Health indicators are quantifiable characteristics of a population which researchers use as supporting evidence for describing the health of a population. The researchers use a survey methodology to gather information about certain people, use statistics in an attempt to generalize the information collected to the entire population, then use the statistical analysis to make a statement about the health of a population. Health indicators are often used by governments to guide health care policy.

List of World Bank accepted inicators for various projects for Nigeria

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. The data includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. Unlike prior years' where the audited financial information was compiled by OPM, the FY 2020 and beyond information in this edition was based upon the self-reporting by municipalities of their own audited data. Note: This dataset includes annually reported data using three types of years: calendar year, fiscal year, and grand list year. The calendar year spans January 1 to December 31. In Connecticut, the state fiscal year runs from July 1 to June 30, with the numerical year indicating when the fiscal year ends (e.g., fiscal year 2022 ended on June 30, 2022). The grand list year refers to the year municipalities assess property values, which occurs annually on October 1. For example, the property values assessed on October 1, 2020, are referred to as "Grand List Year 2020." However, these values are used to levy property taxes for the next fiscal year, spanning July 1, 2021, to June 30, 2022. In this context, grand list year 2020 corresponds to fiscal year ending 2022. Similarly, mill rates for each year are based on the grand list from two years prior. The most recent edition is for the Fiscal Years Ended 2018-2022 published in September 2024. For additional data on net current expenditures per pupil, see the State Department of Education website here: https://portal.ct.gov/sde/fiscal-services/net-current-expenditures-per-pupil-used-for-excess-cost-grant-basic-contributions/documents For additional population data from the Department of Public Health, visit their website here: https://portal.ct.gov/dph/health-information-systems--reporting/population/annual-town-and-county-population-for-connecticut The most recent data on the Municipal Fiscal Indicators is included in the following datasets: Municipal-Fiscal-Indicators: Financial Statement Information, 2020-2022 https://data.ct.gov/d/d6pe-dw46 Municipal-Fiscal-Indicators: Uniform Chart of Accounts, 2020-2022 https://data.ct.gov/d/e2qt-k238 Municipal Fiscal Indicators: Pension Funding Information for Defined Benefit Pension Plans, 2020-2022 https://data.ct.gov/d/73q3-sgr8 Municipal Fiscal Indicators: Type and Number of Pension Plans, 2020-2022 https://data.ct.gov/d/i84g-vvfb Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2020-2022 https://data.ct.gov/d/ei7n-pnn9 Municipal Fiscal Indicators: Economic and Grand List Data, 2019-2024 https://data.ct.gov/d/xgef-f6jp Municipal Fiscal Indicators: Benchmark Labor Data, 2020-2024 https://data.ct.gov/d/5ijb-j6bn Municipal Fiscal Indicators: Bond Ratings, 2019-2022 https://data.ct.gov/d/a65i-iag5 Municipal Fiscal Indicators: Individual Town Data, 2014-2022 https://data.ct.gov/d/ej6f-y2wf Municipal Fiscal Indicators: Totals and Averages, 2014-2022 https://data.ct.gov/d/ryvc-y5rf

This is data for the application envidat — Database of environmental indicators https://www.enviroportal.sk/envidat . The database currently contains about 277 indicators and are organised into 14 thematic areas

Graph and download economic data for Composite Index of Twelve Leading Indicators, Original Trend, Short List for United States (M16072USM351SNBR) from Jan 1948 to Dec 1969 about composite, indexes, and USA.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021. The Grand List is the aggregate valuation of taxable property within a given municipality. The Grand Lists component data provides a breakdown by certain assessment categories and their valuation. Data on the Municipal Fiscal Indicators is included in the following datasets: Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8 Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657 Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

** NEW 2/2021**: new COVID indicators were added linked to support to the vaccination effort.

NEW 11/2020: New reserved CV indicator are added to retain the possibility to extend the list.

