{"doc_desc":{"title":"Belarus Multiple Indicator Cluster Survey 2005","idno":"DDI_BLR_2005_MICS_v01_M","producers":[{"name":"Konoshonok, Inna","abbreviation":"MINSTAT","affiliation":"Ministry of Statistics and Analysis of the Republic of Belarus ","role":"Producer of MICS3 Belarus Archive"},{"name":"Mufel, Natalia","abbreviation":"UNICEF","affiliation":"UNICEF, Belarus ","role":"Coodination of data archiving proccess"},{"name":"Croft, Trevor","abbreviation":"TNC","affiliation":"Blancroft Research International","role":"Producer of generic MICS example archive"},{"name":"Bjelic, Ivana","abbreviation":"SMMRI","affiliation":"Strategic Marketing","role":"Data archiving consultant"},{"name":"James, Rhiannon","abbreviation":"SMMRI","affiliation":"UNICEF","role":"Adaption od MICS3 Belaris Archive for www.childinfo.org"}],"prod_date":"2008-01-31","version_statement":{"version":"Belarus MICS UNICEF 2005 v0.6\nSlightly edited version of UNICEF's DDI ref. DDI-BLR-UNICEF-MICS2005\/1.0-v0.1"}},"study_desc":{"title_statement":{"idno":"BLR_2005_MICS_v01_M","title":"Multiple Indicator Cluster Survey 2005","alt_title":"MICS3 2005"},"authoring_entity":[{"name":"Ministry of Statistics and Analysis of the Republic of Belarus","affiliation":""},{"name":"Research Institute of Statistics","affiliation":""}],"oth_id":[{"name":"The Ministry of Health","affiliation":"","email":"","role":"Technical assistance to the Ministry of Statistics and Analysis of the Republic of Belarus in receiving from medical institutions the lists of the households with children under five and the immunization cards"},{"name":"Galina Gasyuk","affiliation":"The Ministry of Statistics and Analysis","email":"","role":"Survey Coordinator"},{"name":"Svetlana Novoselova","affiliation":"The Ministry of Statistics and Analysis","email":"","role":"Survey Coordinator"},{"name":"Victor Tamashevich","affiliation":"Research Institute of Statistics ","email":"","role":"Technical Director"},{"name":"Irina Bulgakova","affiliation":"The Ministry of Statistics and Analysis","email":"","role":"Sampling Expert"},{"name":"Olga Yakimovich","affiliation":"The Ministry of Statistics and Analysis","email":"","role":"Sampling Expert"},{"name":"Inna Konoshonok","affiliation":"The Ministry of Statistics and Analysis","email":"","role":"Data Processing Expert"}],"production_statement":{"producers":[{"name":"Ministry of Statistics and Analysis of the Republic of Belarus","affiliation":"","role":"Technical implementation and supervision"},{"name":"Research Institute of Statistics","affiliation":"Ministry of Statistics and Analysis of the Republic of Belarus","role":"Methodological conceptualization of the survey"},{"name":"UNICEF, Belrus Country Office","affiliation":"UNICEF","role":"Technical assistance"},{"name":"UNICEF Regional MICS coordinator","affiliation":"UNICEF","role":"International technical assistance"},{"name":"UNICEF Regional M&E officer","affiliation":"UNICEF","role":"International technical assistance"},{"name":"Strategic Information Section, Division of Policy and Planning, UNICEF NYHQ","affiliation":"UNICEF","role":"International technical assistance"}],"copyright":"2007, The Ministry of Statistics and Analysis of the Republic of Belarus","prod_date":"2006-07-20","funding_agencies":[{"name":"UNICEF HQ","abbreviation":"UNICEF","role":"Funding of survey implementation"},{"name":"UNICEF CO","abbreviation":"UNICEF","role":"Funding of survey implementation"},{"name":"Organisation for Economic Co-operation and Development","abbreviation":"OECD","role":"Financial and technical support in data archiving"}],"grant_no":"GC\/2005\/6014-01 GC\/2005\/6014-01"},"distribution_statement":{"contact":[{"name":"Bulgakova, Irina","affiliation":"The Ministry of Statistics and Analysis of the Republic of Belarus","email":"housewife1@mail.ru","uri":"www.belstat.gov.by"},{"name":"Novoselova, Svetlana","affiliation":"The Ministry of Statistics and Analysis of the Republic of Belarus","email":"housewife1@mail.ru","uri":"www.belstat.gov.by"},{"name":"Mufel, Natalia","affiliation":"UNICEF Belarus","email":"nmufel@unicef.org","uri":"www.unicef.org"},{"name":"Hancioglu, Attila","affiliation":"UNICEF","email":"ahancioglu@unicef.org","uri":"www.childinfo.org"}]},"series_statement":{"series_name":"Multiple Indicator Cluster Survey - Round 3 [hh\/mics-3]","series_info":"UNICEF assists countries in collecting and analyzing data in order to fill data gaps for monitoring the situation of children and women through its international household survey initiative the Multiple Indicator Cluster Surveys (MICS).\n\nMICS surveys are typically carried out by government organizations, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops where experts from developing countries are trained on various aspects of MICS (questionnaire content, sampling and survey implementation, data processing, data quality and data analysis, and report writing and dissemination).