Multiple Indicator Cluster Survey 2005
Mongolia, 2005
National Statistical Office of Mongolia
Identification
IDNO
MNG_2005_MICS_v01_M
Title
Multiple Indicator Cluster Survey 2005
Countries
| Name | Code |
|---|---|
| Mongolia | MNG |
Study notes
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 survey has been a joint endeavor of the Government of Mongolia and UNICEF to make an in-depth analysis of Mongolia's child and women health, education, livelihood status and right exercises and to assess the progress of implementation of a National Programme for Child Development and Protection (2002-2010). The data will furnish the preparation process of the national reporting to be presented by the Government of Mongolia at the special session of UN regarding the country's implementation of Declaration of the A World Fit for Children.
Survey Objectives
The primary objectives of “Multiple Indicator Cluster Survey: Child Development 2005-2006” are the following:
- To update the data for assessing the situation of child and women and their right exercises
- To furnish the data needed for monitoring progress towards the goals of Millennium Declaration and the WorldFit for Children as a basis for future action planning
- To contribute to the improvement of data and monitoring systems in Mongolia and strengthen the expertise in the design, implementation and analytical of these systems.
Survey plans
The Mongolia Multiple Indicator Cluster Survey was conducted by the National Statistical Office of Mongolia with the support of the Government of Mongolia and UNICEF. Technical assistance and training for the surveys was 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 primary objectives of “Multiple Indicator Cluster Survey: Child Development 2005-2006” are the following:
- To update the data for assessing the situation of child and women and their right exercises
- To furnish the data needed for monitoring progress towards the goals of Millennium Declaration and the WorldFit for Children as a basis for future action planning
- To contribute to the improvement of data and monitoring systems in Mongolia and strengthen the expertise in the design, implementation and analytical of these systems.
Survey plans
The Mongolia Multiple Indicator Cluster Survey was conducted by the National Statistical Office of Mongolia with the support of the Government of Mongolia and UNICEF. Technical assistance and training for the surveys was 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
Version
Version 1.1: Edited data used for national report in English
Version date
2006-08-06
Scope
Topics
| Topic | Vocab |
|---|---|
| Household's characteristics | MICS Topics |
| Household members | MICS Topics |
| Education | MICS Topics |
| Water and sanitation | MICS Topics |
| Child labour | MICS Topics |
| Child discipline | MICS Topics |
| Child disability | MICS Topics |
| Household income | MICS Topics |
| Salt iodization | MICS Topics |
| Women's characteristics | MICS Topics |
| Women listing | MICS Topics |
| Child mortality | MICS Topics |
| Maternal and infant health | MICS Topics |
| Marriage | MICS Topics |
| Contraception | MICS Topics |
| Attitudes towards family violence | MICS Topics |
| HIV/AIDS knowledge | MICS Topics |
| Children's characteristics | MICS Topics |
| Child listing | MICS Topics |
| Birth registration and pre-schooling | MICS Topics |
| Child development | MICS Topics |
| Vitamin A | MICS Topics |
| Breastfeeding | MICS Topics |
| Care of illness | MICS Topics |
| Immunization | MICS Topics |
| Anthropometry | MICS Topics |
| Houshehold Income | Non MICS Topic |
Coverage
Geographic coverage
The survey is nationally representative and covers the whole of Mongolia.
Unit of analysis
Households (defined as a group of persons who usually live and eat together)
Household members (defined as members 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
Household members (defined as members 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
Universe
The survey covered all 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
Authoring entity
| Name |
|---|
| National Statistical Office of Mongolia |
Producer(s)
| Name | Affiliation | Role |
|---|---|---|
| UNICEF, Mongolia | 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 agencies
| Name | Abbreviation | Role |
|---|---|---|
| UNICEF | UNICEF | Funding of survey implementation |
| Ministry of Finance of Mongolia | MoFM | Funding of survey implementation |
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 MICS - 3 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 MICS - 3 collected data from a nationally representative sample of households, women and children. The primary focus of the MICS - 3 was to provide estimates of key population and health, education, child protection and HIV related indicators for Mongolia as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 5 regions for key indicators. Mongolia is divided into 5 regions. Each region is subdivided into provinces (aimags) and a capital city, and each province into soums, a capital city into districts, each soum into bags and each districts into khoroos. As bag and khoroo household and population listing is annually updated, these were taken as primary sampling units. Bags and khoroos with a large population were divided into 2-3 primary sampling units in order to keep the similar number of households for sampling units. Bag and khoroos (primary sampling unit) were selected with probability proportional to size and 25 households within each of these selected units were sampled using the systematic method. The primary sampling unit variable is the cluster (HH1).
