{"doc_desc":{"title":"Multiple Indicator Cluster Survey (MICS3)-2007","idno":"DDI-NGR-NBS-MICS3 2007-v1.2","producers":[{"name":"National Bureau of Statistics","abbr":"","affiliation":"Federal Republic of Nigeria","role":"Data Producers"}],"prod_date":"2008-08-26","version_statement":{"version":"Version 1.2"}},"study_desc":{"title_statement":{"idno":"NGA-NBS-MICS3 2007-v1.2","title":"Multiple Indicator Cluster Survey MICS3 (2007), Nigeria","sub_title":"Third round","alternate_title":"MICS3,  NIGERIA 2007","translated_title":"English"},"authoring_entity":[{"name":"National  Bureau of Statistics [nbs]","affiliation":"Federal Government of Nigeria"}],"oth_id":[{"name":"Central Bank of Nigeria","affiliation":"CBN [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Nigeria Institute of Social Economic Research","affiliation":"NISER [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Office of the Senior Special Assisstance to the President","affiliation":"MDG office [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"National Agency for the Prohibition of Ttrficking In Persons","affiliation":"NAPTIP [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Federal Ministry of Education","affiliation":"FME [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Federal Ministry of Health","affiliation":"FMH [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"National Population Commission","affiliation":"NPopC [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Federal Ministry of Justice","affiliation":"FMJ [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"National Planning Commission","affiliation":"NPC [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Federal Ministry of Agriculture & Worter Rresouces","affiliation":"FMA&WR [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"National Agency for Food and Drug Administration and Control","affiliation":"NAFDAC [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Ministry of Finance, Budget & Planning","affiliation":"MFB&P [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"Federal Ministry of Environment","affiliation":"FME [FG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"State Planing Commission, Umuahia","affiliation":"ASPC [SG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"State Planing Commission, Calabar","affiliation":"CRSPC [SG]","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"World Health Organisation","affiliation":"WHO","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"},{"name":"The Bridge Inter  Magazine","affiliation":"Jounalism","email":"","role":"Added to the value of questionnaire during stakeholders Meeting"}],"production_statement":{"producers":[{"name":"United Nation Children Educational Fund","abbr":"UNICEF","affiliation":"UNICEF, Nigeria","role":"Funding & Technical assistance in Stakeholders meetings, monitoring"}],"copyright":"(c)NBS 2007","funding_agencies":[{"name":"Fedral Government of Nigeria","abbr":"FG","role":"Funding"},{"name":"United Nation Children Educational Fund","abbr":"UNICEF","role":"Funding"},{"name":"National Bureau of Statistics","abbr":"NBS","role":"Funding"}]},"distribution_statement":{"contact":[{"name":"G.O Adewoye","affiliation":"Director Census & Surveys","email":"goadewoye@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mrs A.N. Adewimbi","affiliation":"Head of  Information and Comnucation Technology Department","email":"taadewnmbi@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Biyi Fafunmi","affiliation":"Data Curator","email":"biyifafunmi@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mrs A. A. Akinsanya","affiliation":"Data Archivist","email":"paakinsanya@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"National Bureau of Statistics","affiliation":"","email":"feedback@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"}],"deposit_date":"","distribution_date":""},"series_statement":{"series_name":"Multiple Indicator Cluster Survey - Round 3 [hh\/mics-3]","series_info":"The Multiple Indicator Cluster Survey, Round 3 (MICS3) is the third round of MICS surveys, previously conducted around 1995 (MICS1) and 2000(MICS2).  MICS surveys are designed by UNICEF, and implemented by National Bureau of Statistics from Nigeria. MICS was designed to monitor various indicators identified at the World Summit for children and the Millennium Development Goals."},"version_statement":{"version":"version 1.2","version_date":"2008-08-26","version_notes":"v1.0 This is the first version used to generate the first set of tables original release in 2007 \nv1.2 The data set was re-edited to fixed the Day, Month and Year under Age variable when missing\n        - Under Education variable was corrected when missing, the program used is also attached under program file in the external resources \n        -The Urban and Rural classfication were corrected"},"bib_citation_format":"","study_authorization":{"date":""},"study_info":{"keywords":[{"keyword":"Total number of father","vocab":"","uri":""},{"keyword":"Total number of women 15-49","vocab":"","uri":""},{"keyword":"Total number of children 5-17","vocab":"","uri":""},{"keyword":"Total number of children under 5-17","vocab":"","uri":""},{"keyword":"Ever attended school","vocab":"","uri":""},{"keyword":"Highest level of school attended","vocab":"","uri":""},{"keyword":"Medical support","vocab":"","uri":""},{"keyword":"Hours worked","vocab":"","uri":""},{"keyword":"Main source of drinking water","vocab":"","uri":""},{"keyword":"Treat water to make safer for drinking","vocab":"","uri":""},{"keyword":"Kind of toilet facility","vocab":"","uri":""},{"keyword":"Religion","vocab":"","uri":""},{"keyword":"Mother