All statistical analyses were performed by GraphPad Prism (version 8.0) and SPSS 26.0 (IBM SPSS Statistics 26.0). A general linear model was used to analyses the effect of Se sources on plasma biochemical parameters at multiple time points using SPSS 26.0 (IBM SPSS Statistics 26.0). Data on the effect of Se sources on growth performance, antioxidant capacity, carcass characteristics, meat quality and Se content of body tissues and organs were analysed using one-way ANOVA. The effect of the Se source was considered significant if statistical tests yielded a p < 0.05 for a particular parameter. For parameters with a significant effect of the Se sources, post hoc analysis was performed using Tukey–Kramer multiple comparisons test to analyses the statistical significance of pairwise differences among the means.
Spss 26
Manufactured by IBM
Sourced in United States, United Kingdom, Germany, Japan, Austria, China, India, Belgium, New Zealand, Italy, Sweden
SPSS 26.0 is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including descriptive statistics, bivariate analysis, predictive analytics, and more. The software is designed to help users gain insights from their data through various analytical techniques.
Automatically generated - may contain errors
Lab products found in correlation
Prism 8, by GraphPad (163 mentions)
Prism 9, by GraphPad (106 mentions)
Prism 7, by GraphPad (54 mentions)
Spss statistics for window, by IBM (18 mentions)
Spss statistics for windows version 26, by IBM (16 mentions)
Prism 6, by GraphPad (15 mentions)
Origin 2021, by OriginLab (13 mentions)
Origin 2018, by OriginLab (13 mentions)
Prism 5, by GraphPad (12 mentions)
Sas 9, by SAS Institute (11 mentions)
Simca 14, by Sartorius (11 mentions)
Spss statistics 26, by IBM (8 mentions)
Spss statistic, by IBM (7 mentions)
Amos 24, by IBM (7 mentions)
R software, by IBM (7 mentions)
R version 4, by R Project for Statistical Computing (7 mentions)
Amos 26, by IBM (6 mentions)
R version 4, by IBM (6 mentions)
Prism v8, by GraphPad (5 mentions)
Illustrator 2020, by Adobe (4 mentions)
3 436 protocols using spss 26
1
Selenium Source Effects on Biochemistry
2
Acceptability of Oral Dispersions: Taste and Grittiness
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Statistical analysis of the questionnaire data was performed using SPSS 26 (IBM SPSS). The related-samples Wilcoxon signed-rank test was employed to test differences in mean scores for taste and taste-masking between dispersions in water and infant formula milk.
The contributions, and association, of taste and grittiness to “acceptability as a medicine” were analysed by both descriptive statistics using participants’ mean taste scores for samples stratified into those deemed “unacceptable as a medicine” and those deemed “acceptable as a medicine”, as well as by a regression analysis. In the descriptive analysis, taste scores for “unacceptable as a medicine” formulations were expressed in terms of a minimum aversion value, and for “acceptable as a medicine” in terms of a maximum aversion score. To assess the relative contributions of taste and grittiness to acceptability, a binary logistic regression analysis was performed using SPSS 26 (IBM SPSS). Grittiness was recoded into two categories of not gritty (grittiness scores of 1–3) and gritty (grittiness scores of 4 and 5).
The contributions, and association, of taste and grittiness to “acceptability as a medicine” were analysed by both descriptive statistics using participants’ mean taste scores for samples stratified into those deemed “unacceptable as a medicine” and those deemed “acceptable as a medicine”, as well as by a regression analysis. In the descriptive analysis, taste scores for “unacceptable as a medicine” formulations were expressed in terms of a minimum aversion value, and for “acceptable as a medicine” in terms of a maximum aversion score. To assess the relative contributions of taste and grittiness to acceptability, a binary logistic regression analysis was performed using SPSS 26 (IBM SPSS). Grittiness was recoded into two categories of not gritty (grittiness scores of 1–3) and gritty (grittiness scores of 4 and 5).
