Multiple Indicator Cluster Survey 2014
Zimbabwe, 2014
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ZWE_2014_MICS_v01_M
Multiple Indicator Cluster Survey 2014
| Name | Country code |
|---|---|
| Zimbabwe | ZWE |
Multiple Indicator Cluster Survey - Round 5 [hh/mics-5]
Since its inception in 1995, the Multiple Indicator Cluster Surveys, known as MICS, has become the largest source of statistically sound and internationally comparable data on women and children worldwide. In countries as diverse as Costa Rica, Mali and Qatar, trained fieldwork teams conduct face-to-face interviews with household members on a variety of topics – focusing mainly on those issues that directly affect the lives of children and women. MICS has been a major source of data on the Millennium Development Goals (MDG) indicators and will be a major data source in the post-2015 era.
The Multiple Indicator Cluster Survey, Round 5 (MICS5) is the fifth round of MICS surveys, previously conducted around 1995 (MICS1), 2000 (MICS2), 2005-2007 (MICS3) and 2009-2011 (MICS4). MICS was originally developed to support countries measure progress towards an internationally agreed set of goals that emerged from the 1990 World Summit for Children.
The fifth round of Multiple Indicator Cluster Surveys (MICS5) is scheduled for 2013-2016 and survey results are expected to be available from 2015 onwards. Data collected in MICS5 will play a critical role in the final assessment of the MDGs in September 2015 and subsequent surveys in MICS6 will provide the baselines for the Sustainable Development Goals that will follow.
Information on more than 130 internationally agreed-upon indicators is being collected through MICS5. In addition to collecting information on intervention coverage, MICS also explores knowledge of and attitudes to certain topics, and specific behaviors of women, men and children, enabling analysts to gain insights into behaviours that may affect women’s and children’s lives. MICS routinely disaggregates data so that disparities associated with age, gender, education, wealth, location of residence, ethnicity and other characteristics are revealed.
The Zimbabwe Multiple Indicator Cluster Survey (MICS) was conducted between February and April in 2014 by the Zimbabwe National Statistics Agency (ZIMSTAT). Technical and financial support for the survey was coordinated by the United Nations Children’s Fund (UNICEF).
The MICS is designed to provide statistically sound and internationally comparable data essential for developing evidence-based policies and programmes and for monitoring progress towards national goals and global commitments, to enhance the welfare of women and children. Among these global commitments are those emanating from the World Fit for Children Declaration and Plan of Action, the goals of the United Nations General Assembly Special Session on HIV/AIDS (UNGASS), the Education for All Declaration (EFA) and the Millennium Development Goals (MDGs). The Zimbabwe MICS 2014 results are critical for final MDG reporting in 2015, and are expected to form part of the baseline data for the post-2015 era. The MICS plays a critical role in informing national policies such as the Zimbabwe Agenda for Sustainable Socio-Economic Transformation (ZimASSET) October 2013 to December 2018. The study covers the following areas: sample and survey methodology, sample coverage and the characteristics of households and respondents, child mortality, child nutrition, child health, water and sanitation, reproductive health, early childhood development, literacy and education, child protection, HIV and sexual behaviour, mass media and information and communication technology, and tobacco and alcohol use.
The Zimbabwe 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.
Sample survey data [ssd]
- individuals
- households
Version
- v01: Edited, anonymous datasets for public distribution.
Scope
The scope of the Multiple Indicator Cluster Survey includes:
- Household: household information panel, listing of household members, education, child discipline for children 1-14 years of age, household characteristics, water and sanitation, handwashing, indoor residual spraying, use of Insect Treated Nets (ITNs), and salt iodisation;
- Women: woman's information panel, her background characteristics, fertility, birth history, desire for last birth, maternal and newborn health, maternal mortality, postnatal care, marriage/union, illness symptoms, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, contraception, unmet need, sexual behaviour, and knowledge on HIV and AIDS;
- Men: man's information panel, his background characteristics, fertility, marriage/union, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, sexual behaviour, circumcision and knowledge on HIV and AIDS;
- Children: children's characteristics, birth registration, early childhood development, breastfeeding and dietary intake, care of illness, immunisation and anthropometry.
Coverage
National
The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all men aged between 15-54 years and all children under 5 living in the household.
