Estimate population figures at state administrative level and different age groups

Estimate population figures at local government administrative level and different age groups

A populated place consisting of more than 15 houses − place or area with clustered or scattered buildings and a permanent human population (city

Natural Science

Abstract

The primary objectives of the 2015 NMIS are to provide information on malaria indicators and malaria prevalence, both at the national level and in each of the country’s 36 states and the Federal Capital Territory. The secondary objectives are to improve knowledge regarding best practices in implementing the survey and enhance the skills of survey-implementing partners in the areas of survey design, training, logistics, data collection monitoring, data processing, laboratory testing, analysis, report drafting, and data dissemination.

Other key objectives of the 2015 Nigeria Malaria Indicator Survey are to: • Measure the extent of ownership and use of mosquito nets • Assess the coverage of preventive treatment programmes for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess knowledge, attitudes, and practices regarding malaria in the general population

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children age 0-5

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2015 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country's six geopolitical zones. Some of these indicators are provided for each of the 36 states and the FCT. Nigeria's geopolitical zones are as follows: 1. North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT 2. North East: Adamawa, Bauchi, Borno,1 Gombe, Taraba, and Yobe 3. North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara 4. South East: Abia, Anambra, Ebonyi, Enugu, and Imo 5. South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers 6. South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

The sampling frame for the 2015 NMIS was the 2006 National Population and Housing Census (NPHC) of the Federal Republic of Nigeria, conducted by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2015 NMIS, was defined on the basis of EAs from the 2006 EA census frame.

A two-stage sampling strategy was adopted for the 2015 NMIS. In the first stage, nine clusters (EAs) were selected from each state, including the FCT. The sample selection was done in such a way that it was representative of each state. The result was a total of 333 clusters throughout the country, 138 in urban areas and 195 in rural areas.

A complete listing of households was conducted, and a mapping exercise for each cluster was carried out in June and July 2015, with the resulting lists of households serving as the sampling frame for the selection of households in the second stage. All regular households were listed. The NPopC listing enumerators used global positioning system (GPS) receivers to record the coordinates of the 2015 NMIS sample clusters.

In the second stage of the selection process, 25 households were selected in each cluster by equal probability systematic sampling. All women age 15-49 who were either permanent residents of the households in the 2015 NMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months were eligible to be tested for malaria and anaemia. This sample size was selected to guarantee that key survey indicators could be produced for each of the country's six geopolitical zones, with approximately 1,338 women in each zone expected to complete interviews. In order to produce some of the survey indicators at the state level for each of the 36 states and the FCT, interviews were expected to be completed with approximately 217 women per state.

For further details of the sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the survey: the Household Questionnaire; the Woman’s Questionnaire, which was administered to all women age 15-49 in the selected households; and the Biomarker Questionnaire.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. Data on age and sex were used to identify women who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and ownership and use of mosquito nets.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: • Background characteristics (e.g., education, media exposure) • Birth history and childhood mortality • Antenatal care and malaria prevention for most recent birth and pregnancy • Malaria prevention and treatment • Knowledge about malaria (symptoms, causes, prevention, drugs used in treatment)

The Biomarker Questionnaire was used to record the results of the anaemia and malaria testing as well as the signatures of the fieldworker and the respondent who gave consent.

Cleaning operations

Data for the 2015 NMIS were collected through questionnaires programmed onto tablet computers. The computers were programmed by an ICF data processing specialist and loaded with the Household, and Woman’s Questionnaires in English and the three major local languages. The tablets were Bluetooth-enabled to facilitate electronic transfer of files, for example, transfer of data from the Household Questionnaires among survey team members and transfer of completed questionnaires to the team supervisor’s tablets. The field supervisors transferred data on a daily basis to the central data processing office using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.

Two data management officers were positioned at the central data office to monitor and supervise daily submission of completed interview data from teams. They also provided technical assistance on the functioning of the tablets and constantly liaised with the central coordination and ICF teams to manage data transfers from the field teams to the central office. They made intermittent visits to assist field teams with serious situations that could not be resolved at the central office, either to replace or fix the tablets.

The Census Survey Processing (CSPro) software program was used for data editing, weighting, cleaning, and tabulation. In the NPopC central office, data received from the supervisors’ tablets were registered and checked for any inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure completeness of work in the field. Any anomalies were communicated to the respective team through field coordinators and the team supervisor. Corrected results were re-sent to the central processing unit. Data processing was completed during the first week of December 2015.

Response rate

A total of 8,148 households were selected for the sample. This does not include six rural clusters in Borno State and one cluster in Plateau State that were dropped from the sample due to security concerns. Of the households selected, 7,841 were occupied. Of the occupied households, 7,745 were successfully interviewed, yielding a response rate of 99 percent. The response rate among households in rural areas was slightly higher (99 percent) than that among households in urban areas (98 percent). No clusters in rural areas of Borno State were visited; thus, estimates for national indicators and indicators in the North East Zone do not include rural Borno State.

