Cut-off values with associated diagnostic sensitivity and specificity were determined with serum samples from ASFV-positive and -negative individual pigs by blocking ELISA. A commercial blocking ELISA kit (ID Screen® African Swine Fever Competition ELISA, IDVET, Grabels, France) was used as a standard evaluating method. Receiver operating characteristic (ROC) analysis, degree of agreement (kappa value) and the sensitivity and specificity of the established ELISA were analyzed by SPSS software for windows, version 25.0 (IBM, Armonk, NY, USA).
Spss software for windows version 25
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SPSS software for Windows version 25.0 is a statistical software package used for data analysis and management. It provides tools for data manipulation, analysis, and visualization. The software is designed to handle a wide range of data types and offers a variety of statistical techniques, including regression analysis, hypothesis testing, and descriptive statistics.
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93 protocols using spss software for windows version 25
1
Blocking ELISA for African Swine Fever
2
Depression Scores Analysis with SPSS
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Data were analyzed and proceeded using the IBM Statistical Package for the Social Sciences (SPSS) software for Windows, version 25.0 (Armonk, NY: IBM Corp). This was important in establishing the central tendencies, dispersion, variance, and skewness of the data. Categorical data variables were described using frequency and percentage, while continuous data variables were presented using mean and standard deviation (SD). An independent sample t-test, or one-way ANOVA was conducted to examine the variances in depression scores based on the demographic and clinical-comorbid variables. Similarly, multiple-linear regression analysis was employed to assess the adjusted effects of independent variables on PHQ-9 scores. A p-value of less than 0.05 was considered statistically significant.
3
Chronotype and Mediterranean Diet Adherence in Children
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Descriptive data are shown as numbers and percentages for categorical variables and as the means and standard deviation (SD) for continuous variables. A preliminary analysis indicated no significant interaction between chronotype and sex according to adherence to the MedDiet. Thus, we analyzed participants of both boys and girls together to increase the statistical power. The chi-square test was applied to verify the association between different KIDMED items or adherence to the MedDiet and different chronotypes (i.e., MT, IT, or ET). In addition, analyses of covariance were performed to estimate differences between mean values of the KIDMED score across different chronotypes.
Binary logistic regression analyses were carried out to determine the association between different chronotypes and the probability of having nonoptimal adherence to the MedDiet.
Corrections of multiple comparisons were performed using the false discovery rate (FDR) p value proposed by Benjamini and Hochberg 43 . All analyses were adjusted for sex, age, socioeconomic status, body mass index, sedentary behavior, physical activity, social jetlag, and energy intake. All analyses were conducted with SPSS software for Windows, version 25.0 (IBM Corp, Armonk, NY). A p value ≤ 0.05 was chosen to determine statistical significance.
Binary logistic regression analyses were carried out to determine the association between different chronotypes and the probability of having nonoptimal adherence to the MedDiet.
Corrections of multiple comparisons were performed using the false discovery rate (FDR) p value proposed by Benjamini and Hochberg 43 . All analyses were adjusted for sex, age, socioeconomic status, body mass index, sedentary behavior, physical activity, social jetlag, and energy intake. All analyses were conducted with SPSS software for Windows, version 25.0 (IBM Corp, Armonk, NY). A p value ≤ 0.05 was chosen to determine statistical significance.
4
Antioxidant Capacity Evaluation Protocol
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TPC, TFC, TAC, DPPH, FRAP and ORAC were expressed as mean ± SD of 6 biological replicates and statistical significance was set at p < 0.05. One-way ANOVA followed by Tukey's HSD test was used for all the factors to determine the differences between groups (p < 0.05) using IBM SPSS® software for Windows version 25.0 (IBM corporation, Armonk, NY, USA) and Dunnett's test to determine any significant effects compared to control (p < 0.05). Pearson correlation tests were interpreted according to the guide Evans (1996) suggested. According to Evans' empirical classification, the correlation strength can be interpreted using absolute values of Pearson correlation, r. less than 0.20 is very weak, 0.20-0.39 is weak, 0.40-0.59 is moderate, 0.60-0.79 is strong and 0.80 or greater is very strong correlation . It is commonly accepted that higher absolute values and smaller associated p values imply a stronger departure from a null hypothesis of no correlation.
5
Statistical Analysis of Research Data
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SPSS software for Windows version 25.0 (Armonk, NY: IBM Corp.) was used for the statistical analyses. Data were tested of normal distribution using the Kolmogorov-Smirnov test with the cut-point P-value of 0.05. The prevalence was presented with a mean and 95% confidence interval (CI). For the univariate analysis, the Kruskal-Wallis test and Chi-square test were used for categorical variables, the Mann-Whitney U test and ANOVA test for quantitative variables. For the multivariate analysis, the binary logistic regression was performed. A P-value <0.05 was considered as statistically significant.
