Multiple Indicator Cluster Survey 2011, Nyanza Province
Kenya, 2011
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Reference ID
KEN_2011_MICS-NP_v01_M
Producer(s)
Kenya National Bureau of Statistics, United Nations Children’s Fund
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Jul 28, 2016
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Jul 28, 2016
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Identification
Survey ID Number
KEN_2011_MICS-NP_v01_M
Title
Multiple Indicator Cluster Survey 2011, Nyanza Province
Subtitle
Nyanza Province
Country/Economy
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
Multiple Indicator Cluster Survey - Round 4 [hh/mics-4]
Series Information
The Multiple Indicator Cluster Survey (MICS) 2011 was conducted to provide comprehensive and disaggregated data to fill the existing gap, particularly at the county level. The survey was the first of its kind to be conducted at the devolved level. MICS was a follow-up to the MICS 2008 conducted in 13 districts in Eastern Province and the 2009 Mombasa Informal Settlement Survey.
Abstract
The Nyanza Province Multiple Indicator Cluster Survey (MICS) was carried out in 2011 by the Kenya National Bureau of Statistics (KNBS) in collaboration with County and Provincial administration. The survey covered all the 6 constituent counties of Nyanza, namely: Siaya, Kisumu, Homa Bay, Migori, Kisii, and Nyamira. Financial and technical support was provided by the United Nations Children's Fund (UNICEF).
The Nyanza Province survey was conducted as part of the fourth global round of MICS surveys (MICS4). MICS is an international household survey program developed by UNICEF, this survey was based on a 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. Additional information on the global MICS project may be obtained from www. Childinfo.org. The objective of Nyanza MICS 2011 was to provide lower-level estimates relating to children and women residing in the six counties of the region. Particular emphasis was on reproductive health, child health and mortality, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, disability, HIV/AIDS, and orphan hood.
The Nyanza MICS is a nationally representative survey of 17,047 households, comprising 14,408 women in the 15-49 years age group. 7,914 men age 15-54 years and 10,223 children under 5 years of age. The sample allows for the estimation of some key indicators at the national, provincial and urban/rural levels. A two stage, stratified cluster sampling approach was used for the selection of the survey sample.
The primary objectives of the 2011 Nyanza Province Multiple Indicator Cluster Survey are:
1. To provide up-to-date information for assessing the situation of children and women in Nyanza Province.
2. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration and other internationally agreed upon goals, as a basis for future action.
3. To contribute to the improvement of data and monitoring systems in Nyanza Province and to strengthen technical expertise in the design, implementation, and analysis of such systems.
4. To generate data on the situation of children and women, including the identification of vulnerable groups and disparities, to inform policies and interventions.
The Nyanza Province survey was conducted as part of the fourth global round of MICS surveys (MICS4). MICS is an international household survey program developed by UNICEF, this survey was based on a 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. Additional information on the global MICS project may be obtained from www. Childinfo.org. The objective of Nyanza MICS 2011 was to provide lower-level estimates relating to children and women residing in the six counties of the region. Particular emphasis was on reproductive health, child health and mortality, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, disability, HIV/AIDS, and orphan hood.
The Nyanza MICS is a nationally representative survey of 17,047 households, comprising 14,408 women in the 15-49 years age group. 7,914 men age 15-54 years and 10,223 children under 5 years of age. The sample allows for the estimation of some key indicators at the national, provincial and urban/rural levels. A two stage, stratified cluster sampling approach was used for the selection of the survey sample.
The primary objectives of the 2011 Nyanza Province Multiple Indicator Cluster Survey are:
1. To provide up-to-date information for assessing the situation of children and women in Nyanza Province.
2. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration and other internationally agreed upon goals, as a basis for future action.
3. To contribute to the improvement of data and monitoring systems in Nyanza Province and to strengthen technical expertise in the design, implementation, and analysis of such systems.
4. To generate data on the situation of children and women, including the identification of vulnerable groups and disparities, to inform policies and interventions.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Individuals
- Households
- Households
Version
Version Description
- v01: Edited, anonymous datasets for public distribution.
