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Statistical package for social science spss program version 23

Manufactured by IBM

SPSS is a software program for statistical analysis. Version 23.0 provides tools for data management, data analysis, and presentation of results. The core function of SPSS is to enable users to perform a variety of statistical procedures, including descriptive statistics, bivariate statistics, prediction for numerical outcomes, and prediction for identifying groups.

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2 protocols using statistical package for social science spss program version 23

1

Fatigue Correlates in Clinical Population

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Data were analysed using the IBM Statistical Package for Social Science (SPSS) program, version 23.0. A P-P Plot analysis was performed to test normality of continuous variable distributions. To compare demographic and clinical characteristics between fatigue and no fatigue groups, Chi-square tests, independent samples t tests and Mann-Whitney U Tests were used, as appropriate. A multiple logistic regression analysis, with the “Enter” method, was conducted to examine independent demographic and clinical correlates of fatigue; fatigue was the dependent variable and other measures that had P values of < 0.05 in univariate analyses were independent variables. To compare overall QOL differences between fatigue and no fatigue groups, an analysis of covariance (ANCOVA) was conducted after controlling for variables that had statistically significant differences in univariate analyses. The significance level was set at P < 0.05 (two-tailed).
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2

Statistical Analysis of PTSS in COVID-19 Survivors

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Data were analyzed using the IBM Statistical Package for Social Science (SPSS) program, version 23.0. A P-P Plot was used to test normality of continuous variables. To compare demographic and clinical characteristics including PCL-1, PCL-2, PCL-3 scores between COVID-19 survivors and healthy controls, and between COVID-19 survivors with and without PTSS, Chi-square tests, independent samples t-tests and Mann-Whitney U Tests were used, as appropriate. Analysis of covariance (ANCOVA) was conducted to compare PTSS total score between COVID-19 survivors and healthy controls and also compare overall QOL between COVID-19 survivors with and without PTSS after controlling for variables that had statistically significant differences in univariate analyses (P < 0.05). Binary logistic regression analysis with the “Enter” method was conducted to examine independent demographic and clinical correlates of PTSS in the COVID-19 survivors, with PTSS as the dependent variable and other measures that had P-values of < 0.05 in univariate analyses as independent variables. The significance level was set at P < 0.05 (two-tailed).
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