In the interviewed households, 8,106 women were identified as eligible for individual interviews. Interviews were completed with 8,034 women, yielding a response rate of 99 percent. The response rate among eligible women did not differ by residence (urban or rural).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Nigeria Malaria Indicator Survey (NMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2015 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample

Comparison of AdaBoost model performance metrics by age and sex.

Journal of Agriculture and Environment International

Abstract

The objectives of the Smallholder Household Survey in Nigeria were to:

• Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships • Segment smallholder households in Nigeria according to the most compelling variables that emerge • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING PROCEDURE

The smallholder household survey in Nigeria is a nationally-representative survey with a target sample size of 3,000 smallholder households. In order to take nonresponse into account, the target sample size was increased to 3,225 households assuming a response rate of 93%. The sample was designed to produce national level estimates as well as estimates for each of the six geo-political zones. Nigeria is comprised of the following states:

• North Central: Benue, Federal Capital Territory (FCT), Kogi, Kwara, Nasarawa, Niger, and Plateau
• North East: Adamawa, Bauci, Borno, Gombe, Taraba and Yobe
• North West: Jigawa, Kaduna, Kano, katsina, Kebbi, Sokoto and Zamfara
• South East: Abia, Anambra, Ebonyi, Enugu, and Imo
• South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and River
• South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

(b) SAMPLING FRAME

Nigeria is divided into 774 local governments (LGAs) and its last housing and population census took place in 2006. In preparation for this last census, the National Population Commission (NPopC) demarcated over 662,000 enumeration areas (EAs) for the country. From these EAs, two hierarchical master sample frames were developed by the Nigeria Bureau of Statistics (NBS): the LGA master frame and the National Integrated Survey of Households (NISH). The smallholder survey used the NISH as sampling frame but retained only the EAs containing agricultural households.

(c) SAMPLE ALLOCATION AND SELECTION

The total sample size was first allocated to the geo-political zones in proportion to their number of agricultural EAs in the sampling frame. Within each zone, the resulting sample was then further distributed to states proportionally to their number of agricultural EAs. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total number of 215 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. A stratum corresponds to a state and the sample was selected independently in each stratum. In the first stage, EAs were selected as primary sampling units with equal probability. A household listing operation was carried out in all selected EAs to identify smallholder households and to provide a frame for the selection of smallholder households to be included in the sample. In the second stage, 15 smallholders were selected in each EA with equal probability. In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviours and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The household listing operation identified fewer than 15 smallholder households in many sampled EAs. As a result, the sample take of 15 households per EA couldn't be implemented in those EAs. To avoid a situation where a sample falls short, the sample take was increased to 17 smallholder households where possible while retaining in the sample all smallholder households in EAs with fewer than 17 smallholder households. This yielded 3,457 sampled households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible. Following the finalization of questionnaires, a script was developed using Dooblo to support data collection on smart phones. The script was thoroughly tested and validated before its use in the field. The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection.

Response rate

• A total of 3,457 households was selected for the survey, of which 3,310 were found to be occupied during data collection. Of these occupied households, 3,026 were successfully interviewed, yielding a household response rate of 91 percent.

• In the interviewed households 6,643 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,128 eligible household members, yielding a response rate of 77 percent for the Multiple Respondent questionnaire.

• Among the 3,206 eligible household members selected for the Single Respondent questionnaire, 2,773 were successfully interviewed, yielding a response rate of 92 percent.

Sampling error estimates

For key survey estimates, sampling errors considering the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Morphometric measurements of the bobo croaker (Pseudotolithus elongatus) obtained from islands and estuary mouth of the Cross River, Nigeria.

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• Concordance and discordance between RDT and microscopy results
• Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

Population estimates using genetic capture–recapture methods from non-invasively collected wildlife samples are more accurate and precise than those obtained from traditional methods when detection and resampling rates are high. Recently, detection dogs have been increasingly used to find elusive species and their by-products. Here we compared the effectiveness of dog- and human-directed searches for Cross River gorilla (Gorilla gorilla diehli) faeces at two sites. The critically endangered Cross River gorilla inhabits a region of high biodiversity and endemism on the border between Nigeria and Cameroon. The rugged highland terrain and their cryptic behaviour make them difficult to study and a precise population size for the subspecies is still lacking. Dog-directed surveys located more fresh faeces with less bias than human-directed survey teams. This produced a more reliable population estimate, although of modest precision given the small scale of this pilot study. Unfortunately, the...

Thirty eight (38) localities were visited in each of the seven (7) focal states of the Enhancing the National Response (ENR) program; the states are Akwa Ibom, Benue, Cross-River, Kaduna, Lagos, Nasarawa and Ogun. In each of these states, nineteen localities were se-lected in the urban sector of the states, and nineteen in the rural stratum; resulting into a total of 266 localities from the seven (7) states. These localities were selected based on probability proportional to size (PPS) after the ordering of the geographical locations. Lists of localities in Nigeria were provided by the National Population Commission (NPC).