Scope
Notes
The scope of the Multiple Indicator Cluster Survey includes:
HOUSEHOLD INFORMATION PANEL
- Household listing form
- Education
- Water and sanitation
- Household characteristics
- Insecticide treated nets
- Indoor residual spraying
- Orphaned and vulnerable children
- Child labor
- Child discipline
- Disability
- Handwashing facility
- Salt iodization
WOMEN'S INFORMATION PANEL
- Child mortality
- Birth history
- Tetanus toxoid (TT)
- Maternal and newborn health
- Marriage/union
- Contraception
- Female genital mutilation/cutting
- Attitudes toward domestic violence
- Sexual behavior
- HIV/AIDS
UNDER-FIVE CHILD INFORMATION PANEL
- Birth registration and early learning
- Child development
- Vitamin A
- Breastfeeding
- Care of illness
- Malaria
HOUSEHOLD INFORMATION PANEL
- Household listing form
- Education
- Water and sanitation
- Household characteristics
- Insecticide treated nets
- Indoor residual spraying
- Orphaned and vulnerable children
- Child labor
- Child discipline
- Disability
- Handwashing facility
- Salt iodization
WOMEN'S INFORMATION PANEL
- Child mortality
- Birth history
- Tetanus toxoid (TT)
- Maternal and newborn health
- Marriage/union
- Contraception
- Female genital mutilation/cutting
- Attitudes toward domestic violence
- Sexual behavior
- HIV/AIDS
UNDER-FIVE CHILD INFORMATION PANEL
- Birth registration and early learning
- Child development
- Vitamin A
- Breastfeeding
- Care of illness
- Malaria
Coverage
Geographic Coverage
National
Universe
The survey covered all de jure household members (usual residents), all women aged between 15-49 years and all children under 5 living in the household.
Producers and sponsors
Primary investigators
| Name |
|---|
| Kenya National Bureau of Statistics |
| United Nations Children’s Fund |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| United Nations Children’s Fund | UNICEF | Financial and technical support |
Sampling
Sampling Procedure
The sample for the Nyanza Province Multiple Indicator Cluster Survey (MICS) was designed to provide estimates for a large number of indicators on the situation of children and women at the provincial level, for urban and rural areas, and for counties: Siaya, Migori, Kisumu, Homa Bay, Kisii, and Nyamira. The urban and rural areas within each County were identified as the main sampling strata and the sample was selected in two stages. The primary sampling units (PSUs) for the survey were the recently created enumeration areas (EAs) based on the 2009 Kenya Population and Housing Census while the households were the ultimate sampling units.
A stand-alone statistical frame for each of the Nyanza counties based on the 2009 census EAs was created for the purpose of MICS. Within each stratum, a specified number of census enumeration areas were selected systematically with probability proportional to size. A complete listing of all households in the selected EAs was undertaken by identifying and mapping all existing structures and households. The listing process ensured that the EAs had one measure of size (MOs). One MOs was defined as an EA having an average of 100 households. EA with less than 50 households was amalgamated with the most convenient adjoining EA. On the other hand, the EAs with more than 149 households were segmented during household listing and eventually one segment scientifically selected and developed into a cluster. After a household listing exercise within the selected enumeration areas, a systematic sample of 25 households was drawn from each of the sampled enumeration area. The sample was stratified by County, urban and rural areas, and is not self-weighting. For reporting provincial level results, sample weights are used. A more detailed description of the sample design can be found in Appendix A.
A stand-alone statistical frame for each of the Nyanza counties based on the 2009 census EAs was created for the purpose of MICS. Within each stratum, a specified number of census enumeration areas were selected systematically with probability proportional to size. A complete listing of all households in the selected EAs was undertaken by identifying and mapping all existing structures and households. The listing process ensured that the EAs had one measure of size (MOs). One MOs was defined as an EA having an average of 100 households. EA with less than 50 households was amalgamated with the most convenient adjoining EA. On the other hand, the EAs with more than 149 households were segmented during household listing and eventually one segment scientifically selected and developed into a cluster. After a household listing exercise within the selected enumeration areas, a systematic sample of 25 households was drawn from each of the sampled enumeration area. The sample was stratified by County, urban and rural areas, and is not self-weighting. For reporting provincial level results, sample weights are used. A more detailed description of the sample design can be found in Appendix A.
