Excel was used to process the raw data. The SPSS 23.0 program was mostly used to analyze clinical data. The chi-square test was used to compare categorical data. The Student’s t test or the Kruskal–Wallis test was used to calculate statistical significance for continuous variables between two groups or more than two groups. R was used to sequence the data, analyze them, and visualize them (v3.6.1). The correlation between each differential gene was determined using the Spearman correlation approach; p < 0.05 was regarded as statistically significant.
Spss 23.0 program
Manufactured by IBM
Sourced in United States
SPSS 23.0 is a statistical software program developed by IBM. It provides data management, analysis, and visualization capabilities. The core function of SPSS 23.0 is to enable users to conduct a variety of statistical tests, including regression analysis, correlation analysis, and hypothesis testing, among others.
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Lab products found in correlation
10 protocols using spss 23.0 program
1
Comprehensive Data Analysis Workflow
2
Correlational Analysis of Maternal and Neonatal Characteristics
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The SPSS 23.0 program was used for statistical analyses. Descriptive statistics were used to give information about maternal and neonatal characteristics. Independent samples t-tests were conducted to examine differences between two independent groups when the dependent variable was numerical. Pearson’s correlation analyses were used to give information about the strength and direction or association between numeric variables. Interpretation of the coefficients was performed based on Colton’s rule as follows: a) an absent or very weak correlation with r from −0.25 to −0.25; b) a reasonable correlation with r from −0.50 to −0.25 and r from 0.25 to 0.50; c) a moderately strong correlation with r from −0.75 to −0.50 and r from 0.5 to 0.75; d) a very strong correlation with r < −0.75 and r > 0.75; e) and a perfect correlation with r = −1 or r = 1. The correlations were considered significant at p < 0.05, very significant at p < 0.01, and extremely significant at p < 0.001.41 ,42 Subsequently, with the R system, the response variables and their determinants in this study were identified using Poisson regression and multiple regression.43
3
Statistical Comparison of Treatment Groups
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The statistical significance between groups was determined using one-way analysis of variance and Dunnett’s comparison. Statistical analyses were performed using SPSS 23.0 program (SPSS, Inc., Chicago, Illinois, USA). Results are expressed as mean ± SEM.
4
Exploring Gender Roles and Quality of Life
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SPSS 23.0 program was used for data analysis in the study. Digit (n), percentage (%), mean, and standard deviation (SD) were used as descriptive statistical methods. The compliance of the data with normal distribution was evaluated by using the Shapiro–Wilk test and QQ diagrams. The relationship between the scale scores was examined through Pearson correlation analysis. The effect of the scale score averages on each other was determined by using linear regression analysis. Linear regression analysis has been used to assess the influence of participants’ gender roles on their levels of quality of life. The statistical significance for all analyses was found as p < 0.05.
5
Statistical Analysis of Categorical and Continuous Data
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The SPSS 23.0 program was used for statistical analysis. Categorical measurements were assessed as number and percentage, continuous measurements are summarized as mean and standard deviation. The chi-square or Fisher’s exact test statistics were used to compare categorical variables. To compare continuous variables between the groups, ranges were assessed, ANOVA or Student’s t-test were used in dual groups for variables in a parametric range. P<0.05 was considered significant for all tests.
6
Mutation Subgroup Efficacy Analysis
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Comparisons of efficacy were conducted by chi-square analysis, with odds ratios (ORa) and 95% confidence intervals (CI) calculated using a logistic regression model. A Kaplan-Meier technique (log-rank tests) was used to estimate the PFS, while a Cox proportional hazards model was used to compute the hazard ratio (HR) and 95 %CI. Univariate logistic regression model and Cox proportional hazards model were performed to compare the efficacy and prognosis of different mutation subgroups, with ORas and HRs as well as 95% CI were calculated, respectively. Statistical significance was determined by a p-value less than 0.05 for two-sided tests. SPSS 23.0 program (SPSS Inc., Chicago, IL, United States) was used for the analysis.
7
Worm Infestation and Autoimmune Diseases
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All data were stored in a common database and statistically analyzed using the SPSS 23.0 program (SPSS Inc., Chicago, IL, USA). Differences in the proportion of individuals who eventually developed type I diabetes mellitus (T1D), Juvenile Rheumatoid Arthritis (JRA) and coeliac disease were compared across groups who had and did not have worm infestations at age 1 year, 5 years and 8 years using the Pearson Chi-Square test. A number of potentially confounding variables were considered, gender of the child, type of locality the child was born into, medications that might mask the infection, low educational attainment (year 9 or lower) of mother and of father. These variables were compared between those who eventually developed each outcome (T1D, JRA and coeliac disease) and those who did not to establish one of the essential criteria for confounding. The association between worm infestation and outcome was re-assessed controlling for potentially confounding variables using unconditional logistic regression. Adjusted associations between worm infestation and outcomes are reported as odds ratios with 95% confidence intervals and p-values.
8
Predictive Factors of Contrast-Induced Acute Kidney Injury
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All statistical studies were carried out using the SPSS 23.0 program (Chicago, IL, USA). Categorical data were reported as percentages, and continuous data were reported as median (25 – 75th percentile) or mean (± SD). The distribution properties of the data were performed using the Kolmogorov-Smirnov test. Dichotomized data were analyzed for the significant difference using Chi-2 or Fisher’s exact test appropriately. Continuous data were analyzed for the significant difference using Student’s t or Mann–Whitney test appropriately. Finally, we identified the predictive factors of CI-AKI using logistic regression analysis.
9
NIHL Group Hearing Comparison
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The SPSS 23.0 program was used for the data analysis. The thresholds for EHFA and both c-VEMPs and o-VEMPs amplitudes and latencies were evaluated with independent sample t tests to investigate differences between the NIHL group and the control group. A Mann–Whitney U test or Wilcoxon Signed-rank test was conducted when the data did not comply with a normal distribution. All analyses were conducted at a 95% confidence interval with a significance level of 0.05. Pearson correlation analyses were performed to define exploratory analyses.
10
Prognostic Factors in Hepatocellular Carcinoma
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Multivariate Cox proportional hazards model was used to analyze the risk factors related with the prognosis. Then, these covariates were matched by PSM with the allowable error at 0.3 (7 (link)-9 (link)) to compare the differences between the patients with RHCC and NHCC. The ratio of RHCC and NHCC patients was 1:4 in the PSM analysis. Nearest neighbor was the matching algorithm. The parameters included hemoglobin, leukocyte, albumin, aminotransferase, maximum tumor diameter, microvascular invasion and Child-Pugh grade were used as the matching variables in the PSM study. Independent t-test was used to analyze continuous data with a normal distribution which was tested by Kolmogorov-Smirnov test. Mann-Whitney U test was adopted for non-normal distributed data. Categorical variables were compared by the χ2 test with Yates’ correction or Fisher’s exact-test. Overall survival (OS) as well as disease-free survival (DFS) curves were generated with Kaplan-Meier method and compared by the log-rank test. The curves differences were examined with Cox proportional hazards analysis. Statistical analyses were performed with SPSS 23.0 program (SPSS Inc., Chicago, IL, USA). P<0.05 was considered as statistically significant.
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