Spss statistics software

Manufactured by IBM

Sourced in United States, Japan, United Kingdom, Germany, Australia, Belgium, Poland

SPSS Statistics is a software package used for statistical analysis. It provides a wide range of analytical tools for data management, statistical analysis, and visualization. The core function of SPSS Statistics is to enable users to analyze and interpret data effectively.

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Lab products found in correlation

Prism software, by GraphPad (33 mentions) Spss statistics for window, by IBM (28 mentions) Prism 8, by GraphPad (17 mentions) Prism 7, by GraphPad (15 mentions) Prism 6, by GraphPad (14 mentions) Spss statistic, by IBM (12 mentions) Prism 9, by GraphPad (12 mentions) Prism 5, by GraphPad (8 mentions) R software, by R Project for Statistical Computing (8 mentions) Origin software, by OriginLab (6 mentions) Matlab, by MathWorks (5 mentions) Simca software, by Sartorius (4 mentions) R version 3, by R Project for Statistical Computing (4 mentions) Spss statistics for windows version 25, by IBM (3 mentions) Design expert software, by Stat-Ease (3 mentions) Originpro software, by OriginLab (3 mentions) Quantity one software, by Bio-Rad (3 mentions) Statview software, by Abacus (3 mentions) Stata statistical software, by StataCorp (2 mentions) Stata software, by StataCorp (2 mentions)

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SPSS Statistics (9452 citations) SPSS statistic software (122 citations) SPSS statistic software version 20 (11 citations) SPSS statistic software version 25 (11 citations) SPSSTM (8 citations) SPSS statistic software package (6 citations) SPSS softwares (4 citations) SPSS 17.0 statistic software (4 citations) SPSS Statistic software, version 18.0 (3 citations) SPSS 13.0 statistic software (2 citations)

3 200 protocols using spss statistics software

Regression Analysis of Consistency and Preference

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Regression analyses between the consistency indices and population preference indices were conducted using IBM SPSS statistics software (version 25).
The LOOCV approach was used to assess the accuracy of the regression model to predict population preference index, and the correlation coefficient and root mean square error were estimated. This LOOCV approach consists of training a model with the complete dataset except for one point, and then that point is predicted by the model, enabling accuracy statistics to be estimated. The process is iterated 18 times, and the root mean square error is calculated. Regression analyses were conducted with IBM SPSS statistics software (version 25).
Regression analyses between compression rates and population preference indices or IPs were conducted with IBM SPSS statistics software (version 25). The statistical significance level was set to p = 0.05 for all statistical tests.

Hoshi A., Hirayama Y., Saito F., Ishiguro T., Suetani H, & Kitajo K. (2023). Spatiotemporal consistency of neural responses to repeatedly presented video stimuli accounts for population preferences. Scientific Reports, 13, 5532.

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Phytochemical Classification of Pu-erh Tea

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All data were subjected separately to PCA and HCA to detect whether phytochemicals could be used to classify AT and TT. HCA and PCA were respectively performed using IBM SPSS Statistics software (Version 22, SPSS Inc., Chicago, IL, USA). An analysis of variance (ANOVA) was also used by IBM SPSS Statistics software (Version 22, SPSS Inc., Chicago, IL, USA) to determine significant differences (p < 0.05). Regarding the establishment of discriminant models, stepwise Fisher discriminant analysis (SFDA) was performed based on the model set. The model set consisted of 20 ATs and 40 TTs chosen randomly from 80 samples, and the other samples (10 ATs and 10 TTs) were the training set to check the accuracy of the model by the leave-one-out method (LOO). Based on discriminant variables from SFDA, DTA was further used to detect the classification of AT and TT, thereby establishing the model. The optimal model of AT and TT was selected by performance indexes and distance measurement. Both SFDA and DTA were applied by IBM SPSS Statistics software (Version 22, SPSS Inc., Chicago, IL, USA), and the evaluation performance indexes including accuracy, precision, recall, and F-score were calculated through the confusion matrix, as shown in Table 2. The formulas were as follows [30 (link)]:

Zhang S.R., Shi Y., Jiang J.L., Luo L.Y, & Zeng L. (2022). Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics. Foods, 11(5), 680.