Producers and sponsors
| Name |
|---|
| United Nations Children’s Fund |
| Zimbabwe National Statistics Agency |
| Name | Role |
|---|---|
| United Nations Children’s Fund | Financial and technical support |
| European Union | Financial and technical support |
| United Nations Population Fund | Financial and technical support |
| United Nations Development Fund | Financial and technical support |
| United States Agency for International Development | Financial and technical support |
| Maternal and Child Health Integrated Programme | Financial and technical support |
Sampling
The primary objective of the sample design for the 2014 Zimbabwe MICS was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the ten provinces of the country namely: Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare and Bulawayo. Urban and rural areas in each of the ten provinces were defined as the sampling strata.
A two-stage, stratified sampling approach was used for the selection of the survey sample.
The sample size for the 2014 Zimbabwe MICS was 17,068 households. For the calculation of the sample size the key indicator used was the birth registration.
The number of households selected per enumeration area/cluster for the 2014 Zimbabwe MICS was determined as 25 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 683 sample clusters would need to be selected nationwide.
Power allocation of the total sample size to the ten provinces was used. In total, 683 clusters were allocated to the ten provinces, with the final sample size calculated as 17 075 households (683 cluster*25 sample households per cluster). In each province, the clusters (primary sampling units) were distributed to the urban and rural domains proportionally to the number of urban and rural households in that province. The table below shows the allocation of clusters to the sampling strata. Of the 683 clusters, one cluster in Masvingo Province could not be covered due to floods which affected the Tokwe Mukosi area. Effectively, (682) clusters were covered during data collection.
The 2012 population census frame was used for the selection of clusters. Census enumeration areas were defined as primary sampling units (PSUs), and were selected from each of the sampling strata by using systematic sampling with probability proportional to size (PPS) sampling procedures the measure of size being the number of households in each enumeration area from the 2012 Population Census frame.
The first stage of sampling was thus completed by selecting the required number of enumeration areas from each of the ten provinces by urban and rural strata.
Since the sampling frame (the 2012 population census) was not up-to-date, a new listing of households was conducted in all the sample enumeration areas prior to the selection of households. Enumerators visited all of the selected enumeration areas and listed all households in each enumeration area. Two hundred enumerators were engaged in the listing operation and each enumerator covered a minimum of three clusters during the listing operation. The household listing operation involves three main steps: locating each cluster, preparing the location and sketch maps of each cluster, and the listing of all households found in each cluster. In some cases, segmentation was required for clusters with 300 or more households. The complete listing of large EAs is not cost effective. For that reason, large EAs were subdivided into smaller segments of which only one was selected and listed. Upon arrival in a large EA that may need segmentation, the enumerator first toured the EA and did a quick count to get the estimated number of households in the EA. The MICS standard recommends that each EA with 300 or more households should be subdivided into 2 or 3 segments. Where possible, the segments were roughly of equal size. However, it was important to adopt segment boundaries that were easily identifiable.
The second stage sampling procedure involved the selection of households after the listing operation. Lists of households and sketch maps were prepared by the listers/mappers in the field for each enumeration area. The households were then sequentially numbered from 1 to n (the total number of households in each enumeration area) at the provincial offices, where the selection of 25 households in each enumeration area was carried out using a household selection template.
The survey also had a questionnaire for men that was administered in every third household in each sampled cluster for interviews with all eligible men.
The sampling procedures are more fully described in "Zimbabwe Multiple Indicator Cluster Survey 2014 - Final Report" pp.333-335.
The Zimbabwe MICS 2014 was based on a representative sample of 17,047 households. Of the sampled households, 33.6 percent (5,723 households) were in urban areas and 66.4 percent (11,324 households) were in rural areas with response rates of 96 percent and 98.7 percent, respectively.
Out of the 17,047 households selected for the sample, 16,041 were found to be occupied. Of these, 15,686 were successfully interviewed yielding a household response rate of 97.8 percent. In the interviewed households, 15,376 women (age 15-49 years) were identified. Of these, 14,408 women were successfully interviewed, yielding a response rate of 93.7 percent.
The survey also sampled men (age 15-54 years), but required only a subsample. A man’s questionnaire, for age 15-54 years, was administered in every third household selected. Nine thousand and eight (9,008) men age 15-54 years were listed in the household questionnaires. Questionnaires were completed for 7,914 eligible men, a response rate of 87.9 percent. The response rates for men in urban areas was lower than for those in rural areas with Harare Province having an even lower response rate compared to Bulawayo Province.