tongue","vocab":"","uri":""},{"keyword":"Cooking location","vocab":"","uri":""},{"keyword":"Mobile phone","vocab":"","uri":""},{"keyword":"Car or truck","vocab":"","uri":""},{"keyword":"Sons living with you","vocab":"","uri":""},{"keyword":"Daughters living with you","vocab":"","uri":""},{"keyword":"Sons living not with you","vocab":"","uri":""},{"keyword":"Daughters not living with you","vocab":"","uri":""},{"keyword":"Has immunization card","vocab":"","uri":""},{"keyword":"Antenatal care","vocab":"","uri":""},{"keyword":"Tested for hiv\/aids","vocab":"","uri":""},{"keyword":"Place of delivery","vocab":"","uri":""},{"keyword":"Child weighed at birth","vocab":"","uri":""},{"keyword":"Ever breastfeed","vocab":"","uri":""},{"keyword":"Avoid pregnancy","vocab":"","uri":""},{"keyword":"Ever circumcised","vocab":"","uri":""},{"keyword":"Condom","vocab":"","uri":""},{"keyword":"Nets","vocab":"","uri":""}],"topics":[{"topic":"economic systems and development [1.4]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"income, property and investment\/saving [1.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"rural economics [1.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"business\/industrial management and organisation [2.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"LABOUR AND EMPLOYMENT [3]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"domestic political issues [4.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"mass political behaviour, attitudes\/opinion [4.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"basic skills education [6.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"compulsory and pre-school education [6.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"post-compulsory education [6.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"vocational education [6.7]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"information society [7.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"childbearing, family planning and abortion [8.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"drug abuse, alcohol and smoking [8.3]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"general health [8.4]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"health care and medical treatment [8.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"environmental degradation\/pollution and protection [9.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"housing [10.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"TRANSPORT, TRAVEL AND MOBILITY [11]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"children [12.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"family life and marriage [12.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"social and occupational mobility [12.8]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"community, urban and rural life [13.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"social change [13.7]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"fertility [14.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"social welfare systems\/structures [15.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"}],"abstract":"The Multiple Indicator Cluster Survey (MICS) was conceptualized to monitor the progress of Child Survival,\nDevelopment, Protection and Participation (CSDPP) Programme as well as to serve as means of data\ngenerating mechanism for measuring the achievement and gaps in the targets of the millennium development\ngoals (MDGs), particularly as it may affect the children and women. At the World Summit for Social\nDevelopment in 1995, the need was also stressed for better social statistics if social development had to\nmove to centre stage for the cause of the children of the world.\nThe first in the series of the Multiple Indicator Cluster Survey (MICS1) was conducted in 1995 by the\nFederal Office of Statistics (FOS), now National Bureau of Statistics (NBS), with technical and funding\nassistance from UNICEF. Since then, MICS has been institutionalized within the National Integrated Survey\nof Households (NISH) in the National Bureau of Statistics, as a process of collecting regular, reliable and\ntimely social statistics. The second round of MICS was conducted in 1999 with a better strategy for the\nexecution of the survey from planning to report writing. Expectedly, the current edition of the Multiple\nIndicator Cluster Survey (MICS3) was better planned, executed and has achieved the aim of providing\nreliable data for monitoring progress of the Nigerian children and women, and the Millennium Development\nGoals.\nThis report would have been impossible without the commitment of UNICEF, which provided technical and\nfinancial assistance for the project. Worthy of mention also is the significant contribution of the officials\nfrom UNICEF, Nigeria, namely: the Representative Mr. Ayalew Abai, Dr. Ahmed El Bashir Ibrahim (Chief,\nPlanning & Communication) and Mr. Johnson Awotunde, M&E Specialist. The National Bureau of Statistics\nacknowledges the support and cooperation from all other stakeholders who took part in the project in various\nforms. These include the National Planning Commission, the Federal Ministry of Health, the Federal\nMinistry of Education, the Federal Ministry of Women Affairs, the Federal Ministry of Information and\nCommunication, the National Population Commission, various Non Government Oganizations. Others\ninclude UNDP, DFID, World Bank and the MDG Office.