Multiple Indicator Cluster Survey 2005
Ukraine, 2005
Get Microdata
Reference ID
UKR_2005_MICS_v01_M
Producer(s)
State Statistics Committee
Created on
Oct 25, 2011
Last modified
Mar 29, 2019
Page views
11720
Downloads
2025
Identification
Survey ID Number
UKR_2005_MICS_v01_M
Title
Multiple Indicator Cluster Survey 2005
Country
| Name | Country code |
|---|---|
| Ukraine | UKR |
Study type
Multiple Indicator Cluster Survey - Round 3 [hh/mics-3]
Series Information
The Multiple Indicator Cluster Survey, Round 3 (MICS3) is the third round of MICS surveys, previously conducted around 1995 (MICS1) and 2000 (MICS2). Many questions and indicators are consistent and compatible with the prior round of MICS (MICS2) but less so with MICS1, although there have been a number of changes in definition of indicators between rounds. Details can be found by reviewing the indicator definitions.
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.
Survey Objectives
The 2005 Ukraine Multiple Indicator Cluster Survey has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in Ukraine
- 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 Ukraine and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Survey Content
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.
Survey Implementation
The surveys is carried out by the State Statistics Committee of Ukraine, 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.
Survey Objectives
The 2005 Ukraine Multiple Indicator Cluster Survey has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in Ukraine
- 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 Ukraine and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Survey Content
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.
Survey Implementation
The surveys is carried out by the State Statistics Committee of Ukraine, 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]
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
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
Version
Version Description
Version 1.0: Edited data used for drafting the final report
Version Date
2007-12-17
Scope
Notes
The Ukraine Multiple Indicator Cluster Survey included the following modules in the questionnaires:
HOUSEHOLD QUESTIONNAIRE : Household listing, education, water and sanitation, household characteristics, child labour, child discipline and salt iodization.
WOMEN'S QUESTIONNAIRE: Women's characteristics, child mortality, maternal and newborn health, marriage and union, security of tenure, contraception, domestic violence and HIV/AIDS knowledge.
CHILDREN'S QUESTIONNAIRE: Children's characteristics, birth registration and early learning, child development, breastfeeding and care of illness.
HOUSEHOLD QUESTIONNAIRE : Household listing, education, water and sanitation, household characteristics, child labour, child discipline and salt iodization.
WOMEN'S QUESTIONNAIRE: Women's characteristics, child mortality, maternal and newborn health, marriage and union, security of tenure, contraception, domestic violence and HIV/AIDS knowledge.
CHILDREN'S QUESTIONNAIRE: Children's characteristics, birth registration and early learning, child development, breastfeeding and care of illness.
Topics
| Topic | Vocabulary |
|---|---|
| Household members | MICS Topics |
| Education | MICS Topics |
| Water and sanitation | MICS Topics |
| Household characteristics | MICS Topics |
| Child labour | MICS Topics |
| Child discipline | MICS Topics |
| Salt iodization | MICS Topics |
| Women's background | MICS Topics |
| Child mortality | MICS Topics |
| Maternal and newborn health | MICS Topics |
| Marriage and union | MICS Topics |
| Security of tenure | MICS Topics |
| Contraception | MICS Topics |
| Attitudes towards domestic violence | MICS Topics |
| HIV/AIDS | MICS Topics |
| Children's background | MICS Topics |
| Birth registration | MICS Topics |
| Early learning | MICS Topics |
| Breastfeeding | MICS Topics |
| Child development | MICS Topics |
| Care of illness | MICS Topics |
Coverage
Geographic Coverage
The survey is nationally representative and covers the whole of Ukraine.
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.
