Spss 22 for windows

SPSS for Windows 22.0 software was used for data analysis. The descriptive statistics consisted of percentages, mean values, standard deviations (SD). The inferential statistical analysis consisted of a two-sample independent t-test, paired t-test, and McNemar’s test [42 (link)]. The statistically significant level was set at less than 0.05.

All collected data were analyzed using SPSS for Windows 22.0 (SPSS, Inc., Chicago, IL, USA). We analyzed the full analysis set according to the intention-to-treat approach using the last observation carried forward method. Descriptive statistics are presented as means (M) and SDs for continuous variables and absolute numbers (N) and percentages for categorical variables. Independent t-tests were used for the comparison of between-group differences for continuous variables, and Fisher’s exact test was used for between-group comparisons of categorical data (baseline data). For the primary outcome, preliminary effect sizes and their 95% confidence interval were calculated using Cohen’s d for continuous variables (between-group effect sizes, pooled SD). In addition, exploratory data analysis that calculated appropriate summary measures for empirical distribution was performed, and descriptive two-sided P-values were calculated. Between-group differences—scores at T₁ and T₂—were analyzed using independent t-tests.
With regard to secondary endpoint, feasibility, acceptability of randomization, and retention rate were reported descriptively. In addition, negative effects of treatment were reported.

Data cleaning and analysis were carried out using SPSS for Windows 22.0 (SPSS Inc., Chicago).
The Emergency Nutrition Assessment (ENA) for SMART software (2010 version) was used for the anthropometric data analysis and reported using WHO 2006 growth reference values with Standardized Monitoring and Assessment of Relief and Transitions (SMART) cut-offs.
A comparative analysis was carried out between normal vaginal and caesarean delivery on postnatal child growth. We conducted two-step hierarchical multiple regression analyses to determine independent predictors of height for age of children under two years. Multicollinearity was investigated by using the variance inflation factor (VIF). VIF (the reciprocal of the tolerance statistics) of greater than 5 is generally considered evidence of multicollinearity.
Explanatory variables that were significant at bivariate analysis at a p value of 0.05 or less were fed into the regression model after confirming the absence of multicollinearity between these independent variables.

Epidata 3.0 was used to establish the database and double-entry was performed by two postgraduates in our research team. All data analyses were carried out in SPSS for Windows 22.0. Descriptive statistics were employed to describe the number, distribution and rank of patients’ RFE and health problems. Student t-test and analysis of variance were used to compare the differences of mean number of RFE or health problems per encounter according to patient characteristics.

We used SPSS software (SPSS for Windows 22.0, SPSS, Chicago, IL) to do some statistical analysis. We used chi-square test for qualitative data and Student t test for quantitative data. P value less than 0.05 indicated significant difference.