There are a few different ways to conduct an ANOVA test in SPSS. The first way is to use the “ANOVA” command. To do this, go to “Statistics” and then select “ANOVA.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click “OK.” Another way to conduct an ANOVA test in SPSS is to use the “Regression” command (van den Bergh et al., 2020). To do this, go to “Statistics” and then select “Regression.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click ” OK. When conducting an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers, one may want to first ensure that the data meets the assumption of normality. This can be done by running a goodness-of-fit test, such as the Kolmogorov-Smirnov test (Liu & Wang, 2021). If the data meet the assumption of normality, one can proceed with conducting the ANOVA. The null hypothesis for this test is that there is no difference in the Interval Depression Scores among the three groups of shift workers. The alternative hypothesis is that there is a difference in at least one of the group means. The purpose of this assignment is to conduct an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers.
Part One
While conducting ANOVA test, it is necessary to determine both the dependent and independent variables. In this case, the independent variable is Shift Worked while the dependent variable is Depression Score (Interval).
H0: There is no statistical significance between the depression score and the shift worked.
H1: There is a statistical significance between the depression score and the shift worked.
| Table 1: ANOVA | |||||
| Shift Worked (nominal) 1=first, 2=second, 3=third | |||||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Between Groups | 10.267 | 12 | .856 | 1.672 | .162 |
| Within Groups | 8.700 | 17 | .512 | ||
| Total | 18.967 | 29 | |||
Table 1 shows ANOVA output between the dependent and independent variables identified in the study. The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked.
Part Two
ANOVA is a statistical technique that is used to test for differences between groups. In this case, we are looking at the Interval Depression Score (IDS) among three groups of shift workers. The ANOVA analysis will help us determine if there are any significant differences between the IDS scores of the different groups (Akbay et al., 2019). To carry out the ANOVA analysis, we first need to gather data from each of the three groups of shift workers. We will need to know the mean IDS score for each group, as well as the number of people in each group. Once we have this information, we can plug it into an ANOVA calculator (there are many freely available online).
The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked. There are a number of possible explanations for why there is no statistical significance between the depression score and the number of shifts worked. It could be that the sample size is too small to detect a difference, or that the relationship between depression and shift work is more complex than a simple linear relationship. Another possibility is that other factors, such as job s
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