All data are expressed as mean ± standard error of mean (SEM) and analyzed using IBM SPSS statistical analysis software, version 23.0 (SPSS Inc., Chicago, IL, USA). Comparisons of two groups were performed using Student’s t-test. Results with p values less than 0.05 were considered statistically significant.
Spss statistical analysis software version 23
Manufactured by IBM
Sourced in United States
SPSS Statistics is a software package used for statistical analysis. It is capable of handling a wide variety of data formats and can perform a broad range of statistical procedures, including regression, cluster analysis, and time series analysis.
Automatically generated - may contain errors
Lab products found in correlation
7 protocols using spss statistical analysis software version 23
1
Evaluation of Cellular Responses
2
Comparative Analysis of TCRVδ-Positive Events
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Data analysis was performed using SPSS statistical analysis software (version 23.0). The mean percentages of TCRVδ-positive events between the two groups were compared using Student's t-test. Comparisons of the count data between groups were performed by McNemar, Kappa tests, χ2 test, or Fisher's exact test. p < 0.05 was considered as statistically significant.
3
Vitamin D Deficiency Risk Factors
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Categorical variables are expressed as percentages, whereas continuous variables as means and standard deviations. The χ 2 test was used to explore the association between categorical variables, using the two-sample z test for proportions for post hoc multiple comparisons. The Kolmogorov-Smirnov test was used to determine normality of distribution of the examined continuous variables. All continuous variables examined in the present study were found to be normally distributed and as such the Student's t test was used for the comparison of mean values between groups. Multivariate logistic regression analysis was also performed in order to assess the associations of sex, surbanisation degree (i.e. urban, semi-urban or rural) and seasonality with vitamin D deficiency and insufficiency respectively. OR and 95 % CI were also derived from these analyses after adjusting for several potential confounding factors. All statistical analyses were performed with the SPSS statistical analysis software version 23.0. All P values reported were two-tailed and the level of statistical significance was set at P < 0•05.
4
Evaluating Seizure Risk After Vaccination
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The acquired data were presented as the median (interquartile range) using SPSS statistical analysis software (version 23). A nonparametric Mann-Whitney U test was used to compare group differences on continuous variables. Categorical variables were analyzed by Pearson’s chi-square test or Fisher’s exact test. Univariate analysis results were entered into a multivariable binary logistic regression model with a statistical significance of p ≤ 0.05. A stepwise logistic regression analysis was applied to determine unique related factors between several variables related to worsening seizures after vaccination, with a statistical significance of p < 0.05. The odds ratio (OR) and 95% confidence interval (CI) were also calculated for each parameter.
5
Statistical Analysis of Intervention Outcomes
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Descriptive statistics (eg, frequency and relative percentages, means, and
standard deviations [SDs]) were used to describe demographic characteristics of
included subjects. The change in outcomes following intervention was compared
using paired t tests and summarized as mean differences along
with 95% confidence intervals (CIs). For the ease of interpretation, summary
measures from continuous data were converted into odds ratio along with 95% CI.15 (link) The statistical significance was set at P < .05 for
all comparisons. All analyses were performed using SPSS statistical analysis
software version 23.
standard deviations [SDs]) were used to describe demographic characteristics of
included subjects. The change in outcomes following intervention was compared
using paired t tests and summarized as mean differences along
with 95% confidence intervals (CIs). For the ease of interpretation, summary
measures from continuous data were converted into odds ratio along with 95% CI.15 (link) The statistical significance was set at P < .05 for
all comparisons. All analyses were performed using SPSS statistical analysis
software version 23.
6
University Students' Knowledge, Attitudes, and Practices Regarding Sugar-Sweetened Beverages and Taxation
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All statistical analyses were performed using SPSS statistical analysis software (version 23). The socio-demographic characteristics, health data, and answers pertaining to the knowledge of and attitudes and practices towards SSBs and SSB taxation were presented as frequencies and percentages. University students’ KAPs regarding SSBs and the taxation questions and responses are presented in Tables S1–S6 . Additionally, the scores pertaining to the knowledge of and attitudes towards SSBs and SSB taxation were presented as mean ± SD. Chi-squared tests were used to examine the associations between the socio-demographic characteristics and knowledge of and attitudes towards SSBs and SSB taxation. Furthermore, a linear regression was conducted to examine the association between the socio-demographic characteristics, knowledge of, and attitudes towards SSBs and SSB taxation scores. Differences were considered statistically significant at p < 0.05.
7
University Students' Knowledge, Attitudes, and Practices Regarding Sugar-Sweetened Beverages and Taxation
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All statistical analyses were performed using SPSS statistical analysis software (version 23). The socio-demographic characteristics, health data, and answers pertaining to the knowledge of and attitudes and practices towards SSBs and SSB taxation were presented as frequencies and percentages. University students’ KAPs regarding SSBs and the taxation questions and responses are presented in Tables S1–S6 . Additionally, the scores pertaining to the knowledge of and attitudes towards SSBs and SSB taxation were presented as mean ± SD. Chi-squared tests were used to examine the associations between the socio-demographic characteristics and knowledge of and attitudes towards SSBs and SSB taxation. Furthermore, a linear regression was conducted to examine the association between the socio-demographic characteristics, knowledge of, and attitudes towards SSBs and SSB taxation scores. Differences were considered statistically significant at p < 0.05.
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