Abstract

The 2010 Nigeria Malaria Indicator Survey (2010 NMIS) was implemented by the National Population Commission (NPC) and the National Malaria Control Programme (NMCP). ICF International provided technical assistance through the MEASURE DHS programme, a project funded by the United States Agency for International Development (USAID), which provides support and technical assistance in the implementation of population and health surveys in countries worldwide. It was carried out from October to December 2010 on a nationally representative sample of more than 6,000 households. All women age 15-49 in the selected households were eligible for individual interviews. During the interviews, they were asked questions about malaria prevention during pregnancy and the treatment of fever among their children. In addition, the survey included testing for anaemia and malaria among children age 6-59 months using finger (or heel) prick blood samples. Test results were available immediately and were provided to the children’s parents or guardians. Thick blood smears and thin blood films were also made in the field and transported to the Department of Medical Microbiology and Parasitology at the College of Medicine, University of Lagos. Microscopy was performed to determine the presence of malaria parasites and to identify the parasite species. Slide validation was carried out by the University of Calabar Teaching Hospital in Calabar.

The 2009-2013 National Strategic Plan for Malaria Control in Nigeria aims to massively scale up malaria control interventions in parts of the country. The 2010 Nigeria Malaria Indicator Survey (NMIS) was, therefore, designed to measure progress toward achieving the goals and targets of this strategic plan by providing data on key malaria indicators, including ownership and use of bed nets, diagnosis and prompt treatment of malaria using artemisinin-based therapy (ACT), indoor residual spraying, and behaviour change communication.

The following are the specific objectives of the 2010 NMIS: - To measure the extent of ownership and use of mosquito bed nets - To assess the coverage of intermittent and preventive treatment programmes for pregnant women - To identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications - To measure the prevalence of malaria and anaemia among children age 6-59 months - To determine the species of plasmodium parasite most prevalent in Nigeria - To assess knowledge, attitudes, and practices regarding malaria in the general population

Geographic coverage

National

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all children age 6-59 months living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2010 Nigeria Malaria Indicator Survey (NMIS) called for a nationally representative sample of about 6,000 households. The survey is designed to provide information on key malaria-related indicators including mosquito net ownership and use, coverage of preventive treatment for pregnant women, treatment of childhood fever, and the prevalence of anaemia and malaria among children age 6-59 months. The sample for the 2010 NMIS was designed to provide most of these indicators for the country as a whole, for urban and rural areas separately, and for each of the six zones formed by grouping the 36 states and the Federal Capital Territory (FCT). The zones are as follows: 1. North Central: Benue, FCT-Abuja, Kogi, Kwara, Nasarawa, Niger, and Plateau 2. North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe 3. North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara 4. South East: Abia, Anambra, Ebonyi, Enugu, and Imo 5. South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers 6. South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

SAMPLING FRAME The sampling frame used for the 2010 NMIS was the Population and Housing Census of the Federal Republic of Nigeria, which was conducted in 2006 by the National Population Commission (NPC). Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 Population Census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2010 NMIS, is defined on the basis of EAs from the 2006 EA census frame.

Although the 2006 Population Census did not provide the number of households and population for each EA, population estimates were published for more than 800 LGA units. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census were used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rual for the survey sample frame.

SAMPLE ALLOCATION The 2010 NMIS sample was selected using a stratified, two-stage cluster design consisting of 240 clusters, 83 in the urban areas and 157 in the rural areas. (The final sample included 239 clusters because access to one cluster was prevented by inter-communal disturbances.) A sample of 6,240 households was selected for the survey, with a minimum target of 920 completed individual women's interviews per zone. Within each zone, the number of households was distributed proportionately among urban and rural areas. A fixed 'take' of 26 households per cluster was adopted for both urban and rural clusters.

SAMPLING PROCEDURE AND UPDATING OF THE SAMPLING FRAME The 2010 NMIS sample is a stratified sample selected in two stages. The primary sampling units (PSUs) are the enumeration areas (EAs) from the 2006 census, and the secondary sampling units (SSUs) are the households. In the first stage of selection, the 240 EAs were selected with a probability proportional to the size of the EA, where size is the number of approximate households calculated within the sampling frame.

A complete listing of households and a mapping exercise for each cluster was carried out from August through September 2010. The lists of households resulting from this exercise served as the sampling frame for the selection of households in the second stage. In addition to listing the households, the NPC listing enumerators used global positioning system (GPS) receivers to record the coordinates of the 2010 NMIS sample clusters.

In the second stage of the selection process, 26 households were selected in each cluster by equal probability systematic sampling. All women age 15-49 who were either permanent residents of the households in the 2010 NMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months were eligible to be tested for malaria and anaemia.