\n\nSince the mid-1990s, the MICS has enabled many countries to produce statistically sound and internationally comparable estimates of a range of indicators in the areas of health, education, child protection and HIV\/AIDS. MICS findings have been used extensively as a basis for policy decisions and programme interventions, and for the purpose of influencing public opinion on the situation of children and women around the world.\n\nMICS1 (1995) - The MICS was originally developed in response to the World Summit for Children to measure progress towards an internationally agreed set of mid-decade goals. The first round of MICS was conducted around 1995 in more than 60 countries.\n \nMICS2 (2000) - A second round of surveys was conducted in 2000 (around 65 surveys), and resulted in an increasing wealth of data to monitor the situation of children and women. For the first time it was possible to monitor trends in many indicators and set baselines for other indicators. \n \nMICS3 (2005-2006) - The third round of MICS, which was carried out in over 50 countries in 2005-06, has been an important data source for monitoring the Millennium Development Goals with 21 MDG indicators collected through MICS3 (particularly indicators related to health, education and mortality). MICS3 was also a monitoring tool for other international goals including the World Fit for Children, the UNGASS targets on HIV\/AIDS and the Abuja targets for malaria.\n\nMICS4 (2009-2011) - In response to an increased demand for data all over the world, starting from MICS4, UNICEF will be prepared to provide assistance to countries at more frequent intervals - every three years instead of every five years. This will provide the opportunity for countries to capture rapid changes in key indicators, particularly the MDGs."},"version_statement":{"version":"Version 1.0: Edited data used for final report","version_date":"2007-12-13"},"study_info":{"topics":[{"topic":"Household members","vocab":"MICS Topics","uri":""},{"topic":"Education","vocab":"MICS Topics","uri":""},{"topic":"Water and sanitation","vocab":"MICS Topics","uri":""},{"topic":"Household characteristics","vocab":"MICS Topics","uri":""},{"topic":"Child labour","vocab":"MICS Topics","uri":""},{"topic":"Child discipline","vocab":"MICS Topics","uri":""},{"topic":"Women's background","vocab":"MICS Topics","uri":""},{"topic":"Child mortality","vocab":"MICS Topics","uri":""},{"topic":"Maternal and newborn health","vocab":"MICS Topics","uri":""},{"topic":"Marriage and union","vocab":"MICS Topics","uri":""},{"topic":"Contraception","vocab":"MICS Topics","uri":""},{"topic":"HIV\/AIDS","vocab":"MICS Topics","uri":""},{"topic":"Children's background","vocab":"MICS Topics","uri":""},{"topic":"Early learning","vocab":"MICS Topics","uri":""},{"topic":"Breastfeeding","vocab":"MICS Topics","uri":""},{"topic":"Care of illness","vocab":"MICS Topics","uri":""},{"topic":"Immunization","vocab":"MICS Topics","uri":""},{"topic":"Anthropometry","vocab":"MICS Topics","uri":""}],"abstract":"The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV\/AIDS and the Abuja targets for malaria. \n\nSurvey Objectives \nThe 2005 Belarus Multiple Indicator Cluster Survey has as its primary objectives: \n- To provide up-to-date information for assessing the situation of children and women in Belarus \n- To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; \n- To contribute to the improvement of data and monitoring systems in Belarus and to strengthen technical expertise in the design, implementation, and analysis of such systems. \n\nSurvey Content \nMICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire. \n\nSurvey Implementation\nThe survey was carried out by the Ministry of Statistics and Analysis of the Republic of Belarus, and Research Institute of Statistics of the Ministry of Statistics and Analysis of the Republic of Belarus with the support and assistance of UNICEF and Ministry of Health. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.","coll_dates":[{"start":"2005-11-01","end":"2005-12-20","cycle":""}],"nation":[{"name":"Belarus","abbreviation":"BLR"}],"geog_coverage":"The survey is nationally representative and covers the whole of Belarus.","analysis_unit":"Households (defined as a group of persons who usually live and eat together) \n\nDe jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household) \n\nWomen aged 15-49 \n\nChildren aged 0-4","universe":"The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.","data_kind":"Sample survey data [ssd]","notes":"The Belarus Multiple Indicator Cluster Survey included the following modules in the questionnaires: \n\nHOUSEHOLD QUESTIONNAIRE : Household characteristics, household listing, education, child labour, water and sanitation, child discipline. \n\nWOMEN'S QUESTIONNAIRE: Women's characteristics, child mortality, maternal and newborn health, marriage\/union, contraception and unmet need, and HIV\/AIDS knowledge. \n\nCHILDREN'S QUESTIONNAIRE: Children's characteristics, early learning, breastfeeding, care of illness, immunization, and anthropometry."