Multiple Indicator Cluster Survey 2005-2006
Gambia, 2005 - 2006
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Reference ID
GMB_2005_MICS_v01_M
Producer(s)
Bureau of Statistics
Collection(s)
Metadata
Created on
Mar 21, 2011
Last modified
Mar 13, 2019
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Identification
Survey ID Number
GMB_2005_MICS_v01_M
Title
Multiple Indicator Cluster Survey 2005-2006
Country/Economy
| Name | Country code |
|---|---|
| Gambia, The | GMB |
Study type
Multiple Indicator Cluster Survey - Round 3 [hh/mics-3]
Series Information
The Multiple Indicator Cluster Survey, Round 3 (MICS3) is the third round of MICS surveys, previously conducted around 1995 (MICS1) and 2000 (MICS2). Many questions and indicators are consistent and compatible with the prior round of MICS (MICS2) but less so with MICS1, although there have been a number of changes in definition of indicators between rounds. Details can be found by reviewing the indicator definitions.
Abstract
The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The Gambia Multiple Indicator Cluster Survey provides valuable information on the situation of children and women in The Gambia and was based, in large part, on the needs to monitor progress towards goals and targets emanating from recent international agreements: the Millennium Declaration, adopted by all 191 United Nations Member States in September 2000, and the Plan of Action of A World Fit For Children, adopted by 189 Member States at the United Nations Special Session on Children in May 2002. Both of these commitments build upon promises made by the international community at the 1990 World Summit for Children.
Survey Objectives:
The 2006 Gambia Multiple Indicator Cluster Survey has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in the Gambia;
- 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 the Gambia and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Survey Content
Following the MICS global questionnaire templates, the questionnaires were designed in a modular fashion customized to the needs of The Gambia. 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).
Survey Implementation
The Gambia Multiple Indicator Cluster Survey (MICS) was carried by The Gambia Bureau of Statistics. Financial and technical support was provided by the United Nations Children's Fund (UNICEF). Technical assistance and training for the survey was provided through a series of regional workshops organised by UNICEF covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.
Survey Objectives:
The 2006 Gambia Multiple Indicator Cluster Survey has as its primary objectives:
- To provide up-to-date information for assessing the situation of children and women in the Gambia;
- 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 the Gambia and to strengthen technical expertise in the design, implementation, and analysis of such systems.
Survey Content
Following the MICS global questionnaire templates, the questionnaires were designed in a modular fashion customized to the needs of The Gambia. 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).
Survey Implementation
The Gambia Multiple Indicator Cluster Survey (MICS) was carried by The Gambia Bureau of Statistics. Financial and technical support was provided by the United Nations Children's Fund (UNICEF). Technical assistance and training for the survey was provided through a series of regional workshops organised by UNICEF 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)
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
Scope
Notes
The Gambia Multiple Indicator Cluster Survey included the following modules in the questionnaires:
The Household Questionnaire: Household Listing; Education; Water and Sanitation; Security of Tenure/Durability of Housing; Malaria-related questions; Child Labour; Child Discipline; Salt Iodization
The Questionnaire for Individual Women: Child Mortality; Tetanus Toxoid; Maternal and Newborn Health; Marriage and Union; Attitudes Towards Domestic Violence; Female Genital Mutilation/Cutting; Sexual Behaviour; HIV knowledge
The Questionnaire for Children Under Five: Birth Registration and Early Learning; Child Development; Vitamin A; Breastfeeding; Care of Illness; Malaria; Immunization; and Anthropometry.
The Household Questionnaire: Household Listing; Education; Water and Sanitation; Security of Tenure/Durability of Housing; Malaria-related questions; Child Labour; Child Discipline; Salt Iodization
The Questionnaire for Individual Women: Child Mortality; Tetanus Toxoid; Maternal and Newborn Health; Marriage and Union; Attitudes Towards Domestic Violence; Female Genital Mutilation/Cutting; Sexual Behaviour; HIV knowledge
The Questionnaire for Children Under Five: Birth Registration and Early Learning; Child Development; Vitamin A; Breastfeeding; Care of Illness; Malaria; Immunization; and Anthropometry.
Coverage
Geographic Coverage
National
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 |
|---|
| Bureau of Statistics |
Producers
| Name | Role |
|---|---|
| Department of State for Education | Writeup on education module |
| Department of State for Health | Writeup on health module |
| National Nutrition Office | Writeup on anthropometry |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| United Nations Children's Fund | UNICEF | Funded the whole survey |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| Edrissa Ceesay | GBOS | Programmer |
| Alieu Ndow | GBOS | Coordinator |
Sampling
Sampling Procedure
The sample for the Gambia's Multiple Indicator Cluster Survey (MICS) was designed to provide estimates on a large number of indicators on the situation of children and women at the national level, for urban and rural areas, and for 8 Local Government Areas (LGA): Banjul, Kanifing, Brikama, Mansakonko, Kerewan, Kuntaur, Janjanbureh and Basse. The LGAs were identified as the main sampling domains and the sample was selected in two stages. Within each LGA, at least 14 and at most 99 census enumeration areas were selected with probability proportional to size. After a household listing was carried out within the selected enumeration areas, a systematic sample of 6,175 households was drawn. The sample was stratified by LGA and urban and rural areas, it is not self-weighting. For reporting national level results, sample weights are used. A more detailed description of the sample design can be found in Appendix A of the final report and among the technical documents in the 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
Of the 6,175 households selected for the sample, 6,171 were found to be occupied. Of these, 6,071were successfully interviewed for a household response rate of 98.4 per cent. In the interviewed households, 10,252 women aged 15-49 were identified. Of these, 9,982 were successfully interviewed, yielding a response rate of 97.4 per cent. In addition, 6,641 under -5 children were listed in the household questionnaire. Copies of the questionnaires were completed for 6,543 of these children. This corresponds to a response rate of 98.5 per cent. Overall response rates of 95.8 per cent and 96.9 per cent are calculated for the women's and under-5's interviews respectively.