The sampling procedures are fully described in Appendix A of "Nigeria Malaria Indicator Survey 2010 - Final Report" pp.69-71.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used in the NMIS: a Household Questionnaire and a Woman’s Questionnaire, which was administered to all women age 15-49 in the selected households. Both instruments were based on the standard Malaria Indicator Survey Questionnaires developed by the Roll Back Malaria and DHS programmes. These questionnaires were adapted to reflect the population and health issues relevant to Nigeria during a series of meetings convened with various stakeholders from the NMCP and other government ministries and agencies, nongovernmental organisations, and international donors. The questionnaires were translated into three major Nigerian languages: Hausa, Igbo, and Yoruba.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for the individual interview and children age 6-59 months who were eligible for anaemia and malaria testing. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof, and walls of the house; ownership of various durable goods; and ownership and use of mosquito nets. In addition, the questionnaire was used to record the results of the anaemia and malaria testing as well as the signatures of the interviewer and the respondent who gave consent. Children’s temperatures were also recorded.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: - Background characteristics (such as age, residence, education, media exposure, and literacy) - Birth history and childhood mortality - Antenatal care and malaria prevention for most recent birth and pregnancy - Malaria prevention and treatment - Knowledge about malaria (symptoms, causes, prevention, and drugs used in treatment)

Cleaning operations

The processing of data for the 2010 NMIS ran concurrently with data collection. Completed questionnaires were retrieved by the zonal coordinators or the trainers and delivered to NPC in standard envelopes, labelled with the sample ID, team number, and state name. The shipment also contained a written summary of any

Grasscutter (Thryonomys swinderianus) is a large-body old world rodent found in sub-Saharan Africa. The body size and the unique taste of the meat of this major crop pest have made it a target of intense hunting and a potential consideration as a micro-livestock. However, there is insufficient knowledge on the genetic diversity of its populations across African Guinean forests. Herein, we investigated the genetic diversity, population structures and evolutionary history of seven Nigerian wild grasscutter populations together with individuals from Cameroon, Republic of Benin, and Ghana, using five mitochondrial fragments, including D-loop and cytochrome b (CYTB). D-loop haplotype diversity ranged from 0.571 (± 0.149) in Republic of Benin to 0.921 (± 0.013) in Ghana. Within Nigeria, the haplotype diversity ranged from 0.659 (± 0.059) in Cross River to 0.837 (± 0.075) in Ondo subpopulation. The fixation index (FST), haplotype frequency distribution and analysis of molecular variance revealed varying levels of population structures across populations. No significant signature of population contraction was detected in the grasscutter populations. Evolutionary analyses of CYTB suggests that South African population might have diverged from other populations about 6.1 (2.6–10.18, 95% CI) MYA. Taken together, this study reveals the population status and evolutionary history of grasscutter populations in the region.

Abstract

Worldwide, the Core Welfare Indicator Questionnaire Survey (CWIQ) is designed to collect household data useful in quantitatively profiling the well-being of the population. The 2006 Nigerian CWIQ was a nationwide sample survey conducted to produce welfare indicators for the population at national and sub-national levels, particularly Zones, States and Senatorial Districts. The Survey compliments 2004 Nigerian Living Standards Survey (NLSS) by NBS which profiled poverty in the country.

Geographic coverage

National coverage

Analysis unit

Community, household, individual

Universe

Households and usual residents from households in the nationally sampled area.

Kind of data

Sample survey data [ssd]

Sampling procedure

The National CWIQ Survey 2006 was designed with Local Government Area (LGA) serving as the reporting domain. Data were then aggregated to give estimates at Federal Constituency (FC), Senatorial, State, zonal (geo-political) and national levels. Basically, a 2-stage cluster sample design was adopted in each LGA. Enumeration Areas (EAs) formed the 1st stage or Primary Sampling Units (PSUs) while Housing Units (HUs) formed the 2nd stage or Ultimate Sampling Units (USUs). The EAs as demarcated by the National Population Commission (NPopC) for the 1991 Population Census served as the sampling frame for the selection of 1st stage sample units. In each LGA, a systematic selection of 10 EAs was made. Prior to the second stage selection, complete listing of Housing Units (and of Households within Housing Units) was carried out in each of the selected 1st stage units. These lists provided the frames for the second stage selection. Ten (10) HUs were then systematically selected per EA and all households in the selected HUs were interviewed.

Sampling deviation

However, only 75,929 households were completely enumerated and this gave a response rate of 98.5 per cent, the remaining 1.5 per cent were recorded cases of respondents not at home, refusals, household not located, moved away and others.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three main instruments were designed for and used during the survey. They included the CWIQ questionnaire, the interviewer’s manual and the supervisor’s manual. The generic scannable CWIQ questionnaire was adapted to suit the country situation. Some modifications were however, made in the questionnaire after the survey in Benue. The modified questionnaire was then used for the CWIQ survey in Abia, Cross-River, Ekiti, Kebbi, Kogi, Yobe, Jigawa and Enugu in May to August 2002. The States covered in year 2003 were repeated for the year 2004. These include Abia, Cross-River, Gombe, Kebbi, Osun and Plateau States. Further modifications was done to the questionnaire in may 2005, there was total overhaul of some sections and the reference number was pre printed and at the same time reduced to 4 digit; while a new methodology which used hand printing recognition was adapted.