Response Rate
Of the 7,500 households selected for the sample 6,994 were found to be occupied. Of these 6,828 were successfully interviewed for a household response rate of 97.6 percent. In the interviewed households 6,581 women (age 15-49 years) were identified. Of these 5,908 were successfully interviewed, yielding a response rate of 89.8 percent within interviewed households. In addition 5,157 children under age five were listed in the household questionnaire. Questionnaires were completed for 5,045 of these children, which corresponds to a response rate of 97.8 percent within interviewed households. Overall response rates of 87.6 percent and 95.5 percent are calculated for the women's and under-5's interviews respectively.
There are some differences in the response rates by urban and rural areas. Overall household responses rates were 98 percent for rural areas and 94 percent for urban areas. The same trends was observed for overall women response rates and under-five overall response rates, in favor of rural areas. At the County levels, household response rates were all above 95 percent, but differences were observed for women response rates across counties.
Overall women response rates were lowest in Nyamira County at 84 percent and highest in Siaya at 95 percent. Given the fact that Nyamira has response rates below 85 percent, the results for this region or residence should be interpreted with some caution, as the response rate is low. Similarly overall under-five response rates were highest in Siaya County and lowest in Nyamira County. The reasons for the lower response rates for Nyamira County are not readily available, but a range of explanations for this lower performance include that a large section of the population who were not reachable on certain prayer days, in addition, heavy downpours affected availability of respondents during the whole day while working on farms.
There are some differences in the response rates by urban and rural areas. Overall household responses rates were 98 percent for rural areas and 94 percent for urban areas. The same trends was observed for overall women response rates and under-five overall response rates, in favor of rural areas. At the County levels, household response rates were all above 95 percent, but differences were observed for women response rates across counties.
Overall women response rates were lowest in Nyamira County at 84 percent and highest in Siaya at 95 percent. Given the fact that Nyamira has response rates below 85 percent, the results for this region or residence should be interpreted with some caution, as the response rate is low. Similarly overall under-five response rates were highest in Siaya County and lowest in Nyamira County. The reasons for the lower response rates for Nyamira County are not readily available, but a range of explanations for this lower performance include that a large section of the population who were not reachable on certain prayer days, in addition, heavy downpours affected availability of respondents during the whole day while working on farms.
Weighting
The Nyanza province Multiple Indicator Cluster Survey sample is not self-weighting. Essentially by allocating equal numbers of households to each of the regions, different sampling fractions were used in each region since the size of the regions varied. For this reason, sample weights were calculated and these were used in the subsequent analyses of the survey data. The major component of the weight is the reciprocal of the sampling fraction employed in selecting the number of sample households in that particular sampling stratum (h) and PSU (i): The term fhi, the sampling fraction for the i-th sample PSU in the h-th stratum, is the product of probabilities of selection at every stage in each sampling stratum: where pshi is the probability of selection of the sampling unit at stage s for the i-th sample PSU in the h-th sampling stratum.
Since the estimated number of households in each enumeration area (PSU) in the sampling frame used for the first stage selection and the updated number of households in the enumeration area from the listing were different, individual sampling fractions for households in each sample enumeration area (cluster) were calculated. The sampling fractions for households in each enumeration area (cluster) therefore included the first stage probability of selection of the enumeration area in that particular sampling stratum and the second stage probability of selection of a household in the sample enumeration area (cluster).
A second component in the calculation of sample weights takes into account the level of non-response for the household and individual interviews. The adjustment for household non-response is equal to the inverse value of: RRh = Number of interviewed households in stratum h/ Number of occupied households listed in stratum h. After the completion of fieldwork, response rates were calculated for each sampling stratum. These were used to adjust the sample weights calculated for each cluster. Response rates in the Nyanza province Multiple Indicator Cluster Survey are shown in Table HH.1 in this report. Similarly, the adjustment for non-response at the individual level (women and under-5 children) for each stratum is equal to the inverse value of: RRh = Completed women's (or under-5) questionnaires in stratum h / Eligible women (or under-5s) in stratum h. The non-response adjustment factors for women's and under-5's questionnaires are applied to the adjusted household weights. Numbers of eligible women and under-5 children were obtained from the roster of household members in the Household Questionnaire for households where interviews were completed.
Since the estimated number of households in each enumeration area (PSU) in the sampling frame used for the first stage selection and the updated number of households in the enumeration area from the listing were different, individual sampling fractions for households in each sample enumeration area (cluster) were calculated. The sampling fractions for households in each enumeration area (cluster) therefore included the first stage probability of selection of the enumeration area in that particular sampling stratum and the second stage probability of selection of a household in the sample enumeration area (cluster).
