relevant columns such as State Abbreviation and Data Value. - Sort the data based on prevalence rates to identify states with the highest and lowest rates of tobacco use. - Visualize this comparison using bar charts or maps for a clearer understanding.

Step 5: Understanding Tobacco Types

To gain insights into different types of tobacco products used: - Analyze the Tobacco

Research Ideas

• Analyzing trends in tobacco use: This dataset can be used to analyze the prevalence of tobacco use over time and across different states. It can help identify patterns and trends in tobacco consumption, which can be valuable for public health research and policy-making.
• Assessing the impact of anti-smoking campaigns: Researchers or organizations working on anti-smoking campaigns can use this dataset to evaluate the effectiveness of their interventions. By comparing the data before and after a campaign, they can determine whether there has been a decrease in tobacco use and if specific groups or regions have responded better to the campaign.
• Understanding demographic factors related to tobacco use: The dataset includes information on age groups, allowing for analysis of how different age demographics are affected by tobacco use. By examining data value variations across age groups, researchers can gain insights into which populations are most vulnerable to smoking-related issues and design targeted prevention programs an...

Dataset AbstractThis dataset stems from a cross-sectional study investigating the association between urban green space environments and the health status of older adults. Data were collected between March and May 2025 across 33 typical residential communities in central Hangzhou, China.The dataset comprises two core components:Survey Data: Obtained through face-to-face, interview-based questionnaires with 1,339 older adults aged 60 and above who had lived in their community for at least two years. The survey covers socio-demographic characteristics, perceived green space quality, green space usage patterns (frequency, duration, intensity), self-rated physical health, and self-rated mental health assessed via the 15-item Geriatric Depression Scale (GDS-15).Objective Green Space Metrics: Derived from Gaofen-6 (GF-6) satellite imagery and OpenStreetMap road network data using GIS techniques. Multiple green space indicators were calculated for a 1000m network-based service area around each residential community. These include park count, green space area, green coverage rate, Normalized Difference Vegetation Index (NDVI), and landscape pattern metrics (fragmentation, connectivity, shape complexity) computed using Fragstats.This dataset integrates detailed subjective survey responses with objective geospatial measurements, providing a high-quality, multi-dimensional resource for research in urban planning, public health, gerontology, and geography on the impact of the built environment, particularly urban green spaces, on the health and well-being of older adults.Detailed Dataset Description1. MethodsStudy Design and Sampling: A cross-sectional study design was employed. Thirty-three residential communities in central Hangzhou were purposively selected as study sites. Respondents were screened using a strict filter questionnaire (age ≥ 60, residence duration ≥ 2 years), and the sample was balanced for socio-demographic attributes like age and gender. A total of 1,339 valid questionnaires were obtained.Data Collection Period: Pre-survey: March 2025; Formal Survey: March to May 2025.Survey Methodology: All questionnaires were administered in an interview format. Trained investigators asked questions orally and recorded the responses to ensure full comprehension and data quality, considering the older age of the respondents.Sources of Objective Green Space Data:Remote Sensing Data: Gaofen-6 (GF-6) satellite imagery (March 2025), with a 2-meter pan-sharpened spatial resolution. Imagery was pre-processed (including radiometric calibration, atmospheric correction, orthorectification, and image fusion) and urban green spaces were extracted using an object-based classification method.Road Network Data: Sourced from the open-source OpenStreetMap (OSM) platform.Park Data: Vector data of Hangzhou's park boundaries, also collected from OpenStreetMap.2. Data Contents and StructureThe dataset primarily consists of two data files:A. Main Data File (Survey_Data.csv) - 1,339 rows × ~40 columnsEach row represents the complete questionnaire response from one older adult. Variables are organized into the following modules:Screening Section: Confirmation of age and residence duration.A. Socio-Demographic Information:A01: Age (in years or categorized)A02: GenderA03: Education LevelA04: Health Insurance CoverageA05: Pension Insurance CoverageA06: Marital StatusB. Perceived Green Space Quality:B0101-B0106: Satisfaction with green space accessibility, safety, quality of activity spaces, facility quality, management, and overall experience (5-point Likert scale from 1="Very Dissatisfied" to 5="Very Satisfied").C. Green Space Usage Patterns:C0101: Days of green space use for leisure in the past week.C0201, C0202...C0501, C0502: For vigorous, moderate, light physical activities, and sedentary behavior in green spaces: days per week and typical daily duration. These can be used to calculate total physical activity levels (e.g., MET-minutes/week).D. Health Status:D0101: Self-rated physical health (5-point scale from 1="Very Healthy" to 5="Very Unhealthy").D0201-D0215: Scores for the 15 items of the Geriatric Depression Scale (GDS-15). The total score indicates the level of depressive symptoms (higher score = more symptoms).Linking ID: A Community_ID variable for merging with the objective green space data file.B. Objective Green Space Data File (GreenSpace_Data.csv) - 33 rows × ~8 columnsEach row represents the green space characteristics for one residential community, calculated within its 1000m pedestrian network service area.Community_ID: Residential community identifier (links to the main survey file).Park_Count: Number of parks.Green_Space_Area: Area of green space (hectares).Green_Coverage_Rate: Percentage of area covered by green space.Fragmentation_PD: Patch Density (quantifying landscape fragmentation).Connectivity_COHESION: Patch Cohesion Index (quantifying landscape connectivity).Shape_Complexity_SHAPE_AM: Area-Weighted Mean Shape Index (quantifying patch shape complexity).NDVI_Mean: Mean Normalized Difference Vegetation Index (quantifying vegetation greenness).3. Data Processing and Quality ControlSurvey Quality Control: The process involved a pre-survey, questionnaire revision, interviewer training, interview-based administration, and rigorous validation (e.g., exclusion of incomplete questionnaires) to ensure data reliability and validity.Remote Sensing Data Processing: Professional processing was conducted using ENVI 5.6 and ArcGIS platforms. Manual visual interpretation was incorporated to correct the automated classification, ensuring high accuracy in green space extraction.Accessibility Analysis: Service Area Analysis based on the actual road network was used, which more accurately reflects the real walking accessibility for older adults compared to traditional straight-line buffer analysis.Data Anonymization: All personally identifiable information (e.g., name, exact address) has been removed. Only a community identifier for analysis is retained.Potential Applications and Reuse ValueThis dataset has significant value for interdisciplinary research, including:Urban Planning and Public Health: Quantifying how different types and configurations of green spaces impact the physical and mental health of older adults, providing evidence for creating "healthy cities" and "age-friendly communities".Environmental Psychology and Sociology: Exploring the relationships between older adults' subjective perceptions of green space (satisfaction), their objective usage patterns, and health outcomes.Geography and Landscape Ecology: Linking micro-scale individual behaviors with macro-scale landscape pattern metrics to deepen the understanding of person-environment interactions.Epidemiology: Serving as cross-sectional baseline data for future longitudinal studies or for building health impact models.Methodological Research: Serving as a case study to compare network-based versus Euclidean distance-based accessibility measures in health research.AcknowledgementsWe extend our sincere gratitude to all the older adults who participated in this study for their time and valuable insights. We acknowledge the providers of the Gaofen-6 satellite imagery and the contributions of the OpenStreetMap community. We also thank all members of the research team for their diligent efforts in data collection, processing, and analysis.

