All datasets were analysed utilizing the IBM SPSS Statistical software package, Version 23. The data was analysed for significant differences among treatments using analysis of variance (ANOVA) for datasets with three or more treatment groups and Student’s t-test where there were only two treatment groups. The significance (P < 0.05) amongst means were determined by Fisher’s protected LSD. Repeated measures ANOVA was done on the data for experiments that were done over different time intervals. Each time point was also analysed separately for significance using the Student’s t-test.
Spss statistical software package version 23
Manufactured by IBM
Sourced in United States
SPSS statistical software package version 23.0 is a data analytics tool developed by IBM. It provides a comprehensive set of features for statistical analysis, data management, and visualization. The software is designed to help users extract insights from data and make informed decisions.
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Lab products found in correlation
23 protocols using spss statistical software package version 23
1
Statistical Analysis of Experimental Datasets
2
Transcranial Direct Current Stimulation Effects
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Data analysis was performed using the IBM SPSS statistical software Package, version 23. Comparison between pre and post session data for HSS rating scales as well as comparison of both FA and MD of the dissected tracts bilaterally between pre- and post-tDCS treatments were performed using paired Student’s t-test. Two-way ANOVA repeated measure analysis with the main factor time (pre and post sessions) X group (real versus sham group) was performed for the assessment of the total HSS and the different components of the scale. To correct for multiple comparisons across the two diffusion measures (FA and MD), the significant threshold was set to p < 0.025 according to the Bonferroni correction. Partial correlation between ΔFA and ΔMD (for measures that showed significant changes after treatment) and ΔHSS (in patients performed MRI) was calculated while controlling for the effect of lesion volume. p < 0.05 was considered significant.
3
Helicobacter pylori Infection Analysis
4
Survival Analysis of Malignant Pleural Effusion
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We used the SPSS statistical software package version 23.0 (SPSS, Chicago, IL, USA) and R software to perform the statistical analysis. All statistical tests were two‐sided and variables with P‐value < 0.05 were considered statistically significant. For continuous variables, one‐way ANOVA (analysis of variance) or Mann‐Whitney U test and χ2 test was used for comparisons of categorical data. The outcome in this study was the time from diagnosis to death or last contact. Kaplan‐Meier curves with log‐rank were used to estimate survival and compare curves when necessary. Cox regression was used to determine the prognostic values of the markers in MPE patients and the results were presented as hazards ratio (HR) and 95% confidential interval (CI).
5
Pesticide Levels in Bee Samples
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The correlation analysis was used to examine the association between pesticide concentrations in bee samples and those in environmental dust samples. Because not every pesticide in a bee or dust sample was detectable, we summarized the detected concentrations by pesticide type (i.e., insecticide, herbicide, and fungicide) for an individual sample, and used them for the correlation analysis. The concentration of each sample that was undetectable was replaced by half the LOD. A general linear model was employed for the two-way analysis of variance (ANOVA) to examine the difference in pesticide residue in healthy and sick/dead bees, and whether the distribution patterns of insecticides, herbicides, and fungicides were different between these two types of bees. All statistical analyses were performed using the SPSS statistical software package version 23.0 (SPSS Inc., Chicago, IL, USA, 2015).
6
Statistical Analysis of Discrete and Continuous Variables
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We analyzed the discrete variables by the chi-square test, whereas the continuous variables by Mann-Whitney U and t-tests. A binary logistic regression model was used to calculate the odds ratio (OR). All determinants with P values of <0.10 were entered together in to the full logistic regression model, and the model was reduced by excluding variables with P values of >0.10. The OR and 95% confidence interval (CI) were used to estimate the risk. The statistically significant was determined by P values <0.05. All statistical analysis in this study was using SPSS statistical software package version 23.0 (SPSS, Inc., Chicago, IL).
7
Splenic Dose Optimization for Survival
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Statistical analysis was performed using the SPSS statistical software package, version 23.0 (SPSS Inc., Chicago, IL, USA). A two-sided p< 0.05 was considered statistically significant. Differences between continuous covariates were compared by the Wilcoxon rank-sum test, and differences between categorical covariates were compared with Fisher’s exact test or χ2 test depending on which was appropriate. Receiver operating characteristic (ROC) analysis and relative area under the curve (AUC) statistics were used, and the ratio closest to the point with the maximum sensitivity and specificity of the measured splenic doses was selected as the optimal cut-off value both for overall (OS) and recurrence-free survival (RFS). NLR and the severity of lymphopaenia were correlated with the clinicopathological variables using the χ2 test or Fisher’s exact test (if necessary for verifying χ2 test). Survival curves were acquired using the Kaplan-Meier method, and groups were compared using the log-rank test. Univariate and multivariate analyses to identify prognostic predictors were performed using Cox proportional hazard regression models. Variables with a p-value ≤ 0.2 on univariate analysis were entered into multivariate analyses [19 (link)].
8
Statistical Analysis of Experimental Data
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SPSS statistical software package version 23.0 (SPSS, Chicago, IL) was used to perform Descriptive statistics, chi‐square, and Student's t test. P ≤ 0.05 was considered as significant. Formulas used for calculating diagnostic accuracy parameters were adopted, as described previously.30
9
Analyzing Insecticide Exposure through Blood and Urine
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Descriptive statistics was performed for each insecticide or metabolite, and a mean value was calculated from all 30 individual concentrations even including non-detects (zero for those under LOD). For correlation analysis between blood and urine data, all concentrations in blood or urine (μg/L) were converted to molar concentrations (nmol/L), which could be summarized within the same category (OP or PYR). Thus, each of the 30 subjects had a pair of a concentration of total insecticides in blood and a concentration of total metabolites in urine. Pearson correlation analysis on the log-transformed values was performed to test the relationship between OP or PYR insecticides in blood and the respective metabolites in urine. Metabolite concentrations in urine (μg/L) were used rather than creatinine-adjusted concentrations, because all urine samples were confirmed to be metabolized normally at prior kit tests, suggesting that such an adjustment might not be necessary. Statistical analysis was performed using SPSS statistical software package version 23.0 (SPSS Inc., Chicago, IL, USA), and descriptive statistics were computed in Microsoft Excel® (Microsoft, Redmond, WA, USA).
10
Gastric Mucosal Inflammation and H. pylori Analysis
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Discrete variables were tested using Pearson’s chi-squared test; the ordinal class variables were tested with the Mann–Whitney U-test for two groups comparison and Kruskall-Wallis test for more than 2 groups comparison. Correlations between PG levels and gastric mucosal inflammation, atrophy, and H. pylori infection status were evaluated by Spearman’s rank coefficients (r). The normality of the continuous variables was evaluated by the Shapiro–Wilk normality test. Receiver-operating characteristic (ROC) curves were constructed to calculate the best cutoff values, including the area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), and accuracy for discriminating H. pylori positivity, chronic gastritis, and atrophic gastritis. A multivariate analysis was performed to determine the odds ratio (OR) for the highest gastric cancer index among the countries, considering confounding factors including age, sex, and H. pylori infection. The entire statistical analysis was performed using SPSS statistical software package version 23.0 (SPSS Inc., Chicago, IL, USA).
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