CHAPTER ONE
INTRODUCTION
1.0 Background of the study
The evaluation of the quality of demographic data is an important aspect of population research and policy-making. Demographic data refers to statistical information about human populations, such as age, sex, race, and income, among others.
Demographic data are used for planning, policy formulation and implementation as well as in program monitoring (Nwogu, et al., 2018). The quality of estimates of demographic parameters required for this purpose depends on the quality of the data from which they were derived. Age and sex are two items on the bases of which data are always collected, tabulated, analyzed and adjusted in all demographic enquiries. Nwogu (2006) Moreover, such demographic phenomenon like fertility, mortality and migration and socio-economic characteristics, like nuptiality, education, occupation and employment are also highly correlated with age and sex. The age and sex structure of a population has important implications (i) for defining the limits of a society’s reproductive potentials, (ii) for determining a nation’s manpower supply and (iii) influences the demand for various essential goods and services. The events of household formation, school attendance and entry into marital union also depend on age. In 2006, a census was conducted in many countries around the world, including the United States, Canada, and Australia (Bassett, et al., 2008).The census is an official count of a population, and it is used to collect demographic data. The 2006 census data is particularly important because it provides a snapshot of the population at a specific point in time, and it allows researchers and policymakers to track changes in the population over time.
To evaluate the quality of demographic data collected during the 2006 census, researchers may use various measures such as the accuracy, completeness, and consistency of the data (Haklay, 2010). Accuracy refers to the extent to which the data collected reflects the true population characteristics, while completeness refers to the extent to which all members of the population were counted. Consistency refers to the degree of agreement between different data sources and over time. For example, to evaluate the accuracy of the demographic data collected during the 2006 census, researchers may compare the census data to other sources of information, such as birth and death records, and assess how well the census data captures the true population characteristics. They may also analyze the response rates to the census survey and assess if certain demographic groups were underrepresented.
Similarly, to evaluate the completeness of the demographic data collected during the 2006 census, researchers may compare the census data to estimates of the population size based on other sources of information, such as administrative records or population registers (Wardrop, et al., 2018). They may also analyze the response rates to the census survey and assess if certain demographic groups were less likely to respond or were missed altogether. To evaluate the consistency of the demographic data collected during the 2006 census, researchers may compare the census data to data collected during previous censuses or to other sources of information, such as surveys or administrative records. They may assess if there are any inconsistencies or discrepancies in the data that may affect its quality.
Overall, evaluating the quality of demographic data collected during the 2006 census is important to ensure that the data is reliable and valid for use in population research and policy-making. The age and sex structure of a population at any time is the result of past trends in fertility, mortality and migration and in turn influences the current levels of crude vital rates and rate of population growth (Coale and Hoover, 2015). The dynamics of a population’s age and sex structure constitute a central subject in demographic analyses because of its significant demographic, economic and social implications (Garavan, et al., 2018). Although the concept of age appears precise and unambiguous, census reports on age are subject to errors arising from a variety of causes. These include deliberate misstatement, a tendency to report ages ending in certain preferred digits and to avoid others, exaggeration of age at older ages, carelessness in reporting, ignorance of correct age, fear, superstition, anticipation of benefits or unfavorable end-points etc, (Nwogu, et al., 2018). Nwogu (2006) has demonstrated that the quality of age and sex data in Nigeria is relatively poor. Ages ending in 1, 3, 7, and 9 are often avoided, whereas the final numbers 0 and 5 are preferred. The pattern of errors noticed in the total census population of Nigerians by age and sex also persisted across the census data by state after evaluation.
One of the main problems in evaluating the quality of demographic data using the 2006 census data is the potential for undercounting and misreporting of certain demographic groups. Despite efforts to promote participation in the census, there may still be individuals or groups who are not included in the count, either because they were missed during the enumeration process or because they chose not to participate in the survey.
Furthermore, there may be certain demographic groups who are more likely to be undercounted or who may provide inaccurate or incomplete information. For example, individuals who are homeless, living in remote areas, or who do not speak the language of the survey may be less likely to participate in the census or to provide accurate information about their demographic characteristics.
Additionally, there may be issues related to data consistency and comparability over time. The 2006 census data may differ in terms of its methodology, sampling design, or survey questions compared to previous or future censuses, which could affect the reliability and validity of the data. Therefore, the problem in evaluating the quality of demographic data using the 2006 census data is to investigate if the data is representative, accurate, and comparable over time, and if potential biases or sources of error can be identified.
1.2 Aim and Objectives
The aim of this project is to evaluate the quality of Nigeria 2006 census data. The objectives includes
- to assess the quality of age distribution of Nigeria data and make possible comparison.
- to assess the quality of age distribution by Geopolitical zones and make possible comparison.
- to assess the quality of age distribution of South Eastern region by states and make possible comparison.
- to assess the quality of age distribution of Ebonyi state L.G.AS data and make possible comparison.
1.3 Significance of the study
The evaluation of the quality of demographic data using the 2006 census data has significant implications for population research, policy-making, and public administration. Accurate demographic data is crucial for understanding the social, economic, and health characteristics of a population. The evaluation of the quality of demographic data using the 2006 census data can provide insights into the accuracy and completeness of this data, which is essential for making informed policy decisions and allocating resources effectively. The 2006 census data provides a snapshot of the population at a specific point in time, and the evaluation of its quality can help researchers and policymakers to track changes and trends in the population over time. Identifying any potential sources of bias or error in the census data is critical for ensuring that the data is representative of the entire population. This can help to address disparities and ensure that vulnerable or marginalized groups are not left out of policy decisions. The evaluation of the 2006 census data can also inform the development of future census methodologies, sampling designs, and survey questions, and improve the quality of demographic data collection and analysis.
The study can provide recommendations for strategies to address any gaps in the demographic data collected during the 2006 census, and can inform efforts to improve data collection and analysis in future censuses and surveys. The evaluation of the quality of demographic data using the 2006 census data is significant for ensuring the ethical use of data in population research and policy-making. Accurate and reliable data is essential for making evidence-based decisions that improve the well-being of individuals and communities.
1.4 Scope and Limitation of the study
The scope of the study on the evaluation of the quality of demographic data using the 2006 census data include, Assessment of the completeness and accuracy of the demographic data collected during the 2006 census. Identification of potential sources of bias or error in the census data, such as undercounting or misreporting of certain demographic groups. Recommendations for improving the quality of demographic data collection and analysis in future censuses and surveys.
However, there are also some limitations to this study, which include: The study may be limited by the availability of data and the representativeness of the sample. It may be challenging to obtain data for all demographic groups or to ensure that the sample is representative of the entire population. The study may be limited by the methodology and sampling design of the 2006 census, which may differ from previous or future censuses or surveys. This could affect the reliability and validity of the data and its comparability over time. The study may be limited by the potential for response bias, as some individuals or groups may be more likely to participate in the census or to provide accurate information than others. The study may be limited by the lack of information on certain demographic characteristics or variables. Some data may be missing or incomplete, making it challenging to draw conclusions about specific population groups or trends. The study may be limited by the ethical considerations surrounding the use of census data, such as privacy concerns or the potential for stigmatization of certain groups based on their demographic characteristics.
