WK7Assgn_Beal_R (1)

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Feb 20, 2024

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1 One Way ANOVA Test Roswell Beal PhD of Science in Public Policy and Administration, Walden University Quantitative Reasoning and Analysis Professor Olivia Yu January 14th, 2024

2 One Way ANOVA The data set used for this week's assignment is the Afrobarometer data set, which has a mean of (Q1) age variable that has a mean of 37.17. As a researcher looking into the social issue of poverty, I am testing the effect that urban, rural or semi urban localities have on those who experience lived poverty. The research question can be answered by using a One Way ANOVA test, which is an inferential statistical method used to test the significance of the relationships between an independent variable and dependent variable in two or more groups (Frankfort- Nachmias et. al., 2020). For this exercise, the dependent variable is lived poverty index, while the independent variable is Urban or Rural Primary Sampling unit which has three categories of Urban, rural or semi urban. Alternative Hypothesis (Ha): Lived poverty index significantly differs by respondent locality. Null Hypothesis (H0): There are no poverty index differences between different locality types. Table 1 In Table 1, (1=Urban), (2=Rural) and (3=Semi-Urban) where it can be observed that Rural localities has the highest mean score of 1.406 with a (SD=.9360), the second highest mean for Semi-Urban at 1.028 (SD=.7767) and lowest mean is for Urban at 1.005 (SD=.9256).

3 Table 2 The above graphic of Table 2 shows the One Way ANOVA test is statistically significant where the results indicate that F(2, 14265)=315.883, p<.001. These findings show that the null hypothesis is rejected. Table 3 Table 3 shows that the Levene’s test of homogeneity of variances is not significant since the significance level is below the threshold of the alpha level of (P> .05). This upholds the rejection of the null hypothesis and that homogeneity is violated.

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4 Table 4 In Table 4, Games-Howell is used since homogeneity is not assumed. The mean LPI score is between (1=Urban) and (2=Rural) at (P<.001) and shows significant difference. Additionally, the mean LPI score shows a significant difference between (2=Rural) and (1=Urban) or (3=Semi-Urban) because (P<.001). Lastly, (3=Semi-Urban) significantly differs from (2=Rural) at (p<.001) but there is no difference between (3=Semi-Urban) and (1=Urban) since P=.919 and is greater than the alpha of .05. Conclusion To conclude this One Way ANOVA test, the research hypothesis is accepted and the null hypothesis is rejected based on the presented statistics in the tables. Based on the results, an implication for social change includes using these findings to evaluate the lived poverty level of Rural, Urban and Semi Urban localities as a way of identifying which locality has the most need of interventions to reduce poverty levels. Further research can conclude what sort of intervention

5 methods are most useful for certain localities and how they differ from one another based on locality.

6 References Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

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