During 2020, many countries and regions will amend their ERDF and ESF programmes to transfer funds to actions targeting the COVID-19 response. Several programmes have discussed with the Commission their plans to add programmes specific indicators for new actions targeting the COVID-19 response to capture the outputs from expenditure. In order to exploit programme specific indicators, which could be commonly applied across Member States, the Commission services proposed a voluntary list of such indicators to the Member States on 12 May, to be used when relevant. The use of the unique codes and names will allow the Commission to make use of the monitoring done by the cooperating programmes. A wide use of these codes and indicators would be highly valued for accountability, transparency and communication at the National and EU levels. It the programmes take up these specific indicators the targets will become available later in 2020 with the first annual report on implementation due in May 2021.

NOTE:
1. On 10 July the file was modified to better reflect the possible uses of the ESF indicators as outputs or result indicators (separate columns added for both types).

This data represents project development objective indicators for the ARTF portfolio (from 2001 through today). This dataset pulls project indicator measurements from ARTF implementation status reports.

Complete list of SDG 4 indicators which includes the current set of 11 global and 32 thematic indicators are agreed upon by the United Nations Statistical Commission and the TCG respectively for the follow-up and review of the SDG 4 - Education 2030 Agenda.

List of indicators and the measures/metrics used to quantify impacts.