The questionnaire served as the main data collection instrument and captured the minimum information that allowed for identification of targets groups, provision of basic welfare indicators for measuring poverty and the capturing of information which measured access, utilization and satisfaction with services provided. The questionnaire did not cover measurement of indicators on child nutrition through anthropometric measurements. This was mainly due to inability to procure early enough, the necessary anthropometric equipment, namely, rollameter, microtoise and mother-and-child weighing scale.

Cleaning operations

During scanning, the scanner took an image of each page of the questionnaire through form processing software (Teleform), which subsequently evaluated the scanned images. Evaluated images that suggested possible errors in the questionnaire were verified and corrected by the data entry operator. Typical errors included unidentified pages that could not be evaluated; unrecognisable hand printed characters or bubbles, which were not completely shaded. The time required for image evaluation and subsequent verification depended on how well and legibly the questionnaire was filled in.

After all potential errors for an EA had been verified by the data entry operator; the data from the questionnaires was transferred to a shared folder in the desktop computer. The output of the scanner was then checked for consistency, omission, skips and other errors; the data was not transferred to the database until all such errors were corrected.

Response rate

A total of 77,062 households were covered from a sample of 77,400 households giving the survey a coverage rate of 99.6 per cent. However, only 75,929 households were completely enumerated and this gave a response rate of 98.5 per cent, the remaining 1.5 per cent were recorded cases of respondents not at home, refusals, household not located, moved away and others. 59,567 households were covered in the rural areas with a response rate of 98.7 per cent while 17,495 households were covered in the urban areas with a response rate of 98.0 per cent. Out of all the six zones, it was only the South-east that has the least response rate of 97.4 per cent followed by South-south with 97.9 per cent. The highest response rate is from the North-east with 99.1 per cent, followed by North-west 99.0 per cent, South-west 98.8 per cent, and North Central 98.4 per cent. Out of all the States Imo State has the least response rate of 94.2 per cent with 2,690 households and Kogi State has the highest response rate of 100.0 per cent.

Abstract

The 2014-2020 National Malaria Strategic Plan is a 7-year major scale up of key interventions resulting from a robust evidence-based data and programme experiences from previous years. The Strategic Plan aims to achieve pre-elimination status and reduction of malaria-related deaths to zero by 2020 in Nigeria. The 2015 Nigeria Malaria Indicator Survey (NMIS), a follow-up to the baseline survey conducted in 2010, was designed to assess the extent of achievements of the 2009-2013 NMSP goals and targets and to provide information for monitoring and evaluation of Nigeria’s National Malaria Elimination Programme in the next 10 years. The primary objectives of the 2015 NMIS are to provide information on malaria indicators and malaria prevalence, both at the national level and in each of the country’s 36 states and the Federal Capital Territory. The secondary objectives are to improve knowledge regarding best practices in implementing the survey and enhance the skills of survey-implementing partners in the areas of survey design, training, logistics, data collection monitoring, data processing, laboratory testing, analysis, report drafting, and data dissemination.

Other key objectives of the 2015 Nigeria Malaria Indicator Survey are to: • Measure the extent of ownership and use of mosquito nets • Assess the coverage of preventive treatment programmes for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Measure the prevalence of malaria and anaemia among children age 6-59 months • Assess knowledge, attitudes, and practices regarding malaria in the general population

Geographic coverage

National coverage

Analysis unit

Households Women 15-49 years Children 6-59 months

Universe

the survey covered all household members, all women aged 15-49 years and all children aged 6-59 months

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2015 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones. Some of these indicators are provided for each of the 36 states and the FCT. Nigeria’s geopolitical zones are as follows:

1. North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT
2. North East: Adamawa, Bauchi, Borno,1 Gombe, Taraba, and Yobe
3. North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara
4. South East: Abia, Anambra, Ebonyi, Enugu, and Imo
5. South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers
6. South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

The sampling frame for the 2015 NMIS was the 2006 National Population and Housing Census (NPHC) of the Federal Republic of Nigeria, conducted by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 census, each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2015 NMIS, was defined on the basis of EAs from the 2006 EA census frame.

A two-stage sampling strategy was adopted for the 2015 NMIS. In the first stage, nine clusters (EAs) were selected from each state, including the FCT. The sample selection was done in such a way that it was representative of each state. The result was a total of 333 clusters throughout the country, 138 in urban areas and 195 in rural areas.

A complete listing of households was conducted, and a mapping exercise for each cluster was carried out in June and July 2015, with the resulting lists of households serving as the sampling frame for the selection of households in the second stage. All regular households were listed. The NPopC listing enumerators used global positioning system (GPS) receivers to record the coordinates of the 2015 NMIS sample clusters.