Weighting
Weights were used for the three datasets. The weighting variables are hhweight for the household datasets, wmweight for the women aged 15-49 years dataset and chweight for the children under five dataset.
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 |
|---|---|
| 2005-11-15 | 2006-02-10 |
Data Collection Mode
Face-to-face [f2f]
Supervision
Supervisor's names: 1. Mr. Gorghi Fye 2. Mr. Alieu Bahoum 3. Mr. Alieu Saho 4. Mr. Baba Suwareh 5. Mr. Baboucarr Samba 6. Mr. Amadou Chorr 7. Mr. Baboucarr Daffeh Seven supervisors were appointed to supervise in the 8 local government areas of the Gambia. Their supervision included regular spot checks and detailed review of questionnaires.
Data Collection Notes
The number of teams of enumerators was 7. Each team had 6 enumerators and a supervisor. One of the teams was responsible for the Banjul and Kanifing LGA and the remaining 6 teams were posted to the remaining 6 LGS. The average duration of an interview was 60 minutes. The field work was planned to last 45 days but due to certain reasons such as a larger work load than expected, call backs and other unavoidable delays, the field work was completed approximately 10 days later than scheduled.
Questionnaires
Questionnaires
The questionnaires are based on the MICS III model questionnaire. Although translated versions of the questionnaires could not be produced for the survey, an attempt was made during the training of data collection personnel to translate all the questions into Mandinka, Fula and Wollof to ensure that there was a common approach to administering the questions to respondents in the local languages. All the questionnaires were pre-tested. Based on the results of the pre-test, modifications were made to the wording of some questions and translation problems identified and suitable alternatives discussed.
Data Processing
Data Editing
The Census and Survey program (CSpro3.1) was used for the data entry application. Eighteen main data entry clerks and 18 verifiers were appointed, and they completed the entry and verification in about 2 and a half months. The coders appointed were 20 in number and they completed coding in about one and a half month. Before the analysis started the datasets were free from all structural and inconsistency errors.
Data editing took place at a number of stages throughout the 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.
Data processing and coding manuals were prepared . The data processing manual has detailed editing instructions in addition to instructions on how to use the data entry applications. Intensive trainings were given to the data entry clerks, coders and editors.
Data editing took place at a number of stages throughout the 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.
Data processing and coding manuals were prepared . The data processing manual has detailed editing instructions in addition to instructions on how to use the data entry applications. Intensive trainings were given to the data entry clerks, coders and editors.
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 in the MICS manual http://www.childinfo.org/mics/mics3/manual.php
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 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 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).
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).
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).
Access policy
Contacts
Confidentiality
Confidentiality of respondents is guaranteed and protected by both the old (1977) and the new Statistics Act (2005).
Access conditions
Survey datasets are distributed at no cost for legitimate research.
Interested users are requested to provide an e-mail address, their name, affiliation and type of institution and country of residence. A short description of the objectives of the research project
must also be provided
Users who download the data agree to provide UNICEF with copies of all reports and publications based on the requested data.
The data may not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of UNICEF.
No attempt will be made to re-identify respondents, and no use will be made of the identity of any person discovered inadvertently. Any such discovery would immediately be reported to UNICEF.
Email: mics@unicef.org <mailto:mics@unicef.org>
Global MICS Coordinator
Statistics and Monitoring
Division of Policy and Practice
UNICEF
Three United Nations Plaza
New York, NY 10017
USA
Requests for access to the datasets should be made through the website: www.childinfo.org.
Interested users are requested to provide an e-mail address, their name, affiliation and type of institution and country of residence. A short description of the objectives of the research project
must also be provided
Users who download the data agree to provide UNICEF with copies of all reports and publications based on the requested data.
The data may not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of UNICEF.
No attempt will be made to re-identify respondents, and no use will be made of the identity of any person discovered inadvertently. Any such discovery would immediately be reported to UNICEF.
Email: mics@unicef.org <mailto:mics@unicef.org>
Global MICS Coordinator
Statistics and Monitoring
Division of Policy and Practice
UNICEF
Three United Nations Plaza
New York, NY 10017
USA
Requests for access to the datasets should be made through the website: www.childinfo.org.
Citation requirements
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download of the data files (for datasets obtained on-line)
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download of the data files (for datasets obtained on-line)
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_WB_GMB_2005_MICS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| The Gambia Bureau of Statistics | GBoS | Department of State for Finance and Economic Affairs | Production and documentation of the study |
| International Household Survey Network | IHSN | Department of State for Finance and Economic Affairs | Review of the metadata |
| Rhiannon James | UNICEF | Customisation of Gambia MICS archive for childinfo.org |
Date of Metadata Production
2008-04-24
DDI Document version
Version 01: Edited version of the previous microdata, both the previous and this version of the metadata are based on final edited datasets. However, the data for certain tables, in particular, the education tables have changed insignificantly in the latest survey report.
Slightly edited version of UNICEF's DDI ref. DDI-GMB-MICS2005-2006v1.2
Slightly edited version of UNICEF's DDI ref. DDI-GMB-MICS2005-2006v1.2