Global Health Cluster's list of indicators for monitoring health conditions, calculating the number of People in Need, and assessing the severity of their needs.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic or targeted sampling design, structured implementation, standard core methods and indicators, electronic data capture and management, and integration with remote sensing. Each record represents a sample visit during which a suite of the BLM Riparian and Wetland AIM methods were applied, with the geometry marking the center of the plot as taken in the Plot Characterization form. Attributes are the BLM Riparian and Wetland AIM core indicators, which include plot-level measures of vegetation and soil condition such as plant species cover and composition, plant height, and woody structure. In addition, some plots may have some contingent and annual use indicators, including measures of hummock cover and characteristics, water quality, stubble height, soil alteration, and riparian woody use. Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (BLM AIM Wetland Database) at the BLM National Operations Center. Annual Use data (i.e., annual use indicators) are omitted from the public version of these data but can be made available upon request. General Definitions The species list used for data collection was originally developed from a full download of all species in USDA PLANTS shown as occurring in BLM-administered states. The state-level occurrence of species in this list have been adjusted over time as individual species were found to be missing from individual state lists. Traits used in indicator calculations for all species observed at a given monitoring plot can be found in the I_SpeciesIndicator feature service, where the traits are listed by plant. A full species list can also be provided by request by the National Riparian and Wetland AIM Team. Once finalized, it will be added to the WetlandAIM database, likely in spring of 2024. In general, traits are assigned at the species-level. Genera and family-level records were only given trait values if all species within that taxonomic group were considered to have one trait (e.g., all species of Tamarix are nonnative, so the genus level code is also considered nonnative). To assign Growth Habit and Duration to unknown plants, information recorded in the Unknown Plants form was used to fill in traits. For example, if a plant was identified as a Carex species (unknown code CAREX_01), the growth habit (graminoid) would be taken from the full species list since all Carex species are graminoids, and the duration would be taken from the plot-specific matching entry in Unknown Plants. Nativity Status: The nativity status of all species was taken from the USDA Plants Database and was ranked at a national scale. All plants identified to species are ‘Native’, ‘Nonnative’, or ‘Cryptogenic’. The term cryptogenic refers to species with both native and nonnative genotypes. Genera and family-level plants were only given a nativity status if all species within that taxonomic group were considered either native or nonnative (e.g. all species of Tamarix are nonnative, so the genus level code is also considered nonnative). Noxious: Noxious status are designated for each political state (i.e. StateCode) developed using the most recent state noxious list available online. Wetland Indicator Status: Wetland Indicator Status was taken from the U.S. Army Corps of Engineers’ National Wetland Plant List (NWPL 2020, version 3.5; https://wetland-plants.usace.army.mil/). Plants are ranked by ecoregion into one of the following rating categories based on an estimated frequency with which it is thought to occur in wetlands: obligate (OBL), facultative wetland (FACW), facultative (FAC), facultative upland (FACU), or upland (UPL), The five rating categories were first developed through an exhaustive review of the botanical literature and best professional judgement of national and regional experts, and has since undergone multiple rounds of revision by a national panel. C-Values: Coefficients of Conservatism (C-values) are assigned to species by a panel of experts, typically at a state level. C-values range from 0 to 10 and represent an estimated probability that a plant is likely to occur in a landscape relatively unaltered from pre-European settlement conditions (see table of C-Value Interpretation below). The Mean C-value is calculated at a plot level by averaging the C-values of all species in a given plot. Mean C-value is a stand-alone indicator of floristic quality, one of several indicators under the Floristic Quality Assessment (FQA) approach to assesses the degree of "naturalness" of an area. C-Value Interpretation 0 = Non-native species. Very prevalent in new ground or non-natural areas 1-3 = Commonly found in non-natural areas 4-6 = Equally found in natural and non-natural areas 7-9 = Obligate to natural areas but can sustain some habitat degradation 10 = Obligate to high-quality natural areas (relatively unaltered from pre-European settlement) C-values were compiled from several sources, listed below. CO = Smith, P., G. Doyle, and J. Lemly. 2020. Revision of Colorado’s Floristic Quality Assessment Indices. Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado. MT = Pipp, Andrea. 2017. Coefficient of Conservatism Rankings for the Flora of Montana: Part III. Report to the Montana Department of Environmental Quality, Helena, Montana. Prepared by the Montana Natural Heritage Program, Helena, Montana. 107 pp. WA = Rocchio, F.J, and R. Crawford. 2013. Floristic Quality Assessment for Washington Vegetation. Washington Natural Heritage Program, Washington Department of Natural Resources, Olympia, WA. (Values for Eastern Washington used). WY = Washkoviak L, B. Heidel, and G. Jones. 2017. Floristic Quality Assessment for Wyoming Flora: Developing Coefficients of Conservatism. Prepared for the U.S. Army Corps of Engineers. The Wyoming Natural Diversity Database, Laramie, Wyoming. 13 pp. plus appendices. AZ, CA, ID, NM, NV, OR, UT = Great Lakes Environmental Center (GLEC), Inc. and M.S. Fennessy. 2019. Project to Assign C-Values to Western State for use in the USEPA National Wetland Condition Assessment. Great Lakes Environmental Center, Traverse City, MI. Live: The Core Methods measure Live vs. Standing Dead plant cover, i.e., if a pin drop hits a dead plant part (even if it’s on a living plant), that hit is considered a dead hit. If a pin hits both a live and a dead plant part in the same pin drop, that hit is considered live. Growth Habit: The form of a plant. In this dataset, plants are either Forb, Graminoid, Shrub, Tree, and, in Alaska only, Liverwort, Moss, Hornwort, and Lichen. Growth habitat was derived from USDA PLANTS. If more than one growth habit was designated in USDA PLANTS, the most common growth habit was determined by consulting the USDA plants database and other literature and was applied across all states where it occurs. Graminoids include all grasses, rushes, sedges, arrow grasses, and quillworts (Poaceae, Cyperaceae, Juncaceae, Juncaginaceae, and Isoetes). Forbs include vascular, non-woody plants, but exclude graminoids. Shrubs are defined as perennial multi-stemmed woody plants usually less than 4-5 m in height. Trees are generally perennial woody plants with a single stem, normally greater than 4 to 5 m in height. Duration: The life length of a plant. In this dataset, plants are either Perennial or Annual. Biennial plants are classified as Annuals. Duration was derived from USDA PLANTS. If more than one duration was designated in USDA PLANTS, the most common duration for each state was determined by consulting the USDA plants database and applied across all administrative states where it occurs. Nonvasculars: Nonvascular species were not included in LPI data collection in the lower-48 except as generic “non-plant” codes. In Alaska, a full list of nonvascular species from the Alaska Vegetation Plots Database (https://akveg.uaa.alaska.edu/) including mosses, hornworts, liverworts, and lichens was used during data collection. In terms of indicator calculations, nonvasculars were not included in plot-level plant counts and cover (i.e. cover of various plant trait categories like nativity, duration, or growth habit), but were instead transferred into the simplified non-plant codes to be calculated into moss and lichen cover indicators. Cover by species of these nonvasculars can be found in the SpeciesIndicators table. Preferred Forbs: A set of specific forb species that are preferred by Sage Grouse birds. State preferred forb lists were developed by state botanists in collaboration with wildlife and sage-grouse experts and were based on a combination of peer reviewed literature and local knowledge. These lists were then combined to create one national list.