\n This report is based on the Nigeria Multiple Indicator Cluster Survey, conducted in 2007 by the National\nBureau of Statistics (NBS), Nigeria with financial and technical support from UNICEF, Nigeria. The survey\nwhich was Nigeria copy of global MICS3 was a response to the needs to monitor progress towards goals and\ntargets emanating from recent international agreements including the Millennium Declaration, adopted by all\n191 United Nations Member States in September 2000, and the Plan of Action of A World Fit For Children,\nadopted by 189 Member States at the United Nations Special Session on Children in May 2002. Both of\nthese commitments build upon promises made by the international community at the 1990 World Summit for\nChildren.\nThe Federal Government of Nigeria has in recent times launched a number of development initiatives to\nimprove the economic and social life of its people. The National Programme for the Eradication of Poverty\n(NAPEP) is concerned with strategies for poverty reduction; the National Action Committee on HIV\/AIDS\n(NACA) has the mandate for planning, implementing and monitoring programmes for control of HIV\/AIDS;\nthe National Economic Empowerment and Development Strategy (NEEDS) focuses on wealth creation,\nemployment generation, corruption elimination and general value orientation; the state and local government\nextensions of NEEDS are State Economic Empowerment and Development Strategy (SEEDS) and Local\nEconomic Empowerment and Development Strategy (LEEDS) respectively. These and other programmes\nare commitments towards targets as those contained in the Millennium Development Goals.\nThe Federal Government has also expressed strong commitment to, and declared as a matter of high priority,\nefforts to monitor and evaluate progress towards the attainment of the benchmarks established in these\nnational and other global goals. The National Bureau of Statistics (NBS) with financial and technical support\nfrom international development partners and donors like UNICEF has been involved in this effort through\nprovision of relevant data to monitor, evaluate and advise necessary adjustments in development policies and\nprogrammes. The NBS, in recent times had conducted a number of national sample surveys mostly within\nglobal generic contexts. The Nigeria Living Standard Survey (NLSS), the General Household Survey (GHS),\nthe Core Welfare Indicator Questionnaire Survey (CWIQ) and the 1999 Multiple Indicator Cluster Survey\n(MICS2) are examples. MICS Nigeria 2007 has been designed to measure progress towards achievements of\nthe Millennium Development Goals (MDG) and other international targets like the Abuja Declaration on\nmalaria which are mainstreamed into the above-stated national commitments. Nigeria\u2019s MICS3 is, therefore,\nbound to improve the country\u2019s data base and provide a valuable tool for evidence-based planning to\nsurmount its development challenges.\nMore specifically, MICS Nigeria 2007 should assist monitoring and evaluating UNICEF country\nprogrammes including those on immunization, vitamin A supplementation, child development, child and\nwomen rights and protection among others. The survey should also build survey capability and enhance data\nanalysis experience at the NBS. This executive summary report presents results on principal topics covered\nin MICS Nigeria 2007 expressed in outcome and impact indicators1 that are important for designing,\nmonitoring and evaluating progress of national programmes and provide a means for comparing the situation\nin Nigeria with that in other countries.\n2. Survey Objectives\nMICS Nigeria 2007 should provide up-to-date information on the situation of children and women in\nNigeria, strengthen national statistical capacity by focusing on data gathering, quality of survey information,\nstatistical tracking and analysis, contribute to the improvement of data and monitoring systems in Nigeria\nand strengthen technical expertise in the design, implementation, and analysis of such systems. The survey\nshould also furnish data needed for monitoring progress toward the Millennium Development Goals, and\ntargets of A World Fit for Children (WFFC) among others, measure progress towards achievements of the\ngoals of NEEDS and its state and local government extensions, provide statistics to complement and assess\nthe quality of data from recent national surveys like Nigeria Living Standard Survey (NLSS), Nigeria Core\nWelfare Indicator Questionnaires (CWIQ) and the National Demographic and Health Survey (NDHS).\n\n1 For more information on the definitions, numerators, denominators and algorithms of Multiple Indicator\nCluster Surveys (MICS) and Millennium Development Goals (MDG) indicators covered in the survey: see\nChapter 1, Appendix 1 and Appendix 7 of the MICS Manual \u2013 Multiple Indicator Cluster Survey Manual 2005:\nMonitoring the Situation of Children and Women, also available at www.childinfo.org.","time_periods":[{"start":"2007-03","end":"2007-04","cycle":"30 Days"}],"coll_dates":[{"start":"2007-03","end":"2007-04","cycle":"30 Days"}],"nation":[{"name":"Nigeria","abbreviation":"NGA"}],"geog_coverage":"National Zone State Local government Sector (Urban,Rural)","analysis_unit":"Individual, Household","universe":"The survey covered:\n\nAll de jure household members (usual residents);\nAll women aged 15-49 years resident in the household and; \nAll children aged 0 <5 years (under age 5) resident in the household.","data_kind":"Sample survey data [ssd]","notes":"HOUSEHOLD\n(1)      Household Information Panel\n(2)      Demographic Characteristics\n(3)      Water and Sanitation\n(4)      Household Characteristics \n(5)      Household use of insecticide treated nets\n(6)      Children Ophaned and made vulnerable