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Comparative Analysis of Treatment Efficacy

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Data are expressed as mean ± standard deviation (SD). ANOVA and Students T-test were used to determine the statistically significant differences between the means of three or more groups and between two groups, respectively, using IBM SPSS Statistics software. Prior to performing ANOVA, the homogeneity of variance was tested using Levene’s test in IBM SPSS Statistics software.

Chen X., He Y., Liu L., Zhu X., Sen B, & Wang G. (2022). Nitrogen Starvation Enhances the Production of Saturated and Unsaturated Fatty Acids in Aurantiochytrium sp. PKU#SW8 by Regulating Key Biosynthetic Genes. Marine Drugs, 20(10), 621.

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Assessing Survey Data Reliability

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We considered the average value to calculate the expected score for each question. Descriptive statistics were generated using the mean and standard deviations or counts/frequencies where appropriate, using IBM SPSS Statistics software (version 22.0; IBM Corp., Armonk, NY, USA). To assess internal consistency reliability for structured/closed-ended survey items graded on the same Likert scale, Cronbach’s alpha calculations were also performed using IBM SPSS Statistics software (version 22.0; IBM Corp., Armonk, NY, USA). The statistical tests and percent stacked bar chart were performed with R 4.1.3 (http://www.r-project.org/).

Chen Y., Qi H., Qiu Y., Li J., Zhu L., Wang H., Gao X, & Jiang G. (2022). Bullet screen in pre-clinical undergraduate pharmacology education: a survey study. BMC Medical Education, 22, 813.

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Genotype-Dependent Carcass and Meat Quality

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Results are presented as the mean ± SE. All data were tested for normality using the Shapiro–Wilk test. Individual data of growth, plasma, carcass and meat quality traits were analysed by one-way ANOVA with genotype as the main effect. For the carcass data, hot carcass weight was included as a covariate in the model. The SPSS Statistics software (IBM SPSS Statistics for Windows, v24.0; IBM Corp., Armonk, NY, USA) was used for data analysis. Mean differences were considered significant when p < 0.05, and values between 0.05 and 0.10 were considered trends.
To determine if the gene expression values were significantly different between the experimental groups, a student’s t-test was executed using IBM SPSS Statistics software (IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.) with an established significance level of p < 0.05. Equal variances of the samples were checked with Levene’s Test for Equality of Variances with values lower 0.05 not considered as equal variances and another Independent Samples Test was performed assuming no equal variances. Equal variances were not assumed for WDR91 (F = 7.643; p = 0.024) and LEP (F = 8.929; p = 0.017). Pearson correlation coefficients and associated p-values were also estimated.

Albuquerque A., Óvilo C., Núñez Y., Benítez R., López-Garcia A., García F., Félix M.D., Laranjo M., Charneca R, & Martins J.M. (2021). Transcriptomic Profiling of Skeletal Muscle Reveals Candidate Genes Influencing Muscle Growth and Associated Lipid Composition in Portuguese Local Pig Breeds. Animals : an Open Access Journal from MDPI, 11(5), 1423.

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Statistical Analysis of Genotype Traits

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All data were subjected to analysis of variance (ANOVA) using completely randomized design (CRD) and a comparison of treatment means was performed using Duncan’s multiple range test (DMRT) at P < 0.05 using IBM SPSS Statistics software. The mean values of individual genotypes for each trait were subsequently used for analyzing summary statistics and graphs using MS Excel software. Histogram and correlation among traits were analyzed by using IBM SPSS Statistics software. Hierarchical cluster analysis was performed using Euclidean distance measure following Unweighted Pair Group Method with Arithmetic Averages (UPGMA) method by using PAleontological STatistics (PAST) software (Hammer, Harper & Ryan, 2001 (link)). The flow of work done is depicted in Fig. S1.

Adhikari S., Kumari J., Bhardwaj R., Jacob S., Langyan S., Sharma S., M. Singh A, & Kumar A. (2023). Unlocking the potential of ancient hexaploid Indian dwarf wheat, Tritium sphaerococcum for grain quality improvement. PeerJ, 11, e15334.

