Spss statistic 21

The normal distribution of each variable was examined by Shapiro-Wilk test, and none of the variables showed normal distribution (all p < 0.01). Descriptive statistics of the two groups (anxiety group and non-anxiety group) were calculated. Categorical variables (family annual income, marital status, etc.) were reported in percentages. Continuous variables (such as age, gestational age, education year, etc.) were expressed as median (Min, Max). The Mann–Whitney U-test or chi-square test was used to test the differences in these variables between the anxiety group and the non-anxiety group. Chi-square test was used to compare the prevalence of anxiety symptom among early, middle, and late pregnancy. A binary logistic regression analysis was performed to test the underlying factors associated with mild to severe anxiety (yes/no). Independent variables were variables that showed significant differences between anxiety group and the non-anxiety group in the previous mentioned Mann–Whitney U-test or chi-square test. SPSS Statistic 21.0 was applied to perform the analyses.

Statistical analysis was performed using SPSS statistic 21.0 software (IBM, Armonk, NY, USA). One-way analysis of variance (p ≤ 0.05) was conducted. Data are means ± SE from three independent biological replicates.

One-way ANOVA followed by the least significant difference test (LSD) were used to analyze the differences in the variables of precautionary measures and depression between the sociodemographic groups. The Pearson correlation was calculated, and a regression analysis was carried out between the variables of precautionary measures and depression. p < 0.05 was considered to be significant. SPSS Statistic 21.0 ^® (IBM SPSS Statistics, Armonk, NY, USA) was used.

We obtained the percentage of responses to each categorical variable by dividing the number of responses to each categorical variable by the total number of responses. We used the Chi-squared test to analyze the differences in categorical variables between the two groups of students. We then analyzed the differences in the degree of DASS-stress, anxiety, depression, and psychological impact using the independent sample t-test to compare the mean score between them. Furthermore, the above cut-off scores of DASS-21 stress (>14), anxiety (>7), depression (>9) subscales, and IES-R (>23) for the respondents were for psychological and post-traumatic stress disorder symptoms, we used binary logistic regressions to calculate the univariable association between independent and dependent variables for these two groups of students, respectively. Finally, internal consistency reliability, construct validity, and criterion validity were used to evaluate the psychometric properties of the AAQ-AHL. Total scores of AAQ-AHL were included in binary logistic regressions models for further investigation. All statistical tests were two-tailed with a significance level of p < 0.001–0.05 using SPSS Statistic 21.0 (IBM Corporation, New York, NY, USA).