Abstract

In response to the COVID-19 pandemic and to mitigate the spread of the disease, UNHCR Thailand MCO procured Non-Food Items (NFI) items, namely soap, cloth masks and hand sanitizers, and distributed to vulnerable refugees who resided in the 9 camps along the border of Thailand-Myanmar. Additionally, blankets were procured and distributed to every household in 4 camps in Mae Hong Son province during the winter. These items were distributed in the second half of 2021 to first half of 2022. Following the distributions, UNHCR conducted the Post Distribution Monitoring (PDM) in the 2nd and 3rd quarter of 2022 to collect refugees’ feedback on the distribution process and suitability of the items for further improvement in the future. The methodology used to determine sample size for this PDM exercise was the scientific method, with sample sizes calculated based on a confidence level of 90% and confidence interval of 5% and the results were entered into KOBO. The overall feedback received was satisfactory. About 98% of respondents faced no challenges in traveling to the distribution points to receive the items. 79% of the respondents reported receiving sufficient NFIs and 95% of respondents stated that the item quality was good.

Geographic coverage

National

Analysis unit

Households

Universe

Recipients on NFIs in Thailand in 2022

Kind of data

Sample survey data [ssd]

Sampling procedure

2200 households were sampled from distribution lists in camps where the distribution has been conducted. Sample size calculated at confidence level 90% and confidence interval 5%. The total sampling frame was approximately 18,000 households.

Mode of data collection

Face-to-face [f2f]

description: This study was undertaken to provide information on the characteristics and distribution of surficial sediments off the eastern United States. Accordingly, long traverses were run across the continental shelf and in most case carrying over the shelf break. This data set includes data from those 9 traverses which were conducted north of Virginia. These data constitute the first systematic sampling of the U.S. Atlantic margin to show the effects of environmental factors (e.g. increasing distance from shore, water depth) on the sediment distribution. Sampling was performed with a primitive grab sampler; navigational methods were not discussed in this report.; abstract: This study was undertaken to provide information on the characteristics and distribution of surficial sediments off the eastern United States. Accordingly, long traverses were run across the continental shelf and in most case carrying over the shelf break. This data set includes data from those 9 traverses which were conducted north of Virginia. These data constitute the first systematic sampling of the U.S. Atlantic margin to show the effects of environmental factors (e.g. increasing distance from shore, water depth) on the sediment distribution. Sampling was performed with a primitive grab sampler; navigational methods were not discussed in this report.

The dataset was created from simulating contamination incidents in a water distribution system and selecting optimal sampling locations to identify the contamination incident. This data includes the number of contamination scenarios still credible after each sampling cycle; the number of nodes correctly identified as being contaminated after each sampling cycle; and the number of nodes identify as likely contaminated, likely not contaminated, and uncertain after each sampling cycle. This dataset is associated with the following publication: Rodriguez, S., M. Bynum, C. Laird, D. Hart, K. Klise, J. Burkhardt, and T. Haxton. Optimal sampling locations to reduce uncertainty in contamination extent in water distribution systems. Journal of Infrastructure Systems. American Society of Civil Engineers (ASCE), Reston, VA, USA, 27(3): 1-31, (2021).

In September 2013, an experiment using Distributed Acoustic Sensing (DAS) was conducted at Garner Valley, a test site of the University of California Santa Barbara (Lancelle et al., 2014). This submission includes one 45 kN shear shaker (called "large shaker" on the basemap) test for three different measurement systems. The shaker swept from a rest, up to 10 Hz, and back down to a rest over 60 seconds.

Lancelle, C., N. Lord, H. Wang, D. Fratta, R. Nigbor, A. Chalari, R. Karaulanov, J. Baldwin, and E. Castongia (2014), Directivity and Sensitivity of Fiber-Optic Cable Measuring Ground Motion using a Distributed Acoustic Sensing Array (abstract # NS31C-3935), AGU Fall Meeting. https://agu.confex.com/agu/fm14/webprogram/Paper19828.html

The e-poster is available at: https://agu.confex.com/data/handout/agu/fm14/Paper_19828_handout_696_0.pdf

