This study was conducted to evaluate some development factors in Southern Nigeria in order to ascertain common factors that explained the interrelationships among them and identify best cities for recommendation. A total sample of 250 cities from different states in three geopolitical zones in Southern Nigeria was used in this study and 11 development factors were considered. 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 are sufficiently large for factor analysis; correlation matrix was computed to confirm the inter-item correlation. In this analysis, principal component factor analysis was the factor extraction method. Varimax rotation technique was used for factor rotation. The result showed that three new factors with eigenvalues greater than 1 were successfully constructed. The three new factors accounted for 71.63% of total variance in the dataset and assigned as the common factors influencing sustainable development in Southern Nigeria. The communalities results ranging from 0.32-0.88 depicted that factor model was adequate. The results of factor analysis were extended to multiple regression analysis. The multiple regression model was fitted using development scores as dependent variable and rotated factors as independent variables. The coefficient of determination,, for the regression model was 99% and this shows that the model is adequate to evaluate the Southern Nigerian cities. The higher the estimated development scores, the better a city. Tolerance and VIF values showed that there was no multicollinearity in the regression model.
Randomisation tests (R-tests) are regularly proposed as an alternative method of hypothesis testing when assumptions of classical statistical methods are violated in data analysis. In this paper, the robustness in terms of the type-I-error and the power of the R-test were evaluated and compared with that of the F-test in the analysis of a single factor repeated measures design. The study took into account normal and non-normal data (skewed: exponential, lognormal, Chi-squared, and Weibull distributions), the presence and lack of outliers, and a situation in which the sphericity assumption was met or not under varied sample sizes and number of treatments. The Monte Carlo approach was used in the simulation study. The results showed that when the data were normal, the R-test was approximately as sensitive and robust as the F-test, while being more sensitive than the F-test when data had skewed distributions. The R-test was more sensitive and robust than the F-test in the presence of an outlier. When the sphericity assumption was met, both the R-test and the F-test were approximately equally sensitive, whereas the R-test was more sensitive and robust than the F-test when the sphericity assumption was not met.
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