Principal Component Factor Analysis of Some Development Factors in Southern Nigeria and Its Extension to Regression Analysis

Eze, Nnaemeka Martin; Asogwa, Oluchukwu Chukwuemeka; Eze, Chinonso Michael

doi:10.9734/jamcs/2021/v36i330351

Cited by 8 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the top 6 components account for 60.286% contribution towards adoption of agro forestry systems. The results are in correlation with that of Martin Ez et al 2021 [2] . The factors where in all collectively accounted for an impressive 71.63% of the total variance in the dataset, signifying their importance in explaining the underlying trends in sustainable development within Southern Nigeria.…”

Section: Communalitiessupporting

confidence: 92%

Analyses of factors affecting the adoption of major Agro-forestry systems in the high altitude zone of Andhra Pradesh

IVY

et al. 2023

Int. J. Stat. Appl. Math.

View full text Add to dashboard Cite

The research study was conducted to analyse the factors affecting the adoption of major agro forestry systems in the high altitude zone of Andhra Pradesh. The high altitude zone of Andhra Pradesh is having the major agro forestry systems viz., cashew + pulses, mango + turmeric, sliver oak + pepper + coffee, eucalyptus, jafra, rubber systems spread over in an area of 12,28,504 ha in the state. Besides analyzing the interrelationships among the factors, the factor affecting the adoption of agro forestry systems were also identified. A total sample of 335 farmers from different districts in high altitude zone of Andhra Pradesh were selected to analyze the identified factors affecting the adoption of six major agro forestry systems. The data pertains to the years 2020-2022. Kaiser-Meyer-Olkin (KMO) of >0.5 was computed to test the sampling adequacy. Bartlett's Test of Sphericity (Significant at 0.001) was conducted to test whether the correlation between the variables was sufficiently large for factor analysis and correlation matrix was computed to confirm the inter-item correlation. The principal component factor analysis was used for the factor extraction and varimax rotation technique was used for factor rotation. The results showed that six factors with eigen values greater than 1 were successfully constructed. The six factors accounted for 60.28% of the total variance in the dataset and were assigned as the factors influencing the agro forestry systems in Andhra Pradesh. The communalities results ranging from 0.47 to 0.72 depicted that the 6 factor model was adequate.

show abstract

Section: Communalitiessupporting

confidence: 92%

Analyses of factors affecting the adoption of major Agro-forestry systems in the high altitude zone of Andhra Pradesh

IVY

et al. 2023

Int. J. Stat. Appl. Math.

View full text Add to dashboard Cite

show abstract

“…The principal component analysis was used for the extraction of the component whose Eigenvalues are more than 1 (Kaiser's criteria) ( Exhibit 5) (Eze et. al; 2021).…”

Section: Discussion and Resultsmentioning

confidence: 99%

A discussion to promote global sustainability techniques for motivating plastic waste to minimize carbon footprints

Kalita

Singh

Baruah

et al. 2023

Environmental Quality Mgmt

View full text Add to dashboard Cite

This paper emphasizes the significant challenges facing the sustainable environment, including managing and handling plastic waste and reducing carbon footprints. To tackle these challenges, it is essential to identify people's awareness levels of waste handling techniques and their pro‐environmental behaviors. The study focuses on Guwahati, one of the most important cities in Northeast India, which generates increasing plastic waste daily. The paper aims to identify the factors contributing to the reduction of carbon footprints resulting from plastic waste management activities. The data collected from 1326 respondents was analyzed using factor analysis, and the reliability of the dataset was confirmed using Cronbach's alpha (0.84 for the awareness level of waste management techniques and 0.780 for the prevalent mode of plastic waste management techniques). KMO (0.796), Bartlett's test of sphericity (p < 0.001), and determinant score (0.019) were used to assess the data adequacy and factorability of the dataset, and the results were found to be satisfactory. Principal component analysis, exploratory factor analysis, and varimax orthogonal rotation method were used to identify high‐loaded factors by reducing the number of variables. The results showed that two highly loaded components, namely awareness level of waste management techniques (AWMT) and prevalent mode of plastic waste management techniques (PWMT), explained 27.53% and 24.34% of the total variance, respectively, with eigenvalues of 3.35 and 2.88. The regression model confirmed the statistical significance of these factors (p < 0.001) and their relationship with the dependent variable, greenhouse reduction (GHGR). The study proposes that minimizing carbon footprints in the environment can be achieved by focusing on a limited number of controllable factors such as AWMT and PWMT. This study provides valuable insights to the authorities in controlling waste generation and achieving a pollution‐free environment.

show abstract

“…Step 1: The data of the independent variables of the GWR model were standardized, then the Kaiser-Mayer-Olkin (KMO) test and Bartlett's test of sphericity were performed on the data. If the KMO value was greater than 0.5 and the p-value of Bartlett's test of sphericity was less than 0.05, there was a strong correlation between the independent variables, and PCA can be performed; otherwise, the data are not suitable for PCA [50].…”

Section: Pca-gwr Modelmentioning

confidence: 99%

Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

et al. 2022

View full text Add to dashboard Cite

In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south.

show abstract

Principal Component Factor Analysis of Some Development Factors in Southern Nigeria and Its Extension to Regression Analysis

Cited by 8 publications

References 38 publications

Analyses of factors affecting the adoption of major Agro-forestry systems in the high altitude zone of Andhra Pradesh

Analyses of factors affecting the adoption of major Agro-forestry systems in the high altitude zone of Andhra Pradesh

A discussion to promote global sustainability techniques for motivating plastic waste to minimize carbon footprints

Spatial Estimation of Regional PM2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

Contact Info

Product

Resources

About