children\n(7)      Child Labour\n(8)      Maternal Mortality\n(9)      Salt Iodization\n\nWOMEN\n(1)     Women Information Panel\n(2)     Child Mortality\n(3)     Tetanus Toxoid\n(4)     Maternal and Newborn Health\n(5)     Marriage\/Union\n(6)     Contraception and UNMET Need\n(7)     Female Genital Mutilation\/Cutting\n(8)     HIV\/AIDS\n(9)     Sexual Behaviour\n\nCHILDREN\n(1)     Information Panel\n(2)     Birth Registration and Early Learning \n(3)     Child Development\n(4)     Vitamin A\n(5)     Breastfeeding\n(6)     Care of Illness\n(7)     Malaria \n(8)     Immunization\n(9)     Anthropometry","ex_post_evaluation":{"completion_date":"","type":""}},"method":{"data_collection":{"data_collectors":[{"name":"National Bureau of Statistics","abbr":"NBS","role":"","affiliation":"Federal Republic of Nigeria"}],"sampling_procedure":"Two - stage cluster sample design was adopted in each state where Enumeration Areas (EAs) form first stage or Primary Sampling Units (PSUs) and Housing Units (HUs) form second stage or Ultimate Sampling Units (USUs)\nEAs demarcated for 1991 Population Census  served as first stage sampling frame \nHousehold listing was conducted in selected first stage units to provide second stage sampling frame\nSample sizes: Within each state of the federation 750 HUs was drawn from 30 EAs. \nThere were 36 states and Federal Capital Territory (FCT), this makes 37, which amounts to 27,750 Housing Units drawn from 1,110 EAs.\n\nThe sample for the Nigeria MICS3 was designed to provide estimates on a large number of indicators on the\nsituation of children and women at the country level, for urban and rural areas; and for each of the 36 States\nof the Federation and the Federal Capital Territory of Abuja. The States were the main reporting domains.\nThe sample design was two-stage in each state, where a systematic sample of 30 census enumeration areas\n(EAs) was selected with equal probability to form the first stage or primary sampling units (PSUs). The\nupdated 1991 Population Census Enumeration Area demarcation was used because the latest demarcation\nwas not available for use at the time MICS3 sample was designed. Also, information about the household\ncomposition of enumeration areas was not available to permit selection of EAs with probability proportional\nto number of households in the enumeration area.\nHousehold listing was conducted in each of the selected EAs to provide an adequate, up-to-date frame of\nhousing units as the secondary sampling units (SSUs). A systematic sample of 25 housing units was\nsubsequently drawn with equal probability within each of the selected EAs and all the households in each of\nthe selected HUs were canvassed. Thus, at state level, 750 HUs were drawn from 30 EAs which meant\n27,750 HUs from 1,110 EAs at the national level. The sample was stratified by states and was hardly self\nweighting at either state or national level. Hence, sample weights were used for reporting state or national\nresults.\nAll the selected enumeration areas were successfully canvassed. Table HH.1 presents a summary of results\nof interviews of households, individual women aged 15 \u2013 49 years and children aged less than five years. A\ntotal of 28,603 households (20,825 rural and 7,778 in the urban sectors) were sampled. The total number of\noccupied sampled households was 28,431 including 20,735 rural and 7,696 urban households. The total\nnumber of interviewed households was 26,735 including 19,569 rural and 7,166 urban households. These\nfigures translated into 94.0 percent response rates for the total, 94.4 percent for the rural and 93.1 percent for\nthe urban. The total number of eligible women was 27,093 with 19,674 and 7,419 for rural and urban sectors,\nrespectively. The corresponding figures of interviewed women were 24,565, 17,928, and 6,637 respectively;\nthese figures amounted to 85.3, 86.0 and 83.3 percent effective response rates respectively for the total, rural\nand urban sectors. Eligible children under-five years of age were 17,093, (12,898 rural and 4,195 urban) and\ninterviews were achieved for 16,549, 12,494 and 4,055 respectively; again the corresponding effective\nresponse rates were 91.0, 91.4 and 90.0 percent respectively.","sample_frame":{"frame_unit":{"is_primary":"","num_of_units":""}},"sampling_deviation":"There were no deviation from sample Designed","coll_mode":["Face-to-face [f2f]"],"research_instrument":"The MICS Generic questionnaire based on MICS3 Model Questionnaire was used with some modifications and additons.\n\nHousehold Questionnaire contained: Household Listing Form; Education; Water and sanitation; Household Characteristics;\nChild Labour; Salt Iodization. Children Orphaned and made Vulnerable by HIV\/AIDS; Insecticide \u2013 Treated Net (ITN); \n\nIndividual women contained: Child Mortality; Tetanus Toxoid; Maternal and Newborn Health HIV\/AIDS; Female Genital Mutilation. Sexual Behaviour; Contraception and Unmet Need \n\nChildren Under Five contained: Birth Registration and Early Learning; Vitamin A; Breastfeeding; Care of Illness; Immunization; Anthropometry; Malaria; Child Development.\n\nHousehold questionnaire was administered to all selected households, the women questionnaire was administered to all women age 15-49 years old in the selected households and children questionnaire to all children below the age of 5years in the selected households.","instru_development_type":"","sources":[{"name":"","origin":"","characteristics":""}],"coll_situation":"Supervisors were NBS staff with experience and familiarization with local terrain. Enumerators were sourced internally and externally of NBS. Female enumerators were engaged at state level while supervisors and editors could be either male or female. Enumerators were fluent in local language for easy translation of the questionnaires content in local language where necessary.