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.
The 2006 Uzbekistan Multiple Indicator Cluster Survey has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in Uzbekistan
- 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;
- To contribute to the improvement of data and monitoring systems in Uzbekistan and to strengthen technical expertise in the design, implementation, and analysis of such systems.
MICS 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.
The survey was conducted by the State Statistical Committee of the Republic of Uzbekistan, 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, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.
Kind of data
Sample survey data [ssd]
Version 1.0: Edited data used for preliminary report
Water and sanitation
Maternal and newborn health
Marriage and union
Care of illness
Durability of housing
Source and cost of supplies
The survey is a nationally representative for households, women, and children of Uzbekistan.
Unit of analysis
Households (defined as a group of persons who usually live and eat together)
De 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)
Women aged 15-49
Children aged 0-4
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.
Producers and sponsors
State Statistical Committee of the Republic of Uzbekistan
State Statistical Committee of the Republic of Uzbekistan
Technical implementation and supervision
UNICEF, Uzbekistan Country Office
UNICEF Regional MICS coordinator
International technical assistance
UNICEF Regional M&E officer
International technical assistance
Strategic Information Section, Division of Policy and Planning, UNICEF NYHQ
International technical assistance
Funding of survey implementation
Funding of survey implementation
The sample for the Uzbekistan Multiple Indicator Cluster Survey was designed to provide estimates
for a large number of indicators on the situation of children and women at the national
level, for urban and rural areas, and for six geo-economical regions of the country, as follows:
1. Western: (Republic of Karakalpakstan & Khorezm oblast)
2. Central: (Bukhara, Navoi & Samarkhand oblasts)
3. Southern: (Kashkadarya & Surkhandarya oblasts)
4. Central-Eastern: (Jjizzakh, Syrdarya & Taskentskaya oblasts)
5. Eastern: (Andizhan, Namangan & Fergana oblasts)
6. Tashkent city
Regions were identified as the main sampling domains and the sample was selected in three stages. At the first stage, 375 primary sampling units were selected with probability proportional to size from a master frame of 14,799 enumeration areas called "mahala" produced by a countrywide population review, conducted by the State Statistical Committee (SSC) in 2002. The list of selected enumeration areas served as the frame for the second stage of selection. Each enumeration area was assigned a measure of size equal to the desired number of "standard segments" it contains by dividing the population size of the enumeration area by 500 and rounding to the nearest whole number. One segment was randomly selected on the basis of a sketch-map prepared for each enumeration area. After a household listing was carried out within the selected segments, a systematic sample of 10,505 households was drawn. All selected enumeration areas were successfully visited.
The distribution of clusters between sampling domains is not proportional to the distribution of population and, consequently neither is the fi nal household distribution. The sample is therefore not a self-weighting household sample. For reporting national level results, sample weights are used.
Following 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 interviewd.
No replacement of households was permitted in case of non-response or non-contactable households. Adjustments were made to the sampling weights to correct for non-response, according to MICS standard procedures.
The sampling procedures are more fully described in the sampling design document and the sampling appendix of the final report.
Deviations from sample design
No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.
Of the 10,505 households selected for the sample, 10,349 were found to be occupied. Of these, 10,198 were successfully interviewed resulting in a household response rate of 98.5 percent. In the interviewed households, 14,205 women (age 15–49) were identifi ed. Of these, 13,919 were successfully interviewed, yielding a response rate of 98 percent. In addition, 5,039 children under age fi ve were listed in the household questionnaire. Questionnaires were completed for 4,986 of these children, which corresponds to a response rate of 98.9 percent. Overall response rates of 96.6 and 97.5 are calculated for the women’s and under-5’s interviews respectively
There are no significant differences in response rates according to regions and urban rural residence. Household, woman and children questionnaires’ response rates are all 95 percent or higher across different regions and urban and rural areas.
The Uzbekistan Multiple Indicator Cluster Survey sample is not self-weighted. Essentially, by allocating equal numbers of households to each of the regions, different sampling fractions were used in each region since the size of the regions varied. For this reason, sample weights were calculated and these were used in the subsequent analyses of the survey data. The major component of the weight is the reciprocal of the sampling fraction employed in selecting the number of sample households in that particular sampling domain
Sample weights were calculated for each of the datafiles.
Sample 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.
Sample 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.
Sample 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.
Dates of collection
Mode of data collection
Data collection supervision
Interviewing was conducted by teams of interviewers. Each interviewing team comprised of 3-4 female interviewers, a field editor and a supervisor, and a driver. Each teams used a 4 wheel dirve vehicle to travel from cluster to cluster (and where necessary within cluster).
Senior staff from the SSC and two national fieldwork coordinators coordinated and supervised the field work activities. An external supervision programme was set up to monitor and provide assistance to the survey field work activities.
