Spss statistics 26

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SPSS Statistics 26 is a comprehensive software package for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of SPSS Statistics 26 is to enable users to perform a wide range of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing.

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Prism 8, by GraphPad (90 mentions) Prism 9, by GraphPad (41 mentions) Prism 7, by GraphPad (28 mentions) Prism 6, by GraphPad (11 mentions) Excel 2016, by IBM (11 mentions) Spss 26, by IBM (8 mentions) Origin 2021, by OriginLab (8 mentions) Excel 2019, by IBM (7 mentions) Prism v8, by GraphPad (7 mentions) Prism 8, by IBM (7 mentions) Simca 14, by Sartorius (5 mentions) Prism 5, by GraphPad (5 mentions) Originpro 2021, by OriginLab (5 mentions) Origin 2022, by OriginLab (5 mentions) Origin 2018, by OriginLab (4 mentions) Originpro 2022, by OriginLab (4 mentions) Origin 2021, by IBM (4 mentions) Spss statistic, by IBM (4 mentions) Excel 365, by IBM (3 mentions) Origin 2021b, by OriginLab (3 mentions)

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SPSS Statistic 26 SPSS Statistic version 26 SPSS Statistic v. 26 SPSS Statistic software (v. 26) SPSS Statistic for Windows, Version 26.0 SPSS Statistic software version 26.0 SPSS Statistics 26.0 for Mac Statistical Package for the Social Sciences (SPSS) Statistics 26.0 SPSS Advanced Statistics Version 26 SPSS Statistics for Window, Version 26.0

2 748 protocols using spss statistics 26

Classroom CO2 and Temperature Analysis

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First, the measurement data of CO₂ and air temperature was extracted from the HOBOs and imported to IBM SPSS Statistics 26.0 (SPSS Inc. Chicago, IL, USA). Then the imported data was screened based on Z-scores, where all the data points with a Z-score (absolute value) higher than three were eliminated as outliers [47 ]. The information collected through the technical questionnaires, inspections, interviews, classroom checklists, and observational forms were manually screened and typed in IBM SPSS Statistics 26.0. All the subsequent statistical analyses were also performed with IBM SPSS Statistics 26.0.
It needs to be noted that for the data analyses, C7, C9 (C9′), C20 (C20’), C30 were excluded because they only had one occupied lesson during at least one of the school visits. C31 was excluded because the indoor CO₂ concentration was most of the time lower than the average outdoor level during the second school visit, which was considered a measurement error. Therefore, the results presented in this paper include 31 classrooms.

Ding E., Zhang D., Hamida A., García-Sánchez C., Jonker L., de Boer A.R., Bruijning P.C., Linde K.J., Wouters I.M, & Bluyssen P.M. (2022). Ventilation and thermal conditions in secondary schools in the Netherlands: Effects of COVID-19 pandemic control and prevention measures. Building and Environment, 229, 109922.

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Thermal Comfort and Learning Performance in Online Education

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All the measurement data of air temperature, skin temperature, relative humidity, and air velocity were imported to IBM SPSS Statistics 26.0 (SPSS Inc. Chicago, IL, USA). Then the data was checked based on Z-scores, and all outliers (the absolute Z-scores higher than three) were filtered out [47 (link)]. The information collected through the questionnaires was manually checked and typed in IBM SPSS Statistics 26.0. All the data were analyzed using two types of methods - descriptive analysis and correlation analysis-with IBM SPSS Statistics 26.0. First, the mean, standard deviation (SD), minimum, and maximum values of all the measurement data and the continuous data collected through questionnaires (personal characteristics, thermal sensation, and performance evaluations) were calculated per group. While for questions about thermal comfort, the frequencies of each answer were calculated per group. Then, to check the influence of personal characteristics and the length of lessons on students' thermal comfort during online learning and the influence of thermal comfort and the length of lessons on students’ online learning performance, a series of Chi-square tests, Pearson correlations, and t-tests (depending on the type of variables) were performed. A p-value less than 0.05 means statistically significant.

