Multiple Indicator Cluster Survey 2006, Monitoring the Situation of Children and Women
Syrian Arab Republic, 2006
Get Microdata
Reference ID
SYR_2006_MICS_v01_M
Producer(s)
Syria Central Bureau of Statistics
Collection(s)
Created on
Mar 22, 2011
Last modified
Sep 26, 2013
Page views
227828
Downloads
5934
Identification
Survey ID Number
SYR_2006_MICS_v01_M
Title
Multiple Indicator Cluster Survey 2006, Monitoring the Situation of Children and Women
Subtitle
Monitoring the Situation of Children and Women
Translated Title
Syria Multiple Indicator Cluster Survey (MICS) 2006
Country/Economy
| Name | Country code |
|---|---|
| Syria | SYR |
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 such as the Millennium Development Goals (MDGs) indicators. It is a flexible tool that is reasonably inexpensive and relatively quick to implement.
Background
MICS was originally developed in response to the 1990 World Summit for Children to measure progress towards an internationally agreed set of goals. The first round of MICS was conducted around 1995 in more than 60 countries. A second round of surveys was conducted in 2000 (around 65 surveys), and resulted in an increasing wealth of data to monitor the situation of children and women. For the first time it was possible to monitor trends in many indicators and set baselines for other indicators.
Purpose
Information on around 20 of the 48 MDG indicators will be collected in the current round of MICS, offering the largest single source of data for MDG monitoring. The current round of MICS is thus 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.
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).
HOUSEHOLD: Household Listing, Education, Water and Sanitation, Household Characteristics, and Child Labour.
WOMEN: Child Mortality, Tetanus Toxoid, Maternal and Newborn Health, Marriage, Contraception, and HIV/AIDS.
CHILDREN: Birth Registration and Early Learning, Vitamin A, Breastfeeding, Care of Illness, Immunization, and Anthropometry.
The surveys are typically carried out by government organizations, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.
MICS is capable of producing statistically sound, internationally comparable estimates of social indicators such as the Millennium Development Goals (MDGs) indicators. It is a flexible tool that is reasonably inexpensive and relatively quick to implement.
Background
MICS was originally developed in response to the 1990 World Summit for Children to measure progress towards an internationally agreed set of goals. The first round of MICS was conducted around 1995 in more than 60 countries. A second round of surveys was conducted in 2000 (around 65 surveys), and resulted in an increasing wealth of data to monitor the situation of children and women. For the first time it was possible to monitor trends in many indicators and set baselines for other indicators.
Purpose
Information on around 20 of the 48 MDG indicators will be collected in the current round of MICS, offering the largest single source of data for MDG monitoring. The current round of MICS is thus 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.
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).
HOUSEHOLD: Household Listing, Education, Water and Sanitation, Household Characteristics, and Child Labour.
WOMEN: Child Mortality, Tetanus Toxoid, Maternal and Newborn Health, Marriage, Contraception, and HIV/AIDS.
CHILDREN: Birth Registration and Early Learning, Vitamin A, Breastfeeding, Care of Illness, Immunization, and Anthropometry.
The surveys are typically carried out by government organizations, with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, 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 final report
Version Date
2008-10-24
Scope
Notes
The scope of the Multiple Indicator Cluster Survey includes:
HOUSEHOLD: Household listing, education, water and sanitation, household characteristics, and child labour.
WOMEN: Women's characteristics, child mortality, tetanus toxoid, maternal and newborn health, marriage, contraception, and HIV/AIDS.
CHILDREN: Children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, immunization, and anthropometry.
HOUSEHOLD: Household listing, education, water and sanitation, household characteristics, and child labour.
WOMEN: Women's characteristics, child mortality, tetanus toxoid, maternal and newborn health, marriage, contraception, and HIV/AIDS.
CHILDREN: Children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, immunization, and anthropometry.