Abstract

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

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

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

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

Abstract

The study included four separate surveys:

1. The LSMS survey of general population of Serbia in 2002

2. The survey of Family Income Support (MOP in Serbian) recipients in 2002 These two datasets are published together.

3. The LSMS survey of general population of Serbia in 2003 (panel survey)

4. The survey of Roma from Roma settlements in 2003 These two datasets are published together separately from the 2002 datasets.

Objectives

LSMS represents multi-topical study of household living standard and is based on international experience in designing and conducting this type of research. The basic survey was carried out in 2002 on a representative sample of households in Serbia (without Kosovo and Metohija). Its goal was to establish a poverty profile according to the comprehensive data on welfare of households and to identify vulnerable groups. Also its aim was to assess the targeting of safety net programs by collecting detailed information from individuals on participation in specific government social programs. This study was used as the basic document in developing Poverty Reduction Strategy (PRS) in Serbia which was adopted by the Government of the Republic of Serbia in October 2003.

The survey was repeated in 2003 on a panel sample (the households which participated in 2002 survey were re-interviewed).

Analysis of the take-up and profile of the population in 2003 was the first step towards formulating the system of monitoring in the Poverty Reduction Strategy (PRS). The survey was conducted in accordance with the same methodological principles used in 2002 survey, with necessary changes referring only to the content of certain modules and the reduction in sample size. The aim of the repeated survey was to obtain panel data to enable monitoring of the change in the living standard within a period of one year, thus indicating whether there had been a decrease or increase in poverty in Serbia in the course of 2003. [Note: Panel data are the data obtained on the sample of households which participated in the both surveys. These data made possible tracking of living standard of the same persons in the period of one year.]

Along with these two comprehensive surveys, conducted on national and regional representative samples which were to give a picture of the general population, there were also two surveys with particular emphasis on vulnerable groups. In 2002, it was the survey of living standard of Family Income Support recipients with an aim to validate this state supported program of social welfare. In 2003 the survey of Roma from Roma settlements was conducted. Since all present experiences indicated that this was one of the most vulnerable groups on the territory of Serbia and Montenegro, but with no ample research of poverty of Roma population made, the aim of the survey was to compare poverty of this group with poverty of basic population and to establish which categories of Roma population were at the greatest risk of poverty in 2003. However, it is necessary to stress that the LSMS of the Roma population comprised potentially most imperilled Roma, while the Roma integrated in the main population were not included in this study.

Geographic coverage

The surveys were conducted on the whole territory of Serbia (without Kosovo and Metohija).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample frame for both surveys of general population (LSMS) in 2002 and 2003 consisted of all permanent residents of Serbia, without the population of Kosovo and Metohija, according to definition of permanently resident population contained in UN Recommendations for Population Censuses, which were applied in 2002 Census of Population in the Republic of Serbia. Therefore, permanent residents were all persons living in the territory Serbia longer than one year, with the exception of diplomatic and consular staff.

The sample frame for the survey of Family Income Support recipients included all current recipients of this program on the territory of Serbia based on the official list of recipients given by Ministry of Social affairs.

The definition of the Roma population from Roma settlements was faced with obstacles since precise data on the total number of Roma population in Serbia are not available. According to the last population Census from 2002 there were 108,000 Roma citizens, but the data from the Census are thought to significantly underestimate the total number of the Roma population. However, since no other more precise data were available, this number was taken as the basis for estimate on Roma population from Roma settlements. According to the 2002 Census, settlements with at least 7% of the total population who declared itself as belonging to Roma nationality were selected. A total of 83% or 90,000 self-declared Roma lived in the settlements that were defined in this way and this number was taken as the sample frame for Roma from Roma settlements.

Planned sample: In 2002 the planned size of the sample of general population included 6.500 households. The sample was both nationally and regionally representative (representative on each individual stratum). In 2003 the planned panel sample size was 3.000 households. In order to preserve the representative quality of the sample, we kept every other census block unit of the large sample realized in 2002. This way we kept the identical allocation by strata. In selected census block unit, the same households were interviewed as in the basic survey in 2002. The planned sample of Family Income Support recipients in 2002 and Roma from Roma settlements in 2003 was 500 households for each group.

Sample type: In both national surveys the implemented sample was a two-stage stratified sample. Units of the first stage were enumeration districts, and units of the second stage were the households. In the basic 2002 survey, enumeration districts were selected with probability proportional to number of households, so that the enumeration districts with bigger number of households have a higher probability of selection. In the repeated survey in 2003, first-stage units (census block units) were selected from the basic sample obtained in 2002 by including only even numbered census block units. In practice this meant that every second census block unit from the previous survey was included in the sample. In each selected enumeration district the same households interviewed in the previous round were included and interviewed. On finishing the survey in 2003 the cases were merged both on the level of households and members.

