SPSS statistical software 26.0 was used to statistically analyze the input data. Normally distributed measures were expressed as means ± standard deviation ( ± s). A one-way ANOVA test was used for the comparison between groups. Count data were expressed as percentages. The chi-square test was used for comparison between groups. Treatment outcomes were analyzed using the intention-to-treat (ITT) and adherence-to-study protocol (per protocol, PP) analyses. p < .05 differences were considered statistically significant. Relevant influences were analyzed using the diagnostic odds ratio (OR) value. Differences were considered statistical when the upper 95% CI was less than one or the lower limit was greater than one.
Spss 26 statistical software
Manufactured by IBM
Sourced in United States
SPSS 26 is a statistical software package developed by IBM. It provides tools for data management, analysis, and visualization. The software is designed to help users analyze and interpret data, identify patterns, and make informed decisions.
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Lab products found in correlation
33 protocols using spss 26 statistical software
1
Comparative Statistical Analysis Protocol
2
Psychometric Assessment of Bariatric Surgery
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Results are expressed as mean values ± standard deviation (SD). After testing the normal distribution of continuous variables via the Shapiro–Wilk test, we applied logarithmic transformation as needed to ensure normality of skewed variables. Student’s t-test was performed to assess the differences between two independent means. Differences in the variables within the same group, before and after BS, were analysed by repeated-measures ANOVA. The comparison of quantitative variables of depressive symptoms and QoL between the groups was based on analysis of variance adjusted for the patients’ sex, age and BMI (ANCOVA). In addition, the effect size of the mean comparisons was estimated with the standardized Cohen’s d coefficient (|d| > 0.20 was considered low, |d| > 0.5 was considered moderate and |d| > 0.8 was considered high) [42 (link)]. The level of significance was set at p < 0.05 for the main effects and the interaction. Relationships between clinical and metabolic data, different serum proteins or miRNA expression with the psychometric test results were analysed via Spearman’s correlation test. All statistical analyses were performed using SPSS statistical software 26.0 (SPSS Inc., Chicago, IL, USA).
3
Psychometric Assessment of Bariatric Surgery
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Results are expressed as mean values ± standard deviation (SD). After testing the normal distribution of continuous variables via the Shapiro–Wilk test, we applied logarithmic transformation as needed to ensure normality of skewed variables. Student’s t-test was performed to assess the differences between two independent means. Differences in the variables within the same group, before and after BS, were analysed by repeated-measures ANOVA. The comparison of quantitative variables of depressive symptoms and QoL between the groups was based on analysis of variance adjusted for the patients’ sex, age and BMI (ANCOVA). In addition, the effect size of the mean comparisons was estimated with the standardized Cohen’s d coefficient (|d| > 0.20 was considered low, |d| > 0.5 was considered moderate and |d| > 0.8 was considered high) [42 (link)]. The level of significance was set at p < 0.05 for the main effects and the interaction. Relationships between clinical and metabolic data, different serum proteins or miRNA expression with the psychometric test results were analysed via Spearman’s correlation test. All statistical analyses were performed using SPSS statistical software 26.0 (SPSS Inc., Chicago, IL, USA).
4
Statistical Analysis of Oncology Outcomes
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SPSS statistical software 26.0 was used to perform statistical analyses in this study. The data were expressed as frequency and/or percentage and analyzed using the Chi-square test or F test. The PFS of patients was analyzed using the Kaplan–Meier method, and the distributions between the curves were compared using the log-rank test. A difference with P < 0.05 indicated statistically significant.
5
Comparing Autism Symptoms in Regressive vs Non-Regressive Groups
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SPSS statistical software 26.0 was used for statistical analysis. The Kolmogorov–Smirnov goodness-of-fit test was used to test the distribution of each dataset for normality before analysis. Continuous variables were described as mean ± standard deviation (M ± SD) and medians (inter-quartile ranges) [M (IQR)]. Categorical variables were described as n (%). Differences in demographic data betweeen groups were assessed by using the Chi-Square test or Mann–Whitney test. Multivariate (adjusted for age and gender) linear regression models were used to compare the scores of the Autism symptom scale and developmental level scale between the regressive and non-regressive groups. A P < 0.05 was considered statistically significant.