World Development Indicators (WDI) Dataset Description The World Development Indicators (WDI) dataset is a comprehensive compilation of relevant, high-quality, and internationally comparable statistics about global development. It presents the most current and accurate global development data available and includes national, regional, and global estimates.

                Data Coverage

Time Period: Varies by indicator, often covering several decadesGeographical Coverage: Includes data from all countries and regions worldwide Indicators and Source URLs The following is a list of indicators included in the dataset, along with their respective source URLs: Here is the revised list with the URL replaced with the provided link: Indicators and Source URLs The following is a list of indicators included in the dataset, along with their respective source URLs: Here is the updated list: Indicators and Source URLs The following is a list of indicators included in the dataset, along with their respective source URLs: Population Total: SP.POP.TOTLWorking Population: SP.POP.1564.TOPopulation Ages 0-14: SP.POP.0014.TOPopulation Ages 15-64: SP.POP.1564.TOPopulation Ages 65 and Above: SP.POP.65UP.TOFemale Population (% of total): SP.POP.TOTL.FE.ZSMale Population (% of total): SP.POP.TOTL.MA.ZSGDP (current US$): NY.GDP.MKTP.CDGDP Growth Rate: NY.GDP.MKTP.KD.ZGGDP per Capita (current US$): NY.GDP.PCAP.CDLabor Force Participation Rate: SL.TLF.CACT.ZSLabor Force Participation Rate, Female: SL.TLF.CACT.FE.ZSLabor Force Participation Rate, Male: SL.TLF.CACT.MA.ZSUnemployment Rate: SL.UEM.TOTL.ZSLife Expectancy at Birth: SP.DYN.LE00.INPrimary School Enrollment: SE.PRM.ENRRSecondary School Enrollment: SE.SEC.ENRRTertiary School Enrollment: SE.TER.ENRRAdult Literacy Rate: SE.ADT.LITR.ZSYouth Literacy Rate: SE.ADT.1524.LT.ZS

Based on the list of recommended available indicators in O'Neill et al. [46], as well as the addition of several new indicators, a final list of 48 potential marine species and food web indicators was compiled. Each indicator was assigned to a specific key attribute (e.g., population size, community composition) based on the literature [14], [19], [20], their previous categorization in Puget Sound [17], [48], [49], and expert opinion.

This global indicator framework was developed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) and agreed to, as a practical starting point at the 47th session of the UN Statistical Commission held in March 2016. The report of the Commission, which included the global indicator framework, was then taken note of by ECOSOC at its 70th session in June 2016. The global indicator list is contained in the Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (E/CN.3/2016/2/Rev.1), Annex IV. The list includes 230 indicators on which general agreement has been reached.

This file contains a list of 446 circular economy (CE) indicators compiled by Nordland Research Institute from an extensive review of the CE monitoring framework literature, carried out as part of the Horizon 2020 project CityLoops (GA no. 821033). Frameworks and indicators with relevance for a European city setting were prioritized for inclusion on the list. In the dataset, each indicator is presented with a brief definition and a literature reference and classified along various dimensions to facilitate filtering of the data. The dataset was compiled during the period 2019-2020. Expired hyperlinks were updated in September 2023.

This list of indicators was developed through the Inform project at SPREP for use by Pacific Islands countries (PICs) to meet their national and international reporting obligations. The indicators are typically adopted by PICs for their State of Environment reports and are intended to be re-used for a range of MEA and SDG reporting targets. The indicators have been designed to be measurable and repeatable so that countries can track key aspect of environmental health over time. The indicators are mapped to key MEA and SDG reporting targets and can be used with the Indicator Reporting Tool (also developed by the Inform project) to reduce the burden of environmental reporting on PICs. Indicators can be used as is, adapted for countries needs, or used in conjunction with other national-scale indicators selected by PICs. This dataset includes a summary pdf document and an associated excel file with more detail.