Stratification: Municipalities are stratified into the following six territorial strata: Vojvodina, Belgrade, Western Serbia, Central Serbia (Šumadija and Pomoravlje), Eastern Serbia and South-east Serbia. Primary units of selection are further stratified into enumeration districts which belong to urban type of settlements and enumeration districts which belong to rural type of settlement.

The sample of Family Income Support recipients represented the cases chosen randomly from the official list of recipients provided by Ministry of Social Affairs. The sample of Roma from Roma settlements was,as in the national survey, a two-staged stratified sample, but the units in the first stage were settlements where Roma population was represented in the percentage over 7%, and the units of the second stage were Roma households. Settlements are stratified in three territorial strata: Vojvodina, Beograd and Central Serbia.

Mode of data collection

Face-to-face [f2f]

Research instrument

In all surveys the same questionnaire with minimal changes was used. It included different modules, topically separate areas which had an aim of perceiving the living standard of households from different angles. Topic areas were the following: 1. Roster with demography. 2. Housing conditions and durables module with information on the age of durables owned by a household with a special block focused on collecting information on energy billing, payments, and usage. 3. Diary of food expenditures (weekly), including home production, gifts and transfers in kind. 4. Questionnaire of main expenditure-based recall periods sufficient to enable construction of annual consumption at the household level, including home production, gifts and transfers in kind. 5. Agricultural production for all households which cultivate 10+ acres of land or who breed cattle. 6. Participation and social transfers module with detailed breakdown by programs 7. Labour Market module in line with a simplified version of the Labour Force Survey (LFS), with special additional questions to capture various informal sector activities, and providing information on earnings 8. Health with a focus on utilization of services and expenditures (including informal payments) 9. Education module, which incorporated pre-school, compulsory primary education, secondary education and university education. 10. Special income block, focusing on sources of income not covered in other parts (with a focus on remittances).

Response rate

During field work, interviewers kept a precise diary of interviews, recording both successful and unsuccessful visits. Particular attention was paid to reasons why some households were not interviewed. Separate marks were given for households which were not interviewed due to refusal and for cases when a given household could not be found on the territory of the chosen census block.

In 2002 a total of 7,491 households were contacted. Of this number a total of 6,386 households in 621 census rounds were interviewed. Interviewers did not manage to collect the data for 1,106 or 14.8% of selected households. Out of this number 634 households or

Abstract

The 2013 NDHS is part of the worldwide Demographic and Health Surveys (DHS) programme funded by the United States Agency for International Development (USAID). DHS surveys are designed to collect data on fertility, family planning, and maternal and child health; assist countries in monitoring changes in population, health, and nutrition; and provide an international database that can be used by researchers investigating topics related to population, health, and nutrition.

The overall objective of the survey is to provide demographic, socioeconomic, and health data necessary for policymaking, planning, monitoring, and evaluation of national health and population programmes. In addition, the survey measured the prevalence of anaemia, HIV, high blood glucose, and high blood pressure among adult women and men; assessed the prevalence of anaemia among children age 6-59 months; and collected anthropometric measurements to assess the nutritional status of women, men, and children.

A long-term objective of the survey is to strengthen the technical capacity of local organizations to plan, conduct, and process and analyse data from complex national population and health surveys. At the global level, the 2013 NDHS data are comparable with those from a number of DHS surveys conducted in other developing countries. The 2013 NDHS adds to the vast and growing international database on demographic and health-related variables.

Geographic coverage

National coverage

Analysis unit

• Households
• Children aged 0-5
• Women aged 15 to 49
• Men aged 15 to 64

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The primary focus of the 2013 NDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas. In addition, the sample was designed to provide estimates of most key variables for the 13 administrative regions.

Each of the administrative regions is subdivided into a number of constituencies (with an overall total of 107 constituencies). Each constituency is further subdivided into lower level administrative units. An enumeration area (EA) is the smallest identifiable entity without administrative specification, numbered sequentially within each constituency. Each EA is classified as urban or rural. The sampling frame used for the 2013 NDHS was the preliminary frame of the 2011 Namibia Population and Housing Census (NSA, 2013a). The sampling frame was a complete list of all EAs covering the whole country. Each EA is a geographical area covering an adequate number of households to serve as a counting unit for the population census. In rural areas, an EA is a natural village, part of a large village, or a group of small villages; in urban areas, an EA is usually a city block. The 2011 population census also produced a digitised map for each of the EAs that served as the means of identifying these areas.

The sample for the 2013 NDHS was a stratified sample selected in two stages. In the first stage, 554 EAs-269 in urban areas and 285 in rural areas-were selected with a stratified probability proportional to size selection from the sampling frame. The size of an EA is defined according to the number of households residing in the EA, as recorded in the 2011 Population and Housing Census. Stratification was achieved by separating every region into urban and rural areas. Therefore, the 13 regions were stratified into 26 sampling strata (13 rural strata and 13 urban strata). Samples were selected independently in every stratum, with a predetermined number of EAs selected. A complete household listing and mapping operation was carried out in all selected clusters. In the second stage, a fixed number of 20 households were selected in every urban and rural cluster according to equal probability systematic sampling.

Due to the non-proportional allocation of the sample to the different regions and the possible differences in response rates, sampling weights are required for any analysis using the 2013 NDHS data to ensure the representativeness of the survey results at the national as well as the regional level. Since the 2013 NDHS sample was a two-stage stratified cluster sample, sampling probabilities were calculated separately for each sampling stage and for each cluster.

See Appendix A in the final report for details

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered in the 2013 NDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from the standard DHS6 core questionnaires to reflect the population and health issues relevant to Namibia at a series of meetings with various stakeholders from government ministries and agencies, nongovernmental organisations, and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organised by the MoHSS from September 25-28, 2012, in Windhoek. The questionnaires were then translated from English into the six main local languages—Afrikaans, Rukwangali, Oshiwambo, Damara/Nama, Otjiherero, and Silozi—and back translated into English. The questionnaires were finalised after the pretest, which took place from February 11-25, 2013.

The Household Questionnaire was used to list all usual household members as well as visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, parents’ survival status was determined. In addition, the Household Questionnaire included questions on knowledge of malaria and use of mosquito nets by household members, along with questions regarding health expenditures. The Household Questionnaire was used to identify women and men who were eligible for the individual interview and the interview on domestic violence. The questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods. The results of tests assessing iodine levels were recorded as well.