In the second stage of the selection process, 25 households were selected in each cluster by equal probability systematic sampling. All women age 15-49 who were either permanent residents of the households in the 2015 NMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 6-59 months were eligible to be tested for malaria and anaemia. This sample size was selected to guarantee that key survey indicators could be produced for each of the country’s six geopolitical zones, with approximately 1,338 women in each zone expected to complete interviews. In order to produce some of the survey indicators at the state level for each of the 36 states and the FCT, interviews were expected to be completed with approximately 217 women per state.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires were used in the survey: the Household Questionnaire; the Woman’s Questionnaire, which was administered to all women age 15-49 in the selected households; and the Biomarker Questionnaire. These questionnaires were adapted to reflect the population and health issues relevant to Nigeria during a series of meetings with various stakeholders from the NMEP and other government ministries and agencies, nongovernmental organisations, and international donors. In addition to English, the questionnaires were translated into the three major Nigerian languages: Hausa, Igbo, and Yoruba. The questionnaires were programmed on tablet computers, and interviewers administered the survey using computer-assisted personal interviewing (CAPI).

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. Data on age and sex were used to identify women who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and ownership and use of mosquito nets.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: • Background characteristics (e.g., education, media exposure) • Birth history and childhood mortality • Antenatal care and malaria prevention for most recent birth and pregnancy • Malaria prevention and treatment • Knowledge about malaria (symptoms, causes, prevention, drugs used in treatment)

The Biomarker Questionnaire was used to record the results of the anaemia and malaria testing as well as the signatures of the fieldworker and the respondent who gave consent.

Cleaning operations

Data for the 2015 NMIS were collected through questionnaires programmed onto tablet computers. The computers were programmed by an ICF data processing specialist and loaded with the Household, and Woman’s Questionnaires in English and the three major local languages. The tablets were Bluetooth-enabled to facilitate electronic transfer of files, for example, transfer of data from the Household Questionnaires among survey team members and transfer of completed questionnaires to the team supervisor’s tablets. The field supervisors transferred data on a daily basis to the central data processing office using the Internet. To facilitate communication and monitoring, each field worker was assigned a unique identification number.

Two data management officers were positioned at the central data office to monitor and supervise daily submission of completed interview data from teams. They also provided technical assistance on the functioning of the tablets and constantly liaised with the central coordination and ICF teams to manage data transfers from the field teams to the central office. They made intermittent visits to assist field teams with serious situations that could not be resolved at the central office, either to replace or fix the tablets.

The Census Survey Processing (CSPro) software program was used for data editing, weighting, cleaning, and tabulation. In the NPopC central office, data received from the supervisors’ tablets were registered and checked for any inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure completeness of work in the field. Any anomalies were communicated to the respective team through field coordinators and the team supervisor. Corrected results were re-sent to the central processing unit. Data processing was completed during the first week of December 2015.

Response rate

A total of 8,148 households were selected for the sample. This does not include six rural clusters in Borno State and one cluster in Plateau State that were dropped from the sample due to security concerns. Of the households selected, 7,841 were occupied. Of the occupied households, 7,745 were successfully interviewed, yielding a response rate of 99 percent. The response rate among households in rural areas was slightly higher (99 percent) than that among households in urban areas (98 percent). No clusters in rural areas of Borno State were visited; thus, estimates for national indicators and indicators in the North East Zone do not include rural Borno State.

In the

Abstract

The 2007 Nigeria Enterprise Survey was part of the UK Department for International Development/World Bank Group Investment Climate Program (ICP) that was launched by the Minister of Finance in August 2007. This program was a response to the request from the Nigeria Federal Minister of Finance to the World Bank Group and UK Department for International Development (DFID) to assist in the development of a diagnostic base on which enterprise and investment climate constraints could be measured and benchmarked internally across the 36 states and the Federal Capital Territory of Nigeria and internationally against key comparator countries, particularly the "BRIC" countries (Brazil, Russia, India and China).

The survey was conducted between September 2007 and February 2008. Data from 2387 establishments was analyzed. The survey was administered across 11 states (Abia, Anambra, Abuja, Bauchi, Cross Rivers, Enugu, Kaduna, Kano, Lagos, Ogun and Sokoto) and included manufacturing and services firms of different sizes.

The objective of the Enterprise Surveys is to obtain feedback from companies in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample includes 2387 establishments: 1891 enterprises have at least 5 full-time employees and 496 are micro establishments with less than 5 full-time workers.

The sample for enterprises with more than four employees was designed using stratified random sampling with strata defined by region, sector and firm size.

Establishments located in 11 states - Abia, Anambra, Abuja, Bauchi, Cross Rivers, Enugu, Kaduna, Kano, Lagos, Ogun and Sokoto - were interviewed.