\n\nThere were two levels of training, the first was at the headquarters meant mainly for trainers at 2nd level (Ttraining of Trainers) It involved NBS Headquaters senior staff and Zonal Controllers. Selection of trainers for 2nd level was based on merit. Training lasted for 5 days. \n\nThe second level of training was for the interviewers, editors and supervisors. NBS Zonal Controllers and state officers also participated. The training was conducted simultaneously at all zonal headquarters of the six geo-political zones of the country. Each location had 2 training centers for easy assimilation of the training which lasted for 10 days.\n\nThe training contents covered among all:\n    - Survey design and roles of survey personnel\n    - Classroom sessions on questionnaires and manuals\n    - Mock interviews and role playing\n    - Questionnaire editing\n    - Field Practice\n\nPilot Test was conducted immediately, after the first level training. 4 states were strategically selected to represent the country (Enugu and Osun states represent the southern part of the country were the training for the engaged enumerators here took place at Enugu state. Also Benue and Kano states represented the Northern part of the country, the training took place at Benue state) for pilot test. The pilot test was conducted between December 26-31, 2006) \n\nTwo roving teams were engaged for data collection per state. A team comprises of 6 persons (1 supervisor, 1 editor and 4 enumerators). Vehicles were provided for each team.  Data collection lasted for 30 days.There were monitoring\/quality checks to assure collection of good quality data.","act_min":"First level monitoring at National level by 18 NBS Headquarters staff and members of Central Technical Committee\n\nSecond level monitoring at state level by NBS  6 Zonal Controllers, 37 State Officers and other member of State Steering Committee\n\nThere were special monitoring Team from Unicef (Nigeria), at every stage of processing, i.e. from planning, trainings, data collection, data editing and entry, analysis, Report writing and dissemination.","weight":"Sample weights were calculated for each of the data files.. Sample weights for the household data were computed as the probability of selection of the household, computed at the sampling domain level (urban\/rural within each state). The household weights were adjusted for non-response at the domain level, and then nomalised by a constant factor so that the total weighted number of households equals the total unweighted number of households. The hosehold weight variable is called HHWEIGHT and is used with the HH data and the HL data\n\nSample weights for the women's data used the un-nomalized 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. \n\nSample weights for the children's data followed the same approach as the women's and used the un-nomalized household weights, adjusted for non-response fr 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 \n\nEstimation Procedures:\nLet the probability of selecting the EA be fj and the probability of selecting the housing unit be fk.  Then the product f = fjfk =  1  where fj = n and fk = h\n                                                               \nYs=Estimate for states\nN  =Total Number of EAs in states\nn  =Selected number of EAs in states\nH = Total number of Housing Units listed in the jth EA   \nh  =Selected number of Housing Units in the jth EA.\nXsj k  =Value of the element in the kth housing unit of jth EA in states.  \nWsjk=Weight of the element in kth housing unit of the jth EA in states.","cleaning_operations":"Data editing began from the feild through the feild data editor and then the feild supervisor before getting to the state officers.\nThen other stages through the processing include\n\n(i) Desk  officers at the zonal offices\n(ii) Trained data editors from the headquarters sent to the zonal offices for data editing during the data entry\n(iii) Data editing through the zonal offices editors before data entry\n(iv) Competent data entry staff"},"method_notes":"Data processing began from the Planning stage. Processing took place in the six geo-political zones of the Federation where the questionnaires were checked against cluster control sheet before the data entry.\n\nIf there were any missing questionnaire, there must be a quick contact with the team from the feild, for a re-interview of the respondent involved. \n\nAll completed quetionnaires were arranged cluster by cluster in numerical order of household number within the cluster (i.e from HH1 to HH10) and despatched to the zonal offices\n\nEach cluster was followed by the selection sheets","analysis_info":{"response_rate":"We had 96% Response Rate\n\nTable HH.1 presents a summary of results of interviews of households; individual women aged 15 \u2013\n49 years and in respect of children aged under-five years. A total of 28,603 households including\n20,825 and 7,778 in the rural and urban sectors respectively were sampled; total number of\noccupied sampled households was 28,431 including 20,735 rural and 7,696 urban households. Total\nnumber of interviewed households was 26,735 including 19,569 rural and 7,166 urban households.