Interpret the post hoc test. When interpreting the post hoc test indicate the mean and standard deviation for each group and indicate which group was signifantly higher or lower from the other. If there is no difference between two groups indicate that as well.

Interpret the post hoc test. When interpreting the post hoc test indicate the mean and standard deviation for each group and indicate which group was signifantly higher or lower from the other. If there is no difference between two groups indicate that as well.

The turkey post-hoc test result uindicated that the level of satisfaction difefred significantly between the groups based on the housing problem. Though, there was no significant difference in level of satisfaction between those people with one housing problem and those with two or more housing problem.

Statistical analysis is a powerful tool that helps researchers gain valuable insights into a set of data and make informed decisions based on the results. Therefore, it is important for nurses and other professionals to have adequate knowledge regarding statistical analyses. There is also a need to know which statistical tests should be used based on the nature of the data set and the purpose of the analysis. Two types of statistical tests that have widely been applied in research are Analysis of Variance (ANOVA) and T-tests (Mishra et al.,2019). ANOVA is applied in to determine whether three or more groups or populations are statistically different. On the other hand, t-tests are applied to determine whether two groups are statistically different (Liang et al.,2019). Therefore, these two tests play a key role since they offer the researcher a chance to understand the nature of variations between variables in research. Therefore, the purpose of this assignment is to summarize the interpretation of the ANOVA statistics provided in the SPSS Output.

The data provided is on the overall satisfaction and material well-being. The data provided covers descriptive statistics, tests for homogeneity of variance, ANOVA and multiple comparisons. The descriptive table shows the standard deviation, mean and 95% confidence interval for the dependent variables for each separate group, which forms part of the study. From the data provided, the mean for “two or more housing problems” was 10.57, the mean for “one housing problem” was 11.97, and the mean for “No housing problem” was 12.71. The standard deviations observed for the three categories are 2.594, 2.588, and 2.353.  It is also important to note that the overall mean for all three groups represented in the study was 11.80.

Another important aspect of this data output is the test of Homogeneity of Variances. Levene’s test was used to accomplish this analysis. This analysis of the F-test when testing the null hypothesis that the variance is equal across all the groups tested (Yi et al.,2022). It is observable that the p-value obtained from Levene’s tests was 0.122, which means that they are not significantly different as the value is greater than 0.05.

The ANOVA output also showed the interaction within the group and between the groups of  “material well-being” and “overall satisfaction” as part of the statistical tests. From the results, it is evident that there was a statistically significant difference between the group means. The p-value obtained for this analysis is 0.000, a value above 0.05, indicating statistical significance. As such, the mean of material well-being and overall satisfaction is statistically significant. Nonetheless, it is not possible to have an idea of how the groups under consideration are different from each other using this test. As such, it is important to apply a computation of multiple comparisons with a Tukey post hoc test.

The next important part of the analysis is the multiple comparisons of “material well-being” and “overall satisfaction”, with a 0.05 used as the level of significance. The analysis shows that the difference between the means of the tested groups is statistically significant. As earlier indicated, a deeper study of the groups requires the use of Tukey post hoc tests, which is the test known and used in accomplishing post hoc tests on one-way ANOVA tests. Therefore, this study employed the Tukey post hoc test since it forms a vital ANOVA. When ANOVA is used to test the similarity of three or more groups’ means, the statistical significance results would show that not all the tested group means are similar (Uysal, et al., 2019).

The ANOVA output fails to identify the particular differences between the mean pairs that are significant. As such, the post hoc tests are key to determining the differences between the means of multiple groups while controlling the standard errors. The difference in overall satisfaction between one housing program and no housing problems was found to be 0.739, which is significant.  The difference in overall satisfaction between no housing problems and two or more housing problems was 2.139, which is also significant. In addition, the difference between one housing problem and two or more housing problems was 1.401, which is also significant.

It is also evident from the table that there was a statistically significant difference between one housing problem and n