Applying Multivariate and Univariate Analysis of Variance on Socioeconomic, Health, and Security Variables in Jordan

Abstract

Many researchers have studied socioeconomic, health, and security variables in developed countries; however, very few studies used multivariate analysis in developing countries. The current study contributes to the scarce literature about the determinants of the variance in socioeconomic, health, and security factors. Questions raised were whether the independent variables (IVs) of governorate and year impact the socioeconomic, health, and security dependent variables (DVs) in Jordan? Whether the marginal mean of each DV in each governorate and each year is significant? Which governorates are similar in the difference between means of each DV? Whether these DVs vary? The main objectives were to determine the source of variances in DVs, collectively and separately, testing which governorates are similar and which diverge for each DV. The research design was a time-series and cross-sectional analysis. The main hypotheses are that IVs affect DVs collectively and separately. Multivariate and univariate analyses of variance were carried out to test these hypotheses. The population of 12 governorates in Jordan and the available data of 15 years (2000-2015) accrued from several Jordanian statistical yearbooks. We investigated the effect of two factors of governorate and year on the four DVs of divorce, mortality, unemployment, and crime. Also, descriptive statistics were calculated for each DV in each governorate and each year. However, we performed a visual and numerical inspection of how each DV changed over time in each governorate compared with DV change in other governorates. The rate of divorce, mortality, and crime, and the percentage of unemployment were used in the analyses. All DVs were transformed into a multivariate normal distribution. Based on the multivariate analysis of variance, we found a significant effect in IVs on DVs with p < 0.001. Based on the univariate analysis, we found a significant effect of IVs on each DV with p < 0.001. Except for the effect of the year factor on unemployment was not significant with p = 0.642. Besides, the grand and marginal means of each DV in each governorate and each year were significant based on a 95% confidence interval. Furthermore, most governorates are not similar in DVs with p < 0.001. We concluded that the two factors produce significant effects on DVs, collectively and separately. Based on these findings, the government can distribute its financial and physical resources to governorates more efficiently. By identifying the sources of variance that contribute to the variation in DVs, insights can help inform focused variation prevention efforts.