},"method":{"data_collection":{"data_collectors":[{"name":"Ministry of Statistics and Analysis Republic of Belarus Ministry of Statistics and Analysis Republic of Belarus Ministry of Statistics and Analysis Republic of Belarus The Ministry of Statistics and Analysis of the Republic of Belarus","abbreviation":"MINSTAT","affiliation":""},{"name":"Research Institute of Statistics","abbreviation":"","affiliation":"Ministry of Statistics and Analysis  Republic of Belarus Ministry of Statistics and Analysis  Republic of Belarus Ministry of Statistics and Analysis  Republic of Belarus The Ministry of Statistics and Analysis of the Republic of Belarus"}],"sampling_procedure":"The principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV\/AIDS. The population covered by the 2005 MICS is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child. \n\nThe 2005 MICS collected data from a nationally representative sample of households, women and children. The primary focus of the 2005 MICS was to provide estimates of key population and health, education, child protection and HIV related indicators for the country as a whole, and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 7 regions for key indicators. Belarus is divided into 7 regions. Each region is subdivided into big cities, small towns and rural areas (selskie sovety). In addition each unit was subdivided into polling stations in urban areas and rural settlements in selskie sovety. In total Belarus includes 20 big cities, 187 small cities and 1388 selskie soveties. \n\nMICS3 is utilizing the sample frame of household surveys that is being used in the republic. To provide uniform distribution of the sample allocation of the households in the republic the selection was carried out in Brest, Vitebsk, Gomel, Grodno, Minsk, Mogilev regions and in Minsk city. \n\nThree stage sampling has been carried out. At the first stage in each of the regions (oblasts) three sampling strata has been created: big cities, small towns and rural areas (selskie sovety); at the second stage - polling stations in urban areas and rural settlements in selskie sovety; at the third stage in the selected settlements the households were selected. Within the strata of big cities, at first stage, 20 big cities were selected with the probability equalling to 1. Within the strata of small towns 29 small towns were sampled systematically with pps and the measure of size was total population of the small towns. The number of small towns in every region (oblast) was selected based on division of the total number of population of all small towns of each region into average household size (2,6), sample share (1\/600) and average load of interviewer (40). \n\nWithin the strata of rural settlements (selskie sovety) at the first stage of sampling 53 rural settlements were selected systematically with pps and the measure of size was number of households in the rural settlement. \n\nOn the second stage of sampling within the big cities and the small towns the polling stations were selected as sampling unit, in the rural settlements - settlements in rural area (selskie sovety). \n\nTo cover the whole territory of the selected city the cartographical materials were used on the second stage of sampling within the big cities. The number of the polling stations was calculated based on division of the population of the city into the average size of the family (2,6), sample share (1\/600) and estimated number of the households in each polling station (20). \n\nThree polling stations were selected in each small town from the list of the polling stations, ranking by number of voters. In rural areas, taking into account the difficulty of access and scattered nature of settlements, the territories of the rural areas (selskie sovety) were divided into zones and the closest rural settlements were grouped. One zone was selected in each rural area (selskie sovety) and within this zone all settlements were investigated. \n\nThroughout the Republic of Belarus there were 304 polling stations and the rural zones in selskie sovery selected in 2005. \n\nOn the third stage of sampling, households were selected from the updated lists systematically taking into account the size of the cluster. In big cities the size of the cluster which is selected from the updated list households within the territory of polling station is 19-20 households, in small towns the size of the cluster is 13-14 households, and in rural areas the size of the cluster is 39-40 households.The size of