3
Workplace Attachment, OCBs, and Moderators
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The mono-factorial structure of the scales measuring secure workplace attachment style, perceived spatial-physical comfort, and difficult relationship with patients was verified through a Principal Component Analysis (on IBM SPSS 26). As concerns the factorial structure of the OCBs scale (see Supplementary Materials, Figure S1, Table S1 ), which has been conceived and then tested as multi-factorial in previous literature (e.g., [31 ]), a confirmatory factor analysis (CFA) was run (on JASP 0.14). The model including the 3-factor structure and the second-order overall factor was confirmed, showing acceptable fit indices (CFI = 0.96; TLI = 0.95; RMSA = 0.07 [90% CI = 0.04, 0.10]; SRNR = 0.04; χ2/df = 1.6).
To test the research hypotheses, model 2 (Figure 1 a; Figure 1 b) of the process macro (on IBM SPSS 26) was used [98 (link)], including a secure workplace attachment style as the independent variable (IV), overall OCB and the three specific OCB subdimensions (Altruism, Conscientiousness, and Civic Virtue) as dependent variables (DVs), and perceived comfort and difficult relationship with patients as moderators.
The simple slope analysis allowed us to interpret the moderation effects of perceived comfort and difficult relationship with patients. Sex, age, education level, and marital status were inserted as covariates.
To test the research hypotheses, model 2 (
The simple slope analysis allowed us to interpret the moderation effects of perceived comfort and difficult relationship with patients. Sex, age, education level, and marital status were inserted as covariates.
4
Gastric Cancer Perforation: Clinicopathological Analysis
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The clinical, surgical, and pathological records of all patients with gastric cancer perforation were reviewed. Patients were retrospectively evaluated for the following factors: gender; age; anatomical location of the tumor in the stomach (defined as upper, middle, or lower third); depth of invasion (T3 or T4); size of the resected specimen; tumor differentiation; TNM staging; neoadjuvant chemotherapy and adjuvant chemotherapy. Quantitative parameters in the study were presented as mean ± standard deviation. When data were not normally distributed, median and interquartile ranges were used instead. Student's t-test was used to compare quantitative data between two groups; and the Mann–Whitney U test was used for skewed distributions by using SPSS 26.0 (IBM Corp., NY, USA). The ggplot2 package in R 4.0 software (https://www.r-project.org/ ) was used to plot OS curves. The Kaplan–Meier method (the log-rank test) was used to compare the OS times between two groups by using SPSS 26.0 (IBM Corp., NY, USA). Differences were considered significant at P < 0.05.
5
Spectroscopic Analysis of Nanoformulation
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The spectral data were pre-processed using Origin 2021 (Massachusetts, USA) to remove the noise and for baseline correction. For principal component analysis (PCA), Origin 2021 (Massachusetts, USA) was used. The pre-processed data from the joint FTIR spectral region (3750–2700 cm−1 and 1800–600 cm−1) was used for PCA analysis. For the Raman spectrum, the whole region (70–2100 cm−1) was used for the analysis. Correlation analysis was performed by Pearson tests using SPSS 26.0 (IBM, USA) between the FTIR/Raman spectral data and the physicochemical parameters of NE. Origin 2021 (Massachusetts, USA) was used for creating the correlation map. The correlation analysis (r) was performed for the data and the statistical significance was reported as p ≤ 0.05 which denotes the significant correlation. TreeMap was constructed for the chemical composition representation for the GTO and NE using Infogram application. The chemical structures of the major constituents of GTO and NE were drawn using ChemSpider. Statistical analysis of the data was performed by one-way analysis of variance (ANOVA) followed by Duncan’s multiple comparison test using SPSS 26.0 (IBM, USA) and the results were denoted as the mean ± standard deviation (SD). Statistical significance was expressed at p < 0.05.
6
Comparative Diagnostic Accuracy of DNA and RNA mNGS
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Utilizing the expert's assessment of the pathogen as the gold standard.The true positives (TP) are defined as cases in which pathogens identified by DNA-mNGS or RNA-mNGS are concordant with the gold standard, while false positives (FP) are cases in which pathogens identified by DNA-mNGS or RNA-mNGS do not align with the gold standard. True negatives (TN) refer to instances in which both DNA-mNGS or RNA-mNGS and the gold standard indicate the absence of pathogens, and false negatives (FN) refer to pathogens missed by DNA-mNGS or RNA-mNGS.the sensitivity [TP/(TP + FN)], specifcity [TN/(TN + FP)], PPV[[TP/(TP + FP)], NPV[TN/(TN + FN)],and accuracy rate[(TP + TN)/(TP + FP + TN + FN)] of DNA based and RNA based mNGS were estimated.We used SPSS 26.0 software for statistical analysis (IBM SPSS 26.0, SPSS Inc.).The Kappa statistic and McNemars test were used for tests of sensitivity and specificity agreement of DNA and RNA based mNGS.Statistical significance: p value, 0.05 threshold.