Identifying Explanation Needs: Towards a Catalog of User-based Indicators

Coding Guidelines: behavior-based and event-based indicators

Summary of Artifact

The uploaded dataset contains the supplementary material for the RE '25 paper "Identifying Explanation Needs: Towards a Catalog of User-based Indicators".
The material can be used to reproduce our survey study.

Authors information

H. Deters, L. Reinhardt, J. Droste, M.Obaidi and K. Schneider (2025). Supplementary Material for Research Paper "Identifying Explanation Needs: Towards a Catalog of User-based Indicators".
IEEE 33th Requirements Engineering Conference (RE'25), Hannover, Germany. https://doi.org/10.5281/zenodo.15798316

Description of Artifact

The pdf-file titled "Survey.pdf" presents the survey questions used for the survey of the paper.
The pdf-file titled "Coding-Guidelines_Phyiscal-Reactions_Emotional-State.pdf" presents the coding guidelines for the physical reaction and emotional state indicators.
The pdf-file titled "Coding-Guidelines_behavior-based_event-based.pdf" presents the coding guidelines for the behavior-based and event-based indicators.
The spreadsheet "CodesForQuestionsQ2Q3Q4.xlsx" presents all participants responses for the second, third and fourth question of the survey.
The spreadsheet "CodesForQuestionsQ5.xlsx" presents all participants responses for the fifth question of the survey.

System Requirements

*Not applicable as our supplementary material is only a dataset.*

Installation Instructions

*Not applicable as our supplementary material is only a dataset.*

Usage Instructions

The dataset presents the study material required to replicate our survey study.

The "Survey.pdf" contains all the questions asked in the online survey and the supposed type of answer that participants had to give. Answer types were either free-text answer fields or single-choice. The survey can be reproduced with an online-survey tool or on paper.

The "Coding-Guidelines_Physical-Reactions_Emotional-State.pdf" contains all the coding guidelines for physical reaction and emotional state indicators. By providing definitions, inclusion and exclusion criteria for each indicator the guidelines assist researchers with categorizing qualitative data from open ended survey responses about physical reactions and emotional states. The survey responses contain participants descriptions of physical reactions and emotional states resulting from an explanation need in a software system. Physical reaction indicators include descriptions of specific physical behaviors that participants might have reported in their responses and how these behaviors need to be assigned to a physical reaction indicator. Emotional state indicators describe different negative or positive emotions participants might have described in their answers. Each emotional state indicator lists multiple synonyms or similar emotions that can be assigned to one specific indicator. Each response can have 0 or more indicators assigned to their reported physical reactions and emotional states. Researchers can use the coding guidelines with our survey responses or new responses from a reproduced survey study. Researcher also might expand the coding guidelines, if they find new inclusion or exclusion criteria for the existing indicators or if they find new indicators.

The "Coding-Guidelines_behavior-based_event-based.pdf" contains all the coding guidelines for behavior- and event-based indicators. By providing definitions, inclusion and exclusion criteria for each indicator the guidelines assist researchers with categorizing qualitative data from open ended survey responses. The survey responses contain participants descriptions of their own user behavior or event-based behavior of a software system resulting from an explanation need in this software system. Some indicators also include keywords that are associated with the indicator. The categorization of the indiciators is splitted in two coding levels. The first-level coding guidelines are applied to identify the indicators. With the second-level coding the already identified indicators can be further specified. Each response can have 0 or more indicators assigned from the first- and second-level of the coding guidelines. Researchers can use the coding guidelines with our survey responses or new responses from a reproduced survey study. Researcher also might expand the coding guidelines, if they find new inclusion or exclusion criteria for the existing indicators or if they find new indicators.

The "CodesForQuestionsQ2Q3Q4.xlsx" contains all participants responses describring their need in a software system, their physical reaction and emotional state from the second to fourth question of the survey and the determined indicator codes. The columns of the spreadsheet are splitted in answer ID, software, need category, answer need translated from german, need original response, behavior-based/event-based indicator code, answer behavior response translated from german, behavior original response, physical reaction indicator code, answer physical behavior translated from german, physical behavior original response, emotional state indicator code, answer emotional state translated from german, original emotional state response. Each line contains all the responses and codes for one named software system, where each coding cell contains 0 or more indicators determined for a response. Every participant response has its own cell. Researchers can use the whole spreadsheet without our indicator codes to reproduce the coding results or use the spreadsheet as a template for their own survey responses and a completely new coding process.