In half of the survey households (the same households selected for the male survey), the Household Questionnaire was also used to record information on anthropometry and biomarker data collected from eligible respondents, as follows: • All eligible women and men age 15-64 were measured, weighed, and tested for anaemia and HIV. • All eligible women and men age 35-64 had their blood pressure and blood glucose measured. • All children age 0 to 59 months were measured and weighed. • All children age 6 to 59 months were tested for anaemia.

The Woman’s Questionnaire was also used to collect information from women age 50-64 living in half of the selected survey households on background characteristics, marriage and sexual activity, women’s work and husbands’ background characteristics, awareness and behaviour regarding AIDS and other STIs, and other health issues.

The Man’s Questionnaire was administered to all men age 15-64 living in half of the selected survey households. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

CSPro—a Windows-based integrated census and survey processing system that combines and replaces the ISSA and IMPS packages—was used for entry, editing, and tabulation of the NDHS data. Prior to data entry, a practical training session was provided by ICF International to all data entry staff. A total of 28 data processing personnel, including 17 data entry operators, one questionnaire administrator, two office editors, three secondary editors, two network technicians, two data processing supervisors, and one coordinator, were recruited and trained on administration of questionnaires and coding, data entry and verification, correction of questionnaires and provision of feedback, and secondary editing. NDHS data processing was formally launched during the week of June 22, 2013, at the National Statistics Agency Data Processing Centre in Windhoek. The data entry and editing phase of the survey was completed in January 2014.

Response rate

A total of 11,004 households were selected for the sample, of which 10,165 were found to be occupied during data collection. Of the occupied households, 9,849 were successfully interviewed, yielding a household response rate of 97 percent.

In these households, 9,940 women age 15-49 were identified as eligible for the individual interview. Interviews were completed with 9,176 women, yielding a response rate of 92 percent. In addition, in half of these households, 842 women age 50-64 were successfully interviewed; in this group of women, the response rate was 91 percent.

Of the 5,271 eligible men identified in the selected subsample of households, 4,481 (85 percent) were successfully interviewed.

Response rates were higher in rural than in urban areas, with the rural-urban difference more marked among men than among women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview

Dataset name: Future Orientation and Perceived Goal-Related Social Context Scientific domain: Psychology Category: Survey data Language of the study: Latvian Research ethics: The study protocol was reviewed and approved by the Research Ethics Committee of the Institute of Cardiology and Regenerative Medicine of the University of Latvia (No 125/2020). Language of the description: English File type: IBM SPSS Statistics data file (.sav)

Content: The dataset involves a convenience sample of 415 university students aged 18 to 30. Data were collected in Latvia in 2020. The dataset consists of demographic characteristics and measures of individual future orientation (Kolesovs, 2017, 2023), including future-oriented behavior [Variable label – FO behavior] and self- [Goals Self] and other-oriented [Goals Others] goals; perceived goal-related support (Freibergs & Kolesovs, 2021), including mesosystem [Support Meso] and university [Support University] support; and sense of country (Kolesovs, 2022), including perceived influence [SOCI Influence] on the country, two components of belonging to it [ SOCI REL Belonging and SOCI SPT Commitment], and opportunities for fulfillment self- and other-oriented personal goals [SOCI Opportunities Self and SOCI Opportunities Others].

Points under investigation: The factor structure of sense of belonging to the country. The factor structure of individual future orientation, goal-related perceived support, and sense of country. Links among future orientation, goal-related perceived support, and sense of country.

References Freibergs, Z., & Kolesovs, A. (2021). University students’ growth goals, opportunities for goal fulfillment, and perceived university and mesosystem support. In: Society. Integration. Education. Proceedings of the International Scientific Conference, Vol. 7, pp. 86–94. https://doi.org/10.17770/sie2021vol7.6159. Kolesovs, A. (2017). Individual future orientation and demographic factors predicting life satisfaction. In: Society. Integration. Education. Proceedings of the International Scientific Conference, Vol. 1, pp. 534–543. https://doi.org/10.17770/sie2017vol1.2448. Kolesovs, A. (2022). Sense of country: General and specific factors covary with social identification and predict emigration plans. Frontiers in Psychology, 13, e992028. https://doi.org/10.3389/fpsyg.2022.992028. Kolesovs, A. (2023). General and specific factors of future orientation link to awareness of meaning in life. In: Society. Integration. Education. Proceedings of the International Scientific Conference, Vol. 2, pp. 429–437. https://doi.org/10.17770/sie2023vol2.7104.

Abstract

The 2011 Bangladesh Demographic and Health Survey (BDHS) is the sixth DHS undertaken in Bangladesh, following those implemented in 1993-94, 1996-97, 1999-2000, 2004, and 2007. The main objectives of the 2011 BDHS are to: • Provide information to meet the monitoring and evaluation needs of health and family planning programs, and • Provide program managers and policy makers involved in these programs with the information they need to plan and implement future interventions.

The specific objectives of the 2011 BDHS were as follows: • To provide up-to-date data on demographic rates, particularly fertility and infant mortality rates, at the national and subnational level; • To analyze the direct and indirect factors that determine the level of and trends in fertility and mortality; • To measure the level of contraceptive use of currently married women; • To provide data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS; • To assess the nutritional status of children (under age 5), women, and men by means of anthropometric measurements (weight and height), and to assess infant and child feeding practices; • To provide data on maternal and child health, including antenatal care, assistance at delivery, breastfeeding, immunizations, and prevalence and treatment of diarrhea and other diseases among children under age 5; • To measure biomarkers, such as hemoglobin level for women and children, and blood pressure, and blood glucose for women and men 35 years and older; • To measure key education indicators, including school attendance ratios and primary school grade repetition and dropout rates; • To provide information on the causes of death among children under age 5; • To provide community-level data on accessibility and availability of health and family planning services; • To measure food security.