Abstract

More than 900,000 Rohingya refugees are living in extremely congested camps in Cox’s Bazar, Bangladesh. Since their arrival in Bangladesh, they have been dependent on humanitarian aid for their survival, including food, core-relief items, shelter and other basic services. From January to the end of August 2021, UNHCR distributed 726 Core Relief Item kits to newly arrived refugee families. Each such kit includes tarpaulins for shelter construction, a kitchen set, blanket, jerry can, bucket, sleeping mat and solar lamp. UNHCR conducts Post Distribution Monitoring (PDM) to collect refugees’ feedback on the quality, sufficiency, utilization, and effectiveness of the assistance we provide, which helps improve and adapt the services as per the refugees’ needs. For this PDM exercise, a mixed methodology incorporating both qualitative and quantitative methods was used. By qualitative method, FGD (Focus Group Discussion) disaggregated by gender and age were conducted with recipients of NFI in 16 camps. 49 groups discussions for NFI recipients took place between 03 and 20 October 2021. The PDM survey found that items distributed by UNHCR and partners, including NFI items, WASH Hygiene kits, Female Hygiene kits and LPG generally met the declared household needs and the minimum quality standards for NFIs as approved by the Bangladesh Shelter/NFI Sector in Cox's Bazar. LPG, NFI, WASH and female hygiene kit distribution has considerably improved from last year’s PDM with an average of 97% satisfied with the organisation of the distribution. 81% of respondents reported receiving enough quantity of NFI which represents 4% decrease compared to last year.

Geographic coverage

The survey covers 16 camps in Cox's Bazar, Bangladesh

Analysis unit

Households

Universe

Recipients of NFIs in Cox' Bazar in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

Probabilistic and non-probabilistic samplings were used to prepare the samples and groups for the interviews. Stratified random sampling (probability sampling) methodology was used to create the sample of households to be interviewed using camp as a stratum with head of household gender consideration. The NFI sample size was determined from the recipient households on 95% confidence level and 5% margin of error. The sample was increased by 15% for contingency and non-response rate purposes. A total of 2139 households randomly selected were interviewed.

Mode of data collection

Face-to-face [f2f]

Stata module that implements Potter's (1990) weight distribution approach to trim extreme sampling weights. The basic idea is that the sampling weights are assumed to follow a beta distribution. The parameters of the distribution are estimated from the moments of the observed sampling weights and the resulting quantiles are used as cut-off points for extreme sampling weights. The process is repeated a specified number of times (10 by default) or until no sampling weights are more extreme than the specified quantiles.

This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models., Web of Science and SCOPUS databases were searched on the 13/02/2023 using the following search string: ALL=(("species distribution*" OR SDM OR "environmental niche" OR ENM OR "resource selection" OR "habitat selection" OR suitability OR occurrence) AND ("presence-only" OR “presence data†OR "presence-background" OR “pseudo absence†OR opportunistic OR “citizen science†OR preferential OR maxent OR biomod)) After removing duplicates, the search returned 8564 unique studies, and these were further filtered to remove studies that fell outside of the review subject area based on the title and abstract and then the remaining studies were filtered by content based on the criteria that they involved the building of SDMs using PO data (i.e. no absence information, including inferred absences from complete species lists) and that the study included a direct comparison between SDMs that attempted to correct models for SSB and models without this correction. To avoid ambiguity, studies were requir..., The file can be opened with any software capable of reading a .csv file., ---

title: Data for the meta-analysis of the effects of spatial sampling bias correction on presence-only species distribution models. output: pdf_document: default

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This dataset contains information extracted from 220 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the usage and effect of spatial sampling bias correction methods in presence-only species distribution models.

Description of the Data and file structure

The dataset contains the following columns:

• study_id = Unique number for identifying each study.
• bibtexkey = Author/year key.
• journal = Name of journal publishing the study.
• title = Title of article.
• volume = Volume number of published study.
• year = Year of publication for study.
• doi = doi of study.
• focal_kingdom = Kingdom of focal species (all named, separated by '|').
• f...

Water quality sample year information to be used in conjunction with CMI Water Quality Sampling Sites. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

The dataset presents the results of the validation of the method by intensity fluorimetry to quantify formaldehyde contents in samples of fresh white cheese. Additionally, the results of the analysis of 412 cheese samples over a period of 12 months are shown. Of the total samples, 32.9% (n=135) have quantified levels of formaldehyde and distributed in four seasons: late dry, transitional dry to rainy, rainy, transitional rainy to dry and early dry. The highest percentages of formaldehyde-positive samples are concentrated in the seasons with the highest temperature values of the year: late dry (60.9%, 27.5 °C) and dry to rainy transitional season (79.7%, 28.3 °C) and tend to decrease in rainy (25.4%, 26.9 °C) and in rainy to dry transition (1.5%, 26.7 °C), characterized by having the lowest temperature records. The association between the prevalence of formaldehyde-positive samples and temperature was shown to be statistically significant, providing evidence that would indicate the use of formaldehyde to prevent the deterioration of milk and/or dairy product on the shelf due to elevated temperature values.

