Spss statistics software ver 24

The SPSS statistics software (ver. 24, Chicago, IL, USA) and Mann-Whitney nonparametric test were used to analyze the obtained data. P value ≤0.05 was statistically significant.

Data analysis was undertaken using SPSS Statistics software (ver. 24). Furthermore, missing data were assessed and deleted before the analysis. Descriptive analyses were performed to test the participants’ characteristics. Then bivariate analysis was carried out to classify elements regarding high levels of PTSD signs.
Categorical data were summarized by frequencies and percentages, and continuous data were offered as M ± SD. In DASS-21, the three dimensions of stress, depression, and anxiety were identified, and each domain was categorized into five classifications: normal, mild, moderate, severe, and very Severe. The scores of GHQ-12 were divided into two categories: high and low. PRQ was divided into three categories: low risk, moderate risk, and high risk. The Chi-square (χ²) test was applied for comparing between‐group differences. Univariate/multivariate ordinal logistic regression models were applied to uncover the possible predictors of anxiety during the COVID-19 pandemic. Logistic regression was used to analyze the PTSD questionnaire. To analyze the risk perception and DASS-21, first, univariate logistic regression was performed for all variables, and then the variables whose p-value was less than 0.2 were entered into the multivariate logistic regression model.

All statistical analyses were performed using the SPSS Statistics software (ver.24) (IBM Corp., Somers, N.Y., the United States). Kolmogorov-Smirnov test was thus employed to assess the normality of the variables. Accordingly, the assumption of normality was confirmed for all variables. In the present study, descriptive statistics, viz., Mean±SD, percentage, and frequency were further employed to describe the participants’ characteristics.
The Chi-square test and independent-samples t-test were respectively used to compare the qualitative and quantitative demographic data between the intervention groups. To examine the effect of CASES in both BL-based approaches,
we also used the paired-samples t-test, while the one-way ANCOVA (analysis of covariance) test to analyse the difference between the pre-and post-test mean scores of the CASEs in the BL methods, controlling for the pre-test mean scores.

All statistical analyses were performed in IBM SPSS Statistics software (ver. 24). Data was first ensured to have normal distribution using the Shapiro-Wilk test. Independent-samples t-test was used in between-group (younger vs. older) comparisons for CKC strength, number of averages, sustained field peak-to-peak amplitude, acceleration magnitude and postural sway. Paired-samples t-test was used to compare CKC strength, number of averages, sustained field peak-to-peak amplitude, MEG power and acceleration magnitude for dominant and non-dominant legs, and to compare postural sway between eyes-open and eyes-closed tasks. Pearson correlation coefficient was computed across all subjects between CKC strength, and MEG response amplitude and background-MEG power to estimate the effect of the MEG signal strength to the CKC strength. Linear multiple regression analysis was performed to estimate the importance of CKC strength (for non- and dominant legs and F0 and F1 separately), body mass, height, gender, age, group (younger, older) on predicting postural stability (during eyes open and closed conditions, and their difference) using the enter method in SPSS. Results are indicated as mean ± standard deviation.

Multivariate linear correlation analysis is a useful method to statistically determine the relationships between pollutants or other atmospheric factors influencing the air quality and to reveal the most significant parameters on the concentrations of atmospheric pollutants [12 (link),43 (link),77 (link)]. In this work, IBM^® SPSS^® statistics software (Ver. 24) was used to perform descriptive statistics, sample comparison, and multivariate linear correlation analyses for the hourly mean values of the measured eBC concentrations and meteorological parameters. Bivariate polar plots of concentrations have recently been used to reveal the collective impact of wind velocity and wind direction on air pollutants’ observed concentrations [78 (link),79 (link),80 (link)]. In this research, bivariate polar plots were drawn to illustrate the impact of wind components on hourly mean values of eBC concentrations through Pre-LD, LD, and Post-LD phases.

Data analysis in this study was performed using the SPSS Statistics software (ver. 24) (SPSS Inc., IBM Corp., USA). To identify the missing data, they were also checked and cleaned before analysis. To describe the quantitative variables, descriptive analyses, such as mean± standard deviation (SD), were employed. Moreover, frequency and percentage were applied to explain the qualitative variables. The prevalence rates of depression, anxiety, and stress caused by COVID‐19 were correspondingly reported and the Chi‐square test (χ²) was utilized to compare between‐group differences. Univariate/multivariate logistic regression models were further used to explore the potential predictors of anxiety in times of this crisis. p‐values less than .05 were also considered statistically significant.