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. The Pan Arab Population and Family Health Project(PAPFAM) is a programme conducted to enable national health institutions in the Arab region to obtain a timely and integrated flow of reliable information suitable for formulating, implementing, monitoring and evaluating the family health and reproductive health policies and programs in a cost-effective manner.
MICS and PAPFAM are capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS/PAPFAM is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.
The 2006 Somali Multiple Indicator Cluster Survey (MICS)/Pan Arab Population and Family Health Project(PAPFAM) has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in Somalia
- 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 Somalia and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Following the MICS global questionnaire templates, the questionnaires were designed in a modular fashion customized to the needs of Somalia. The questionnaires 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).
The Somalia MICS/PAPFAM was carried out by UNICEF with the support and assistance the Ministry of Planning and International Cooperation of the Somali Transitional Federal Government, the Ministry of National Planning and Coordination of Somaliland and the Ministry of Planning and International Cooperation of Puntland. Technical assistance and training for the survey was provided through a series of regional workshops organised by UNICEF and PAPFAM, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.
Kind of data
Sample survey data [ssd]
Version 1.0: Edited data used for final report
Water and sanitation
Maternal and newborn health
Marriage and union
Care of illness
Female genital cutting
Attitudes towards domestic violence
The Somali 2006 MICS/PAPFAM covers all regions of Somalia. For the purposes of this survey, the analysis refers to the North West Zone, the North East Zone and Central South Zone according to prewar boundaries for Somaliland and Puntland and does not imply any recognition of administrative boundaries by the United Nations or the League of Arab States.
Unit of analysis
Households (defined as a group of persons who usually live and eat together)
De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
Women aged 15-49
Children aged 0-4
The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household. The survey also included a full birth history module which covered all live births born to ever-married women aged 15-49.
Producers and sponsors
UNICEF Somalia Support Centre
Pan Arab Population and Family Health Project
League of Arab States
International technical assistance
Strategic Information Section, Division of Policy and Planning, UNICEF NYHQ
International technical assistance
Funding of survey implementation
Part funding of survey preparation
Part funding of data entry and dissemination
Part funding of data collection
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/PAPFAM 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 2006 MICS/PAPFAM collected data from a nationally representative sample of households, women and children. The primary focus of the 2006 MICS/PAPFAM was to provide estimates of key population and health, education, child protection and HIV related indicators for the country as a whole, for the North West, North East and Central South Zones and for urban and rural areas separately. Somalia is divided into 18 regions. Each region is subdivided into districts, and each district into settlements and towns. The sample frame for this survey was based on the list of settlements developed from the 2005-2006 UNDP Settlement Survey and WHO vaccination campaign data.
The Sampling design follows a 4 stage-sample approach. The first stage is the selection of the districts in each of the 18 regions of the country selected using probability proportional to size (pps). The second stage is the selection of the secondary sampling units which are defined as permanent and temporary settlements. The third stage is the selection of the cluster(s) within the settlement and the fourth stage is the selection of the households to be interviewed.
Once the districts had been selected great efforts went into compiling a complete list of permanent and temporary settlements within these districts. The main source was the WHO immunisation campaign data, this data was later backed up by the UNDP settlement survey for at least two out of the three zones. Other sources also contributed such as FAO data on water points which could act as proxy for surrounding nomadic areas and temporary settlements. Finally lists were shown to the NGO partners implementing the survey and UNICEF staff on the ground for additional contributions to recent movement of internally displaced persons and nomads. The settlement lists were then sorted into urban and non urban. The first two stages of sampling were thus completed by selecting the required number of clusters from each of the 3 zones by urban and rural areas separately.
Mapping and Listing Activities
For settlements over the estimated size of 150 households some form of segmentation through sketch mapping was necessary. For several district capitals it was possible to use maps from UN Habitat to assist the personnel deployed in sketch mapping. However for most of the larger non-urban settlements there were no maps available. The most important aspect of the sketch mapping was to divide the settlements into roughly equal sizes by estimating the number of households and to clearly delineate the segments using identifiable boundaries.
Once sketch maps were prepared survey coordinators were then in a position to randomly select the cluster(s) where household would be selected. It must be added at this point that finding people trained in cartographic techniques is rare in Somalia. Thus the quality of the maps varied significantly across the country and resources and time also did not allow for a full household count.
Selection of Households
For the final stage of sampling, the Somali MICS/PAPFAM had no other option than to use the method used in MICS 2 of the Expanded Program for Immunization (EPI) random walk method; the expense of household/dwelling listing would simply be too considerable.
Whilst the EPI method is quick and approximately self-weighting, it is recognised that this is not a probability sample, and so cannot ensure objectivity of household selection. In order to try and avoid the subjectivity involved in selecting households some measures were put in place. For example instead of relying on an arbitrary decision regarding the central point of a cluster, supervisors selected at least three or four possible starting points and then randomly choose one of them. Moreover only supervisors were able to select and number the households, not interviewers. Significant time was spent training supervisors on how to select households in order to avoid some of the criticisms typically directed towards this method.
For clusters falling in nomadic areas (the temporary settlements) the survey teams were instructed to interview the first 24 households that they came across. Typically nomads do not move in large numbers, therefore in order to ensure representation of nomads in the sample it was necessary to assume a more purposive method of sampling for this group.
Deviations from sample design
No major deviations from the original sample design were made. All clusters were accessed and successfully interviewed with good response rates.
