Statisticians who are independent from the research team will be responsible for the statistical analysis. The SPSS 12.0 software for Windows (Chicago, IL, USA) will be used. Categorical variables will be presented with frequencies or percentages and continuous variables will be presented as mean and standard deviation. The main analysis will compare the efficacy of acupuncture treatment group with the control group, including primary and secondary outcomes. Changes in all outcome measurements before and after the treatment and between groups will be analyzed. Demographic and clinical characteristics of two groups will be compared on admission using unpaired two-sample t-tests (continuous data) and Chi-square analysis (categorical data). Nonparametric methods will be used when assumptions of normality are violated. We will also conduct an intention-to-treat analysis if participants are lost to follow-up. The statistical significance threshold will be set at 0.05 (two-sided), with 95 % confidence intervals.
Spss 12.0 software for windows
Manufactured by IBM
Sourced in United States
SPSS 12.0 is a software package for statistical analysis. It provides tools for data management, analysis, and presentation. The software is designed to run on the Windows operating system.
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Lab products found in correlation
9 protocols using spss 12.0 software for windows
1
Evaluating Acupuncture Efficacy: A Rigorous Analysis
2
Statistical Analysis of Group Differences
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Results were expressed as mean ± SD, and analysis was conducted using the SPSS12.0 software for Windows (SPSS, Chicago, IL, USA). Variance analysis (ANOVA) with the Fisher’s PLSD test was employed to determine the statistical differences among groups. Probabilities (P) below 0.05 were regarded as significant.
3
Atherosclerosis Risk Factors Analysis
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Statistical analyses were performed using SPSS 12.0 software for Windows (SPSS, Chicago, IL, USA). Pearson correlation was used to determine agreement on c-IMT. Results are expressed as means ± standard deviation for continuous variables. Parameters that did not fullfil normal distribution were mathematically transformed to improve symmetry for subsequent analyses. One-way ANOVA with Bonferroni correction as the post-hoc test, were used to seek differences in clinical variables among groups. We used Student's t-test to determine differences in quantitative variables. The relation between variables was tested using Pearson's test and stepwise multiple linear regression analysis. The general linear model was also used to identify independent predictors of atherosclerosis after adjusting for cardiovascular risk factors (BMI, blood pressure or LDL-cholesterol). Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic potential. Statistical significance was set at p<0.05.
4
Longitudinal Student Performance Evaluation
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The quiz scores were presented as means ± standard deviation (SD). Data were analyzed using SPSS 12.0 software for Windows (SPSS Inc., Chicago, Illinois, USA). ANOVA, T-test and LSD were used to determine differences among 2015, 2017, and 2018 classes. For the results of the questionnaires, the collected data were analyzed using SPSS with Chi-square test. A P-value of <0.05 was considered statistically significant.
5
Statistical Analysis of In Vitro Experiments
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Statistical analysis was performed using SPSS 12.0 software for Windows. All of the in vitro experiments were performed in triplicate and data were presented as mean ± SD. Statistical significance was assessed using ANOVA with a SNK post-hoc analysis, where P-values <0.05 were considered statistically significant.
6
Kruskal-Wallis Analysis of Variance
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Data were collected and statistically analyzed by means of SPSS 12.0 software for Windows (SPSS Inc., Chicago, IL, USA) and a Kruskal–Wallis one-way analysis of variance (ANOVA) test, followed by the Mann–Whitney U test at a significance level of p < 0.05.
7
Survival Analysis of Risk Factors
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Data were expressed as mean ± standard deviation or number and percentage in parentheses unless otherwise stated. All variables were tested for normal distribution using the Kolmogorov-Smirnov test. The Student's t-test was used to compare the means of continuous variables and normally distributed data. Otherwise, the Mann-Whitney U test was used for nonnormally distributed data. Categorical data were analyzed using the chi-square test. Finally, risk factors were assessed by univariate Cox regression analysis, and variables that were statistically significant (P < 0.05) were included in multivariate analysis by applying multiple Cox regression analysis based on forward elimination of data [11 (link)]. The cumulative survival curves as a function of time were generated using the Cox regression survival approach. All statistical tests were 2-tailed, with P values less than 0.05 being considered statistically significant. Data were analyzed using SPSS 12.0 software for Windows (SPSS, Inc., Chicago, IL).
8
Statistical Analyses in Clinical Research
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Statistical analyses were performed using SPSS 12.0 software for Windows (SPSS Inc., Chicago, IL, USA). Fisher’s exact test was used for dichotomous variables, and statistical significance was set at a p value of < 0.05. Descriptive data are presented as the mean with standard deviation for quantitative variables and as the number with frequency for categorical variables.
9
Statistical Analysis Methods Outlined
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All statistical analyses were conducted using GraphPad Prism 5 (GraphPad Software, La Jolla, CA) and SPSS 12.0 software for Windows (Chicago, IL). Statistically significant differences were determined by Student’s t-test, Wilcoxon rank sum test, one-way analysis of variance, the chi-squared test, or multivariate logistic analysis. P values less than 0.05 were considered statistically significant in all cases.
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