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Variability Analysis of Wasp Venom

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Statistical analysis was applied to demonstrate the variability of the 134 batches of wasp venom samples. On the basis of the HPLC-DAD data, the similarity of these samples was calculated using software named Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2012.130723, Chinese Pharmacopoeia Commission, Beijing, China), which was recommended by the National Medical Products Administration (NMPA). Principal component analysis (PCA) was performed on the common chromatographic peaks in the HPLC fingerprints using the IBM SPSS Statistics software (IBM, Version 23, New York, NY, USA). Simultaneously, the hierarchical clustering analysis (HCA) was based on squared Euclidean distance to distinguish samples utilizing IBM SPSS Statistics software (IBM, Version 23, New York, NY, USA).

Zhou S.T., Luan K., Ni L.L., Wang Y., Yuan S.M., Che Y.H., Yang Z.Z., Zhang C.G, & Yang Z.B. (2019). A Strategy for Quality Control of Vespa magnifica (Smith) Venom Based on HPLC Fingerprint Analysis and Multi-Component Separation Combined with Quantitative Analysis. Molecules, 24(16), 2920.

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Statistical Analyses for Survival and Correlation

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We used GraphPad Prism Version 8 for data analysis and plotting. For survival analyses, Kaplan–Meier curves were calculated using IBM SPSS statistics software. To determine differences, Log-rank tests were performed. Kruskal–Wallis tests were performed to determine the differences between the cell lines in the in vitro experiments. Correlation analyses were also performed using IBM SPSS statistics software. The Cox proportional hazards model was used to calculate the hazard ratios of CIC rates in central cancer and clinicopathological characteristics. Covariates with p < 0.3 in univariate analysis were included in multivariate Cox regression. The proportional hazards assumption was tested by the visual inspection of log minus log curves and was found to be satisfactory for all multivariate covariates. p-values < 0.05 were considered statistically significant.

Bauer M.F., Hildebrand L.S., Rosahl M.C., Erber R., Schnellhardt S., Büttner-Herold M., Putz F., Ott O.J., Hack C.C., Fietkau R, & Distel L. (2022). Cell-In-Cell Structures in Early Breast Cancer Are Prognostically Valuable. Cells, 12(1), 81.

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Binary Logistic Regression in SPSS

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Data was analyzed using binary logistic regression, using IBM SPSS statistics software. Collinearity was checked using IBM SPSS statistics software. For categorical variables, one category was required to be used to compare the remaining categories' odds ratios (O.R.). An O.R. greater than 1 indicates that a variable is more likely to be associated with the noted outcome, and an O.R. less than 1 indicates that the variable is less likely to be associated with the outcome. Each O.R. was reported with a p value. A p value of <0.05 was used to determine statistical significance of the results.

Carroll A., Carman R., Bannerman T, & Pancholi P. (2024). Carbapenemase producing Enterobacterales at a large teaching hospital in Ohio: comparison to state surveillance and retrospective analysis of patient characteristics. Infection Prevention in Practice, 6(2), 100366.

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Surgical Site Infection Risk Factors

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Statistical analysis was performed using IBM SPSS Statistics software (version 22.0; IBM Corp., Armonk, NY USA). For categorical variables, results are expressed as n (%) and compared using Pearson’s chi-squared test or Fisher’s exact test. For continuous variables, results are reported as mean±standard deviation (SD) or median and interquartile (IQR) and compared using Student’s t-test and Mann-Whitney U test, as appropriate. Logistic regression models were used to explore independent risk factors for surgical site infections. Univariate analysis was done for each risk factor, and all biologically plausible variables with P value of <0.10 in the univariate analysis were included in the logistic regression model during the multivariate analysis. The results are shown as odds ratio with 95% confidence intervals. All analyses were processed using IBM SPSS Statistics software (version 20.0; IBM Corp., Armonk, NY, USA). Differences were considered to have reached the significance level with a two-tailed P<0.05.

Chien Y.S., Chen H.T., Chiang H.T., Luo T.S., Yeh H.I., Sheu J.C, & Li J.Y. (2024). Effect of Standardized Bundle Care and Bundle Compliance on Reducing Surgical Site Infections: A Pragmatic Retrospective Cohort Study. Medical Science Monitor : International Medical Journal of Experimental and Clinical Research, 30, e943493-1-e943493-8.

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