Producers and sponsors
Primary investigators
| Name |
|---|
| State Statistics Committee |
Producers
| Name | Affiliation | Role |
|---|---|---|
| State Statistics Committee of Ukraine | Technical implementation and supervision | |
| UNICEF, Ukraine Country Office | UNICEF | Technical assistance |
| UNICEF Regional MICS coordinator | UNICEF | International technical assistance |
| UNICEF Regional M&E officer | UNICEF | International technical assistance |
| Strategic Information Section, Division of Policy and Planning, UNICEF NYHQ | UNICEF | International technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| UNICEF Ukraine | UNICEF | Funding of attending the first and second MICS workshops for implementing institution and UNICEF CO representative, as well as for sampling, and data input software development and printing of MICS fieldwork material and it partly covered the funding for IT supply |
| UNICEF Ukraine | UNICEF | Funding of attending the 3rd and 4th MICS workshops for implementing institution and UNICEF CO representative, as well as for development of MICS tabulations, data quality procedures and the development of the final report. Funding of attending the 3rd and 4th MICS workshops for implementing institution and UNICEF CO representative, as well as for development of MICS tabulations, data quality procedures and the development of the final report. Funding of attending the 3rd and 4th MICS workshops for implementing institution and UNICEF CO representative, as well as for development of MICS tabulations, data quality procedures and the development of the final report. |
| UNICEF HQ | UNICEF | Fieldwork implementation and partial coverage of IT supplys |
| UNICEF HQ | UNICEF | Translation of the instructions for the interviewers. |
| United States Fund for UNICEF | US fund | Analysis of salt samples |
| United Kingdom National Committee for UNICEF | UK NatCom | Analysis of salt samples and procurement of salt test kits |
| Organisation for economic co-operation and development | OECD | Financial and technical support in data archiving |
Sampling
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.
The 2005 MICS collected data from a nationally representative sample of households, women and children. The primary focus of the 2005 MICS was to prodvide 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. Administrative units of the country are called oblast, there are 24 oblasts in the country plus AR Crimea and Kyiv. Each oblast is devided into rayons (which can be urban and rural) and cities. In addtion, each rayon or city, according to the 2001 census, was subdivided into instruction units. In total Ukraine includes 38,091 instruction units. The sample frame for this survey was based on the list of instruction units developed from the 2001 population census.
The primary sampling unit (PSU), the cluster for the 2005 MICS, is defined on the basis of the instruction units from the census frame.
The three-stage sampling was implemented. At the first stage of the selection process 100 primary selection units (cities or rural rayons) were selected with a probability proportional to the country population. At the second stage of selection secondary selection units (National Census 2001 instruction units) were selected; one in each selected city or rural rayon. The selection at the second was implemented proportionally to the population of the instruction units. At the third stage two lists of households were compiled in every instruction unit (secondary selection units). The first list included households with children under 5 and the second contained all the rest of households. 28 households with children aged 0-4 and 28 other households were systematically selected in each of the two lists in every secondary selection unit.
The sample is stratified by region and is not self-weighted. Sample weights were used when preparing the reports at the national level.
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 will be provided at the sampling appendix of the final report, once it is finished.
The 2005 MICS collected data from a nationally representative sample of households, women and children. The primary focus of the 2005 MICS was to prodvide 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. Administrative units of the country are called oblast, there are 24 oblasts in the country plus AR Crimea and Kyiv. Each oblast is devided into rayons (which can be urban and rural) and cities. In addtion, each rayon or city, according to the 2001 census, was subdivided into instruction units. In total Ukraine includes 38,091 instruction units. The sample frame for this survey was based on the list of instruction units developed from the 2001 population census.
The primary sampling unit (PSU), the cluster for the 2005 MICS, is defined on the basis of the instruction units from the census frame.
The three-stage sampling was implemented. At the first stage of the selection process 100 primary selection units (cities or rural rayons) were selected with a probability proportional to the country population. At the second stage of selection secondary selection units (National Census 2001 instruction units) were selected; one in each selected city or rural rayon. The selection at the second was implemented proportionally to the population of the instruction units. At the third stage two lists of households were compiled in every instruction unit (secondary selection units). The first list included households with children under 5 and the second contained all the rest of households. 28 households with children aged 0-4 and 28 other households were systematically selected in each of the two lists in every secondary selection unit.
The sample is stratified by region and is not self-weighted. Sample weights were used when preparing the reports at the national level.