The 2011 BDHS was conducted under the authority of the National Institute of Population Research and Training (NIPORT) of the Ministry of Health and Family Welfare. The survey was implemented by Mitra and Associates, a Bangladeshi research firm located in Dhaka. ICF International of Calverton, Maryland, USA, provided technical assistance to the project as part of its international Demographic and Health Surveys program (MEASURE DHS). Financial support was provided by the U.S. Agency for International Development (USAID).

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Universe

The 2011 BDHS covers the entire population residing in noninstitutional dwelling units in the country.

Kind of data

Sample survey data

Sampling procedure

Sample Design The sample for the 2011 BDHS is nationally representative and covers the entire population residing in noninstitutional dwelling units in the country. The survey used as a sampling frame the list of enumeration areas (EAs) prepared for the 2011 Population and Housing Census, provided by the Bangladesh Bureau of Statistics (BBS). The primary sampling unit (PSU) for the survey is an EA that was created to have an average of about 120 households.

Bangladesh has seven administrative divisions: Barisal, Chittagong, Dhaka, Khulna, Rajshahi, Rangpur, and Sylhet. Each division is subdivided into zilas, and each zila into upazilas. Each urban area in an upazila is divided into wards, and into mohallas within a ward. A rural area in the upazila is divided into union parishads (UP) and mouzas within a UP. These divisions allow the country as a whole to be easily separated into rural and urban areas.

The survey is based on a two-stage stratified sample of households. In the first stage, 600 EAs were selected with probability proportional to the EA size, with 207 clusters in urban areas and 393 in rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second-stage selection of households. In the second stage of sampling, a systematic sample of 30 households on average was selected per EA to provide statistically reliable estimates of key demographic and health variables for the country as a whole, for urban and rural areas separately, and for each of the seven divisions. With this design, the survey selected 18,000 residential households, which were expected to result in completed interviews with about 18,000 ever-married women. In addition, in a subsample of one-third of the households, all evermarried men age 15-54 were selected and interviewed for the male survey. In this subsample, a group of eligible members were selected to participate in testing of the biomarker component, including blood pressure measurements, anemia, blood glucose testing, and height and weight measurements.

Note: See Appendix A (in final survey report) for the details of the sample design.

Sampling deviation

The 2007 BDHS sampled all ever-married women age 10-49. The number of eligible women age 10-49 was 11,234, of whom 11,051 were interviewed for a response rate of 98.4 percent. However, there were very few ever-married women age 10-14 (55 unweighted cases or less than one percent). These women have been removed from the data set and weights recalculated for the 15-49 age group. The tables in the survey report discuss only women age 15-49.

Mode of data collection

Face-to-face

Research instrument

The 2011 BDHS used five types of questionnaires: a Household Questionnaire, a Woman’s Questionnaire, a Man’s Questionnaire, a Community Questionnaire, and two Verbal Autopsy Questionnaires to collect data on causes of death among children under age 5. The contents of the household and individual questionnaires were based on the MEASURE DHS model questionnaires. These model questionnaires were adapted for use in Bangladesh during a series of meetings with a Technical Working Group (TWG) that consisted of representatives from NIPORT, Mitra and Associates, International Centre for Diarrheal Diseases and Control, Bangladesh (ICDDR,B), USAID/Bangladesh, and MEASURE DHS. Draft questionnaires were then circulated to other interested groups and were reviewed by the 2011 BDHS Technical Review Committee. The questionnaires were developed in English and then translated and printed into Bangla.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, information was collected about the dwelling unit, such as the source of water, type of toilet facilities, materials used to construct the floors and walls, and ownership of various consumer goods. The Household Questionnaire was also used to record for eligible individuals: • Height and weight measurements • Anemia test results • Measurements of blood pressure and blood glucose

The Woman’s Questionnaire was used to collect information from ever-married women age 12-49. Women were asked questions on the following topics: • Background characteristics (e.g., age, education, religion, and media exposure) • Reproductive history • Use and source of family planning methods • Antenatal, delivery, postnatal, and newborn care • Breastfeeding and infant feeding practices • Child immunizations and childhood illnesses • Marriage • Fertility preferences • Husband’s background and respondent’s work • Awareness of AIDS and other sexually transmitted infections • Food security

The Man’s Questionnaire was used to collect information from ever-married men age 15-54. Men were asked questions on the following topics: • Background characteristics (including respondent’s work) • Marriage • Fertility preferences • Participation in reproductive health care • Awareness of AIDS and other sexually transmitted infections

The Community Questionnaire was administered in each selected cluster during the household listing operation. Data were collected by administering the Community Questionnaire to a group of four to six community leaders who were knowledgeable about socioeconomic conditions and the availability of health and family planning services/facilities, in or near the sample area (cluster). Community leaders included such persons as government officials, social workers, teachers, religious leaders, traditional healers, and health care providers.

The Community Questionnaire collected information about the existence of development organizations in the community and the availability and accessibility of health services and other facilities. During the household listing operation, the geographic coordinates and altitude of each cluster were also recorded. The information obtained in these questionnaires was also used to verify information gathered in the Woman’s and Man’s Questionnaires on the types of facilities accessed and health services personnel seen.

The Verbal Autopsy Questionnaires were developed based on the work done by an expert group led by the WHO, consisting of researchers, data users, and other stakeholders under the sponsorship of the Health Metrics Network (HMN). The verbal autopsy tools are intended to serve the various needs of the users of mortality information. Two questionnaires were used to collect information related to the causes of death among young children; the first questionnaire collected data on neonatal deaths (deaths at 0-28 days), and the

The main objectives of this data collection effort were to assemble a set of cross-nationally comparable microdata samples for Economic Commission for Europe (ECE) countries based on the 1990 national population and housing censuses in countries of Europe and North America, and to use these samples to study the social and economic conditions of older persons. The samples are designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. The Finland microdata sample contains information on persons aged 50 and over and on the persons who reside with them. Variables included in this dataset provide information on geographic area, type of residency, type of dwelling, household characteristics and demographic characteristics such as age, sex, year of birth, household composition, marital status, number of children, education, income, religion, and occupation.

This dataset provides a comprehensive view of student performance and learning behavior, integrating academic, demographic, behavioral, and psychological factors.