Wong L.T., Chan M.T., Zhang D, & Mui K.W. (2023). Impact of thermal comfort on online learning performance. Building and Environment, 236, 110291.

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Analyzing Microbial Succession and Metabolites

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Amplicon sequencing data were analyzed using QIIME (V.1.8) (52 (link)). Analysis of variance (ANOVA) was calculated by using SPSS Statistics 26 (IBM SPSS Statistics, Chicago, IL) to determine differences between different fermentation groups. Sankey diagram was identified by Hmisc (version 4.7-0), networkD3 (version 0.4), and plotly (version 4.10.2) packages in R studio (version 3.6.1). Kruskal-Wallis H test was calculated in SPSS Statistics 26. The mantel test correlation was analyzed using the ggcor packages (version 0.9.8.1), and the statistical analyses (significant differences and Procrustes analysis) were calculated by ANOSIM using vegan package (version 2.6-4) in R studio (version 3.6.1) (53 (link)). The mantel test (mantel’ R- and P-value) was calculated using the vegan package (version 2.6-4) to assess Pearson’s correlation between abundances of metabolites and microbial succession (deterministic assembly and stochastic assembly) in R studio (version 3.6.1). The heatmap of MST and succession-related microbiota was figured using TBtools (version 1.108). The relationships between inoculation conditions, microbial succession and metabolites were calculated by establishing SEM using AMOS 21 in SPSS Statistics 26.

Ban S., Cheng W., Wang X., Niu J., Wu Q, & Xu Y. (2024). Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making. mSystems, 9(2), e00586-23.

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Statistical Analysis of Survival Curves

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Here, SPSS Statistics 26.0 (IBM SPSS, Chicago, IL, USA) and GraphPad prism V8.0.2 (GraphPad Software, San Diego, CA, USA) software were used for statistical analyses. Survival curves were established using K–M analysis and compared using a two-tailed log-rank test. Univariate and multivariate Cox proportional-hazards regression models were performed using IBM SPSS Statistics 26. All experimental data are presented as (mean ± standard deviation). Differences between groups were compared using Student’s t-test or one-way analysis of variance. * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001 were considered statistically significant.

Sun C., Huang X., Li J., Fu Z., Hua Y., Zeng T., He Y., Duan N., Yang F., Liang Y., Wu H., Li W., Zhang Y, & Yin Y. (2023). Exosome-Transmitted tRF-16-K8J7K1B Promotes Tamoxifen Resistance by Reducing Drug-Induced Cell Apoptosis in Breast Cancer. Cancers, 15(3), 899.

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Statistical Analysis of Research Data

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Statistical analysis was performed using Excel and SPSS Statistics 26.0 (IBM SPSS, Chicago, IL). Measurement data, such as age, were expressed in mean differences ± standard deviation, and compared by the independent sample t-test if they were normally distributed. For those who did not conform to normal distribution, nonparametric Mann–Whitney U test was applied. The enumeration data, which was displayed by the number of cases (percentage), were analyzed by the χ² test between the 2 groups. If the value of expected cases in 1 cell was ≥1 but <5, we adopted continuity-adjusted formula for chi-square test. Fisher Exact test was used if a cell had few expected cases (i.e.,<1) in the table. A 2-tailed P < .05 was considered to indicate statistical significance. The pie charts were completed by SPSS Statistics 26.0 according to the number of cases in different groups.

Wang T., Bai Y., Bai L, & Wang N. (2023). The acceptance and hesitancy of COVID-19 vaccination among chronic obstructive pulmonary disease (COPD) patients. Medicine, 102(26), e33923.

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Physicochemical and Sensory Analysis of Food

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Data obtained from the physicochemical analyses, TPC, and RSA were analysed by one-way ANOVA with Duncan’s post hoc test at a 95% confidence level using SPSS Statistics 26 software (IBM-SPSS, Inc., Chicago, IL, USA).
The Kruskal–Wallis test (test H) and one-way ANOVA were used to process the values obtained by the consumer acceptance test using the SPSS Statistics 26 software (IBM-SPSS Inc., Chicago, IL, USA).