The survey estimates the indicators on the child and women situation by national level, rural, urban areas and regions. Five regions (Western, Khangai, Central, Eastern and Ulaanbaatar) were the main sampling domains and a two stage sampling design was used. Within each region households were selected with probability proportional to size.
A total of 6325 households in 253 primary sampling units were selected to represent 21 aimags and Ulaanbaatar city. Sample weights were used for estimating the data collected from each of the sampled households.
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 MICS - 3 collected data from a nationally representative sample of households, women and children. The primary focus of the MICS - 3 was to provide estimates of key population and health, education, child protection and HIV related indicators for Mongolia as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates for each of the 5 regions for key indicators. Mongolia is divided into 5 regions. Each region is subdivided into provinces (aimags) and a capital city, and each province into soums, a capital city into districts, each soum into bags and each districts into khoroos. As bag and khoroo household and population listing is annually updated, these were taken as primary sampling units. Bags and khoroos with a large population were divided into 2-3 primary sampling units in order to keep the similar number of households for sampling units. Bag and khoroos (primary sampling unit) were selected with probability proportional to size and 25 households within each of these selected units were sampled using the systematic method. The primary sampling unit variable is the cluster (HH1).
The survey estimates the indicators on the child and women situation by national level, rural, urban areas and regions. Five regions (Western, Khangai, Central, Eastern and Ulaanbaatar) were the main sampling domains and a two stage sampling design was used. Within each region households were selected with probability proportional to size.
A total of 6325 households in 253 primary sampling units were selected to represent 21 aimags and Ulaanbaatar city. Sample weights were used for estimating the data collected from each of the sampled households.
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.
Deviations from sample design
No major deviations from the original sample design were made. All primary sampling units were accessed and successfully interviewed with good response rates.
Response rate
6325 households were selected for the sample. Of these, 6325 were occupied households and 6220 were successfully interviewed for a response rate of 98.3%. Within these households, 8057 eligible women aged 15-49 were identified for interview, of which 7459 were successfully interviewed (response rate 92.6%), and 3568 children aged 0-4 were identified for whom the mother or caretaker was successfully interviewed for 3547 children (response rate 99.4%). These give overall response rates (household response rate times individual response rate) for the women's interview of 91.0% and for the children's interview of 97.8%.
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 (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.
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.
Data Collection
Dates of collection
| Start | End |
|---|---|
| 2005-11 | 2005-12 |
Mode of data collection
Face-to-face [f2f]
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).
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 staffs on a periodic basis during fieldwork. The senior staff of the NSO 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 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 staffs on a periodic basis during fieldwork. The senior staff of the NSO also made 3 visits to field teams to provide support and to review progress.
Questionnaires
The questionnaires for the MICS were structured questionnaires based on the MICS - 3 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's characteristics, household listing, education, water and sanitation, child labour, child discipline, child disability, and salt iodization.
To reflect the country specific characteristics, module “Salt Iodization” of household questionnaire was enlarged by the question about the vitamin enriched flour and module “child discipline” was added with sub-module child behaviour. These additions were made based on the decisions made by work group members and Steering Committee.
In the meantime, the salt used for household cooking was on site tested to measure the iodine content.
Household questionnaire was administered to an adult household member who can best represent other members, women questionnaire to women themselves and under-five questionnaire to mothers or caretakers of children under 5 years. Child weights and heights were measured during the interviews.
The women's questionnaire includes women's characteristics, women listing, child mortality, maternal and infant health, marriage, contraception, attitudes towards family violence, and HIV/AIDS knowledge.
The children's questionnaire includes children's characteristics, child listing, birth registration and pre-schooling, child development , “A” vitamin supplement, breastfeeding, care of illness, immunization, and anthropometry.
The questionnaires were developed in Mongolian from the MICS3 Model Questionnaires, and were translated into English.
In order to check the clarity and logical sequence of questions and determine the interview duration per household, the pretest of questionnaires was made in September 2005 covering the selected households in Erdene soum of Tuv aimag. Based on the findings of the pretest, wording and logical sequence of the questions were improved.