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 will be provided at the sampling appendix of the final report, once it is finished.
Deviations from the 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.
Response Rate
Of the 5600 households selected for the sample, 5,595 were found to be occupied. Of these, 5,243 were successfully interviewed for a household response rate of 93,7 percent. In the interviewed households, 6,174 women (age 15-49) were identified. Of these, 6,164 were successfully interviewed, yielding a response rate of 99,8 percent. In addition, 3,049 children under age five were listed in the household questionnaire. Questionnaires were completed for all 3,049 children, which corresponds to a response rate of 100 percent. Overall response rates of 93.6 and 93.7 are calculated for the women's and under-5's interviews respectively.
The response rates for households, women and children under 5 differ depending on the household location - in urban or rural areas. In urban areas the household response rate was at 91.2 percent, response rate for women was 91.0 percent and for children under 5 it stood as 91.2 percent. The response rates in rural areas were considerably higher: 98.2 for households, 98.1 for women, and 98.2 for children. This situation is generally characteristic for Ukraine.
The response rates for households, women and children under 5 differ depending on the household location - in urban or rural areas. In urban areas the household response rate was at 91.2 percent, response rate for women was 91.0 percent and for children under 5 it stood as 91.2 percent. The response rates in rural areas were considerably higher: 98.2 for households, 98.1 for women, and 98.2 for children. This situation is generally characteristic for Ukraine.
Weighting
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 (household with children under 5 or without children under five in each cluster). 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.
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 (household with children under 5 or without children under five in each cluster). 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.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2005-11-02 | 2005-12-27 |
Data Collection Mode
Face-to-face [f2f]
Supervision
Interviewing was conducted by teams of interviewers. Each interviewing team comprised of 3 interviewers and a supervisor. Each teams used a 4 wheel drive vehicle to travel from cluster to cluster (and where necessary within cluster).
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 supervisor was also 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.
Responsibilities of the supervisor 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 State Statistics Committee of Ukraine also made 3 visits to field teams to provide support and to review progress.
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 supervisor was also 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.
Responsibilities of the supervisor 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 State Statistics Committee of Ukraine also made 3 visits to field teams to provide support and to review progress.
Data Collection Notes
The pretest for the survey took place in September 2005.
The data were collected by 26 teams. The exact size of each team depended upon the workload (number of clusters surveyed), but average team consisted out of 4 persons, 1 supervisor and 3 interviewers. Data collection took place from November 2, 2005 until December 27, 2005.
Interviews averaged 25 minutes for the household questionnaire (excluding salt testing), 30 minutes for the women's questionnaire, and 20 for the under five children's questionnaire. Interviews were conducted primarily in Ukrainian and Russian.
Five staff members of State Statistics Committee of Ukraine provided overall fieldwork coordination and supervision. The overall field coordinator was Kateryna Kupchynska from StatInformConsulting Company. This Company was involved upon the recommendation of the SSCU.
The data were collected by 26 teams. The exact size of each team depended upon the workload (number of clusters surveyed), but average team consisted out of 4 persons, 1 supervisor and 3 interviewers. Data collection took place from November 2, 2005 until December 27, 2005.
Interviews averaged 25 minutes for the household questionnaire (excluding salt testing), 30 minutes for the women's questionnaire, and 20 for the under five children's questionnaire. Interviews were conducted primarily in Ukrainian and Russian.
Five staff members of State Statistics Committee of Ukraine provided overall fieldwork coordination and supervision. The overall field coordinator was Kateryna Kupchynska from StatInformConsulting Company. This Company was involved upon the recommendation of the SSCU.
Data Collectors
| Name | Abbreviation |
|---|---|
| State Statistics Committee of Ukraine | SSCU |
Questionnaires
Questionnaires
The questionnaires for the Ukraine 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, and orphanhood status. The household questionnaire includes household listing, education, water and sanitation, household characteristics, child labour, child discipline and salt iodization.
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 includes women's characteristics, child mortality, maternal and newborn health, marriage and union, security of tenure, contraception, domestic violence and HIV/AIDS knowledge.