clusters is not uniform. Variation in cluster sizes for urban and rural settlements was done on purpose since existing sampling plan was considering load of one interviewer, as one of the parameters, and distribution of sampled population into the sampling domains - proportionally to the distribution in general population. \n\nBesides, taking into account the limited representation of children under 5 in the household sample, the additional sub-sample of households with children aged 0-4 was formed. For this purpose, in each of the 304 clusters the lists of households was updated with the information on households with under 5 children through local out-patient health institutions. From these lists with higher probability then for households without children, the households with children aged 0-4 were selected. \n\nThe resulting number of households for MICS3 sample in the Republic of Belarus was 7,000, including 2,857 households with children aged 0-4. \n\nFollowing standard MICS data collection rules, if a household was actually more than one household when visited, then a) if the selected household contained two households, both were interviewed, or b) if the selected household contained 3 or more households, then only the household of the person named as the head was interviewed.","sampling_deviation":"No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.","coll_mode":"Face-to-face [f2f]","research_instrument":"The questionnaires for the Belarus MICS were structured questionnaires based on the MICS3 Model Questionnaire. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household listing, education, water and sanitation, household characteristics, child labour, and child discipline. \n\nIn addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child. \n\nThe women's questionnaire include women's characteristics, child mortality, maternal and newborn health, marriage\/union, contraception, and HIV\/AIDS. \n\nThe children's questionnaire includes children's characteristics, early learning, breastfeeding, care of Illness, immunization and anthropometry. \n\nThe questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Russian. After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Russian version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. \n\nThe Russian questionnaires were both piloted as part of the survey pretest. \n\nAll questionnaires and modules are provided as external resources.","coll_situation":"The pretest for the survey took place in Minsk city and Minsk region during September 2005. \n\nTraining for the fieldwork was conducted for 5 days in early November 2005. \n\nThe data were collected by 14 teams; each was comprised of 4-5 interviewers, one driver, one editor\/measurer and a supervisor. Two teams were working in each of the 7 regions (oblasts). Fieldwork began in November 2005 and concluded in December 2005. \n\nInterviews averaged 40 minutes for the household questionnaire, 25 minutes for the women's questionnaire, and 30 for the under five children's questionnaire (excluding the anthropometry). Interviews were conducted in Russian. \n\n14 staff members of The System of the Ministry of Statistics and Analysis provided overall fieldwork coordination and supervision. The overall field coordinators were Galina Gasyuk and Svetlana Novoselova.","act_min":"Interviewing was conducted by teams of interviewers. Each interviewing team comprised of 4-5 interviewers, one driver, one editor\/measurer and a supervisor. Each teams used a 4 wheel dirve vehicle to travel from cluster to cluster (and where necessary within cluster). \n\nThe role of the supervisor was to coordinate field data collection activities, including management of the field teams, supplies and equipment, finances, maps and listings, coordinate with local authorities concerning the survey plan and make arrangements for accomodation and travel. Additionally, the field supervisor assigned the work to the interviewers, spot checked work, maintained field control documents, and sent completed questionnaires and progress reports to the central office.\n\nThe field editor was responsible for reviewing each questionnaire at the end of the day, checking for missed questions, skip errors, fields incorrectly completed, and checking for inconsistencies in the data. The field editor also observed interviews and conducted review sessions with interviewers. \n\nResponsibilities of the supervisors and field editors are described in the Instructions for Supervisors and Field Editors, together with the different field controls that were in place to control the quality of the fieldwork.","weight":"Sample weights were calculated for each of the datafiles. \n\nSample weights for the household data were computed as the inverse of the probability of selection of the household, computed at the sampling domain