Topics
| Topic | Vocabulary |
|---|---|
| Household members | Syria MICS Topics |
| Education | Syria MICS Topics |
| Water and sanitation | Syria MICS Topics |
| Household characteristics | Syria MICS Topics |
| Child labour | Syria MICS Topics |
| Women's background | Syria MICS Topics |
| Child mortality | Syria MICS Topics |
| Tetanus toxoid | Syria MICS Topics |
| Maternal and newborn health | Syria MICS Topics |
| Marriage | Syria MICS Topics |
| Contraception | Syria MICS Topics |
| HIV/AIDS | Syria MICS Topics |
| Children's background | Syria MICS Topics |
| Birth registration | Syria MICS Topics |
| Early learning | Syria MICS Topics |
| Vitamin A | Syria MICS Topics |
| Breastfeeding | Syria MICS Topics |
| Care of illness | Syria MICS Topics |
| Immunization | Syria MICS Topics |
| Anthropometry | Syria MICS Topics |
Coverage
Geographic Coverage
The survey is nationally representative and covers the whole of Syria.
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 | Affiliation |
|---|---|
| Syria Central Bureau of Statistics | CBS |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Ministry of Health | Health | International technical assistance |
| UNICEF, Syria | UNICEF | Technical assistance |
| Pan Arab Project For Family Health,PAPFAM/League of Arab States | PAPFAM | International technical assistance |
| Strategic Information Section, Division of Policy and Planning, UNICEF NYHQ | UNICEF | International technical assistance |
| UNICEF, Regional MICS Coordinator | UNICEF | International technical assistance |
| UNICEF, Regional M&E Officer | UNICEF | International technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| UNICEF | UNICEF | 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 2006 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 sample for the Multiple Indicator Cluster Survey of the Syrian Arab Republic was designed to estimate a number of indicators on the situation of women and children at the national, and governorate levels, for urban and rural areas. The framework of the 2004 Overall Census of Inhabitants and Dwellings was used as the sampling frame. The sample was selected in two stages in each area. In the first stage, the clusters were drawn as census areas in proportion to size with a total of (1000) clusters. In the second stage, the counting units were drawn (households) in a regular arbitrary manner. The sample is not selfweighting. 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 appendix of the final report and can also be found in the list of technical documents within this archive.
The sample for the Multiple Indicator Cluster Survey of the Syrian Arab Republic was designed to estimate a number of indicators on the situation of women and children at the national, and governorate levels, for urban and rural areas. The framework of the 2004 Overall Census of Inhabitants and Dwellings was used as the sampling frame. The sample was selected in two stages in each area. In the first stage, the clusters were drawn as census areas in proportion to size with a total of (1000) clusters. In the second stage, the counting units were drawn (households) in a regular arbitrary manner. The sample is not selfweighting. 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 appendix of the final report and can also be found in the list of technical documents within this archive.
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
Out of (20022) households selected for sampling, (19870) were actually found, while the dwellings of the remaining households were either not occupied, or else the households themselves were out. (19019) households were successfully interviewed yielding a household response rate of (95.7) percent. In the interviewed households (25563) women aged 15-49 were identified. Out of these (25026) women were interviewed, yielding a response rate of (97.9) percent. The number of children under five listed in the household questionnaire totaled (11104). Out of these, (11017) children were interviewed, which corresponds to a response rate of 99.2%. The overall response rate for the women’s questionnaires was (93.7) percent, while the one for the children under five was (95) percent. The response rate was similar in urban and rural areas, as Table (HH1) shows.
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 Data Collection
| Start | End |
|---|---|
| 2006-04-19 | 2006-05-30 |
Data Collection Mode
Face-to-face [f2f]
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 team 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 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 The Central Bureau of Statistics also made 3 visits to field teams to provide support and to review progress.
Data Collection Notes
Members of the fieldwork staff were trained for ten days in early April, 2006. 35 teams collected the data, each comprising 4 interviewers, a team head, and a supervisor for each governorate. Fieldwork began on April 19th, 2006 and lasted until the end of May, 2006. Interviews averaged 35 minutes for the household questionnaire, 23 minutes for the women's questionnaire, and 27 for the under five children's questionnaire (excluding the anthropometry). Interviews were conducted in Arabic and Kurdish.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Syria Central Bureau of Statistics | CBS | CBS |
Questionnaires
Questionnaires
The questionnaires for the Syria 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 and relationship with the household head. The household questionnaire includes household listing, education, water and sanitation, household characteristics, and child labour
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, tetanus toxoid, maternal and newborn health, marriage, contraception, and HIV/AIDS.