Cantele C., Tedesco M., Ghirardello D., Zeppa G, & Bertolino M. (2022). Coffee Silverskin as a Functional Ingredient in Vegan Biscuits: Physicochemical and Sensory Properties and In Vitro Bioaccessibility of Bioactive Compounds. Foods, 11(5), 717.

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Selenium Source Effects on Biochemistry

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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.

Huang Q., Wang S., Yang X., Han X., Liu Y., Khan N.A, & Tan Z. (2023). Effects of organic and inorganic selenium on selenium bioavailability, growth performance, antioxidant status and meat quality of a local beef cattle in China. Frontiers in Veterinary Science, 10, 1171751.

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Comparative Statistical Analysis of Omics Data

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Data groups (two groups) with normal distributions were compared using the two-sided unpaired Student’s t-test. Comparisons between multiple groups were assessed by a one-way ANOVA with Turkey’s multiple comparisons. Mann-Whitney-Wilcoxon test was used for comparison of different groups in analysis results from scRNA-seq data. The association between the abundance of isoleucine and the serum creatinine (fold of pre-operation) was determined by Pearson correlation analysis. ∗P<0.05; ∗∗P<0.01; ∗∗∗P<0.001; ∗∗∗∗P<0.0001; ns, not significant. Data are expressed as mean ± SEM. The receiver operating characteristic (ROC) curve analysis were conducted for isoleucine with the area under mass spectrum peak by SPSS Statistics 26, and their predictive abilities were assessed using the AUC. Statistical analysis was performed with GraphPad Prism 8.3.1 (Graphpad Software, USA, RRID: SCR_002798) and SPSS Statistics 26 (IBM, USA, RRID: SCR_016479).

Shan D., Wang Y.Y., Chang Y., Cui H., Tao M., Sheng Y., Kang H., Jia P, & Song J. (2023). Dynamic cellular changes in acute kidney injury caused by different ischemia time. iScience, 26(5), 106646.

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Anonymous Likert Survey Analysis

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Researchers accessed anonymous responses through Microsoft Forms with responses downloaded to Microsoft Excel. Both complete and partially completed forms were eligible for analysis. Questions with responses rated on a five-point Likert scale were coded with 1 being Strongly Disagree and 5 being Strongly Agree and then downloaded into SPSS Statistics 26 for analysis. Collated responses were represented as number (percentage) for categorical data and mean (standard deviation) for Likert scale responses as calculated by SPSS Statistics 26. Qualitative data were thematically analysed, and direct responses were utilised as appropriate.

Bernaitis N., Grant G, & Hall S. (2022). Provision of externally accredited immunisation certification within an Australian Professional Pharmacy University programme: a survey of graduates on benefits and outcomes. The International Journal of Pharmacy Practice, 30(3), 273-278.

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Interrater Reliability Analysis Using Fleiss' Kappa

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Statistical analysis was performed with SPSS Statistics 26 (SPSS Inc., Chicago, IL, USA) and Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA). The interrater reliability was calculated using Fleiss' Kappa.
Interrater reliability was determined for every abnormality by using Fleiss' Kappa coefficient: <0.00 poor, 0.00-0.20 slight, 0.21-0.40 fair, 0.41-0.60 moderate, 0.61-0.80 substantial, 0.81-1.00 almost perfect agreement [28] . Statistical analysis was performed with SPSS Statistics 26 (SPSS Inc.) and Microsoft Excel 2019 (Microsoft Corporation).

Uhrmann M.F., Peto T., Bullmann T., Andrassi-Darida M., Schumann M., Schmitz-Valckenberg S., Holz F.G, & Lorenz B. (2022). The Feasibility of Using Ultra-Widefield Retinal Imaging to Identify Ocular Pathologies amongst Those with Systemic Medical Disease Attending a Tertiary Healthcare Facility at a University Hospital. Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde, 245(5).

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