Following the ISIC (revision 3.1) classification, the following industries were targeted: all manufacturing sectors (group D), construction (group F), retail and wholesale services (subgroups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub-group 72 of group K). For establishments with five or more full-time permanent paid employees, this universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15); 2. Manufacturing: Garments (Group D, sub group 18); 3. Manufacturing: Other Manufacturing (Group D excluding sub-groups 15 and 18); 4. Retail Trade: (Group G, sub-group 52); 5. Rest of the universe, including: • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

The sampling frame of establishments with 5 employees and more was built with lists sourced from the Nigeria Manufacturer Association, the National Bureau of Statistics in Abia, Anambra, Abuja, Cross River, Enugu, Kaduna, Lagos, the ministry of commerce and industry in Ogun, Kano, Bauchi, and from the Abuja Business Directory, the Sokoto Business Directory. This master list was used to set the target sample size for each stratum. During the survey period, the list was updated as new information regarding establishments that had closed or were out-of-scope was gathered. The final population size in all strata and locations was 771018 with the vast majority of establishments operating in the micro and manufacturing strata. The sample (including the entire rest of universe and retail sample in each state) was selected at random from the master list by a computer program.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees. The implementing agency (EEC Canada) selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all states of the survey.

First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to local sources. The EEC team then went in the field to verify the sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At the head office, the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Nigeria, enumerators were sent to each zone with instructions how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro Establishments Questionnaire (for establishments with 1 to 4 employees).

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on

IntroductionTerrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation.MethodsWe compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000–2018 and captured bighorn in 2010–2016 to deploy GPS collars and test for disease. From 2011–2015, we non-invasively collected fecal samples and genotyped them at 9–16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capture-recapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model.Results and discussionFrom 2010–2018, 3,176 sightings of bighorn were reported, with sightings of 56–145 bighorn annually on formal surveys. From 2012–2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011–2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022–0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536–552 (95% CrI range 451–647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems.

Abstract

The survey was conducted in Nigeria between April 2014 and February 2015 as part of Enterprise Surveys roll-out, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

In Nigeria, data from 2,676 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, region and size.

• Industry stratification For panel firms, the universe was stratified into manufacturing industries and two service sectors (retail and other services). For fresh firms, the universe was stratified into seven manufacturing industries (food & beverage, garments, fabricated metal products, non-metallic mineral products, furniture, publishing, and other manufacturing) and six service sectors (retail, wholesale, transport, hotels & restaurants, repair of motor vehicles, and other services).

• Regional stratification 19 states: Abia, Abuja, Anambra, Cross River, Enugu, Gombe, Jigawa, Kaduna, Kano, Katsina, Kebbi, Kwara, Lagos, Nasarawa, Niger, Ogun, Oyo, Sokoto, Zamfara.

• Size stratification Small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).

For the Nigeria ES, two sample frames were used. The fresh sample frame was built using data compiled from the NBS, as well as local and municipal business registries. Due to the fact that the previous round of surveys utilized different stratification criteria in the 2007 and 2009 survey samples, the following convention was used. The presence of panel firms was limited to a maximum of 50% of the achieved interviews in each cell. That sample is referred to as the panel.

The sample design for the Nigeria Enterprise Survey was generated with the aim of obtaining interviews at 2,640 establishments.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The eligible manufacturing industries have been surveyed using the Manufacturing Module Questionnaire (includes a common set of core variables, plus manufacturing specific questions). Eligible service establishments have been covered using the Services Module Questionnaire. Each variation of the questionnaire is identified by the index variable, a0.

All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names proceeded by a prefix "NG" indicate questions specific to Nigeria, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.49. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.13.

Abstract

The Multiple Indicator Cluster Survey (MICS) is a primary source of information on women and children as it provides statistical indicators that are critical for the measurement of human development. It is an international household survey programme developed by United Nations Children’s Fund (UNICEF). The MICS is designed to collect statistically sound and internationally comparable estimates of key indicators that are used to assess the situation of children and women in the areas of health, education, child protection and HIV/AIDS. It can also be used as a data collection tool to generate data for monitoring the progress towards national goals and global commitments which aimed at promoting the welfare of children and women such as MDGs and SDGs.

OBJECTIVES

The primary objectives of Multiple Indicator Cluster Survey (MICS) Nigeria 2016-17 are:

• To provide up-to-date information for assessing the situation of children and women in Nigeria;

• To generate data for the critical assessment of the progress made in various programme areas, and to identify areas that require more attention;

• To contribute to the generation of baseline data for the SDG;

• To furnish data needed for monitoring progress toward goals established in the post Millennium Declaration and other internationally agreed goals, as a basis for future action;

• To provide disaggregated data to identify disparities among various groups to enable evidence based actions aimed at social inclusion of the most vulnerable.

Geographic coverage

National, rural/urban, states as well as the 6 geo-political zones of Nigeria.

Analysis unit

• Individuals

• Households

Universe

All household members (usual residents), all women age 15-49 years, all men age 15-49 years and all children under 5 years of age.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE AND SAMPLE ALLOCATION

The sample size for the Nigeria MICS5 was calculated as 37,440 households.

