2.3 Statistical Analysis
The quantitative data used in the study were summarized as arithmetic
mean ± standard deviation and qualitative data as numbers (percent). As
the four dependent variables in the study, DASS-21 anxiety, depression
and stress subfields, and IES-R total scores were selected, and the
related data were converted into binary categorical data according to
the following criteria. A cut-off of the IES-R total score ≥33 was used
to reflect moderate-to-severe impact.14 Similarly,
individuals with a score of 7 and above in the depression subscale, 6
and above in the anxiety subscale, 10 or above in the stress subscale
cut-off points were used to determine the moderate and above
psychological influence reflected on DASS-21.8 Since
the number of dependent variables is four, four different binary
logistic regression models were applied to the data set. Before applying
the related models, variable selection algorithms based on each
dependent variable were applied to the data, and independent variables
considered to have no contribution to modeling were removed from the
data set. As a variable selection method, LASSO (Least absolute
shrinkage and selection operator)15 logistic
regression technique was applied. The goodness of fit and coefficients
of the created models were evaluated by Hosmer-Lemeshow (p>
0.05) and Omnibus (p <0.05) tests, respectively. In logistic
regression models, significance level for model coefficients was
determined as p <0.05. In the analysis, “BKSY: Information
Discovery Process Software” developed by Inonu University Faculty of
Medicine Department of Biostatistics and Medical Informatics was used
for the data analysis.16