The children's questionnaire includes children's characteristics, birth registration and early learning, child development, breastfeeding and care of illness.
The questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Ukrainian and Russian.
The Ukrainian and Russian questionnaires were both piloted as part of the survey pretest.
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 includes women's characteristics, child mortality, maternal and newborn health, marriage and union, security of tenure, contraception, domestic violence and HIV/AIDS knowledge.
The children's questionnaire includes children's characteristics, birth registration and early learning, child development, breastfeeding and care of illness.
The questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Ukrainian and Russian.
The Ukrainian and Russian questionnaires were both piloted as part of the survey pretest.
Data Processing
Data Editing
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.
The data were entered on 26 microcomputers and carried out by 26 data entry operators and 26 data entry supervisors. All data entry was conducted at the Oblast' s Statistics offices 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.
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
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.
The data were entered on 26 microcomputers and carried out by 26 data entry operators and 26 data entry supervisors. All data entry was conducted at the Oblast' s Statistics offices 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.
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
Other Processing
For tabulation and analysis SPSS version 14.0 was used.
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.
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.
Data Appraisal
Estimates of Sampling Error
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.
Sampling 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. 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 2005-2006 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 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.
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 will be presented in the sampling errors appendix to the report and in the sampling errors table presented in the external resources, as soon as they are calculated.
Sampling 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. 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 2005-2006 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 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.
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 will be presented in the sampling errors appendix to the report and in the sampling errors table presented in the external resources, as soon as they are calculated.
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
Population pyramid
The results of each of these data quality tables is shown in the appendix of the final report.
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).
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
Population pyramid
The results of each of these data quality tables is shown in the appendix of the final report.
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).
Access policy
Contacts
Confidentiality
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.
Access conditions
Survey datasets are distributed at no cost for legitimate academic research, with the condition that we receive an abstract or a detailed description of any research project that will be using the data prior to authorizing their distribution. Once received, the datasets must not be passed on to other researchers without the written consent of either State Statistics Committee of Ukraine or UNICEF. Copies of all reports and publications based on the requested data must be sent to State Statistics Committee of Ukraine: Irina Kalachova (I.Kalachova@ukrstat.gov.ua) and UNICEF: UNICEF Ukraine (kiev@unicef.org).
Requests for access to the datasets may be made through the website www.childinfo.org.
Requests for access to the datasets may be made through the website www.childinfo.org.
Citation requirements
State Statistics Committee of Ukraine, Ukraine. Multiple Indicator Cluster Survey: Household , household listing, women and children's files, 2005 [Computer file]. Kyiv, Ukraine: State Statistics Committee of Ukraine [producer], 2006. Kyiv, Ukraine: State Statistics Committee of Ukraine and New York: Strategic Information Section, Division of Policy and Planning, UNICEF [distributors], 2006.
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| UNICEF Ukraine | UNICEF | kiev@unicef.org | www.uniceg.org/ukraine |
| State Statistics Committee of Ukraine | SSCU | www.ukrstat.gov.ua |
Disclaimer and copyrights
Disclaimer
UNICEF Ukraine and State Statistics Committee of Ukraine provide these data to external users without any warranty or responsibility implied. UNICEF Ukraine and State Statistics Committee of Ukraine accept no responsibility for the results and/or implications of any actions resulting from the use of these data.
Metadata production
DDI Document ID
DDI_UKR_2005_MICS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Bjelic, Ivana | SMMRI | Strategic Marketing Research, Belgrade | Data producer and customization of generic template |
| Croft, Trevor | TNC | Blancroft Research International | Producer of generic example |
| Kobzarenko, Andriy | UNICEF Ukraine | UNICEF | Support in data archiving |
| Holmberg, Emma | UNICEF | UNICEF | Customization of the Ukraine MICS3 Archive for childinfo.org |
Date of Metadata Production
2011-10-25
DDI Document version
Version 01 (October 2011) - Slightly edited version of UNICEF's DDI ref. DDI-UKR-UNICEF-MICS2005/1.0-v0.1