The Delphi technique method allows the generation of consensus through a working group of experts on a specific topic, using an interactive process of individual feedback. The method is widely used in many fields of medicine to obtain consensus when formal recommendations are lacking or for areas in which clinical evidence is insufficient [4 (link), 5 ]. A scale from 1 to 10, with 1 indicating complete disagreement and 10 indicating complete agreement, was used, with a cut-off of 7 considered to indicate approval with 70% of participants voting on the summary synthesis for all rounds of voting. Discrete variables were expressed as counts (percentage), and continuous variables were expressed as means with the standard deviation or medians with the 25–75th percentiles, as appropriate. Statistical analyses were performed using IBM SPSS Statistics 25.0 for Macintosh (IBM Corp., Armonk, NY, USA).
Spss statistics 25.0 for macintosh
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SPSS Statistics 25.0 for Macintosh is a software package used for statistical analysis. It provides tools for data management, statistical modeling, and visualization. The software is designed to run on the Macintosh operating system.
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8 protocols using spss statistics 25.0 for macintosh
1
Delphi Technique for Consensus Building
2
Salivary Nitric Oxide as a Prognostic Biomarker in Asthma
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Statistical analyses were performed using SPSS Statistics 25.0 for Macintosh (IBM, Tokyo, Japan). The Shapiro-Wilk test was used to evaluate the normality of regression. In Table 1 , the variables are expressed as mean ± SD. The Mann-Whitney U test was used to identify sex-related differences in the participants’ characteristics. In Figure 1 , the Spearman’s rank correlation coefficients (rs) were used to express linear correlations between the variables. In Figure 2 , data are expressed as mean ± SE. The Mann-Whitney U test was used to compare the two groups. In Figure 3 , to assess the reliability of salivary NO levels as a prognostic variable by using receiver-operating characteristic (ROC) curves, we calculated the sensitivities and specificities in predicting asthma at cut-off values. In all the tests, a value of P < 0.05 was considered to indicate a statically significant difference.
3
Assessing Vaccine Hesitancy Factors
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Continuous variables were initially tested for normal distribution (D’Agostino and Pearson omnibus normality test), where the corresponding p-value was <0.10; “normal” distribution was assumed as rejected; and bivariate correlations between continuous variables were compared using Spearman’s rank test. On the other hand, bivariate correlation between variables passing the normality check (D’Agostino and Pearson p-value ≥ 0.10) was assessed by calculating the Pearson’s correlation. Categorical variables were reported as percentages, and their distributions with respect of the outcome variables were initially analyzed using a chi-squared test. Two outcome variables were specifically assessed: reporting an appropriate vaccination status for TeV; showing any vaccine hesitancy.
All categorical variables that, at univariate analysis, were associated with the aforementioned statuses with a p-value < 0.05 were included as explanatory variables in a stepwise binary logistic regression analysis model of having an appropriate TeV status and exhibiting any vaccine hesitancy. Adjusted odds ratios (adjOR) and their respective 95% confidence intervals (95%CI) were calculated accordingly. All statistical analyses were performed by means of IBM SPSS Statistics 25.0 for Macintosh (IBM Corp. Armonk, NY, USA).
All categorical variables that, at univariate analysis, were associated with the aforementioned statuses with a p-value < 0.05 were included as explanatory variables in a stepwise binary logistic regression analysis model of having an appropriate TeV status and exhibiting any vaccine hesitancy. Adjusted odds ratios (adjOR) and their respective 95% confidence intervals (95%CI) were calculated accordingly. All statistical analyses were performed by means of IBM SPSS Statistics 25.0 for Macintosh (IBM Corp. Armonk, NY, USA).
4
Statistical Analysis of Technique Agreement
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Discrete variables were expressed as counts (percentage) and continuous variables as means ± standard deviation (SD) or median (25th–75th percentiles) as appropriate. The Kolmogorov–Smirnov test was used and histograms and normal quartile plots were examined to verify the normality of distribution of continuous variables. The agreement between the three techniques and the three raters was evaluated using Cohen’s kappa or Fleiss kappa coefficient (≤ 0 no agreement, 0.01–0.20 slight, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 substantial, 0.81–1.00 as almost perfect agreement), as appropriate. Correlations were tested using Pearson or Spearman test, as appropriate.
All tests were two-tailed and a p < 0.05 was considered statistically significant. Statistical analyses were performed using IBM SPSS Statistics 25.0 for Macintosh (Armonk, NY, USA).
All tests were two-tailed and a p < 0.05 was considered statistically significant. Statistical analyses were performed using IBM SPSS Statistics 25.0 for Macintosh (Armonk, NY, USA).
5
Wingate Test Performance Analysis
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Data were processed using IBM SPSS 25.0 Statistics for Macintosh (IBM Corp., Armonk, NY, USA). Data were expressed as mean ± standard deviation. The normality distribution of the variables was analysed using the Shapiro–Wilk test and the homogeneity of variances using the Levene test. Student’s t-test and a two-way ANOVA (group effect + time effect) were used. The percentage change during the Wingate test was calculated every 5 s. A p < 0.05 was considered as statistically significant.
6
Physiological Responses in Endurance Race
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Data were processed with IBM SPSS 25.0 Statistics for Macintosh (IBM Corp., Armonk, NY, USA). A descriptive analysis was performed to show the means and standard deviations. The variables’ normality distribution was analyzed by the Shapiro–Wilk test and the homogeneity of variances by the Levene test. The Student’s t-test was applied to determine differences in the participants’ characteristics and the percentages of change (pre-race vs. post-race). A two-way ANOVA (group effect + race effect) was used to show differences between the studied variables. Effect size was calculated for the two-way ANOVA using partial eta-squared (η2) as a low effect (0.01–0.06), moderate effect (0.06–0.14) and high effect (>0.14) [42 (link)]. The p < 0.05 differences were considered statistically significant.
7
Effects of Strength Conditioning on Gender
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Data were processed in IBM SPSS 25.0 Statistics for Macintosh (IBM Corp., Armonk, NY, USA) and expressed as the mean ± standard deviation. The normality of the distribution of variables was analyzed by the Shapiro–Wilk test and the homogeneity of variances by the Levene test. The Student’s t-test was employed to determine the differences in percentages of change (pre-SC vs. post-SC). A two-way ANOVA (gender effect and SC effect) was used to show any differences in the studied variables. Effect size was calculated by the two-way ANOVA, using partial eta-squared (η2), where 0.01–0.06 was a small effect size, 0.06–0.14 was a moderate effect size, and >0.14 was a large effect size [33 (link)]. p < 0.05 differences were considered to be statistically significant.
8
Two-way ANOVA Analysis of Study Variables
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A descriptive analysis was performed to show means and standard deviations. The normality of the distribution of the variables was analysed using the Shapiro-Wilk test and the homogeneity of the variances using the Levene test. A two-way ANOVA (Group effect and Match effect) was used to show differences between study variables. The differences between after and before (delta values = ∆) were determined. p < 0.05 differences were considered statistically significant. Data were processed in IBM SPSS 25.0 Statistics for Macintosh (IBM Corp., Armonk, NY, USA).
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