It was created by merging two publicly available Kaggle datasets, resulting in a unified dataset of 14,003 student records with 16 attributes. All entries are anonymized, with no personally identifiable information.

Key Features

• Study behaviors & engagement → StudyHours, Attendance, Extracurricular, AssignmentCompletion, OnlineCourses, Discussions
• Resources & environment → Resources, Internet, EduTech
• Motivation & psychology → Motivation, StressLevel
• Demographics → Gender, Age (18–30 years)
• Learning preference → LearningStyle
• Performance indicators → ExamScore, FinalGrade

Objectives & Use Cases

The dataset can be used for:

• Predictive modeling → Regression/classification of student performance (ExamScore, FinalGrade)
• Clustering analysis → Identifying learning behavior groups with K-Means or other unsupervised methods
• Educational analytics → Exploring how study habits, stress, and motivation affect outcomes
• Adaptive learning research → Linking behavioral patterns to personalized learning pathways

Analysis Pipeline (from original study)

The dataset was analyzed in Python using:

• Preprocessing → Encoding, normalization (z-score, Min–Max), deduplication
• Clustering → K-Means, Elbow Method, Silhouette Score, Davies–Bouldin Index
• Dimensionality Reduction → PCA (2D/3D visualizations)
• Statistical Analysis → ANOVA, regression for group differences
• Interpretation → Mapping clusters to LearningStyle categories & extracting insights for adaptive learning

File

• merged_dataset.csv → 14,003 rows × 16 columns Includes student demographics, behaviors, engagement, learning styles, and performance indicators.

Provenance

• Source: Zenodo – Student Performance and Learning Behavior Dataset
• Creator: Kamal Najem (2024)
• License: CC BY 4.0 (per Zenodo terms)

This dataset is an excellent playground for educational data mining — from clustering and behavioral analytics to predictive modeling and personalized learning applications.

Description

The research employed a mixed methods online survey to understand better the meaning, use, and development of academic research software at the University of Illinois Urbana-Champaign. Other objectives include understanding academic research software support and training needs to make projects successful at Illinois, as well as investigating the use of generative AI tools in using and creating research software.

At the beginning of the survey, all participants gave informed consent. The University of Illinois Urbana-Champaign Institutional Review Board (IRB Protocol no.: Project IRB24-0989) reviewed the study and gave it an exempt determination.

Data collection took place from August 2024 to October 2024. Prior to data analysis, identifiable respondent details were removed during the data cleaning process. Not Applicable and Unsure style responses were used for descriptive statistics, but these responses were excluded for inferential statistics.

Survey design

At the beginning of the online survey, a consent form was provided based on guidelines from the University of Illinois Institutional Review Board to the respondents stating the aims of the study, its benefits and risks, ethical guidelines, being a voluntary survey for participation and withdrawal, privacy and confidentiality, data security, estimated time for survey completion, and contact information of researchers for asking questions. Respondents clicked to indicate their consent. Survey questions were divided into four parts: demographic information, using software for research, creating software for research, and the protocol of citing software for research. The survey had to stop points, whereby not all questions applied to respondents, which led to different sample sizes at the stop points. At the opening of the survey, the number of respondents was 251 with the funding demographic question being answered by all respondents, while other demographic questions had between 225 and 228 respondents answering them. For the first stop question, using research software in their research, the total respondents was 212, and at the last stop question, respondents considering themselves to be research developers, the total number of respondents was 74. The last question of the survey was answered by 71 respondents. Respondents may also have left the survey for other reasons. The questions were primarily closed-type questions with single choice, multiple choice, or Likert scale, as well as a few open-ended questions. Likert scale responses were created utilizing validated scales from Vagias' (2006) Likert Type Scale Response Anchors.

Sampling

Survey Respondents’ Demographics

While most respondents were Tenure Track Faculty (34.7%, f=227), other key categories included Principal Investigator (22.4%, f=227) and Research Scientist (12.1%, f=227). Computer Science, Information Science, Mathematics, and Engineering fields combined for 16% (f=228) of the respondents surveyed, but it should be noted the remaining respondents were from various academic fields across campus from various arts, humanities, and social science fields (25%, f=228) to agriculture (10%, f=228), education (5%, f=228), economics (3%, f=228), medical sciences (4%, f=228), and politics and policy/law (1%, f=228). Most respondents were likely to receive funding from various government agencies. A more detailed breakdown of the demographic information can be found in the supplemental figures. Of the 74 respondents who answered whether they were a research software developer, most respondents did not consider themselves a research software developer, with respondents stating Not at All (39%, n=74) and Slightly (22%, n=74). In addition, open-ended questions asked for further detail about research software titles used in research, research software developer challenges, how generative AI assisted in creating research software, and how research software is preserved (e.g., reproducibility).

Table 1: Survey Respondents’ Demographics

Characteristics

Respondent (%)

Age

 18-24

 25-34

 35-44

 45-54

 55-64

 Over 64

 Preferred Not Answer

3%

14%

33%

27%

14%

7%

2%

Gender

 Woman

 Man

 Non-binary / non-conforming

 Prefer not to answer

49%

44%

2%

4%

Race

 Asian

 Black or African American

 Hispanic or Latino

 Middle Eastern or North African (MENA; new)

 White

 Prefer not to answer

 Other

12%

5%

6%

1%

67%

8%

1%

Highest Degree

 Bachelors

 Masters

 Professional degree (e.g., J.D.)