To reflect the country specific characteristics, module “Salt Iodization” of household questionnaire was enlarged by the question about the vitamin enriched flour and module “child discipline” was added with sub-module child behaviour. These additions were made based on the decisions made by work group members and Steering Committee.
In the meantime, the salt used for household cooking was on site tested to measure the iodine content.
Household questionnaire was administered to an adult household member who can best represent other members, women questionnaire to women themselves and under-five questionnaire to mothers or caretakers of children under 5 years. Child weights and heights were measured during the interviews.
The women's questionnaire includes women's characteristics, women listing, child mortality, maternal and infant health, marriage, contraception, attitudes towards family violence, and HIV/AIDS knowledge.
The children's questionnaire includes children's characteristics, child listing, birth registration and pre-schooling, child development , “A” vitamin supplement, breastfeeding, care of illness, immunization, and anthropometry.
The questionnaires were developed in Mongolian from the MICS3 Model Questionnaires, and were translated into English.
In order to check the clarity and logical sequence of questions and determine the interview duration per household, the pretest of questionnaires was made in September 2005 covering the selected households in Erdene soum of Tuv aimag. Based on the findings of the pretest, wording and logical sequence of the questions were improved.
Data collector(s)
| Name | Abbreviation |
|---|---|
| National Statistical Office of Mongolia | NSO of Mongolia |
Data Processing
Data editing
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 in the MICS manual http://www.childinfo.org/mics/mics3/manual.php.
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 in the MICS manual http://www.childinfo.org/mics/mics3/manual.php.
metadata.study_desc.method.data_collection.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:
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 in the MICS manual http://www.childinfo.org/mics/mics3/manual.php
Data entry was conducted by 8 data entry operators in tow shifts, supervised by 1 data entry supervisors, using a total of 9 computers (8 data entry computers plus one supervisor's computer). All data entry was conducted at the NSO 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 in the 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 13.0 and 14.0 were used. Version 13.0 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, and 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. Conventionally, household economic status is being is defined by the data of household income and expenditure. This conventional method of data collection is time consuming (each household member is asked numerous questions by each of income sources). Besides such a method can result in incompleteness of data (interviewee may be unaware of income and expenditures of other members) and be challenged by irregularity of household economic activities and difficulties of capturing the higher incomes. Therefore, the current survey has estimated the indicator “wealth Index” to measure the household wealth which can be captured by a few and simple questions. For this purpose, it is quite possible to use the questions asked to measure other indicators (drinking water, sanitation facilities, housing type, access to electricity). One advantage of this index is to lessen the data effect of seasonal and temporary income sources as the index concentrates on assets or capitals accumulated over the longer period. (Rutstein & Johnson, 2004). The survey results were estimated by five equally weighted groups of wealth index. This includes the indicators of household type, condition, drinking water, sanitation facility, access to electricity, household consumerables (communications and transportation means, household electrical appliances). Using these indicators, each household was then weighted by the number of household members, and the household population was divided into five groups of equal size, from the poorest quintile to the richest quintile, based on the wealth scores of households they were living in. Total households were put in five groups with the following categories: poorest (I), second (II), middle (III), fourth (IV), richest (V).
The survey data has been disaggregated by national average, regions, urban and rural areas with household location and estimated by women education level and five wealth groups of household with equal weighting.
Regions: Western, Khangai, Central, Eastern and Ulaanbaatar
Location: Capital city, Aimag center, Soum center, Countryside
Urban, rural areas: Capital city and aimag centers are counted for urban areas and soum centers and the countryside makes up the category of rural areas.
Wealth index quintiles: Poorest (I), Second (II), Middle (III), Fourth (IV), Richest (V)
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 in the MICS manual http://www.childinfo.org/mics/mics3/manual.php
Data entry was conducted by 8 data entry operators in tow shifts, supervised by 1 data entry supervisors, using a total of 9 computers (8 data entry computers plus one supervisor's computer). All data entry was conducted at the NSO 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 in the 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 13.0 and 14.0 were used. Version 13.0 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, and 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. Conventionally, household economic status is being is defined by the data of household income and expenditure. This conventional method of data collection is time consuming (each household member is asked numerous questions by each of income sources). Besides such a method can result in incompleteness of data (interviewee may be unaware of income and expenditures of other members) and be challenged by irregularity of household economic activities and difficulties of capturing the higher incomes. Therefore, the current survey has estimated the indicator “wealth Index” to measure the household wealth which can be captured by a few and simple questions. For this purpose, it is quite possible to use the questions asked to measure other indicators (drinking water, sanitation facilities, housing type, access to electricity). One advantage of this index is to lessen the data effect of seasonal and temporary income sources as the index concentrates on assets or capitals accumulated over the longer period. (Rutstein & Johnson, 2004). The survey results were estimated by five equally weighted groups of wealth index. This includes the indicators of household type, condition, drinking water, sanitation facility, access to electricity, household consumerables (communications and transportation means, household electrical appliances). Using these indicators, each household was then weighted by the number of household members, and the household population was divided into five groups of equal size, from the poorest quintile to the richest quintile, based on the wealth scores of households they were living in. Total households were put in five groups with the following categories: poorest (I), second (II), middle (III), fourth (IV), richest (V).
