Reviewer’s report
Title: 
Factors associated with health-related quality of life in women using path
analyses: Mediation effect of the adiposity traits
Version: 
0 
Date: 
25 Jan 2021
Reviewer's report:
Is the study design appropriate for the research question (considering whether the
analyzed population accurately reflects the design and whether you see any
problems with control/comparison groups, e.g., likely confounders)?
No - there are minor issues
Are methodologies adequate and well implemented (considering whether
assumptions are addressed and whether analyses are robust)?
No - there are major issues
Are the analyses adequately communicated (considering whether reporting
details are adequate and whether figures and tables are well labeled and
described)?
No - there are major issues
Does the interpretation accurately reflect the analyses without overstatement
(considering whether limitations/bias are acknowledged and whether accurate
descriptors, e.g., 'significant', are used)?
No - there are major issues
Could an appropriately REVISED version of this work represent a statistically
sound contribution? 
Maybe - with major revisions
STATISTICAL REVIEWER COMMENTS:
In the literature, there are hundreds of studies that investigated the relationships
among eating behavior, quality of life, sleep quality, and obesity. According to
the author(s), the novelty of this study was to investigate possible association of
eating behavior, quality of life, and sleep quality with obesity using SEM. I
recommend the author(s) to explain why the use of SEM is so important to
justify this research. It reads that "Structural equation modeling (SEM) is a
powerful statistical tool that combines factor analysis and mathematical
modeling to test hypothesized." Leaving aside the typo, in the present study,
factor analysis was not combined with mathematical modeling to test the
hypotheses. The main reason is that path analysis was used rather than SEM.
REQUESTED REVISIONS: 
Structural equation modeling is a statistical methodology for modeling
multivariate relations. Therefore, it is possible to include all study variables in
one model. I strongly suggest the use of one model rather than four different
models. 
The title of this manuscript and the use of the term structural equation modeling
(SEM) is misleading. As it stands, path analysis was used in this version of the
manuscript. Path analysis can be considered as a special case of structural
equation modeling. In path analysis only single indicators are used for each of
the variables in the causal model. In other words, in path analysis a structural
model is estimated, but no measurement model in included. Path analysis
assumes that all variables are measured without error, while SEM uses latent
variables to account for measurement error. How did you ascertain that that all
variables are measured without error? Otherwise, I strongly suggest the use of
latent variables. The use of latent variables is one of the main advantages of
SEM.
It reads that "the information of two individuals were not entered into the
analysis because of missing information about demographic data." Case deletion
is just one option when it comes to missing data handling. Was a missing data
analysis done, and if so, what was the procedure and the results? If not, why? I
suggest using missing handling procedures recommended by Graham (2009).
Reliability and validity for some of the measures are unknown. 
It is written that "All measurements and structural models were assessed based
on model fit to the data (how well the data fits the hypothesized relationships)"
however, as I said before, a measurement model was not included in this
analysis. 
It reads that "The path coefficient's strength was defined as weak (r < 0.2),
moderate (r = 0.2-0.5), or strong (r > 0.5), and significance threshold was defined
as p < 0.05 (74)." However, the path coefficient in SEM is not equivalent to
Pearson' r. therefore, these criteria used to assess path coefficient's strength are
not valid.
It seems that maximum likelihood estimate was used. Is it correct? If yes, did
you test the underlying assumptions of multivariate normality? Assessment of
nonnormality is an important assumption in the use of SEM analyses in general.
Another important prerequisite for SEM is the assessment of multivariate
outliers. Did you determine the presence of multivariate outliers in the data?
Determining sample size requirements for SEM is crucial. I strongly recommend
the used of Monte Carlo data simulation techniques to evaluate sample size
requirements for SEM (e.g., Wolf et al., 2013).
In the figures, the significance of path coefficients was NOT indicated by an
asterisk. Moreover, it is not clear if standardized or unstandardized coefficients
were reported. Finally, another important thing is to be consistent in how you
report the number of decimal places.
The current interpretation of the results is not reasonable and is overstated. For
instance, I strongly disagree with the following sentence "current study was
performed in a relatively large sample size and its results are generalizable." 
The limitation and future study section is too brief to demonstrate the careful
reflection of the authors. I suggest expanding somewhat on the limitations. For
example, you did not mention self-selection bias. About half of the 564 eligible
participants refused to participate. Reasons for refusal were not mentioned. 
References
Graham, J. W. (2009). Missing data analysis: Making it work in the realworld.
Annual Review of Psychology, 60, 549-576.
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample
Size Requirements for Structural Equation Models: An Evaluation of Power,
Bias, and Solution Propriety. Educational and psychological measurement,
76(6), 913-934. https://doi.org/10.1177/0013164413495237
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Is the study design appropriate to answer the research question (including the use
of appropriate controls), and are the conclusions supported by the evidence
presented?
No
Are the methods sufficiently described to allow the study to be repeated?
No
Is the use of statistics and treatment of uncertainties appropriate?
No
Is the presentation of the work clear?
Yes
Are the images in this manuscript (including electrophoretic gels and blots) free
from apparent manipulation?
Yes