\nThese figures translated into 94.0 percent response rates for the total, 94.4 percent for the rural and\n93.1 percent for the urban. Total figure of eligible women was 27,093 including 19,674 and 7,419\nfor rural and urban sectors respectively while corresponding figures of interviewed women were\n24,565, 17,928, and 6,637 respectively; these figures translated into 85.3, 86.0 and 83.3 effective\nresponse rates respectively. Numbers of eligible under-five children were 17,093, 12,898 and 4,195\nand interview was completed for 16,549, 12,494 and 4,055 respectively; again the corresponding\noverall response rates were 91.0, 91.4 and 90.0 percent respectively. Urban-rural disparities in\nresponse rates were quite marginal.\nTable HH.1: Results of household and individual interviews\nNumbers of households, women and children under 5 by results of the household, women's and under-five's interviews, and household,\nwomen's and under-five's response rates, Nigeria, 2007\nHouseholds\u2019 response rates varied from 81 percent in Osun State to 100 percent in Katsina State;\nbut the variations have been bridged across geopolitical zonal aggregates although the northern\nzones show greater household response rates. This pattern of variation is true also of women and\nunder-five children response rates respectively. No immediate explanations could be adduced for\nthese differentials beyond the fact that the less educated North is ever more prepared to cooperate\nwith the interviewer and that the terrain in the North is friendlier for purposes of interviewing.\n\nDetailed information attached as external document","sampling_error_estimates":"The sample of respondents selected in the Nigeria Multiple Indicator Cluster Survey is only one of the\nsamples that could have been selected from the same population, using the same design and size. Each of\nthese samples would yield results that differ somewhat from the results of the actual sample selected.\nSampling errors are a measure of the variability between all possible samples. The extent of variability is\nnot known exactly, but can be estimated statistically from the survey results.\nThe following sampling error measures are presented in this appendix for each of the selected indicators:\n?? Standard error (se): Sampling errors are usually measured in terms of standard errors for particular\nindicators (means, proportions etc). Standard error is the square root of the variance. The Taylor\nlinearization method is used for the estimation of standard errors.\n?? Coefficient of variation (se\/r) is the ratio of the standard error to the value of the indicator\n?? Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used\nin the survey, to the variance calculated under the assumption of simple random sampling. The\nsquare root of the design effect (deft) is used to show the efficiency of the sample design. A deft value\nof 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value\nabove 1.0 indicates the increase in the standard error due to the use of a more complex sample design.\n?? Confidence limits are calculated to show the interval within which the true value for the population\ncan be reasonably assumed to fall. For any given statistic calculated from the survey, the value of that\nstatistics will fall within a range of plus or minus two times the standard error (p + 2.se or p \u2013 2.se) of\nthe statistic in 95 percent of all possible samples of identical size and design.\nFor the calculation of sampling errors from MICS data, SPSS Version 15 Complex Samples module has\nbeen used. The results are shown in the tables that follow. In addition to the sampling error measures\ndescribed above, the tables also include weighted and unweighted counts of denominators for each\nindicator.\nSampling errors are calculated for indicators of primary interest, for the national total, for the regions,\nand for urban and rural areas. Three of the selected indicators are based on households, 8 are based on\nhousehold members, 13 are based on women, and 15 are based on children under 5. All indicators\npresented here are in the form of proportions. Table SE.1 shows the list of indicators for which sampling\nerrors are calculated, including the base population (denominator) for each indicator. Tables SE.2 to SE.9\nshow the calculated sampling errors.\nTable SE.1: Indicators selected for sampling error calculations\nList of indicators selected for sampling error calculations, and base populations (denominators)\nfor each indicator, Nigeria 2007\nTable SE.2: Sampling errors: Country\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE.3: Sampling errors: Urban\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nConfidence limits Table\nTable SE.4: Sampling errors: Rural\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE.5: Sampling errors: North East\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE6: Sampling errors: North East\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE.7: Sampling errors: South East\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE.8: Sampling errors: South South\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007\nTable SE.9: Sampling errors: South West\nStandard errors, coefficients of variation, design effects (deff), square root of design effects (deft) and confidence intervals for selected\nindicators, Nigeria 2007","data_appraisal":"A series of tables and graphs were genenrated\nTable DQ.1: Age distribution of household population\nSingle-year distribution of household population by sex (weighted), Nigeria, 2007\nTable DQ.2: Age distribution of eligible and interviewed women\nHousehold population of women age 10-54, interviewed women age 15-49, and percentage of\neligible women who were interviewed (weighted), by five-year age group, Nigeria, 2007\nTable DQ.3: Age distribution of