Similar to the other spreadsheet "CodesForQuestionsQ5.xlsx" contains all participants responses describing their own ideas of needs in different software systems and the determined indicator codes. The columns of the spreadsheet are splitted in answer ID, original participants response, participants response translated from german and the behavior-based/event-based indicator code. Each line contains all the responses and codes for one named software system, where each coding cell contains 0 or more indicators determined for a response. Every participant response has its own cell. Researchers can use the whole spreadsheet without our indicator codes to reproduce the coding results or use the spreadsheet as a template for their own survey responses and a completely new coding process.

Steps to Reproduce

*Not applicable as our supplementary material is only a dataset.*

The UNDESERT List of Indicators of Desertification and Degradation provides a set of indicators to assess desertification and degradation processes. The list contains spatial and plot-based indicators. The eight spatial indicators were compiled from remote sensing data (medium resolution satellite images), thematic maps, digital elevation and climate models. The 27 plot-based indicators represent a refinement of the spatial indicators at site level. Both indicator types are to be used parallel to assess all aspects of desertification and degradation processes. The eight relevant spatial indicators for desertification/degradation were grouped in four thematic classes: Land cover/use change, vegetation indices, human pressure and fire. The 29 relevant plot-based indicators for desertification/degradation were grouped in four thematic classes: Vegetation, human pressure, soil and topography. Furthermore the interested user gets an insight of the data needed, the methodology applied and the indicators related. Finally there is a list of bibliographic reference.

Abstract

The Lao Social Indicator Survey (LSIS) II provides a set of single national figure on social indicators. It combines the Multiple Indicator Cluster Survey (MICS) and the Demographic and Health Survey modules to maximize government resources for a nationally representative sample survey. LSIS II follows the first LSIS I survey which was carried out in 2011-12 jointly by the Ministry of Health (MoH) and the Lao Statistics Bureau (LSB) of the Ministry of Planning and Investment in collaboration with other line ministries. The LSIS I provided baseline data for the 7th National Socio-Economic Development Plan (NSEDP) and the Millennium Development Goals.

The LSISII 2017 of Lao PDR has as its primary objectives:

• To provide up-to-date information that will assist with the selection of data on key social development indicators to support the monitoring of the Sustainable Development Goals (SDGs);

• To establish a baseline for national development plans and priorities including the 8th National Socio- Economic Development Plan (NSEDP), provincial core social development indicators data, as well as supporting the data for Least Developed Country Graduation;

• To produce a range of population and social indicators that are statistically sound and based on internationally comparable methodology and best practices; and

• To continue reinforcing coordination mechanisms on supporting and strengthening social statistics in Lao PDR and making use of its findings to formulate and advocate for policies, programme formulation and monitoring.

Geographic coverage

The sample for the Lao Social Indicator Survey 2017 was designed to provide estimates at the national level, for urban and rural areas, including rural with roads and rural without roads, for three regions including: North, Central and South and 18 provinces including: Vientiane Capital, Phongsaly, Luangnamtha, Oudomxay, Bokeo, Luangprabang, Huaphanh, Xayabury, Xiengkhuang, Vientinae, Borikhamxay, Khammuane, Savannakhet, Saravane, Sekong, Champasack, Attapeu and Xaysomboun.

Analysis unit

• Individuals

• Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The major features of the sample design are described in this appendix. Sample design features include defining the sampling frame, target sample size, sample allocation, listing in sample clusters, choice of domains, sampling stages, stratification, and the calculation of sample weights.

The primary objective of the sample design for the 2017 Lao Social Indicator Survey (LSIS 2017) was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the 18 provinces of the country.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The primary sampling units (PSUs) selected at the first stage were villages (PSU and Village are used interchangeably in this Chapter). A listing of households was conducted in each sample village, and a sample of households was selected at the second stage.