There were 10,223 children under 5 years of age listed for the household questionnaires. Questionnaires were completed for 9,884 of these children, a response rate of 96.7 percent. Overall response rates of 91.6 percent, 85.9 percent and 94.5 percent were calculated for the individual interviews of women, men and under-5s, respectively. The household response rates by province were generally high across all provinces ranging from 95 percent for Harare Province to 99.3 percent for Midlands Province.
The 2014 Zimbabwe MICS sample is not self-weighting. Essentially, by allocating an equal number of households to each of the clusters, different sampling fractions were used in each province since the sizes of the provinces 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 and PSU.
Since the number of households in each cluster (PSU) from the 2012 Population Census frame used for the first stage selection and the updated number of households in the enumeration area from the listing are generally different, individual overall probabilities of selection for households in each sample cluster were calculated.
A final 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 in each stratum is equal to: 1/RRh.
Where RRh is the response rate for the sample households in stratum h, defined as the proportion of the number of interviewed households in stratum h out of the number of selected households found to be occupied during the fieldwork in stratum h.
The non-response adjustment factors for the individual women, men, and under-5 questionnaires were applied to the adjusted household weights. Numbers of eligible women, men, and under-5 children were obtained from the list of household members in the Household Questionnaire for households where interviews were completed.
The design weights for the households were calculated by multiplying the inverse of the probabilities of selection for the households in each enumeration area by the non-response adjustment factor for the corresponding stratum. These weights were then standardised (or normalised), one purpose of which was to make the weighted sum of the interviewed sample units equal to the total sample size at the national level. Normalisation was achieved by dividing the full sample weights (adjusted for non-response) by the average of these weights across all households at the national level. This was performed by multiplying the sample weights by a constant factor equal to the unweighted number of households at the national level divided by the weighted total number of households (using the full sample weights adjusted for nonresponse). A similar standardisation procedure was followed in obtaining standardised weights for the individual women, men, and under-5 questionnaires. Adjusted (normalised) weights varied between 0.325 and 2.25 in the 682 sample clusters.
Sample weights were merged to all data sets and analyses were performed by weighting households, women, men, or under-5s with these sample weights.
Since interviews with eligible men were conducted in every third household, the sample weight for men included an additional factor of 3, in addition to the non-response adjustment factor.
Survey instrument
The questionnaires for the Generic MICS were structured questionnaires based on the MICS5 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household information panel, listing of household members, education, child discipline for children 1-14 years of age, household characteristics, water and sanitation, handwashing, indoor residual spraying, use of Insect Treated Nets (ITNs), and salt iodisation.
In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, men age 15-49 and children under age five. The questionnaire was administered to the mother or primary caretaker of the child.
The women's questionnaire includes woman's information panel, her background characteristics, fertility, birth history, desire for last birth, maternal and newborn health, maternal mortality, postnatal care, marriage/union, illness symptoms, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, contraception, unmet need, sexual behaviour, and knowledge on HIV and AIDS.
The men's questionnaire includes man's information panel, his background characteristics, fertility, marriage/union, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, sexual behaviour, circumcision and knowledge on HIV and AIDS.
The children's questionnaire includes children's characteristics, birth registration, early childhood development, breastfeeding and dietary intake, care of illness, immunisation and anthropometry.
The questionnaires are based on the MICS5 model questionnaire. From the MICS5 model English version, the questionnaires were customised and translated into Chichewa and Tumbuka and were pre-tested in Kasungu district during October 2013. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.
In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, observed the place for handwashing, and measured the weights and heights of children age under 5 years. Details and findings of these observations and measurements are provided in the respective sections of the report.
Data collection
| Start | End |
|---|---|
| 2014-02 | 2014-04 |
| Name |
|---|
| Zimbabwe National Statistics Agency |
Training for the fieldwork was conducted for 20 days in February 2014. Training included presentations on interviewing techniques and the contents of the questionnaires. Mock interviews were conducted among trainees to gain practice in asking questions. In addition, trainees received instructions and practiced weighing and measuring the height of children under five years of age. Salt testing, for the presence of iodine, was practiced as part of the training. Towards the end of the training period, trainees spent three days practicing interviewing in areas outside the MICS sample.
The data were collected by 29 mobile teams; each team comprised a team leader, a measurer, four to five interviewers and a driver. Teams were supported by a provincial and national supervisors. The survey did not use Field Editors, even though it is one of the recommendations of MICS and part of the field protocols of the survey programme. However, their duties were assumed by the Field Supervisors. Fieldwork began in February and ended in April 2014.