eligible and interviewed under-5s\nHousehold population of children age 0-7, children whose mothers\/caretakers were interviewed\nand percentage of under-5 children whose mothers\/caretakers were interviewed (weighted), by\nfive-year age group, Nigeria, 2007\nTable DQ.4: Age distribution of under-5 children\nAge distribution of under-5 children by 3-month groups (weighted), Nigeria, 2007\nTable DQ.5: Heaping on ages and periods\nAge and period ratios at boundaries of eligibility by type of information collected (Household\nquestionnaire, weighted), Nigeria, 2007\nTable DQ.6: Percentage of observations missing information for selected questions and indicators\n(Under-5 questionnaire, weighted), Nigeria, 2007\nTable DQ.7: Presence of mother in the household and the person interviewed for the under-5 questionnaire: Distribution of children under five by\nwhether the mother lives in the same household, and the person interviewed for the under-5 questionnaire (weighted), Nigeria, 2007\nTable DQ.8: School attendance by single age\nDistribution of household population age 5-24 by educational level and grade attended in the current year, Nigeria, 2007\nTable DQ.9: Sex ratio at birth among children ever born and living\nSex ratio at birth among children ever born, children living, and deceased children by age of women (weighted), Nigeria, 2007\nTable DQ.10: Distribution of women by time since last birth\nDistribution of women aged 15-49 years with at least one live birth (weighted), by months since\nlast birth, Nigeria, 2007\nQuality assessment study of the data has confirmed a number of quality problems in MICS Nigeria 2007. In the\nfollowing paragraphs we set out these problems offering the likely causes as well as some of the possible\nimplications for data quality and accuracy of estimates of characteristics and indicators emanating from the data\nAge Heaping\nLarge amount of heaping exists at ages with digits ending in 0 and 5 except at age 15.This exception is not genuine\nbeing yet evidence of some other quality problem (Table DQ.1 Table DQ.5 and, Figure DQ.1)). Illiteracy\nparticularly un respect of women respondents, cultural bias for figures ending with 0 and 5, cultural practice that\ncounts in 5s, poor book keeping habit, burden of length of questionnaire, and other reasons Age heaping is also\nevident in the male age data. This problem could lead to a false impression of the age structure resulting from some\nover-representation of persons of ages ending in digits 0 and 5. There could be bias in weighted estimate of any\ncharacteristic that depends on age structure e.g. mortality rate. Effect is less in respect of characteristics that depend\non age grouping where the ages ending 0 or 5 are less important and where differentials in respect of the\ncharacteristics of interest about the heaps are trivial.\nOut-Transfer of Ages of Women and Children\nLarge out-transfer of children from target group 0-4 year old (Table DQ.3, Figure DQ.2) and of women from the\ntarget group 15-49 year-old was evident; a proof is the unlikely pyramidal structure of age distribution; some\nchildren of genuine age 4 (or even lower) must have had their ages recorded as 5 or more years; also a good number\nof women with true age 15 years or higher must have been recorded as 14 years old or younger; and some women\ntruly aged 49 years or lower have had their ages recorded as 50 or higher (Figure DQ.3). Possible effects of the outtransfers\ncould include a substantial detraction from the quality of the data and from the general accuracy of those\nindicators that use differential weights that are derived from the relative frequency distribution of the ages. This\nmeans that children aged 4 years and women aged 15 and 49 years respectively may have been poorly reflected in\nthe sample; it means that these children and women have been under-sampled, that is children aged 0-4 and women\naged 15-24, 45-49 and 15-49 may have been quite severely under-represented.\nEstimates of group characteristics of the children under 5 and of women in each of the affected age groups stand\nadequate and credible as long as sample size posed no serious precision problem. But combined estimates derived\nfrom weighted estimation would have problem of bias particularly if there are differences across ages and age\ngroups.\nLower Response Rates Among Younger Women.\nDifferential response rates are noted across age group, lower among the younger women aged 15-24 years (Table\nDQ.2) (Figure DQ.4); this translates in to differential representation and data accuracy across the age groups. The\nlikely effect includes a distortion of the weights and a bias in estimates. But response rate ranged from 86 to 95\npercent; bottom 86 percent seems quite adequate though quite less than MICS3 suggested bottom figure of 90\npercent The fear is that some bias in favour of the older women may result particularly in combined estimates across\nages; inevitably, this could detract from the accuracy of results particularly if the non-respondents coincide with a\nsub-group with characteristics that are distinct from the rest of the population.\nIncomplete information on dates, month, year of birth and marriage\nAge data featured disproportionately large amount of \u2018missing\u2019 and \u2018don\u2019t know\u2019 in data on dates of marriages of\nwomen and births of children and adults. This is a a problem of the poor or the uneducated or the rural person the\npoor; it is a problem aggravated by characteristic inadequate birth registration and poor record keeping habits. The\ncost could be a substantial reduction in effective sample size impacting adversely on the accuracy of estimates of\nchild outcomes that require an accurate recollection of dates of birth of the child and of landmarks in child history\ne.g. weaning, breastfeeding food supplementation, vaccination, pre-school development. Good recollection of dates\nof events is also a vital requirement for quality of results on mortality rates.