 Doctorate

6%

19%

5%

70%

Professional Title

 Tenure Track Faculty

 Principal Investigator

 Research Scientist

 Staff

 Research Faculty

 Other

 Teaching Faculty

 Postdoc

 Research Assistant

 Research Software Engineer

35%

22%

12%

8%

7%

4%

4%

4%

2%

2%

Academic Field

 Biological Sciences

 Other

 Agriculture

 Engineering

 Psychology

 Earth Sciences

 Physical Sciences

 Education

 Medical & Health Sciences

 Computer Science

 Library

 Chemical Sciences

 Human Society

 Economics

 Information Science

 Environment

 Veterinary

 Mathematical Sciences

 History

 Architecture

 Politics and Policy

 Law

18%

10%

10%

9%

8%

6%

6%

5%

4%3%

3%

3%

3%

3%

2%

2%

2%

2%

1%

1%

1%

0%

Years Since Last Degree

 Less than 1 Year

 1-2 Years

 3-5 Years

 6-9 Years

 10-15 Years

 More than 15 Years

4%

8%

11%

14%

24%

40%

Receive Funding

 Yes

 No

73%

27%

Funders for Research

 Other

 National Science Foundation (NSF)

 United States Department of Agriculture (USDA)

 National Institute of Health (NIH)

 Department of Energy (DOE)

 Department of Defense (DOD)

 Environmental Protection Agency (EPA)

 National Aeronautics and Space Administration (NASA)

Bill and Melinda Gates Foundation

Advanced Research Projects Agency - Energy (ARPA-E)

Institute of Education Sciences

Alfred P. Sloan Foundation

W.M. Keck Foundation

Simons Foundation

Gordon and Betty Moore Foundation

Department of Justice (DOJ)

National Endowment for the Humanities (NEH)

Congressionally Directed Medical Research Programs (CDMRP)

Andrew W. Mellon Foundation

22%

18%

18%

11%

9%

5%

4%

4%

2%

2%

1%

1%

1%

1%

1%

1%

0%

0%

0%

Table 2: Survey Codebook

QuestionID

Variable

Variable Label

Survey Item

Response Options

1

age

Respondent’s Age

Section Header:

Demographics Thank you for your participation in this survey today! Before you begin to answer questions about academic research software, please answer a few demographic questions to better contextualize your responses to other survey questions.

What is your age?

Select one choice.

Years

1-Under 18

2-18-24

3-25-34

4-35-44

5-45-54

6-55-64

7-Over 64

8-Prefer not to answer

2

gender

Respondent’s Gender

What is your gender?

Select one choice.

1-Female

2-Male

3-Transgender

4-Non-binary / non-conforming

5-Prefer not to answer

6-Other:

3

race

Respondent’s Race

What is your race?

Select one choice.

1-American Indian or Alaska Native

2-Asian

3-Black or African American

4-Hispanic or Latino

5-Middle Eastern or North African (MENA; new)

6-Native Hawaiian or Pacific Islander

7-White

8-Prefer not to answer

9-Other:

4

highest_degree

Respondent’s Highest Degree

What is the highest degree you have completed?

Select one choice.

1-None

2-High school

3-Associate

4-Bachelor's

5-Master's

6-Professional degree (e.g., J.D.)

7-Doctorate

8-Other:

5

professional_title

Respondent’s Professional Title

What is your professional title?

Select all that apply.

1-professional_title_1

Principal Investigator

2-professional_title_2

Tenure Track Faculty

3-professional_title_3

Teaching Faculty

4-professional_title_4

Research Faculty

5-professional_title_5

Research Scientist

6-professional_title_6

Research Software Engineer

7-professional_title_7

Staff

8-professional_title_8

Postdoc

9-professional_title_9

Research Assistant

10-professional_title_10

Other:

6

academic_field

Respondent’s most strongly identified Academic Field

What is the academic field or discipline you most strongly identify with (e.g., Psychology, Computer Science)?

Select one choice.

1-Chemical sciences

2-Biological sciences

3-Medical & health sciences

4-Physical sciences

5-Mathematical sciences

6-Earth sciences

7-Agriculture

8-Veterinary

9-Environment

10-Psychology

11-Law

12-Philosophy

13-Economics

14-Human society

15-Journalism

16-Library

17-Education

18-Art & Design Management

19-Engineering

20-Language

21-History

22-Politics and policy

23-Architecture

24-Computer Science

25-Information science

26-Other:

7

years_since_last_degree

Number of years since last respondent’s last degree

How many years since the award of your last completed degree?

Select one choice.

1-Less than 1 year

2-1-2 years

3-3-5 years

4-6-9 years

5-10-15 years

6-More than 15 years

8

receive_funding_for_research

Whether respondent received funding for research

Do you receive funding for your research?

1-Yes

0-No

9

funders_for_research

Respondent’s funding sources if they answered yes in Question 8

Who funds your research or work (e.g., NIH, Gates Foundation)?

Select all that apply.

1-funders_for_research_1

United States Department of Agriculture (USDA)

2-funders_for_research_2

Department of Energy (DOE)

3-funders_for_research_3

National Science

Chinese Longitudinal Healthy Longevity Survey (CLHLS) WELCOME! The Chinese Longitudinal Healthy Longevity Survey (CLHLS) has been supported by NIA/NIH grants R01 AG023627-01 (PI: Zeng Yi) (Grant name: Demographic Analysis of Healthy Longevity in China) and P01 AG 008761 (PI: Zeng Yi; Program Project Director: James W. Vaupel), awarded to Duke University, with Chinese matching support for personnel costs and some local expenses. UNFPA and the China Social Sciences Foundation provided additional support for expanding the 2002 CLHLS survey. The Max Planck Institute for Demographic Research has provided support for international training since the CLHLS 1998 baseline survey. Finally, in December 2004 the China Natural Sciences Foundation and the Hong Kong Research Grants Council (RGC) partnered with NIA/NIH, providing grants to partially support the CLHLS project. Until present, the CLHLS conducted face-to-face interviews with 8,959, 11,161, 20,421, 18,524 and 19,863 individuals in 1998, 2000, 20002, 2005, and 2008-09, respectively, using internationally compatible questionnaires. Among the approximately 80,000 interviews conducted in the five waves, 14,290 were with centenarians, 18,910 with nonagenarians, 20,743 with octogenarians, 14,416 with younger elders aged 65-79, and 10,569 with middle-age adults aged 35-64. At each wave, survivors were re-interviewed, and deceased interviewees were replaced with new participants. Data on mortality and health status before dying for the 17,721 elders aged 65-110 who died between waves were collected in interviews with a close family member of the deceased. The CLHLS has the largest sample of centenarians in the world according to a report in Science (see the report). Our general goal is to shed new light on a better understanding of the determinants of healthy longevity of human beings. We are compiling extensive data on a much larger population of the oldest-old aged 80-112 than has previously been studied, with a comparison group of younger elders aged 65-79. We propose to use innovative demographic and statistical methods to analyze longitudinal survey data. Our goal is to determine which factors, out of a large set of social, behavioral, biological, and environmental risk factors, play an important role in healthy longevity. The large population size, the focus on healthy longevity (rather than on a specific disease or disorder), the simultaneous consideration of various risk factors, and the use of analytical strategies based on demographic concepts make this an innovative demographic data collection and research project. Our specific objectives are as follows: Collect intensive individual interview data including health, disability, demographic, family, socioeconomic, and behavioral risk factors for mortality and healthy longevity. Follow up the oldest-old and the comparison group of the younger elders, as well as some of the elders’ adult children to ascertain changes in their health status, care needs and costs, and associated factors. We will also ascertain mortality and causes of death, as well as care needs, costs, and health/disability status before death. Analyze the collected data to estimate the impacts of social, behavioral, environmental, and biological risk factors that are determinants of healthy longevity and mortality in the oldest-old. Compare the findings with results from other studies of large populations at advanced age.