The principal domain of reporting to which the sample size n refers in this calculation is the state. For this sample design, determination of the sample size is based on the indicator stunting prevalence in under-5 children as the design variable. The results from the MICS4 of 2011 reported stunting prevalence at 35.8 percent at the national level. This estimate had a relatively high design effect (deff) of 4.85, indicating a large clustering effect for this characteristic. However, with the more efficient sample design for the MICS 2016-17 it was expected that the deff will be lower, so a value of 3.5 was assumed for the deff in calculating the sample size. The value for pb (percentage of children aged 0-4 years in the total population) based on the results of the MICS4 2011 and NDHS 2013 is 17.1; and Average Size (average household size) is 5.0. For state-level results, it is reasonable to use a relative margin of error (RME) of 18%. Based on previous survey results, the household response rate is assumed to be 95%.

For 34 states and the FCT Abuja a sample of 60 EAs was selected per state and 16 households per EA, which gives a sample size of 960 households in each of these states. Six (6) replicates containing ten (10) EAs/clusters each was selected from the NISH2 master sample for each of these states. In the case of Kano and Lagos States, additional results were needed at the level of the three senatorial districts in each state. Therefore, a sample of 40 EAs per senatorial district was selected in these two states from the NISH2 master sample, for a total of 120 sample EAs and 1,920 sample households in each state. The total sample size for Nigeria was 37,440 households. And the selection of 16 households per EA slightly reduces the design effects compared to the MICS 2011, in which 20 households were selected per EA

SAMPLING FRAME AND SELECTION OF CLUSTERS

The MICS sample clusters were selected from the NISH2 master sample, based on the 2006 census frame. For the NISH2 master sample the census enumeration areas were defined as primary sampling units (PSUs), stratified by state. The first stage of sampling for MICS was completed by selecting the required number of enumeration areas from the NISH2 master sample for each of the 36 states of the federation and FCT Abuja which cut across urban and rural areas.

LISTING ACTIVITIES

Since the sampling frame (the 2006 Census) was not up-to-date, a new listing of households was conducted in November 2015 for all the sample enumeration areas prior to the selection of households. For this purpose, listing teams were formed who visited all of the selected enumeration areas and listed all households in each enumeration area. Selected staff of the National Bureau of Statistics (NBS) in all the states carried out the listing exercise. Six (6) teams were constituted that carried out the listing exercise in each state except Lagos and Kano where twelve teams were constituted respectively. Each team comprises of 2 enumerators and one (1) supervisor who supervised two (2) teams. There were three (3) supervisors in each of the 35 states, and six (6) supervisors for Lagos and Kano states respectively. The listing exercise lasted for twelve (12) days. Out of the 2,340 enumeration areas selected for the household listing, one hundred and one (101) of them were not visited because they were inaccessible due to insecurity during the listing exercise.

SELECTION OF HOUSEHOLDS

Lists of households were prepared by the listing teams 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 National Bureau of Statistics (Field Services and Methodology Department), where the selection of 16 households in each enumeration area was carried out using random systematic selection procedures.

The survey also included a questionnaire for individual men aged 15 to 49 years. It was administered in eight out of sixteen sampled households. Households with even number in each sample cluster were selected and all eligible men were interviewed.

Within each state, a sub-sample of 30 enumeration areas was systematically selected for the water quality test. In each of these sampled EAs, a systematic sub sample of three households out of sixteen (16) MICS sample households was selected for the water quality tests.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires are based on the MICS5 questionnaire3 model (English version), customized and pre-tested in Cross River, Enugu, Gombe, Lagos, Kaduna, Kano, Nasarawa and Oyo states in April 2016. Based on the results of the pre-test, modifications were made to the wording of the questionnaires. A copy of the Nigeria MICS questionnaires is provided as Related Material.

In addition to the administration of questionnaires, salt iodization and water quality tests were conducted. Weight and height of children age under 5 years were also measured.

Cleaning operations

Data were analyzed using the Statistical Package for Social Scientists (SPSS) software, Version 21. Model syntax and tabulation plans developed by UNICEF MICS team were customized and used for this purpose.

Response rate

Out of 37,440 households sampled, 35,747 households were visited, 34,289 were found to be occupied and 33,901 were successfully interviewed, representing a household response rate of 98.9 percent.

In the interviewed households, 36,176 women (age 15-49 years) were identified. Of these, 34,376 were successfully interviewed, yielding a response rate of 95.0 percent within the interviewed households.

The survey also sampled men (age 15-49), but required only a subsample. All men (age 15-49) were identified in 17,868 households selected for the men questionnaire; 16,514 men (age 15-49 years) were listed in the household questionnaires. Questionnaires were completed for 15,183 eligible men, which corresponds to a response rate of 91.9 percent within eligible interviewed households.

There were 28,578 children under age five listed in the household questionnaires. Questionnaires were completed for 28,085 of these children, which corresponds to a response rate of 98.3 percent within interviewed households.

Overall response rates of 93.9, 90.9 and 97.2 are calculated for the individual interviews of women, men, and under-5s, respectively.

Sampling error estimates

The sample of respondents selected in the Multiple Indicator Cluster Survey (MICS) 2016 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. 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