This dataset provides O2/N2, CO2, Ar/N2, and stable isotope ratios of CO2 measured in flasks collected by the Medusa Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Medusa collected 32 cryogenically dried, flow, and pressure-controlled samples per flight. The samples are collected by an automated sampler into 1.5 L glass flasks that integrate over 25 seconds. Medusa provides discretely-sampled comparisons for onboard in situ O2/N2 ratio and CO2 measurements and unique measurements of Ar/N2 and 13C, 14C, and 18O isotopologues of CO2. Medusa flasks are analyzed on a sector-magnet mass spectrometer and a LiCor non-dispersive infrared CO2 analyzer by the Scripps O2 Program at Scripps Institution of Oceanography.

Highest mean environmental characteristics, as identified by single variable ANOVA’s, in bold.

This product summarizes the collection and analysis of bed material sample grain size distribution collected from the Iron Gate, Copco, and J.C. Boyle Reservoirs located in Northern California and Southern Oregon on the Klamath River. Samples were collected on June 16, 2020 from cores (less than 1m depth) and processed for the full size distribution.

Biotic interactions have been rarely included in traditional species distribution models, wherein Joint Species Distribution Models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.

This dataset provides atmospheric concentrations of halocarbons and hydrocarbons measured by the UC-Irvine Whole Air Sampler (WAS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The analysis of samples from the UCI WAS provides measurements of more than 50 trace gases, including C2-C10 NMHCs, C1-C2 halocarbons, C1-C5 alkyl nitrates, and selected sulfur compounds. Species were identified and measured using an established technique of airborne whole air sampling followed by laboratory analysis using gas chromatography (GC) with flame ionization detection (FID), and mass spectrometric detection (MSD). The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a Master Sample of Primary Sampling Units (PSUs) which comprises of EAs that are drawn from across the country.

The sample is designed to be representative at the provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Mode of data collection

Face-to-face [f2f]

Polyethylene passive samplers were used to detect the vertical distribution of truly dissolved POPs at two sites in the Atlantic Ocean. Samplers were deployed at five depths covering 26-2535 m in the northern Atlantic and Tropical Atlantic, in approximately one year deployments. Samplers of different thickness were used to determine the state of equilibrium POPs reached in the passive samplers. Concentrations of POPs detected in the North Atlantic near the surface (e.g., sum of 14 polychlorinated biphenyls, PCBs: 0.84 pg L-1) were similar to previous measurements. At both sites, PCB concentrations showed subsurface maxima (tropical Atlantic Ocean -800 m, North Atlantic -500 m).

Abstract

The Vietnam Multiple Indicator Cluster Survey (MICS 2011) was conducted from December 2010 to January 2011 by the General Statistics Office of Vietnam, in collaboration with the Ministry of Health (MOH) and the Ministry of Labour, Invalids and Social Affairs (MOLISA). Financial and technical support for the survey was provided by the United Nations Children's Fund (UNICEF). Financial support was also provided by the United Nations Population Fund (UNFPA) in Vietnam.

MICS 2011 gives valuable information and the latest evidence on the situation of children and women in Vietnam, updating information from the previous 2006 Vietnam MICS survey as well as earlier data collected in the first two MICS rounds carried out in 1996 and 2000.

The survey presents data from an equity perspective by indicating disparities by sex, region, area, ethnicity, living standards and other characteristics. MICS 2011 is based on a sample of 11,614 households interviewed and provides a comprehensive picture of children and women in Vietnam's six regions.

Geographic coverage

National

Analysis unit

• individuals,
• households.

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Vietnam MICS 2011 was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the six regions of Vietnam: Red River Delta, Northern Midlands and Mountainous areas, North Central area and Central Coastal area, Central Highlands, South East and Mekong River Delta. Urban and rural areas in each of the six regions were designated as the sampling strata.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Vietnam MICS 2011 was calculated as 12,000 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children aged 0-4 years.

