Data were analyzed through SPSS statistic software (Version 19.0., SPSS Inc., Chicago, IL, USA). The difference between multiple groups was determined by one-way analysis of variance followed by Tukey’s HSD post hoc test. Statistical significance was considered with p values ≤ 0.05.
Spss statistic software
Manufactured by IBM
Sourced in United States
SPSS is a software package used for statistical analysis. It provides a wide range of statistical and data management capabilities, including data manipulation, visualization, and modeling. SPSS is designed for use in various fields, such as social sciences, market research, and healthcare. The software offers a user-friendly interface and a comprehensive set of analytical tools to help users analyze and interpret their data effectively.
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Lab products found in correlation
Matlab script, by MathWorks (1 mentions)
Jasp software, by JASP Stats (1 mentions)
Prism 8, by GraphPad (1 mentions)
V amp system, by Brain Products (1 mentions)
R package, by R Project for Statistical Computing (1 mentions)
Prism software, by GraphPad (1 mentions)
Spss statistics software, by IBM (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Quantstudio design analysis software, by Thermo Fisher Scientific (1 mentions)
Matlab, by MathWorks (1 mentions)
Spss statistics for macintosh, by IBM (1 mentions)
122 protocols using spss statistic software
1
Statistical Analysis of Experimental Data
2
Histological Changes in Animal Model
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Data are presented as mean ± standard deviation. One-way analysis of variance (ANOVA) was used to analyze the differences between the groups. This analysis was followed by Tukey’s Multiple Comparison Test. Mann-Whitney U was looked upon for histologic changes testing. Statistical analysis of data was carried out using SPSS statistic software (Version 17.0). A level of p < 0.05 was considered significant.
3
Statistical Analysis of Parametric and Non-Parametric Data
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Data distribution was controlled using the Kolmogorov–Smirnov test. The data were normally distributed and analyzed using parametric techniques. Regarding quantitive data comparison, Turkey’s test and One-way analysis of variance (ANOVA) were chosen. And, Mann- Whitney U was looked upon for histologic changes’ testing. Statistical analysis of data was carried out using SPSS statistic software (Version 17.0). Also, statistical significance was defined as P < 0.05.
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Data distribution was controlled using the Kolmogorov–Smirnov test. The data were normally distributed and analyzed using parametric techniques. Regarding quantitive data comparison, Turkey’s test and One-way analysis of variance (ANOVA) were chosen. And, Mann- Whitney U was looked upon for histologic changes’ testing. Statistical analysis of data was carried out using SPSS statistic software (Version 17.0). Also, statistical significance was defined as P < 0.05.
4
Analyzing Neurophysiological Responses in Motor Tasks
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Neurophysiological data were analyzed offline using a custom written MATLAB script (MathWorks) and SPSS Statistic software (version 24.0.0.1; SPSS Inc.). For each participant, the area under the rectified MEP curve (MEP area) was calculated within an 8- to 43-ms time window after the TMS pulse. MEP area was chosen instead of peak-to-peak distance because it is more relevant in the context of polyphasic muscle responses (37 (link)). To normalize data distribution, MEP area values for each participant were transformed into z-scores, and values deviating more than 2.5 SDs from the grand average of all of the trials were excluded as outliers (3%). To prevent contamination of MEP measurements by background EMG activity, we computed EMG background activity as the area under the rectified EMG signal within a −38- to −3-ms time window before the TMS pulse. Trials characterized by a pre-TMS background exceeding 2.5 times the average were excluded as precontracted trials (<1%) (28 (link)). Trials in which MEPs were not identified (2%) were also discarded from further analysis. The remaining normalized MEP data were analyzed in a 2 × 2 repeated-measures ANOVA with intention (to pour, to drink) and time bin (25%, 100%) as within-subjects factors. A significance threshold of P < 0.05 was set for all statistical tests, and Bonferroni correction was applied for post hoc pairwise comparisons.
5
Cognitive Impairment in Multiple Sclerosis
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Statistical analyses were performed using SPSS statistic software (SPSS Inc., Chicago, IL, USA, version 24), while figures and graphs were designed using GraphPad Prism (GraphPad, La Jolla, CA, USA, version 8).
To evaluate the cognitive status of MS patients, cognitive impairment classifications were compared between baseline and follow-up for each participant.