7
Intramural Uterine Adhesions: Surgical Outcomes
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Data are summarised as medians (interquartile range (IQR)) for continuous variables and frequencies (%) for categorical variables. Comparison of baseline characteristics and study outcomes between the two surgical modalities were performed using the Mann-Whitney test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables. All statistical tests were performed using SPSS 26.0 (IBM Corporation, New York) with a two-sided p-value < 0.05 considered statistically significant. Multivariate logistic regression analysis was performed to assess the association between the presence/absence of IUA and adjusted for confounding factors. All statistical tests were performed using SPSS 26.0 (IBM Corporation, New York) with a two-sided p-value < 0.05 considered statistically significant.
8
Propensity Score Matching for IVF Outcomes
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All data were statistically analyzed using SPSS 26.0 (IBM SPSS 26.0, SPSS Inc.). Categorical variables are expressed as percentages, and the chi square test and Fisher's exact test were used for analysis. As the data did not meet the assumption of a normal distribution, continuous variables are expressed as the median (25th percentile–75th percentile), and a nonparametric test was used. P < 0.05 indicated a significant difference. To reduce selection bias and eliminate the interference of confounding factors, the PSM method was used in this study, and the nearest neighbor PSM was employed to match the two groups. PSM was used mainly to match the baseline variables of the two groups, including age, body mass index (BMI), follicle stimulating hormone (FSH), luteinizing hormone (LH), number of oocytes retrieved, polycystic ovary syndrome (PCOS), endometrial preparation scheme for FET, natural cycle, program cycle, ovarian stimulation cycle, number of transferred embryos, endometrial thickness, endometrial thickness on HCG trigger day, and previous conception. The matching ratio for PSM was 1 : 4 within a caliper width of 0.1 for a satisfactory match.
9
Analysis of Intraprocedural Hypoxemia Factors
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The results of each scale were entered into a computer for score conversion and statistical analysis was performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA), with measured data expressed as mean and standard deviation and counted data expressed as frequencies and percentages. Statistical analysis between groups was performed using t-test and Chi-square test. Variables with differences after t-test or Chi-square test were included in binary logistics regression analysis was used to analyze the factors influencing the presence of intraprocedural hypoxemia and prognosis. A two-sided P < 0.05 was considered statistically significant.
SPSS 26.0 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis of the data, with measured data presented as mean ± standard deviation and counted data presented as frequencies and percentages. The t-test and Chi-square test were used for the analysis of between-group variability. Variables that differed by t-test or Chi-square test were included in a binary logistic regression analysis to analyze the presence of intraprocedural hypoxemia and factors influencing prognosis. A two-sided P < 0.05 was considered statistically significant.
SPSS 26.0 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis of the data, with measured data presented as mean ± standard deviation and counted data presented as frequencies and percentages. The t-test and Chi-square test were used for the analysis of between-group variability. Variables that differed by t-test or Chi-square test were included in a binary logistic regression analysis to analyze the presence of intraprocedural hypoxemia and factors influencing prognosis. A two-sided P < 0.05 was considered statistically significant.
10
Statistical Analysis of Experimental Data
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Principal component analysis (PCA) and heat map were performed using the combination of SPSS 26.0 (IBM Inc., United States) and OriginPro 2023b (OriginLab Corporation, Northampton, United States). Data were normalized before applying the feature selection algorithms and weighting results presented between 0 and 1, in which weight closer to 1 indicated the priority of the studied strains. All experiments were repeated three times and all data were expressed as means ± standard deviation. The statistical analysis was completed using SPSS 26.0 (IBM Inc., United States) and significant differences between groups were evaluated by the Duncan test. The statistical significance level was defined as p < 0.05.
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