level (urban\/rural within each region). The household weights were adjusted for non-response at the domain level, and were then normalized by a constant factor so that the total weighted number of households equals the total unweighted number of households. The household weight variable is called HHWEIGHT and is used with the HH data and the HL data. \n\nSample weights for the women's data used the un-normalized household weights, adjusted for non-response for the women's questionnaire, and were then normalized by a constant factor so that the total weighted number of women's cases equals the total unweighted number of women's cases. \n\nSample weights for the children's data followed the same approach as the women's and used the un-normalized household weights, adjusted for non-response for the children's questionnaire, and were then normalized by a constant factor so that the total weighted number of children's cases equals the total unweighted number of children's cases.","cleaning_operations":"Data editing took place at a number of stages throughout the processing (see Other processing), including: \na) Office editing and coding \nb) During data entry \nc) Structure checking and completeness \nd) Secondary editing \ne) Structural checking of SPSS data files \n\nDetailed documentation of the editing of data can be found in the data processing guidelines","method_notes":"Data were processed in clusters, with each cluster being processed as a complete unit through each stage of data processing. Each cluster goes through the following steps: \n1) Questionnaire reception \n2) Office editing and coding \n3) Data entry \n4) Structure and completeness checking \n5) Verification entry \n6) Comparison of verification data \n7) Back up of raw data \n8) Secondary editing \n9) Edited data back up After all clusters are processed, all data is concatenated together and then the following steps are completed for all data files: \n10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5) \n11) Recoding of variables needed for analysis \n12) Adding of sample weights \n13) Calculation of wealth quintiles and merging into data \n14) Structural checking of SPSS files \n15) Data quality tabulations \n16) Production of analysis tabulations \n\nDetails of each of these steps can be found in the data processing documentation, data editing guidelines, data processing programs in CSPro and SPSS, and tabulation guidelines. \n\nThe data were entered on four computers and carried out by 11 data entry operators and 5 data entry supervisors. All data entry was conducted at the MINSTAT head office using manual data entry. Data processing began simultaneously with data collection in December 2005 and was completed in January 2006. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach, that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included inthe data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses (\"Other\" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire. \n\nStructure and completeness checking ensured that all questionnaires for the cluster had been entered, were structurally sound, and that women's and children's questionnaires existed for each eligible woman and child. \n\n100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files. \n\nAfter completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility. \n\nFor tabulation and analysis SPSS version14.0 were used. Version 10.0 was originally used for all tabulation programs, except for child mortality. Later version 14.0 was used for child mortality, data quality tabulations and other analysis activities. \n\nAfter transferring all files to SPSS, certain variables were recoded for use as background characteristics in the tabulation of the data, including grouping age, education, geographic areas as needed for analysis. In the process of recoding ages and dates some random imputation of dates (within calculated constraints) was performed to handle missing or \"don't know\" ages or dates. Additionally, a wealth (asset) index of household members was calculated using principal components analysis, based on household assets, and both the score and quintiles were included in the datasets for use in tabulations."