Of the 6000 households selected for the sample 5969 were successfully interviewed for a household response rate of 99.5 percent. In the interviewed households, 7277 women (age 15-49) were identified. Of these, 6764 were successfully interviewed, yielding a response rate of 93 percent. In addition, 6373 children under age five were listed in the household questionnaire. Of these, questionnaires were completed for 6305 which corresponds to a response rate of 98.9 percent. Overall response rates of 92.5 percent and 98.4 are calculated for the women's and under-5's interviews respectively
Sample weights were calculated for each of the datafiles.
Sample weights for the household data were computed as the inverse of the probability of selection of the household, computed at the sampling domain level (urban/rural within each region). The household weights were adjusted for non-response at the domain level, and were then normalized by a constant factor so that the total weighted number of households equals the total unweighted number of households. The household weight variable is called HHWEIGHT and is used with the HH data and the HL data.
Sample weights for the women's data used the un-normalized household weights, adjusted for non-response for the women's questionnaire, and were then normalized by a constant factor so that the total weighted number of women's cases equals the total unweighted number of women's cases.
Sample weights for the children's data followed the same approach as the women's and used the un-normalized household weights, adjusted for non-response for the children's questionnaire, and were then normalized by a constant factor so that the total weighted number of children's cases equals the total unweighted number of children's cases.
Dates of collection
Mode of data collection
Data collection supervision
Interviewing was conducted by teams of interviewers. Each interviewing team comprised of 4 female interviewers and 4 male interviewers, a field editor and a supervisor. Each team used 4 wheel drive vehicles to travel from cluster to cluster (and where necessary within cluster).
The role of the supervisor was to coordinate field data collection activities, including management of the field team, supplies and equipment, finances, maps and listings, coordinate with local authorities concerning the survey plan and make arrangements for accommodation 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.
The Somali MICS also recruited field coordinators who were responsible for coordinating the work of several field teams. Field visits were made by coordinators throughout fieldwork. Planning, monitoring and evaluation staff of UNICEF also made regular visits to field teams to provide support and to review progress. The Somali MICS Coordinator visited several field teams during data collection across the country.
The questionnaires for the Somali MICS/PAPFAM 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.
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 questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Af-Somali. Differences in translation were reviewed and resolved in collaboration with the original translators.
The English and Somali questionnaires were both piloted as part of the survey pretest.
The questionnaires contained the following modules:
HOUSEHOLD: Household characteristics, household listing, orphaned children, education, child labour, water and sanitation, household use of insecticide treated mosquito nets, salt iodization and maternal mortality.
CHILDREN: Children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, malaria, immunization and anthropometry.
Women Care (Middle Shabelle)
Sean Deveroux Human Rights Organisation (Lower Juba)
Natural Resource Management and Information Centre (Bakool)
African Rescue Committee (Middle Juba)
Community Care Centre (Bay Region)
Community Research and Development Group (Benadir Region)
Himilo Relief, Rehabilitation and Development Association (Gedo Region)
Hiran HIV/AIDS Prevention and Child Protection (Hiran Region)
Training and Research Group (Somaliland)
Ministry of Planning and International Cooperation
Government of Puntland
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 found in the global MICS manual http://www.childinfo.org/mics/mics3/manual.php
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 5 files (hh - household, hl - household members, wm - women, ch - children under 5 bh - birth history)
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 12 data entry operators in tow shifts, supervised by 2 data entry supervisors, using a total of 7 computers (6 data entry computers plus one supervisors computer). All data entry was conducted at the GenCenStat 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.
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 2005-2006 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 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 2005-2006 MICS. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.
Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).
Details of the sampling errors are presented in the sampling errors appendix to the final report.
Other forms of data appraisal
A series of data quality tables and graphs are available to review the quality of the data and include the following:
Age distribution of the household population
Age distribution of eligible women and interviewed women
Age distribution of eligible children and children for whom the mother or caretaker was interviewed
Age distribution of children under age 5 by 3 month groups
Age and period ratios at boundaries of eligibility
Percent of observations with missing information on selected variables
Presence of mother inthe household and person interviewed for the under 5 questionnaire
School attendance by single year age
Sex ratio at birth among children ever born, surviving and dead by age of respondent
Distribution of women by time since last birth
Scatterplot of weight by height, weight by age and height by age
Graph of male and female population by single years of age
The results of each of these data quality tables is shown in the appendix of the final report.
The generral rule for presentation of missing data in the final report tabulations is that a column is presented for missing data if the percentage of cases with missing data is 1% or more. Cases with missing data on the background characteristics (e.g. education) are included in the tables, but the missing data rows are suppressed and noted at the bottom of the tables in the report (not in the SPSS output, however).
Users of the data agree to keep confidential all data contained in these datasets and to make no attempt to identify, trace or contact any individual whose data is included in these datasets.
Survey datasets are distributed at no cost for legitimate research, with the condition that we receive 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 UNICEF Somalia: firstname.lastname@example.org
Requests for access to the datasets may be made through the website: www.childinfo.org
UNICEF Somalia, Multiple Indicator Cluster Survey: Household , household listing, women, children's and birth history files, 2006.Nairobi provided by New York: Strategic Information Section, Division of Policy and Planning, UNICEF, www.childinfo.org.
Disclaimer and copyrights
UNICEF provides these data to external users without any warranty or responsibility implied. UNICEF accepts no responsibility for the results and/or implications of any actions resulting from the use of these data.
Chief of Social Policy and Planning Section, UNICEF Somalia
Producer of the Somali MICS Archive
Producer of the Generic MICS Archive Example
Somali MICS 2006 v0.2
Slightly edited version of UNICEF's DDI ref. DDI-SOM-UNICEF-MICS-2006-V1