The children's questionnaire includes children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, immunization, and anthropometry.
The questionnaires are based on the MICS3 model questionnaire and were pre-tested during February, 2006. Based on the results of the pre-test, modifications were made to the wording of the questionnaires.
In addition to the administration of questionnaires, fieldwork teams measured the weights and heights of children age under-five years.
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, tetanus toxoid, maternal and newborn health, marriage, contraception, and HIV/AIDS.
The children's questionnaire includes children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, immunization, and anthropometry.
The questionnaires are based on the MICS3 model questionnaire and were pre-tested during February, 2006. Based on the results of the pre-test, modifications were made to the wording of the questionnaires.
In addition to the administration of questionnaires, fieldwork teams measured the weights and heights of children age under-five years.
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/mics3_manual.html)
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/mics3_manual.html)
Other Processing
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 entry was conducted by 30 data entry operators in one shifts, supervised by 2 data entry supervisors, using a total of 31 computers (15 data entry computers plus one supervisors computer). All data entry was conducted at the CBS SYRIA 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.
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 entry was conducted by 30 data entry operators in one shifts, supervised by 2 data entry supervisors, using a total of 31 computers (15 data entry computers plus one supervisors computer). All data entry was conducted at the CBS SYRIA 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.
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 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).
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).
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 Central Bureau of Statistics of Syria or UNICEF. Copies of all reports and publications based on the requested data must be sent to:
1) Central Bureau of Statistics, Soumaya@scs-net.org
2) Central Bureau of Statistics, Y.Jumaa70@gmail.com
Requests for access to the datasets may be made through the website www.childinfo.org.
1) Central Bureau of Statistics, Soumaya@scs-net.org
2) Central Bureau of Statistics, Y.Jumaa70@gmail.com
Requests for access to the datasets may be made through the website www.childinfo.org.
Citation requirements
Central Bureau of Statistics, Ministry of Health, Syria. Multiple Indicator Cluster Survey: Household , household listing, women and children's files, 2006 [Computer file]. Damascus, Central Bureau of Statistics, Ministry of Health [producer], 2006. Damascus, Syria: Central Bureau of Statistics, Ministry of Health and New York: Strategic Information Section, Dvision of Policy and Planning, UNICEF [distributors], 2006.
Access authority
| Affiliation | URL | |
|---|---|---|
| Syria Central Bureau of Statistics | cbs@mail.sy | Link |
Disclaimer and copyrights
Disclaimer
Central Bureau of Statistics of Syria and UNICEF provide these data to external users without any warranty or responsibility implied. Central Bureau of Statistics of Syria and UNICEF accept no responsibility for the results and/or implications of any actions resulting from the use of these data.
Copyright
2008, Central Bureau of Statistics, Syria
Metadata production
DDI Document ID
DDI_SYR_2006_MICS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Fahmi Alfaouri | CBS | Central Bureau of Statistics | Data producer and customization of Syria Data |
| Raghdaa Khattab | CBS | Central Bureau of Statistics | Data producer and customization of Syria Data |
| Croft,Tevor | UNICEF | Blancroft Research International | Data producer and customization of Syria Data |
| UNICEF, Syria | UNICEF | Customization of generic template | |
| PAPFAM | Pan Arab Project for Family Health | Customization of generic template | |
| Bjelic,Ivana | Data archiving consultant | ||
| Holmberg, Emma | UNICEF | UNICEF | Customization of the Syria MICS3 Archive for childinfo.org |
Date of Metadata Production
2009-05-20
DDI Document version
Syria MICS3 2006 v0.1
Slightly edited version of UNICEF's DDI ref. DDI-CBS-SYR-MICS2006/1.0-v0.2
Slightly edited version of UNICEF's DDI ref. DDI-CBS-SYR-MICS2006/1.0-v0.2