Profile of psychiatric symptoms among people with schizophrenia and attending the follow-up service at Jimma University Medical Center, psychiatric clinic (n = 351).

The 2015-16 Armenia Demographic and Health Survey (2015-16 ADHS) is the fourth in a series of nationally representative sample surveys designed to provide information on population and health issues. It is conducted in Armenia under the worldwide Demographic and Health Surveys program. Specifically, the objective of the 2015-16 ADHS is to provide current and reliable information on fertility and abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of young children, childhood mortality, maternal and child health, domestic violence against women, child discipline, awareness and behavior regarding AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking, tuberculosis, and anemia. The survey obtained detailed information on these issues from women of reproductive age and, for certain topics, from men as well.The 2015-16 ADHS results are intended to provide information needed to evaluate existing social programs and to design new strategies to improve the health of and health services for the people of Armenia. Data are presented by region (marz) wherever sample size permits. The information collected in the 2015-16 ADHS will provide updated estimates of basic demographic and health indicators covered in the 2000, 2005, and 2010 surveys.The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the NSS. The 2015-16 ADHS also provides comparable data for longterm trend analysis because the 2000, 2005, 2010, and 2015-16 surveys were implemented by the same organization and used similar data collection procedures. It also adds to the international database of demographic and health–related information for research purposes.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

Safeguarded versions of MCS studies:
The Safeguarded versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Polygenic Indices
Polygenic indices are available under Special Licence SN 9437. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).

Release of Sweeps 1 to 4 to Long Format (Summer 2020)
To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard Safeguarded Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

International Data Access Network (IDAN)
These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website.

PHQ-9 Depression Assessment

14-Days of Ambulatory Mood Dynamics in a General Population

By [source]

About this dataset

This dataset contains 14 days of ambulatory assessment (AA) data related to depression symptoms and mood ratings, as well as findings from a retrospective Patient Health Questionnaire (PHQ-9) designed for depression screening purposes. Furthermore, it contains demographic information about the participants such as their age and gender.

This dataset is composed of various fields including: phq1, phq2, phq3, phq4, phq5, phq6, phq7,ph q8 ,ph q9 ,age ,sex ,q10 ,e11 ,12 w13 w14 e16 e46 e47 happiness.score time period name start time Ph Q day The data gathered through this survey allows us to gain insight into the daily fluctuations in self-reported symptoms experienced by these individuals at different stages of their lives. In addition to providing important clues about possible causes or triggers associated with depressive episodes, this type of survey can also help identify interventions that may prove successful in reducing symptom severity and frequency. Our hope is that we can use this extensive collection of data to inform treatment decisions and ultimately improve outcomes for those affected by depression

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How to use the dataset

This dataset contains information about the Patient Health Questionnaire (PHQ-9) depression screening assessment, which is used to assess the severity of depressive symptoms over the past two weeks. This dataset can be used to gain insights into depression in a general population sample.

The data is broken down into several categories: PHQ Score (1-9), Age and Gender of participant, Questions 10-47 (Numeric Scores), Happiness score, Time/Period Name/Start Time, and PHQ Day.

In order to use this dataset effectively and accurately analyze your results it is important to understand how each column impacts your results. The PHQ Score column contains information on the severity of depressive symptoms in a scale from 1-9. The Age and Gender columns contain demographic information related to participants while Questions 10-47 represent a range of mental health subject including anhedonia, fatigue, sleep disturbance and changes in appetite or weight that are rated on a numeric scale from 0-4. The Happiness score reflects individual’s subjective ratings at time of assessment with higher scores reflecting greater positivity toward life as reported by participant during study period. Finally the Time/Period Name/Start Time columns provide date and time information related to study period while the PHQ Day represents total number of days elapsed since onset of clinical trial at beginning of assessment period.

By understanding how each category contributes as well as any relationships that may exist between variables researchers can use this data set effectively when analyzing their results for more detailed insights into depression in general population samples across different lengths of time or months scoring methodologies employed reflected by total PHQ scores attained over course on particular month interval included within scope defined for particular study group being considered for analysis by researcher during evaluation protocol being employed developed data research development team assigned project develop analysis offers potential obtainable from working current model designed herein designed incorporated iteration included questionnaires offer basis obtainable utilizing utilized platform outlined herethrough model presented currently established outcome metrics thereby providing tool required necessary review evaluate found current project implementation structure framework wherein needed result may provided evaluated research rationale procedures ultimately yielding findings potentially productive goals desired analytical outcomes original objective initial efforts made implement intended protocol design methodological measures prescribed evaluator's evaluation criteria reported therewith provide result assist uncovering needed research answers discoverable platform established herein presented purpose obviate further attempts previously reviewed limitations encountered earlier trials thus executing member's logbook objectives upgraded format allow corporate setting without interruption driven process overhaul project initiation iterative systemic component procedure triage session estimation techniques management applicable foundational principles

Research Ideas

• Developing an AI-driven screening tool that can rapidly identify and monitor symptom...

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150