Paired t-tests were applied to compare social cognition performance between baseline and follow-up. Furthermore, depending on distribution normality, either Pearson or Spearman correlation analyses were carried out between SC and amygdala measures (lesions and volume), both at baseline and at follow-up, and also between change in SC performance and everyday measures (emotional state, fatigue, and quality of life). Moreover, stepwise regression models were performed using baseline and follow-up MRI data to predict follow-up SC performance.
Results were expressed as mean ± SD. A p-value less than 0.05 was considered significant.
To evaluate the cognitive status of MS patients, cognitive impairment classifications were compared between baseline and follow-up for each participant.
Paired t-tests were applied to compare social cognition performance between baseline and follow-up. Furthermore, depending on distribution normality, either Pearson or Spearman correlation analyses were carried out between SC and amygdala measures (lesions and volume), both at baseline and at follow-up, and also between change in SC performance and everyday measures (emotional state, fatigue, and quality of life). Moreover, stepwise regression models were performed using baseline and follow-up MRI data to predict follow-up SC performance.
Results were expressed as mean ± SD. A p-value less than 0.05 was considered significant.
6
Statistical Analysis of Experimental Data
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The experimental data were analyzed via SPSS statistic software (Version 19.0., SPSS Inc., Chicago, IL, USA). The difference between multiple groups were determined by one-way analysis of variance followed by Tukey’s HSD post-hoc test. The p value less than 0.05 was indicated as statistically significant.
7
Analyzing SPHK/S1P System Genes
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Analysis was performed using SPSS statistic software (SPSS Inc. IBM Chicago, USA) and the two-sample Wilcoxon rank sum test, or the Kruskal-Wallis test for analyses of more than two groups. Correlation with lung function, smoking, age, gender and presence of lung cancer on mRNA expression levels of SPHK/S1P system genes were determined using data from all subjects and Pearson’s correlation coefficient with significance set at p<0.05.
8
Statistical Analysis of Experimental Data
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Values are expressed as mean ± standard deviation (SD). Kolmogorov-Smirnov analysis was used to check for normality of data. ANOVA one-way with Tukey's post-hoc analysis was applied to determine significance among more than two groups of parametric data. Paired T test and Wilcoxon matched-pairs signed rank test were used to analyze differences between two paired parametric and non-parametric data groups, respectively. Kruskall-Wallis analysis was used to determine significance among groups and Mann-Whitney test to find differences between two groups of non-parametric data. One sample T test and Wilcoxon Signed Rank test were used to determine differences of normalized parametric and non-parametric data, respectively. Analyses were performed using the GraphPad Prism software (6.01 version) and the SPSS statistic software (19.0.1 version, SPSS Inc., Chicago, IL), and differences were considered significant when p<0.05.
9
Statistical Analysis of Quantitative and Qualitative Data
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Quantitative data were described as mean, standard deviation (SD), and 95% confidence interval (CI; lower and upper limits). For the analysis of the qualitative data, the Chi-square test was used to analyze the dependency relationship between the variables through cross tables. For the analysis between a categorical and a quantitative variable, normality tests were performed using the Shapiro–Wilk test to determine the most appropriate test based on the behavior of the data. To compare independent samples when the variables’ values met the normality criteria, the T test was used for two groups or ANOVA for three groups. When the variable to be analyzed did not meet the normality criteria, the Mann–Whitney U-test was used for two groups or the Kruskal–Wallis test for three groups. For related samples where the values of the variables were in accordance with normality, the t-test was used. To compare more than two related groups when the variable to be studied did not meet the normality criteria, Friedman’s two-dimensional analysis of variance by ranges was used.IBM SPSS Statistic software (v25, SPSS Inc., Chicago, IL, USA) was used for the data analysis and statistically significant differences were established at p < 0.05 with 95% CI.
10
Estrogen Receptor Alpha and Cell Proliferation
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All data and statistical significance were determined using SPSS statistic software (version 19.0, Chicago, IL, USA). Quantitative data were expressed as mean ± SD. The normality test and the homogeneity of variance test were applied. One-way ANOVA and the LSD test were used to determine differences among the groups. When the variance analysis conditions did not agree, the Kruskal-Wallis test was used. Qualitative data were analyzed using Fisher’s exact test. In addition, Student’s t-test was applied to determine urine concentration differences. Spearman’s correlation analysis was applied between ERα and PCNA immunochemistry scores, and between serum E2 concentration and ERα immunochemistry scores. Differences were considered statistically significant at P < 0.05.
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