6
Gender and Obesity Effect on Gene Expression
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The results are expressed as mean values ± SD unless otherwise stated. Normality of the distribution of continuous variables was tested using the Shapiro–Wilk test. Logarithmic or square root transformations were applied as needed to ensure the normal distribution of data. Univariate and multivariate general linear models (GLMs) were used to determine, within a single analysis, the influence of group (women and men), obesity (normal weight and obese), and the interaction of both factors on clinical variables, P4-ATPases and miR gene expression, while adjusting the level of significance to compensate for the multiple comparisons involved. Correlation analysis was assessed using Spearman’s correlation test. Regression analysis was assessed using a backward stepwise linear regression. All of the analyses were performed with SPSS statistical software 26.0 (SPSS Inc., Chicago, IL, USA), and p values < 0.05 were considered statistically significant.
7
Comparative Analysis of Ruptured and Unruptured Groups
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All analyses were conducted using IBM SPSS statistical software 26.0 (IBM Corp., Armonk, NY, USA). Statistical significance was set at p < 0.05 for a 95% confidence interval (CI). The original baseline differences between the ruptured and unruptured groups were evaluated using a t-test for continuous variables and the Chi-squared test for categorical variables. Given the potential confounding effects of selection and routine biases, we compared the characteristics in the present study with those of other published medical literature.
8
Statistical Analysis of Genetic Factors
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In this study, IBM SPSS statistical software 26.0 was used for statistical analysis. The measurement data conforming to the normal distribution were expressed by the mean ± standard deviation (Mean ± SD), and the comparison between groups was analyzed by Student’s t-test or one-way ANOVA. The measurement data that did not conform to the normal distribution were expressed by the median (interquartile range) [Median (IQR)], and the comparison between groups was performed by the Nonparametric tests. Counting data were expressed in percentage (%) and were compared by χ2 test or Fisher’s exact test. The deviation of the Hardy-Weinberg equilibrium (HWE) for genotype frequencies was analyzed by χ2 test. Logistic regression was used to analyze the relationship among genotypes, 25 (OH) D levels and NH risk. Generalized multifactor dimensionality reduction (GMDR) was used to find out the potential interaction combinations between SNPs and environmental factors, including SNP-SNP synergy and SNP-vitamin D synergy effect. Linkage disequilibrium (LD) and haplotypebased case-control analysis were performed by Haploview 4.2 software[25 ]. And a P-value less than 0.05 was regarded as statistically significant.
9
Psychological Distress Predictors in Infection
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The analyses were performed using the SPSS statistical software (26.0) (IBM, Armonk, NY, USA). The presence or absence of psychological distress was assessed for each independent variable (information received, preventive measures taken, level of concern about transmitting the infection or getting infected, beliefs, and level of knowledge about the infection). Subsequently, bivariate analyses were performed, including Chi-squared test and student’s T-test for the independent variables, depending on their type. Crammer’s V and Cohen’s d effect size indexes were also calculated with the following cut-off points: 0 to 0.19, negligible; 0.20 to 0.49, small; 0.50 to 0.79, medium; from 0.80 on, high.
Then, with the aim of studying the predictive ability for psychological distress of the different sets of variables, logistic regression analyses (controlled by sex and age) were carried out including variables with p value <0.05. Finally, variables that manifested to have a predictive nature in each of the models were included in a global model (Model 5).
Odds ratios (ORs) were calculated with a 95% confidence interval.
Then, with the aim of studying the predictive ability for psychological distress of the different sets of variables, logistic regression analyses (controlled by sex and age) were carried out including variables with p value <0.05. Finally, variables that manifested to have a predictive nature in each of the models were included in a global model (Model 5).
Odds ratios (ORs) were calculated with a 95% confidence interval.
10
Evaluating QTc Interval in Diabetic Neuropathy
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Statistical analyses were carried out using the SPSS statistical software 26.0 (IBM Corp., Armonk, NY, USA). In all cases, P‐values <0.05 (P < 0.05) were considered statistically significant. Data were tested for normality using the Shapiro–Wilk test. The mean ± standard deviation, n (%) and median (25th and 75th interquartile range) presented continuous variables with normal distributions, categorical variables and continuous variables with skewed distributions, respectively. The differences were compared by the independent t‐test, Mann–Whitney test and the χ2‐test. Binary logistic regression analysis was carried out to compare the independent influence of the QTc interval and other metabolic parameters on the DPN, and the odds ratio (OR) and 95% confidence interval (95% CI) were determined. Receiver operating characteristic (ROC) analysis and the area under curve (AUC) were used to assess the accuracy and discriminatory ability of QTc in diagnosis of DPN.
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