The survey data has been disaggregated by national average, regions, urban and rural areas with household location and estimated by women education level and five wealth groups of household with equal weighting.
Regions: Western, Khangai, Central, Eastern and Ulaanbaatar
Location: Capital city, Aimag center, Soum center, Countryside
Urban, rural areas: Capital city and aimag centers are counted for urban areas and soum centers and the countryside makes up the category of rural areas.
Wealth index quintiles: Poorest (I), Second (II), Middle (III), Fourth (IV), Richest (V)
Data Appraisal
metadata.study_desc.method.analysis_info.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 MICS - 3 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 MICS - 3 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 different 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 errors 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 MICS - 3 sample is the result of a two-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 MICS - 3. 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.
Sampling errors can be evaluated statistically. The sample of respondents to the MICS - 3 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 different 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 errors 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 MICS - 3 sample is the result of a two-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 MICS - 3. 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.
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 in the 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
Population pyramid
The results of each of these data quality tables are 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 in the 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
Population pyramid
The results of each of these data quality tables are 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).
Data access
Access authorities
| Name | Affiliation | URI | |
|---|---|---|---|
| O.Baigalmaa | National Statistical Office of Mongolia | obaigalmaa@yahoo.com | www.nso.mn |
| D.Khurelmaa | UNICEF, Mongolia | kdashdorj@unicef.org | www.unicef.org |
metadata.study_desc.data_access.dataset_use.conf_dec
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 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. Copies of all reports and publications based on the requested data must be sent to the National Statistical Office of Mongolia : nso@magicnet.mn and UNICEF: ulaanbaatar@unicef.org.
Requests for access to the datasets may be made through the website www.childinfo.org and www.nso.mn.
Requests for access to the datasets may be made through the website www.childinfo.org and www.nso.mn.
Citation requirements
The following statement must be used as citation: "Source of data: National Statistics Office of Mongolia, Multiple Indicator Cluster Survey: Child Development 2005-2006, Version 1.1 of the dataset (August 2006), provided by UNICEF"
Disclaimer and copyrights
Disclaimer
The National Statistical Office of Mongolia and UNICEF provide these data to external users without any warranty or responsibility implied. The National Statistical Office of Mongolia and UNICEF accept no responsibility for the results and/or implications of any actions resulting from the use of these data.
Contacts
Contact(s)
| Name | Affiliation | URI | |
|---|---|---|---|
| O.Baigalmaa | National Statistical Office of Mongolia | obaigalmaa@yahoo.com | www.nso.mn |
| Z.Munkhzul | National Statistical Office of Mongolia | zulaa_z@yahoo.com | www.nso.mn |
| D.Khurelmaa | UNICEF, Mongolia | kdashdorj@unicef.org | www.unicef.org |
Metadata production
IDNo
DDI_MNG_2005_MICS_v01_M
Producers
| Name | Affiliation | Role |
|---|---|---|
| Z.Munkhzul | National Statistical Office of Mongolia | Data producer and customization of the metadata |
| Daniel Clarke | ESCAP, Microdata Management Project | Review and editing |
| Rhiannon James | UNICEF | Customisation of Mongolia MICS DDI for childinfo.org |
Production date
2008-02-08
Version
Mongolia MICS 2005 v1.3 (with edits from ESCAP, Microdata Management Project and UNICEF)
Slightly edited version of UNICEF's DDI ref. DDI-MNG-UNICEF-MICS2005 v0.1
Slightly edited version of UNICEF's DDI ref. DDI-MNG-UNICEF-MICS2005 v0.1