While reviewing the results, I noticed a potential reporting error in Table 1 that might be worth clarifying for readers trying to interpret the strength of the findings.
The paper reports eta-squared (η²) values, a measure of effect size, for several ANOVA comparisons. In social science research, η² tells us the proportion of total variance explained by a factor. By convention, values like 0.01, 0.06, and 0.14 are considered small, medium, and large effects, respectively.
In this table, however, some of the reported η² values are quite high. For instance:
— Sleep quality is listed with an η² of 1.00 across all well-being dimensions.
— Years in profession shows η² values of 0.99 for self-esteem and 0.94 for balance.
An η² of 1.00 would mean that sleep quality alone explains 100% of the differences in well-being, with no other factors playing a role, which is a statistical impossibility in this context. Values of 0.99 and 0.94 are similarly extraordinary and unrealistic for this type of research.
My best guess is that there was an error in preparing the table, and the values in the η² column might actually be the F-statistics from the ANOVAs (which are reported in the text and are much more plausible).
It would be helpful if the authors could confirm this or provide the corrected η² values. Getting this right is key for readers to accurately gauge the practical significance of these important results, beyond just their statistical significance.