Data were analysed by using the WHO/HAI preprogrammed Excel workbook,13 IBM Statistical Package for the Social Sciences (SPSS) V.22.0 and R V.3.5.1 (codename ‘Feather Spray’).
Statistical package for the social sciences spss v 22
Manufactured by IBM
Sourced in United States
SPSS V.22.0 is a software package used for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS is to enable users to perform a variety of statistical procedures, including regression, correlation, and hypothesis testing.
Automatically generated - may contain errors
Lab products found in correlation
4 protocols using statistical package for the social sciences spss v 22
1
Analyzing Statistical Data with WHO/HAI, SPSS, and R
2
Statistical Analysis of Experimental Data
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Statistical analysis was carried out using the IBM Statistical Package for the Social Sciences (SPSS, v22.0, Chicago, IL, United States). The Levene and Shapiro–Wilk tests were used to determine the equality of variances and normal distribution of the data, respectively. After their verification, a one-way ANOVA test was applied. When significant differences were determined, Bonferroni’s post hoc test was carried out between groups. The Kruskal–Wallis test was used when data were neither equals nor normally distributed. In the case of significant differences between groups, the Mann–Whitney U test was applied. On the other hand, a repeated-measures ANOVA test was performed to assess time-dependent variables (e.g., BW). Data are represented as mean ± standard error. Significant differences were considered when P < 0.05.
3
Soil Properties and Disturbance Analysis
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Prior to analysis of variance (ANOVA), data normality was assessed using the Kolmogorov-Smirnov test. ANOVA was performed to analyze response variables by study areas (SA1 vs. SA2 vs. SA3) and soil disturbance types (UA vs. BT vs. ST). Additionally, soil physical properties were separately analyzed within different soil layers (0–10 cm and 10–20 cm depths). Post-hoc tests were conducted using Scheffe's test and Dunnett's (T3) test, and Pearson's correlation coefficients were used to determine relationships between Ksat and bulk density. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) v. 22.0 (International Business Machines (IBM) Corp., 2013 ).
4
Neuroimaging Analysis of COVID-19 Impacts
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All demographic and clinical data analyses were performed using the IBM Statistical Package for the Social Sciences (SPSS) V 22.0 statistical software (Armonk, NY, United States). As the age variable did not follow a normal distribution, the Mann-Whitney U test was selected. The chi-square test was used for the statistical analysis of sex data. When the p value is less than 0.05, it indicates statistical signi cance. We used the statistical module of Data Processing & Analysis of Brain Imaging (DPABI, http://www.rfmri.org/dpabi) for statistical analysis(Yan et al., 2016). Group differences in imaging parameters (sALFF, dALFF, sFC, and dFC) between the COVID-19 group under study and the HCs group were analyzed using a two-sample t-test. Age, sex, and FD were included as covariates in the analysis. Two-sample t-tests between the two groups were all corrected by the Gaussian random eld (GRF), and the thresholds for multiple comparisons were voxel-level p < 0.001 and cluster-level p < 0.05.
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