\nLarge Over-Age Children in Pre-School and Primary Schools\nThere are large numbers of household members\u2019 age 8+ attending pre-school, similar unexpected numbers of\nhousehold members at quite unexpected ages are attending other levels of schools including the primary . If these\nare confirmed as errors, then they probably suggest incorrect trend and a misrepresentation of pre-school\ndevelopment and primary school attendance; it means an under-estimation of primary school attendance ratio and a\ngeneral loss of accuracy in the results\nOn the other hand, it is evident that there is a strong diagonal feature if we take the ages in groups e.g. 5-7, 6-8, etc.,\nthis suggests there could be some late starts in primary school enrolment, a feature that splits over into the higher\ngrades of the primary school and beyond.\nLarge Male-Female Ratio\nSex ratios at birth are consistently above the expected 1.05-1.06 level (Tables DQ.1 & DQ.9 and Figures DQ.4-\nDQ.5) This usually indicates that some female children are not declared. This criticism suggests possible undersampling\nof the female and in its wake an under-representation of the female children; it would also suggest a tilt to\nmale sex domination beyond the norm.\nUnder-declaration of female children necessarily distorts sex ratio figure and gender balance; an under-sampling of\nthe girl-children reduces the sample size and the precision of estimate of girl-child outcomes. It could also affect\nestimates of sex differentials.\nLarge Exclusion o Children in the Calculation of Anthropometrical Child Outcomes\nA large number of children are excluded from the tabulations on malnourishment, because of missing data (Table\nDQ.5) Some 29 percent of all children under 5 are excluded from the analysis. This figure includes 11 percent who\nwere excluded because the weight and\/or height measurements were out of range, and 17 percent for who date of\nbirth was incomplete; the exclusions were 17% due to missing date or year of birth and other causes. The missing\ncases could as well be children of the most poorly educated mothers or children in the poorest wealth index\nquintiles. Hence malnutrition could be more prevalent and more intense among them. In effect, the true state of\nmalnutrition in the country could be more serious than depicted by the data\nHeaping of height and weight measurements\nConsiderable heaping of height and weight measurements around decimal point 0 and 0.5 most especially around 0\nhas been observed. Apparently figures ending 0.1, 0.2, 0.3, 0.4 were rounded down to next whole number below.\nFigures ending 0.6, 0.7, 0.8, 0.8 were rounded\nup to the whole number above while figures ending 0.5 were left alone because canvassers would not know whether\nto round up or down (Figures 8a 8b). The errors here could mutually cancel out; the mean and the standard deviation\nmay not be significantly distorted, and the bias minimal. But if the individual measurement is considered against an\ninterval to decide the level of malnourishment of the individual child, then the effect of the difference of\nmagnitude 0.1 to 0.4 arising from rounding up or down of the individual measurement may be more than trivial\nThe extent of distortions associated with the tabulated results would depend on the extent to which differences of 0.1\nto 0.4 in measurements of individual weight and height respectively influence the placement of an individual on the\nweight for age (underweight), height for age (stunting) and weight for height (wasting) scales respectively. Weights\nare measured in kg and height in cm; it is unlikely that differences of magnitude 0.1 \u2013 0.4 cm in height and 0.1-0.4\nkg in weight would make any significant difference in these placements.\nLow Child Mortality Rates\nEstimates of infant and under-5 mortality rates by MICS Nigeria 2007 are low.. Some inconsistency,\nincomparability and incompatibility with previous survey results is suspected. Criticism that the figures are underestimates,\nif well-founded means that child deaths have been under reported, or age structures of the children and of\nthe ,others have been misreported or that the calculating method is sensitive to such misreporting."}},"data_access":{"dataset_availability":{"access_place":"","access_place_url":""},"dataset_use":{"conf_dec":[{"txt":"The confidentiality of the individual respondent is protected by law (Statistical Act 2007)\nThis is published in the Official Gazette of the Federal republic of Nigeria No. 60 vol. 94 of 11th June 2007. See section 26 para.2. Punitive measures for breeches of confidentiality are outlined in section 28 of the same Act.","required":"yes","form_no":"","form_uri":""}],"contact":[{"name":"National Bureau of Statistics","affiliation":"","email":"feedback@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"}],"cit_req":""National Bureau of Statistics, Multiple Indicators Cluster Survey (MICS3, Nigeria 2006), version 1.2"","conditions":"A comprehensive data access policy is been developed by NBS, however section 27 of the Statistical Act 2007outlines the data access obligation of data producers which includes the realease of properly anonymized micro data.","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."}}},"schematype":"survey"}