},"analysis_info":{"response_rate":"Of the 7,000 households selected for the sample, 100% were found to be occupied. Of these, 6,707 were successfully interviewed for a household response rate of 95.8% percent. In the interviewed households, 5,906 women (age 15-49) were identified. Of these, 5,895 were successfully interviewed, yielding a response rate of 99.8 percent. In addition, 3,051 children under age five were listed in the household questionnaire. Of these, questionnaires were completed for 3,051 which corresponds to a response rate of 100 percent. Overall response rates of 95.6% and 95.8% are calculated for the women's and under-5's interviews respectively. \n\nDifferentials in household response rates by regions were from 94.4 % in Mogilev region to 96.7% in Gomel region.","sampling_error_estimates":"Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically. \n\nSampling errors can be evaluated statistically. The sample of respondents to the 2005 MICS is only one of many possible 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 differe somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability in the results of the survey between all possible samples, and, although, the degree of variability is not known exactly, it can be estimated from the survey results.\nThe sampling erros are measured in terms of the standard error for a particular statistic (mean or percentage), which is the square root of the variance. Confidence intervals are calculated for each statistic within which the true value for the population can be assumed to fall. Plus or minus two standard errors of the statistic is used for key statistics presented in MICS, equivalent to a 95 percent confidence interval. \n\nIf the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the 2005 MICS sample is the result of a multi-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the 2005 MICS. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS. \n\nSampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+\/-2 standard errors). \n\nDetails of the sampling errors are presented in the sampling errors appendix to the report and in the sampling errors table presented in te external resources.","data_appraisal":"A series of data quality tables and graphs are available to review the quality of the data and include the following: \nAge distribution of the household population \nAge distribution of eligible women and interviewed women \nAge distribution of eligible children and children for whom the mother or caretaker was interviewed \nAge distribution of children under age 5 by 3 month groups \nAge and period ratios at boundaries of eligibility \nPercent of observations with missing information on selected variables \nPresence of mother inthe household and person interviewed for the under 5 questionnaire \nSchool attendance by single year age \nSex ratio at birth among children ever born, surviving and dead by age of respondent \nDistribution of women by time since last birth \n\nThe results of each of these data quality tables is shown in the appendix of the final report and is also given in the external resources section. \n\nThe general rule for presentation of missing data in the final report tabulations is that a column is presented for missing data if the percentage of cases with missing data is 1% or more. Cases with missing data on the background characteristics (e.g. education) are included in the tables, but the missing data rows are suppressed and noted at the bottom of the tables in the report (not in the SPSS output, however)."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"Users of the data agree to keep confidential all data contained in these datasets and to make no attempt to identify, trace or contact any individual whose data is included in these datasets.","required":"yes","form_no":"","uri":""}],"contact":[{"name":"Ministry of Statistics and Analysis Republic of Belarus Ministry of Statistics and Analysis Republic of Belarus Ministry of Statistics and Analysis Republic of Belarus The Ministry of Statistics and Analysis of the Republic of Belarus","affiliation":"","email":"minstat@mail.belpak.by","uri":"www.belstat.gov.by"}],"cit_req":"The Ministry of Statistics and Analysis of the Republic of Belarus, Belrus. Multiple Indicator Cluster Survey: Household , household listing, women and children's files, 2005 [Computer file]. Minsk, Belarus: The Ministry of Statistics and Analysis of the Republic of Belarus [producer], 2005. Minsk, Belarus: The Ministry of Statistics and Analysis of the Republic of Belarus and New York: Strategic Information Section, Division of Policy and Planning, UNICEF [distributors], 2007.","conditions":"Survey datasets are distributed at no cost for legitimate research, with the condition that we receive a description of the objectives of any research project that will be using the data prior to authorizing their distribution. Copies of all reports and publications based on the requested data must be sent to MINSTAT (housewife1@mail.ru) and UNICEF Belarus (nmufel@unicef.org) . \n\nRequests for access to the datasets may be made through the website www.childinfo.org.","disclaimer":"MINSTAT and UNICEF provides these data to external users without any warranty or responsibility implied. MINSTAT and UNICEF accepts no responsibility for the results and\/or implications of any actions resulting from the use of these data."}}},"schematype":"survey","tags":[{"tag":"noDOI"}]}