A second component in the calculation of sample weights takes into account the level of non-response for the household and individual interviews. The adjustment for household non-response is equal to the inverse value of: RRh = Number of interviewed households in stratum h/ Number of occupied households listed in stratum h. After the completion of fieldwork, response rates were calculated for each sampling stratum. These were used to adjust the sample weights calculated for each cluster. Response rates in the Nyanza province Multiple Indicator Cluster Survey are shown in Table HH.1 in this report. Similarly, the adjustment for non-response at the individual level (women and under-5 children) for each stratum is equal to the inverse value of: RRh = Completed women's (or under-5) questionnaires in stratum h / Eligible women (or under-5s) in stratum h. The non-response adjustment factors for women's and under-5's questionnaires are applied to the adjusted household weights. Numbers of eligible women and under-5 children were obtained from the roster of household members in the Household Questionnaire for households where interviews were completed.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2011-10-01 | 2011-12-01 |
Data Collection Mode
Face-to-face [f2f]
Supervision
The teams were led by a Supervisor, overseen by the District Statistical Officer (DSO) on a daily basis, who also attended the 4 days training program. The county team was led by a county coordinator who was in charge of managing all the quality assurance activities of the teams in each County. One team was given two days to list an EA. The whole exercise of listing was also monitored by the UNICEF independent team that included a consultant.
Data Collectors
| Name | Abbreviation |
|---|---|
| Kenya National Bureau of Statistics | KNBS |
Questionnaires
Questionnaires
Three sets of questionnaires were used in the survey:
1. A household questionnaire which was used to collect information on all de jure household members (usual residents), the household, and the dwelling
2. A women's questionnaire administered in each household to all women aged 15-49 years.
3. An under-5 questionnaire, administered to mothers or caretakers for all children under 5 living in the household.
1. A household questionnaire which was used to collect information on all de jure household members (usual residents), the household, and the dwelling
2. A women's questionnaire administered in each household to all women aged 15-49 years.
3. An under-5 questionnaire, administered to mothers or caretakers for all children under 5 living in the household.
Data Processing
Data Editing
Data were entered using the CSPro software. The data were entered into microcomputers by 23 data entry operators and 4 data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 program and adapted to the Nyanza Province questionnaire were used throughout. Data processing began three weeks after commencing data collection in October 2011 and was completed in January 2012.Data were analyzed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.
Data Appraisal
Estimates of Sampling Error
Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.The sample of respondents selected in the Nyanza province Multiple Indicator Cluster Survey is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected.
The following sampling error measures are presented in this appendix for each of the selected indicators:
- Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc.). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors.
- Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error.
- Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design.
- Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r - 2.se) of the statistic in 95 per cent of all possible samples of identical size and design.
For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.
Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions. Tables SE.1 to SE.9 show the list of indicators for which sampling errors were calculated for each indicator and for several domains i.e. whole province, urban areas, rural areas and the six counties.
The following sampling error measures are presented in this appendix for each of the selected indicators:
- Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc.). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors.
- Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error.
- Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design.
- Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r - 2.se) of the statistic in 95 per cent of all possible samples of identical size and design.
For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.
Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions. Tables SE.1 to SE.9 show the list of indicators for which sampling errors were calculated for each indicator and for several domains i.e. whole province, urban areas, rural areas and the six counties.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| MICS Program Manager | UNICEF | mics@unicef.org | Link |
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 research, with the condition that we receive a description of the objectives of any research project that will be using the data prior to authorizing their distribution.
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.
Example:
United Nations Children's Fund, Kenya National Bureau of Statistics. Kenya Multiple Indicator Cluster Survey (MICS) 2011, Ref. KEN_2011_MICS_v01_M. Dataset downloaded from [url] on [date].
- 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.
Example:
United Nations Children's Fund, Kenya National Bureau of Statistics. Kenya Multiple Indicator Cluster Survey (MICS) 2011, Ref. KEN_2011_MICS_v01_M. Dataset downloaded from [url] on [date].
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| MICS Program Manager | UNICEF | mics@unicef.org | Link |
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_KEN_2011_MICS-NP_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2016-07-07
DDI Document version
v01 (July 2016)