IBM SPSS statistics software version 22.0 for Windows (IBM Corp., Armonk, NY, USA) was used to carry out one-way analysis of variance (ANOVA) among different samples using three replications. Duncan’s multiple range test (DMRT) was used to determine significant differences (p < 0.05).
Spss statistics software version 22.0 for windows
Manufactured by IBM
Sourced in United States
SPSS Statistics software version 22.0 for Windows is a statistical analysis and data management software. It provides tools for data access, data manipulation, and statistical analysis. The software is designed to handle a wide range of data types and enables users to perform a variety of analytical techniques.
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Lab products found in correlation
7 protocols using spss statistics software version 22.0 for windows
1
One-way ANOVA Analysis of Samples
2
Postural Control in Healthy and Elderly Participants
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In the healthy participants group, parametric pairwise multiple comparisons were performed using one-way repeated-measures analysis of variance (ANOVA), because the data were normally distributed. Comparisons were then made for each pair in the four postural conditions based on the estimated marginal mean. Additionally, due to presumed multiplicity, P-values and confidence intervals were adjusted using the Bonferroni correction. The results are presented as mean ± standard error.
In the elderly participants group, non-parametric pairwise multiple comparisons were performed using Friedman's test, because the data in this group were not normally distributed. Post-hoc comparisons were performed using the Wilcoxon signed-rank test. In addition, due to presumed multiplicity, P-values and confidence intervals were adjusted using the Bonferroni correction. The results are presented as medians and interquartile ranges.
As a preliminary analysis, the Shapiro–Wilk test was performed to confirm the normality of the data. Statistical analyses were performed using IBM SPSS Statistics software version 22.0 for Windows (IBM, Chicago, IL, USA). The statistical significance level was set at < 5%.
In the elderly participants group, non-parametric pairwise multiple comparisons were performed using Friedman's test, because the data in this group were not normally distributed. Post-hoc comparisons were performed using the Wilcoxon signed-rank test. In addition, due to presumed multiplicity, P-values and confidence intervals were adjusted using the Bonferroni correction. The results are presented as medians and interquartile ranges.
As a preliminary analysis, the Shapiro–Wilk test was performed to confirm the normality of the data. Statistical analyses were performed using IBM SPSS Statistics software version 22.0 for Windows (IBM, Chicago, IL, USA). The statistical significance level was set at < 5%.
3
Evaluating K-LINQ for COPD Symptom Assessment
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The baseline characteristics of patients are presented as absolute variables for categorical variables and means with standard deviations for numerical variables. For concurrent validity, the Pearson correlation test of K‐LINQ and other symptom‐relate questionnaires for COPD patients were analysed. The test‐retest reliability was measured using ICC for agreement. The group was divided according to the PR clinic visited, and independent t tests were performed on continuous variables which had normal distribution. A value of p < 0.05 was considered statistically significant. All data analyses were performed with IBM SPSS statistics software, version 22.0 for Windows (Armonk, NY, USA).
4
Supplement Effects on Strength and Performance
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Individual group and time data are presented throughout the text and in all tables as means (± SD), while group effects are presented as means (± SEM). All related variables were grouped and analyzed using repeated measures MANOVA in IBM SPSS Statistics Software version 22.0 for Windows (IBM Corporation, Armonk, NY, USA) to assess values observed and changes from pre-lift levels in response to the supplement administered. Post-hoc LSD pairwise comparisons were used to analyze any significance among groups where needed with Cohen’s d calculations employed to determine effect magnitude. Data was considered statistically significant when the probability of error was less than 0.05 and considered to be trending when the probability of error was less than 0.10. Statistical trends (p < 0.05 to p < 0.10) were noted as is common practice in studies with relatively small sample size [55 (link)].
5
Antioxidant Capacity Analysis Protocol
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All assays were performed in triplicate. The results were expressed as mean ± SD. IC50 and IC25 values were determined by the linear regression analysis of RSC (Origin Pro 2016 software, version 9.3). The data that have a normal distribution were subjected to two-way analysis of variance (ANOVA)and multivariate analysis of variance (MANOVA). Tukey’s test was used to determine significant differences (p < 0.05) between the extracts. Nonparametric Friedman tests and posthoc LSD tests were used for data that do not have a normal distribution. The correlation between antioxidant capacity and total phenols content was established using the Pearson’s product-moment correlation (normally distributed data) and Spearman correlation (data that do not have a normal distribution). The statistical analysis was performed using the IBM SPSS Statistics software version 22.0 for Windows.
6
Evaluation of Intervention's Impact
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The data were analyzed with IBM SPSS Statistics software version 22.0 for Windows (IBM Corp., Armonk, NY, USA). A descriptive analysis was conducted relative to the study’s variables. The mean and standard deviation were utilized for quantitative variables, and frequencies and percentages were used for categorical ones.
As most measurement results did not obtain a normal distribution, the pre- and post-data were subjected to a Bootstrap analysis [46 (link)]. The comparison between the pre- and post-intervention scores was performed using Student’s t-test with a Bonferroni correction for multiple comparisons. For each variable, the effect size was calculated using Cohen’s d to assess the magnitude of the effect of the intervention, with the use of the values proposed by Ferguson [47 (link)], in which 0.41 indicated a small effect, 1.15 indicated a medium effect, and 2.70 indicated a large effect.
As most measurement results did not obtain a normal distribution, the pre- and post-data were subjected to a Bootstrap analysis [46 (link)]. The comparison between the pre- and post-intervention scores was performed using Student’s t-test with a Bonferroni correction for multiple comparisons. For each variable, the effect size was calculated using Cohen’s d to assess the magnitude of the effect of the intervention, with the use of the values proposed by Ferguson [47 (link)], in which 0.41 indicated a small effect, 1.15 indicated a medium effect, and 2.70 indicated a large effect.
7
Identifying Risk Factors for Viral Resistance
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Variables were summarized as proportions for categorical variables, the median and range for continuous variables. The x 2 test or Fisher's exact test was used to compare categorical variables and the Mann -Whitney U-test was used to compare continuous variables between groups. The 95% CIs were computed using a binomial distribution. A logistic regression model was used to identify which population groups had the greatest risk of being infected with resistant viruses. Analyses were performed with IBM SPSS statistics software version 22.0 for Windows (SPSS Inc., Chicago, IL, USA).
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