The resulting number of households from this exercise was 2,050 households which is the sample size needed in each region - thus yielding about 12,000 in total. The average number of households selected per cluster for the Vietnam MICS 2011 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 100 sample clusters would need to be selected in each region.

Equal allocation of the total sample size to the six regions was used. Therefore, 100 clusters were allocated to each region, with the final sample size calculated at 12,000 households (100 clusters * 6 regions * 20 sample households per cluster). In each region, the clusters (primary sampling units) were distributed to urban and rural domains, proportional to the size of urban and rural populations in that region.

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2011 - Final Report" pp.215-218.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, insecticide treated bednets, indoor residual spraying, child labour, child discipline, handwashing and salt Iodisation.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. The questionnaire for children under 5 years of age was administered to mothers or caregivers of all children under 5 years of age living in the households.

The women's questionnaire includes woman's background, child mortality, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior and HIV/AIDS.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria, immunization and anthropometry.

Cleaning operations

Data were entered using CSPro software on eight small computers. Ten operators working in shifts performed data entry under supervision of two data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS 4 programme and adapted to the Viet Nam questionnaire were used throughout. Data processing began on 27 December 2010 and was completed on 21 March 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 19. The model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 12,000 households selected for the sample, 11,642 were present at the time of the survey. Of these, 11,614 successfully completed the interview, resulting in a household response rate of 99.8 percent. In the interviewed households, 12,115 women (aged 15-49 years) were identified. Of these, 11,663 completed the interview, yielding a response rate of 96.3 percent compared to eligible respondents in interviewed households. In addition, 3,729 children under 5 years were listed in the household questionnaire. Questionnaires were completed for 3,678 of these children, which corresponds to a response rate of 98.6 percent within interviewed households. The overall response rates (household response rate times the woman and child response rates within households) were 96 and 98.4 percent for the survey of women and of children under 5 years of age, respectively.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of the household population
• Age distribution of eligible and interviewed women
• Age distribution of children under 5 in household and children under 5 questionnaires
• Completeness of reporting
• Completeness of information for anthropometric indicators
• Heaping in anthropometric measurements
• Observation of bednets places for hand washing
• Observation of women's health cards
• Observation of children under 5 birth certificates
• Observation of vaccination cards
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• Selection of children age 2-14 years for the child discipline module
• School attendance by single age
• Sex ratio at birth among children ever born and living

The results of each of these data quality tables are shown in appendix D in document "Multiple Indicator Cluster Survey 2011 - Final Report"

A key task in understanding and mapping the complex mass transport pathways and potential transformation processes of contaminants in coastal regions such as the German Bight is to determine and evaluate the most significant contribution sources into coastal areas. Rivers represent one key input source within this context. As part of a river campaign in June 2016, sediment and freshwater samples were taken from the Weser river and its tributaries to identify their elemental and isotopic fingerprint and to investigate potential inputs to the German Bight. All sediment samples were taken using a Van Veen grab sampler and were analyzed for their grain size distribution by laser diffraction.

Abstract

The Nepal Multiple Indicator Cluster Survey (NMICS) 2010 is a subnational survey of 7,372 women aged 15–49 years and 3,574 children under five from 6,000 households in the Mid- and Far Western Regions (MFWR) of Nepal. NMICS 2010 was implemented as part of the fourth round of the global MICS household survey programme with technical and financial support from UNICEF Nepal in collaboration with the Government of Nepal. The main purpose of NMICS 2010 is to support the government to generate statistically sound and comparable data for monitoring the situation of children and women in the MFWR of the country. NMICS 2010 covers topics related to nutrition, child health, water and sanitation, reproductive health, child development, literacy and education, child protection, HIV and AIDS, mass media and the use of information and communication technology, attitude towards domestic violence, the use of tobacco and alcohol, and life satisfaction. In addition, NMICS 2010 is the first survey in Nepal to provide baseline information on the prevalence of chaupadi (women who live in a separate house or animal shed during menstruation) in the MFWR and evidence on women’s life satisfaction.

