Statistical software SPSS, V27.0 for Windows was used for data analysis. A 5% significance level was considered as significant. The Shapiro–Wilk test was used for testing the normality of the present data. For qualitative data, frequency analysis (n, %) was performed for sample characterization; for quantitative data, median (percentile 25%–percentile 75% (Q1–Q3)) were used. To compare age and BMI, eosinophil blood count, eosinophil count in the appendicular wall, and the IL-5 blood levels between the appendicular specimen’s histology (the three study groups NPA, APA, and AGA), the Kruskal–Wallis test was used, since the normality assumption was not verified and given the discrepancy in the size of the groups. When statistically significant differences were detected, Kruskal–Wallis multiple comparison tests were used. To verify whether the distribution of gender and allergies (presence/absence) was homogeneous among the three groups under study, the Chi-Square test and the Chi-Square test by Monte Carlo simulation were used (since the assumptions of applicability of the Chi-Square test were not verified), respectively. The Spearman correlation test was used to measure the degree of association between two variables.
Spss v 27.0 for windows
Manufactured by IBM
Sourced in United States
SPSS V.27.0 for Windows is a statistical software package developed by IBM. It is designed to analyze and manage data, perform statistical analyses, and generate reports. The software provides a wide range of statistical techniques, including descriptive statistics, regression analysis, and advanced modeling techniques. SPSS V.27.0 for Windows is compatible with the Windows operating system.
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Lab products found in correlation
7 protocols using spss v 27.0 for windows
1
Appendicular Histology Comparison
2
Longitudinal Evaluation of Metabolic Markers
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Data are presented as mean or percentage±SD. Differences between baseline and follow-up visits were tested for statistical significance using paired t-tests. P values (two sided) were considered significant if p<0.05. All statistical analyses were performed using SPSS V.27.0 for Windows.
3
Associations between OPA and Cardiometabolic Factors
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Continuous variables were presented as the mean and standard deviation (SD), and categorical variables were presented as percentages. Shapiro−Wilk test was used for assessing the parametricity of the variables. ANOVA was used to compare continuous variables, and the chi-square test was used to compare the categorical variables. Logistic regression models were used to analyze the associations between OPA and cardiometabolic risk factors regarding categorical (dichotomic) variables, and multivariate linear regression models were used for continuous variables. All of the models were adjusted by age, sex, alcohol consumption, and global PA. All data were analyzed using SPSS V.27.0 for Windows (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set to p < 0.05.
4
Evaluating Psychosocial Factors and Variables
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SPSS V.27.0 for Windows facilitated data analysis. Missing data were excluded, and descriptive and inferential statistics were employed to evaluate PS. Subscale scores were calculated, and relationships between different factors and PS were evaluated. The non-normal distribution of data was confirmed through the Kolmogorov-Smirnov test and a non-parametric Kruskal-Wallis test was used to assess the relationship between PS and sociodemographic variables with a p <0.05 was taken as statistically significant.
5
Assessing Motor Abilities in Children
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Normality was tested with Kolmogorov-Smirnoff’s test. Normally distributed variables are presented as mean with SD, and variables with skewed distributions are presented as median with IQR. When comparing the proportion of children with an MABC-2 score ≤15th or ≤5th percentile or an FTF score >90th percentile to norms, we used a binomial test. McNemar test was used to investigate if the proportion of children with an abnormal MABC-2 score differed significantly between the two assessment occasions. A two-sided p-value <0.05 was considered statistically significant for all analyses. Analyses were done using GraphPad Prism V.9.3.1 for macOS (GraphPad Software, San Diego, California, USA; www.graphpad.com ) or SPSS V.27.0 for Windows (IBM, Armonk, New York, USA).
6
Analyzing Muscular Fitness Across Age and Gender
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Statistical Package for the Social Sciences—SPSS v 27.0 for Windows (IBM) was used for data processing and analysis. Boys and girls were analyzed separately. Data were tested for normality using the Kolmogorov‐Smirnov analysis for each age group (n = 3) and for both genders separately. Values outside the interval of ±3 SDs (outliers) were excluded from further analysis.16 It should be noted that all analyses were performed on three previously specified age groups: 6–10, 11–14, and 15–19 years. Analysis of covariance (ANCOVA) was used to compare differences in muscular fitness between the decades in each age and gender group. To eliminate the effect of anthropometry, body height, weight, and BMI were used as covariates, with the decade being a fixed factor. The Bonferroni post‐hoc test was used several times for comparisons in both types of analysis. A p‐value of 0.05 was considered statistically significant. Results in the figures are presented as means, while results in the tables are presented as percentages (relative differences).
7
Semen Quality during Ramadan
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Data were analyzed using SPSS v.27.0 for Windows (IBM Corp., Armonk, NY, USA). Descriptive parameters are expressed as mean ± standard deviation or median ± interquartile range, as required. Frequencies are presented as percentages. Wilcoxon sign test was used for paired variables with nonnormal distribution. The McNemar test was used for categorical parameters.
The sample size needed to attain a power of 80% and a level of significance of 5% (two-sided) was calculated. Ninety pairs of semen samples were required to achieve a 30% difference in semen parameters between the Ramadan and control periods. A p value <0.05 was considered statistically significant. All statistical tests were two-tailed.
The sample size needed to attain a power of 80% and a level of significance of 5% (two-sided) was calculated. Ninety pairs of semen samples were required to achieve a 30% difference in semen parameters between the Ramadan and control periods. A p value <0.05 was considered statistically significant. All statistical tests were two-tailed.
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