SAMPLING FRAME AND STRATIFICATION

The sampling frame for this survey consisted of a list of all villages in the country, arranged by province, with appropriate size estimates (number of households) and other relevant information about each village. The village register is maintained by Lao Statistics Bureau (LSB). It is updated in December each year. The version used as sampling frame was the village register of December 2015.

The 18 provinces were defined as the sampling strata. Within provinces a further, implicit, stratification - on village category - was achieved by systematic sampling from a list of villages ordered by village category.

SAMPLE SIZE AND SAMPLE ALLOCATION

The overall sample size for the 2017 Lao Social Indicator Survey was calculated as 23,400 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children age 0-4 years. Since the survey results are tabulated at the provincial level, it was necessary to determine the minimum sample size for each province.

The number of households selected per cluster for the survey was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 1,170 sample clusters would need to be selected for the survey.

The sample allocation over provinces was determined by a procedure where the sample at first was allocated proportionally to the square root of the number of households in each province. This allocation was further adjusted so that provinces getting less than 1,100 households in the preliminary allocation were given additional households up to 1,100. These additional households were taken from the three provinces that had the largest samples according to the preliminary allocation. The sample sizes for provinces vary between 1,100 and 1,680 households. The justification for using different sample sizes is that the standard errors for national estimates will be lower than the standard errors that would have been achieved with equal sample sizes over the provinces.

Within province the sample was allocated over implicit strata defined by village category. This was achieved by systematic sampling from a list of villages ordered by village category. This way of sampling resulted in approximately proportional allocation of the province sample over the implicit strata urban villages, rural villages with road and rural villages without road.

SELECTION OF VILLAGES (CLUSTERS)

Villages were selected from each of the sampling strata (provinces) by using systematic probability proportional to size (PPS) sampling procedures. The measure of size was the number of households in the village; the number was obtained from the LBS village register. Altogether 32 villages were so large in size so they had the probability equal to one to be selected to the sample. These large villages were thus selected to the sample with certainty.

LISTING ACTIVITIES

A new listing of households was conducted in all the sample villages prior to the selection of households. For this purpose, listing teams were trained to visit all the sampled villages and list all households in the village. The listing operation took place from December 2016 to February 2017 with 70 listing team members. In each Province, there were two teams each consisting of a lister and a mapper, except in Champasack, where three teams were assigned.

Listing could not be done in four villages. In two of the villages the area had been completely cleared of dwellings due to preparations for dam construction. One village was not accessible by car or motorcycle due to poor roads and one village could not be properly identified due to village mergers.

Large villages, where the number of households exceeded 300 households, were divided into two or more segments, and one segment was picked randomly before listing. Segmentation was done in 216 villages.

SELECTION OF HOUSEHOLDS

Lists of households were prepared by the listing teams in the field for each village. The households were then sequentially numbered from 1 to Mhi (the total number of households in each village or segment) at the Lao Bureau of Statistics, where the selection of 20 households in each village was carried out using random systematic selection procedures. The MICS6 spreadsheet template for systematic random selection of households was adapted for this purpose.

The survey also included a questionnaire for individual men that was to be administered in half of the sample of households. The MICS household selection template includes an option to specify the proportion of households to be selected for administering the individual questionnaire for men, and the spreadsheet automatically selected the corresponding subsample of households. All men age 15 to 49 years in the selected households were eligible for interview.

LSIS 2017 also included water quality testing for a subsample of households within each sample cluster. A subsample of 3 of the 20 selected households was selected in each sample cluster using random systematic sampling for conducting water quality testing, for both water in the household and at the source. The MICS household selection template includes an option to specify the number of households to be selected for the water quality testing, and the spreadsheet automatically selected the corresponding subsample of households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the survey: 1) a household questionnaire which was used to collect basic demographic information, the household, and the dwelling; 2) a water quality testing questionnaire administered in three households in each cluster of the sample; 3) a questionnaire for individual women; 4) a questionnaire for individual men; 5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

Questionnaires to capture anthropometry measurements among children under 5 years and to record anaemia test results for children under 5 years and women age 15-19