Geographic coverage

Mid- and Far- Western regions, both urban and rual areas. (Mid-Western Mountains, Mid-Western Hills, Mid-Western Terai, Far Western Mountains, Far Western Hills,and Far Western Terai)

Analysis unit

• individuals
• households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for NMICS 2010 was to produce statistically reliable estimates of most indicators at each of the six subregions: Mid-Western Mountains, Mid-Western Hills, Mid-Western Terai, Far Western Mountains, Far Western Hills and Far Western Terai. It also provides estimates in aggregate at urban and rural areas of the combined Mid- and Far Western Regions of Nepal. In subregions where urban areas exist, (i.e., four of six subregions), urban and rural areas were defined as the sampling strata.

A two-stage, cluster sampling design was used for the selection of the survey sample.

The target sample size for NMICS 2010 was calculated as 6,000 households. For the calculation of the sample size, the key indicator used was the comprehensive knowledge about the HIV transmission among women aged 15-49 years.

The resulting number of households from this exercise was 1,000 households, which is the sample size needed in each subregion-thus yielding about 6,000 in total. The average number of households selected per cluster for NMICS 2010 was determined as 25 households, based on a number of considerations, including the design effect, intra-class correlation coefficient, 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 40 sample clusters would need to be selected in each subregion.

Equal allocation of the total sample size to the six subregions was used. Therefore, 40 clusters were allocated to each subregion, with the final sample size calculated at 6,000 households (40 clusters * 6 subregions * 25 sample households per cluster). In each subregion, the clusters (primary sampling units) were distributed to urban and rural domains, proportional to the size of urban and rural households in that subregion.

The sampling procedures are more fully described in "Nepal Multiple Indicator Cluster Survey 2010 - Final Report" pp.196-200.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010 Nepal MICS used the standard MICS4 questionnaires and included several country-specif ic questions and modules. Three sets of questionnaires were used in the survey.

Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, child labour, de-worming (Nepal-specific module), child discipline, handwashing and salt iodisation.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. The questionnaire for children under 5 years of age was administered to mothers or caregivers of all children under 5 years of age living in the households.

The women's questionnaire includes woman's background, access to mass media and use of information communication technology, desire for last birth, maternal and newborn health, illness symptoms, contraception, unmet need, attitudes toward domestic violence (Nepal-specific module), marriage/union, HIV/AIDS Tobacco and alcohol use and life satisfaction.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria, immunization and child grant (Nepal-specific module).

Cleaning operations

Data were entered using the CSPro software on four microcomputers by four data-entry operators and two data-entry supervisors. In order to ensure a high level of quality control, all questionnaires were double-entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the Nepal questionnaires were used throughout. Data entry started in November 2010 and was completed in March 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software programme, Version 18. The model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 6,000 households selected for the sample, 5,917 were found to be occupied. Of these, 5,899 were successfully interviewed, giving a household response rate of 99.7 percent. In interviewed households, 7,674 women (aged 15–49 years) were identified. Of these, 7,372 were successfully interviewed, yielding a response rate of 96.1 percent within interviewed households. In addition, 3,688 children under five were listed in the household questionnaire. Questionnaires were completed for 3,574 of these children, giving a response rate of 96.9 percent within interviewed households. Overall response rates of 95.8 percent and 96.6 percent are calculated for women’s and under-fives’ interviews, respectively. Response rates for households, women and children under five were similar (above 95 percent) between urban/rural areas and across all subregions.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: • Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. • Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error. • Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. • Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from NMICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the sub national level, for the subregions, and for urban and rural areas. One of the selected indicators is based on households, five are based on household members, 15 are based on women, and 15 are based on children under five. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of the household population
• Age distribution of eligible and interviewed women
• Age distribution of under-5s in household and under-5 questionnaires
• Women's completion rates by socio-economic characteristics of households

- Completion rates for under-5 questionnaires by socio-economic characteristics of households

This report describes the results of a bee survey coordinated by Leo Shapiro under contract with USFWS, working in close collaboration with Sam Droege of Patuxent Wildlife Research Center (USGS). We had two main goals for this pilot survey of native bees on selected USFWS Region 5 units:(1) Obtain initial assessments of bee species richness and diversity on fields across Region 5 refuge properties.(2) Establish and test protocols for large-scale bee sampling using geographically distributed volunteers. The appropriateness and practicality of both our statistical sampling design and our methods for handling the mechanics of processing, identifying, and databasing a large flow of samples must be demonstrated before we scale up further. One question of particular interest was whether four fields elected and sampled on a refuge by volunteers could reasonably be treated as statistical replicate samples from that refuge.