Title: Sense of coherence and health in women: A 25-year follow-up study
Version: 1 Date: 24 Jul 2023
Reviewer's report:
PEER REVIEWER ASSESSMENTS:
OBJECTIVE - Full research articles: is there a clear objective that addresses a testable research
question(s) (brief or other article types: is there a clear objective)?
No - there are major issues
DESIGN - Is the current approach (including controls and analysis protocols) appropriate for
the objective?
No - there are major issues
EXECUTION - Are the experiments and analyses performed with technical rigor to allow
confidence in the results?
No - there are major issues
STATISTICS - Is the use of statistics in the manuscript appropriate?
No - there are issues with the statistics in the study
INTERPRETATION - Is the current interpretation/discussion of the results reasonable and not
overstated?
No - there are major issues
OVERALL MANUSCRIPT POTENTIAL - Is the current version of this work technically
sound? If not, can revisions be made to make the work technically sound?
Maybe - with major revisions
PEER REVIEWER COMMENTS:
GENERAL COMMENTS: This research addresses an important theoretical issue and reveals
some interesting results. However, I have some major concerns about the aims, the lack of
hypothesis, the sampling, the choice of measures, the statistical methods, and the practical and
theoretical contribution of the paper. I detail these and other concerns below.
REQUESTED REVISIONS:
The theory of SOC is well-developed and has a long history. Moreover, the literature (e.g., prior
research testing the assumption of the SOC theory) is huge and I do not see the reason why this
study has an exploratory nature. I would like to see an introduction that leads clearly and directly
to hypotheses. The lack of testable and specific hypotheses is a major flaw of the present study. I
ask the authors to clearly define the aims of each study and to formulate the hypotheses. Finally,
I would like to see a close linkage between the aims/hypotheses proposed and the analyses.
The aims should be greatly revised. A well-worked-up hypothesis is half the answer to the
research question (see https://doi.org/10.4103/0972-6748.62274).” For instance, it says “How
life events, depression/anxiety, educational level, family situation, and work/family conflicts are
associated with SOC and SRH after 25 years.” However, the nature of this expected relationship
is not clear. Is it a linear relationship? Or curvilinear? Is it a positive or negative relationship?
Did the author(s) carry out missing data analysis? What was the amount of missingness? How
did the author(s) handle item-level missingness as well as longitudinal missingness?
Please calculate and report the response rate for each wave and the attrition rate. Was an analysis
of the drop-out pattern carried out?
What were the inclusion and exclusion criteria for participants?
Please describe any agreements, rewards, and payments made to participants.
Another concern is about the measurement of these factors. Without solid measures, we cannot
be completely sure what was measured. More information about the measures is needed. Were
participants Swedish citizens? Were the instruments translated into the Swedish language? If
yes, it seems to me that measures are not validated in the Swedish language. The measures were
used without any information regarding psychometric properties or supporting documentation
for their use in the Swedish language. For instance, what about the cross-cultural validation of
these instruments? It is challenging to use an instrument that has been validated in a culture
and adapt it in a culturally relevant and comprehensible form while maintaining the original
meaning. Cross-cultural validation of the measures is needed (e.g., investigating whether an
instrument originally generated in a specific culture can be applicable, meaningful, and thus
equivalent in another culture).
How did you establish the validity of the cut-off scores? The use of a cut-off score is justified
based on the calculation of sensitivity and specificity for this specific population (Swedish
people). For instance, the ability of the Swedish version of the GAD to discriminate cases from
non-cases should be assessed by receiver operating characteristics analysis, and its curve should
be used to select cut-off points. Using cut-off scores that are not valid and supported by previous
validation studies regarding the Swedish language and related culture provides biased results.
It is said “In accordance with previous studies with screening purpose, a cut-off level of 10 was
used (17).” However, Norhayati reported three studies conducted in Sweden using the EPDS,
two of which used a cut-off level of 12.
The SOC variable was dichotomized (into <=75th percentile and >75th percentile) without
providing any theoretical and methodological arguments supporting this decision. There is no
need to dichotomize variables unless there are strong theoretical reasons. I strongly advise
against the dichotomization of variables without providing any theoretical and methodological
arguments supporting this decision. See Dawson and Weiss’ article entitled “Dichotomizing
continuous variables in statistical analysis: A practice to avoid” (Journal: Medical Decision
Making). The reason is that categorization results in lost information, reduced power of statistical
tests, and increased probability of a Type II error (see Streiner’s article entitled “Breaking up
is Hard to Do: The Heartbreak of Dichotomizing Continuous Data”). There are other types of
statistical tests that can be used (e.g., OLS regression, ordinal regression).”
The theoretical and practical implications of the study findings are not clear to me. I am left
wondering, "so what"? How do these insights inform theory development and interventions?
I would like to see a discussion about the magnitude of the effects, not P values. These are the
words of two of the most influential statistician-researchers of the past half-century:
“Statistical significance is the least interesting thing about the results. You should describe the
results in terms of measures of magnitude –not just, does a treatment affect people, but how
much does it affect them.” Gene V. Glass
“The primary product of a research inquiry is one or more measures of effect size, not P values.”
-Jacob Cohen