Statistical Analysis
Data analysis was conducted with the Statistical Package for the Social
Science (SPSS) V.21 (Armonk, NY, USA) for Mac OS. A Kolmogorov-Smirnoff
test was done to analyze the normal distribution of the data (normal if
p>0.05). After checking the sample homogeneity, student
t-tests for independent samples were used to assess gender differences
for age, height, weight, BMI, thorax circumference during maximum
inspiration and expiration, or respiratory moment differences for
ultrasonographic features.
Multiple linear regression analyses were conducted to determine
variables significantly contributing to the variance within the pleura
depth. First, a correlation analysis between pleura depth with
anthropometric features and respiratory moment was performed using
Pearson’s correlation coefficients (r) for normal distributed variables.
Values ranging 0-0.3 were considered as poor correlation, 0.3-0.5 as
fair, 0.6-0.8 as moderate and 0.8-1.0 as strong18.
Those statistically significant variables (p<0.05) were
included in a stepwise multiple linear regression model to estimate the
proportion of variance explaining the pleura depth. To avoid risk of
bias during the regression model, a multicollinearity and shared
variance analyses between the variables (defined as r>0.80)
were also calculated.
Hierarchical regression models were conducted to determine those
variables that contributed significantly to pleura depth determination.
The significance criterion of the critical F value for entry into the
regression equation was set as p<0.05. The adjusted changes in
R2 were reported step by step in the regression model
to determine the association of each additional variable.