As part of the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, observed the place for handwashing, types of exterior walls, roofing materials, flooring materials and mosquito nets. Children under five years of age had their weight and height measured and were assessed for oedema. Details and findings of these observations and measurements are provided in the respective sections of the report.
Data processing
Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. The data were entered on 32 desktop computers by 42 data entry operators and nine data entry supervisors. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS programme and adapted to the Zimbabwe questionnaire were used throughout. Data entry started two weeks into data collection in March 2014 and was completed in May 2014. Data were analysed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by the Global MICS team were customized and used for this purpose.
Data appraisal
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 following sampling error measures are presented in this appendix for each of the selected indicators:
- Standard error (se): Standard error is the square root of the variance of the estimate. For survey indicators that are means, proportions or ratios, the Taylor series linearization method is used for the estimation of standard errors. For more complex statistics, such as fertility and mortality rates, the Jackknife repeated replication method is used for standard error estimation.
- Coefficient of variation (se/r) is the ratio of the standard error to the value (r) 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 based on the same sample size. 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 of the survey is as efficient as a simple random sample for a particular indicator, while a deft value above 1.0 indicates an 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 percent of all possible samples of identical size and design.
The Talyor series variance estimation method was used in the calculation of sampling errors. The variance estimator takes into account the different aspects of the sample design, such as the stratification and clustering.
For the calculation of sampling errors from MICS data, programs developed in CSPro Version 5.0, SPSS Version 21 Complex Samples module and CMRJack103 have 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. Given the use of normalized weights, by comparing the weighted and unweighted counts it is possible to determine whether a particular domain has been under-sampled or over-sampled compared to the average sampling rate. If the weighted count is smaller than the unweighted count, this means that the particular domain had been over-sampled.
Sampling errors are calculated for indicators of primary interest, for the national level, for urban and rural areas, and for all provinces. Three of the selected indicators are based on households members, 12 are based on women, 3 are based on men, and 4 are based on children under 5.
A series of data quality tables are available to review the quality of the data and include the following:
- Age distribution of the household population
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Age distribution of children under 5 in household and children under 5 questionnaires
- Birth date reporting: Household population
- Birth date and age reporting: Women
- Birth date and age reporting: Men
- Birth date and age reporting: Under-5s
- Birth date reporting: Children, adolescents and young people
- Birth date reporting: First and last births
- Completeness of reporting
- Completeness of information for anthropometric indicators: Underweight
- Completeness of information for anthropometric indicators: Stunting
- Completeness of information for anthropometric indicators: Wasting
- Heaping in anthropometric measurements
- Observation of birth certificates
- Observation of child health cards
- Observation of women’s health cards
- Observation of bednets and places for handwashing
- Presence of mother in the household and the person interviewed for the under-5 questionnaire
- Selection of children age 1-14 years for the child labour and child discipline modules
- School attendance by single age
- Sex ratio at birth among children ever born and living
- Births by periods preceding the survey
- Reporting of age at death in days
- Reporting of age at death in months
- Completeness of information on siblings
- Sibship size and sex ratio of siblings
The results of each of these data quality tables are shown in appendix D in document "Zimbabwe Multiple Indicator Cluster Survey 2014 - Final Report" pp.364-391.
Access policy
UNICEF
UNICEF
http://mics.unicef.org/surveys
Cost: None
Depositor information
| Name |
|---|
| United Nations Children's Fund |
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| Childinfo | UNICEF | http://www.childinfo.org/mics4_surveys.html | mics@unicef.org |
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | 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 a description of the objectives of any research project that will be using the data prior to authorizing their distribution.
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, Zimbabwe National Statistics Agency. Zimbabwe Multiple Indicator Cluster Survey (MICS) 2014, Ref. ZWE_2014_MICS_v01_M. Dataset downloaded from [url] on [date].
Disclaimer and copyrights
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.
Contacts
| Name | Affiliation | |
|---|---|---|
| Evelyn Marima | Zimbabwe National Statistics Agency | emarima@zimstat.co.zw |
| Handrix Chigiji | Zimbabwe National Statistics Agency | hchigiji@zimstat.co.zw |
Metadata production
DDI_ZWE_2014_MICS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Data Group | The World Bank | Documentation of the DDI |
2015-11-16
Metadata version
Version 01 (November 2015)