Analysis On Education Trust Fund Allocation To Teritiary Institution In Six Geo- Political Zones Of Nigeria (1999-2007).
ABSTRACT
The average allocation to zones, method of distributions, extraction of principal components, classification of the components into factors, and testing if there is any significant difference in the allocation among the zones were all carried out in this project titled statistical analysis on education trust fund allocation to tertiary institutions in six geopolitical zones of Nigeria. During the review period, the average allocation to all zones was $14,605,429,76. The allocation to zones was normally distributed, indicating that the allocations were unbiased. University allocation is the most important factor component in ETF allocation among institutions, with 0.201 in the first component, followed by monotechnics and polytechnics. and educational institutions. Polytechnics, monotechnics, and colleges of education were lumped together in one factor, while universities were lumped together in another. According to the results, no zone is more favored, and their distribution is unbiased.
CONTENTS TABLE
TITLE PAGE…………………………………………………………………..
CERTIFICATION ……………………………………………….ii
ACKNOWLEGMENT …………………………………………..iii
DEDICATION ……………………………………………………iv
ABSTRACT ……………………………………………………….v
TABLE OF CONTENT ……………………………….………….vi-vii
CHAPITRE ONE
1.0 INTRODUCTION…………………………………………….
1
1.1 STUDY BACKGROUND……………………………….1
1.2 SOME EDUCATION FACTS IN NIGERIA………..4
1.3 PROBLEM STATEMENT ………………………….…12
1.4 STUDY PURPOSE………………………………..12
1.5 THE STUDY’S SIGNIFICANCE…………………………12
1.6 STUDY SCOPE…………………………………….13
1.7 GOALS AND OBJECTIVES………………………………….13
1.8 HYPOTHESIS TEST………………………………………….14
1.9 OPERATION KEY WORDS……………………………………. 14
1.10 SYMBOLIZATIONS ……………………………………………15
REVIEW OF LITRATURE IN CHAPTER TWO
2.0 INTRODUCTION……………………………………………..16
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THE METHODOLOGY IN CHAPTER THREE
3.0 INTRODUCTION…………………………………………………………….27
3.1 THE SAMPLED POPULATION………………………………27
3.2 DATA COLLECTION METHODS……………………….28
3.3 PRINCIPAL COMPONENT ANALYSIS………………………28
3.4 FACTOR ANALYSIS……………………………………………30
3.5 KRUSKAL-WALLIS TEST………………………………………34
CHAPITRE FOUR
4.0 DATA ANALYSIS…………………………………………..36
4.1 TO BE AWARE OF THE
ETF ALLOCATION DISTRIBUTION
TO NIGERIAN TERTIARY INSTITUTIONS…………………36
4.2 KRUSKAL-WALLIS TEST……………………………………..37
4.3 PRINCIPALCOMPONENTANALYSIS………………………..39
TIME SERIES 4.4
…………………………………………………….42
4.5 ANALYSIS OF FACTORS …………………………………………..43
CHAPITRE FIVE
5.0 SYNOPSIS ………………………………………………………..49
5.1 CLARIFICATION …………………………………………………….51
5.2 RECOMMENDATION……………………………………………52
REFERENCES …………………………………………………………54
APPENDIX ………………………………………………………………56
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CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF STUDY
Principal Components Analysis (PCA) and Factor Analysis (FA)
Analysis (FA) is the process of extracting from a set of P variables a reduced set of M components or factors that account for the majority of the variance in the set of P variables; in other words, we want to reduce a set of P variables to a set of M underlying superordinate dimensions.
The correlations between the P variables reveal these underlying factors. The weighted sum of the P variables is used to estimate each factor. The factor is as follows:
W1X1 + Wi2X2 + W1pXp + K = F1.
Each of the P variables can also be expressed as a linear combination of the M factors.
Aij F1 + A2j F2 + Amj Fm + k+ Uj = Xj
Where Uj denotes the variance
Variable j is unique, and variance
None of the common factors can account for it. A variable reduction technique is principal component analysis.
Procedure that specifies the sample size and number of items required for each component. It is also
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demonstrates how to calculate the number of components to keep, interpret the rotated solution, generate factor scores, and summarize the results.
It is useful when you have obtained measures for a number of observed variables and want to create a smaller number of artificial variables known as Principal Components that will account for the majority of the variance in the observed variables. Following that, the principal components can be used as predictor variables in subsequent analysis.
A linear combination of optimally weighted observed variables is defined as a principal component.
bination” and “optimally weighted” refer to the fact that scores on a component are created by adding together scores on the observed variables being analyzed, and the terms “linear combination” and “optimally weighted” refer to the fact that the observed variables are weighted in such a way that the resulting components account for the greatest amount of variance in the data set.
Factor analysis is a mathematical tool that can be applied to a wide variety of data sets. It is the most well-known multivariate procedure in the behavioral sciences; it includes both component analysis and common factor analysis.
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The factor analysis. In factor analysis, only the correlation or covariance matrix is required, not the actual scores. The goal of factor is to find simple patterns in patterns.
of relationship among the variables. It specifically seeks to determine whether the observed variable can be explained largely or entirely in terms of a much smaller number of variables known as factors.
The distinction between factor analysis and principal component analysis was defined by Onyeagu (2003). Factor analysis is based on covariance (or correlation). In principal component analysis, all components must produce the same inter-correlation (covariance). A few factors in factor analysis will exactly reproduce the inter-correlations (covariance).
Wang (2007) (2007) Differentiate between principal component analysis and factor analysis because the primary goal of principal component analysis is to select a number of components that will express as much of the total variance in the data as possible.
However, the factors identified through factor analysis are
generated in order to identify the latent variables
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contributing to the common variance in the data. A factor analysis attempts to exclude unique variance, whereas a principal component analysis does not distinguish between common and unique variance. PCA examines variance, whereas FA examines covariance.
The PCA and FA share some characteristics, such as an interval or ratio measurement scale, a linear relationship between observed variables, and a normal distribution for each observed variable. PCA and FA are both variable reduction techniques, and each pair of observed variables has a bivariate normal distribution. If communalities are large, close to one, the results may be comparable.
1.2 SOME EDUCATION FACTS IN NIGERIA
The educational and literacy characteristics of
The population aged 6 and up were
Counted in the 1991 population census. Male literacy was 60% and female literacy was 40%. The country’s literacy level appears to have improved over time, but the gender gap in literacy among people aged 35-39 was nearly twice as large for males (68.3%) and females (35.8%). In comparison, the
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In the age group 10-14, male literacy rates (76.6%) are slightly higher than female rates (74.7%). This pattern did not differ across states, indicating that there was increased awareness in all states that education of a female child is as important as that of a male child, even for heads of households.
The literacy rate among people aged 15 and up was discovered. to be 44.3% on a national scale. Adult literacy rates were lowest in Lagos State (19.8%) and highest in Yobe State (68.6%). Niger State (61.8%), Taraba (64.4%), Sokoto (64.5%), and Kebbi (66.1%) also have very high adult literacy rates, while 46% have no education. Such a high rate of illiteracy has serious implications for education, social and economic development. Similarly, more males than females completed primary, secondary, or tertiary education, and this situation may have resulted from a long neglect of women’s education needs and a lack of funds for our educationa.
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