6
Cetuximab Impacts Cell Growth
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Experiments were repeated at least three times. All statistical analyses were conducted using SPSS software for Windows Version 25.0 (Armonk, NY: IBM Corp). Cell-growth rates were assessed by one-way ANOVA. Cell motilities and mKO2-expression levels were analyzed using the Mann–Whitney U test. The protein expression levels, which were calculated by the number of pixels × density on WB analysis, were expressed as mean ± standard error; differences between the control- and the cetuximab-treated group were tested by t test. Two-sided P-values of < 0.05 were considered to reflect statistically significant differences.
7
Evaluating Factors Affecting Survival in Localized Ewing's Sarcoma
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For demographic data, descriptive statistics were used. Tumor assessment was done using the Response Evaluation Criteria in Solid Tumors version 1.1. OS was analyzed by the Kaplan–Meier method. Univariate analysis was done to assess the effect of age, gender, primary tissue type, primary site of the disease, and size of the primary tumor on OS in localized ES. All statistical analyses were done using SPSS software for Windows version 25.0 (IBM Corp. Released 2017. Armonk, NY, USA.).
8
Qualitative Data Analysis Methodology
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The information obtained of a qualitative nature was subjected to a general analysis process that consists of recording the expressions and situations as they appear in reality to later initiate a process of data reduction followed by the provision and transformation and then arrive at obtaining and verification of conclusions. For a better understanding of the results, a quantification of the qualitative data is carried out using the analysis of frequencies and percentages, as well as descriptive statistics such as the minimum, the maximum, the mean, the standard deviation and the t-test for independent samples considering a 95% confidence interval (p≤0.05). Measurable data are statistically processed using IBM SPSS software for Windows version 25.0.
9
Motor Skills and Physical Activity Assessment
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Statistical analysis was performed using IBM® SPSS® software for Windows, version 25.0 (IBM SPSS Software, 2017; Chicago, IL, USA). Participant data are described using mean and standard deviation for normally distributed data, or median (25, 75 percentile) for non-normally distributed data. Categorical variables are summarized as frequencies and percentages. The Shapiro–Wilk test and normality plot were used to evaluate the normality of distributions. Student’s t-test, Mann–Whitney, ANOVA and Kruskal–Wallis explored relationships between TGMD-2 (locomotor skills, object control skills and gross motor quotient), accelerometry (levels of activity; light, medium and vigorous PA, and kcal/hour) and other variables (age and sex). Interactions between age and sex were tested using ANOVA. The post hoc Tukey test was used to explore multiple comparisons. Pearson’s correlation was used to examine relationships between two normally distributed quantitative variables and Spearman’s correlation coefficient was used where one or both variables were non-parametric. Statistical significance was set at p < 0.05 and accepted effect size metrics and interpretations were followed.
10
Autism Serum Antibody Profile Analysis
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Data are presented as mean ± SD, and were analysed using IBM SPSS software for
Windows, version 25.0 (IBM, Armonk, NY, USA). Between-group differences in
demographic and clinical data were analysed using χ2-test and
independent-samples t-test. Biochemical and clinical data were
assessed for normality and Log transformed as necessary. Multivariate analysis
of covariance within the General Linear Model in SPSS was used to compare case
versus control serum antibodies, and to account for confounding variables.
Pairwise differences (post-hoc analyses) were Bonferroni corrected for multiple
comparisons. Relationships between ADOS scores and serum antibodies were
explored using Pearson’s correlation coefficient. Statistical significance was
set to ensure 95% confidence intervals, conferring 0.80 power and an alpha error
of 0.05 (two-tailed P value ≤0.05).
Windows, version 25.0 (IBM, Armonk, NY, USA). Between-group differences in
demographic and clinical data were analysed using χ2-test and
independent-samples t-test. Biochemical and clinical data were
assessed for normality and Log transformed as necessary. Multivariate analysis
of covariance within the General Linear Model in SPSS was used to compare case
versus control serum antibodies, and to account for confounding variables.
Pairwise differences (post-hoc analyses) were Bonferroni corrected for multiple
comparisons. Relationships between ADOS scores and serum antibodies were
explored using Pearson’s correlation coefficient. Statistical significance was
set to ensure 95% confidence intervals, conferring 0.80 power and an alpha error
of 0.05 (two-tailed P value ≤0.05).
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