WK7Assgn

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Walden University *

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8210

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Statistics

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Jan 9, 2024

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doc

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8

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1 Testing for One-Way ANOVA: High School Long Survey DrPh, Walden University RSCH-8210R: Quantitative Reasoning and Analysis Dr. Nancy Rea July, 2020
2 Testing for One-Way ANOVA: High School Long Survey Testing a researcher’s hypotheses is crucial for understanding the actual outcomes of studies and making decisions for communities. One benefit of using a one-way ANOVA is the ability to compare means of groups to help the researcher determine if there is statistical significance between the groups (Frankfort-Nachmias et al., 2020). The mean of the High School Longitudinal Survey will be calculated (IBM SPSS vs25, 2017). A research question, null hypotheses, and alternative hypotheses will be determined and the results of the hypotheses. Furthermore, an evaluation of the assumptions, post hoc test, effect size, displaying of the data and social change implications will be competed. Verification of Dataset Table 1 reveals, the mode of parent highest level of education is 2. Table 1 Parent Highest Level of Education N Valid 3332 Missing 1368 Mean 2.99 Median 3.00 Mode 2 From “High School Longitudinal Survey: Dataset B,” IBM SPSS v25, 2017. One-Way ANOVA Research Question and Hypotheses The research question is to what extent is there a difference between school locations and the socioeconomic status composition? The null hypotheses (Ho) there is no difference between the four school locations and the socioeconomic status composition. The alternative hypotheses (Ha) there is at least one significant difference between the school locations and the
3 socioeconomic status composition. Based on the conducted one-way ANOVA, a decision of the null hypotheses will be decided. Assumptions The assumptions of conducting a one-way ANOVA are the dependent variable is to be measured as a continuous variable and the independent variable should have three or more categorical groups (Frankfort-Nachmias et al., 2020). A test of homogeneity of variances is to be conducted for the review of Levene’s Test for Homogeneity of Variances to provide the determination of the use of the Bonferroni or Games-Howell post-hoc test for statistical significance (Mishra et al., 2019). ANOVA Test Results Levene’s Interpretation To test the null hypotheses that there are no significant differences between the mean and of statistical locations of schools and socioeconomic status composition, a one-way ANOVA was done (Laureate Education, 2018). The statistical location of schools was measured in the city ( M = .1402, SD = .837, N = 1219), suburb ( M = .1352, SD = .767, N = 1531), town ( M = -.1114, SD = .736, N = 491) and rural ( M = -.0835, SD = .697, N = 1038) as shown in Table 2. Table 3 reveals, Levene’s Test for Homogeneity of Variances between the three groups was not equal: F (3, 4275) = 13.53, p < .05. Hence, the assumption of homogeneity of variances was violated. The F value assists in determining the significance of the test (Laureate Education, 2016h). The Homogeneity was violated, and the null hypotheses is rejected. ANOVA Analysis The results for the ANOVA Analysis showed significant difference in the statistical location between and socioeconomic composition: F (3, 17.4) = 29.49, p < .05 as shown in Table
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