The role of the supervisor was to coordinator 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
The 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.
Responsibilities 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.
Field visits were also made by a team of central staff on a periodic basis during fieldwork. The senior staff of SSC also made 3 visits to field teams to provide support and to review progress.
The questionnaires for the Uzbekistan MICS were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship. The household questionnaire includes household characteristics, education, child labour, water and sanitation, and salt iodization, with optional modules child disability, maternal mortality and durability of housing.
In 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.
The women's questionnaire include women's characteristics, child mortality, maternal and newborn health, marriage, contraception, and HIV/AIDS knowledge, with optional modul for sexual behavior.
The children's questionnaire includes children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, immunization, and anthropometry, with an optional module for child development and county specific immunization module by data from medical institution
The questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Russian and Uzbek. 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 and Uzbek version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators.
The English and Russian and Uzbek questionnaires were both piloted as part of the survey pretest.
All questionnaires and modules are provided as external resources.
State Statistical Committee
Data editing took place at a number of stages throughout the processing (see Other processing), including:
a) Office editing and coding
b) During data entry
c) Structure checking and completeness
d) Secondary editing
e) Structural checking of SPSS data files
Detailed documentation of the editing of data can be found in the data processing guidelines
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:
1) Questionnaire reception
2) Office editing and coding
3) Data entry
4) Structure and completeness checking
5) Verification entry
6) Comparison of verification data
7) Back up of raw data
8) Secondary editing
9) 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:
10) Export to SPSS in 4 files (hh - household, hl - household members, wm - women, ch - children under 5)
11) Recoding of variables needed for analysis
12) Adding of sample weights
13) Calculation of wealth quintiles and merging into data
14) Structural checking of SPSS files
15) Data quality tabulations
16) Production of analysis tabulations
Details 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.
Data were entered on six microcomputers using the CSPro software. In order to ensure quality control, double entry of questionnaires was considered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS3 project and adapted to the Uzbekistan questionnaire were used throughout. An additional set of data quality control tables was developed by the data collection team and was used along the data entry to monitor the quality of incoming data and provide feedback to data collection teams. Data processing began simultaneously with data collection in April 2006 and finished in early June 2006.
All data entry was conducted at the SSC head office using manual data entry. 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.
Structure 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.
100% 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.
After completion of all processing in CSPro, all individual cluster files were backed up before concatenating data together using the CSPro file concatenate utility.
For tabulation and analysis SPSS versions 10.0 and 14.0 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.
After 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.
Primary and secondary data processing programmes can be found under technical documents.
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 2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors can be evaluated statistically. The sample of respondents to the 2006 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. The 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.
If 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 2006 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 2006 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.
Sampling 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).
Details 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.
Other forms of data appraisal
A series of data quality tables and graphs are available to review the quality of the data and include the following:
Age distribution of the household population
Age distribution of eligible women and interviewed women
Age distribution of eligible children and children for whom the mother or caretaker was interviewed
Age distribution of children under age 5 by 3 month groups
Age and period ratios at boundaries of eligibility
Percent of observations with missing information on selected variables
Presence of mother inthe household and person interviewed for the under 5 questionnaire
School attendance by single year age
Sex ratio at birth among children ever born, surviving and dead by age of respondent
Distribution of women by time since last birth
Scatterplot of weight by height, weight by age and height by age
Graph of male and female population by single years of age
The 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.
The 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).
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.
Survey datasets are distributed at no cost for legitimate research, with the condition that we receive and approve 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 UNICEF email@example.com.
Requests for access to the datasets may be made through the website www.childinfo.org.
State Statistical Committee of the Republic of Uzbekistan. Multiple Indicator Cluster Survey: Household , household listing, women and children's files, 2006 [Computer file]. Tashkent, Uzbekistan: State Statistical Committee of the Republic of Uzbekistan [producer], 2006. Tashkent, Uzbekistan: State Statistical Committee of the Republic of Uzbekistan and New York: Strategic Information Section, Dvision of Policy and Planning, UNICEF [distributors], 2006.
Disclaimer and copyrights
SSC and UNICEF provides these data to external users without any warranty or responsibility implied. SSC and UNICEF accepts no responsibility for the results and/or implications of any actions resulting from the use of these data.
State Statistical Committee of the Republic of Uzbekistan
Djamila de Vaulgrenant
Blancroft Research International
Producer of Generic example
Customization of Uzbekistan archive
Adaption of Uzbekistan archive for childinfo.org
Uzbekistan MICS 2006 v0.1
Slightly edited version of UNICEF's DDI ref. DDI-UZB-CSO-MICS2006/1.0-v0.1