Spss 22.0 statistical analysis

Manufactured by IBM

Sourced in United States

SPSS 22.0 is a statistical analysis software package developed by IBM. It provides tools for data management, analysis, and visualization. The software is designed to handle a wide range of statistical techniques, including descriptive statistics, regression analysis, and hypothesis testing.

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SPSS 22.0 (7633 citations) SPSS 22.0 statistical (297 citations) SPSS 22.0 statistical analysis software (19 citations) SPSS statistical 22.0 software (9 citations) SPSSTM (8 citations) SPSS 22.0 IBM (5 citations) SPSS 22.0 analysis (4 citations)

8 protocols using spss 22.0 statistical analysis

Immune Profiles Predict Composite Endpoints

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SPSS 22.0 statistical analysis software was used for data analysis. The Shapiro-Wilk normality test and the variance homogeneity test were used for measurement data. The measurement data with normal distribution were described as means ± standard deviations, and the skewed data with non-normal distribution were described as medians (P25–P75). The differences between the two endpoint groups were compared.
The measurement data with normal distribution and homogeneity of variance were compared between groups using a two-independent-samples t-test. Measurement data satisfying the normal distribution but not the homogeneity of variance criteria were compared between groups using the Satterthwaite t-test. According to the applicable conditions, the chi-square test, corrected chi-square test, and Fisher’s exact probability method were used. The Spearman rank test was used to analyse the correlations between measurement data. The influence of lymphocyte subsets on composite endpoint events was analysed by stepwise forward logistic regression. ROC curves were used to calculate the cut-off points for classifying the T lymphocyte subsets. Finally, Kaplan-Meier survival curves were used to analyse the influence of immune parameters on the occurrence of composite endpoint events; α = 0.05 was considered significant.

Zhang W., Li L., Liu J., Chen L., Zhou F., Jin T., Jiang L., Li X., Yang M, & Wang H. (2020). The characteristics and predictive role of lymphocyte subsets in COVID-19 patients. International Journal of Infectious Diseases, 99, 92-99.

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Statistical Analysis of CRC Molecular Markers

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SPSS 22.0 statistical analysis software was employed for statistical analysis of the experimental data. The significance of differences between groups was estimated by Student’s t test. Additionally, multiple group comparisons were analyzed with one-way ANOVA. Statistically significant correlations between SNHG6 and ULK1 expression levels in CRC tissues and cell lines were analyzed by Pearson’s correlation analysis. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001 were considered significant; ns indicates no significance.

Wang X., Lan Z., He J., Lai Q., Yao X., Li Q., Liu Y., Lai H., Gu C., Yan Q., Fang Y., Zhang Y., Li A, & Liu S. (2019). LncRNA SNHG6 promotes chemoresistance through ULK1-induced autophagy by sponging miR-26a-5p in colorectal cancer cells. Cancer Cell International, 19, 234.

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Survival Analysis of Treatment Outcomes

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SPSS 22.0 statistical analysis software was used to statistically process the collected data. The median survival time and survival rate were calculated by Kaplan Meier method (K-M method), the survival curve was drawn, and the survival curve was compared by log rank sum test (log rank method). Cox stepwise regression model was used for multivariate analysis. Inspection level α=0.05.

Ran F, & Liu Q. (2022). Prognostic factors of 112 elderly patients with advanced non-small cell lung cancer. Pakistan Journal of Medical Sciences, 38(6), 1674-1679.

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COVID-19 Self-Disclosure, Peer Relationships, and Loneliness

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In this study, Excel was first used to clean, sort, and code the data set, and then the data set was exported to SPSS.
First, descriptive statistics and correlations between the main variables were conducted. Second, to examine the relationship between COVID-19 self-disclosure and loneliness, a serial mediation was performed with COVID-19 self-disclosure as the independent variable, peer relationship as mediators in sequence, and loneliness as the dependent variable. Finally, SPSS 22.0 statistical analysis software was used to conduct variance analysis, independent sample t-test, Pearson correlation analysis, and simple effect analysis on the data. Confirmatory factor analysis was conducted on the data through AMOS 21.0 software and Bootstrap software to establish a structural equation model.

Chen L., Cheng R, & Hu B. (2021). The Effect of Self-Disclosure on Loneliness in Adolescents During COVID-19: The Mediating Role of Peer Relationships. Frontiers in Psychiatry, 12, 710515.

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Statistical Analysis of Biomarker Accuracy

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SPSS 22.0 statistical analysis software package (Chicago, USA) was applied for data analysis. The χ2 test was adopted for comparison of categorical variables and the diagnostic accuracies of markers. For the combinations of 2 markers, the test was considered positive if either or both exceeded the cutoff value. The Kaplan–Meier method was used to analyse the survival rate; the log rank test was applied for single-factor analysis; the Cox proportional hazard model was employed for multiple-factor analysis; and logistic analysis was used to analyse the relationship between EGFR gene mutation and multiple factors. P < 0.05 was regarded as statistically significant.

Wu Z., Dai Y, & Chen L.A. (2019). The Prediction Of Epidermal Growth Factor Receptor Mutation And Prognosis Of EGFR Tyrosine Kinase Inhibitor By Serum Ferritin In Advanced NSCLC. Cancer Management and Research, 11, 8835-8843.

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Statistical Analysis of Experimental Data

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SPSS 22.0 statistical analysis software was employed for data analysis, and the measurement index was expressed as mean ± SEM. A t-test was adopted for the data in conformity to normal distribution. The comparison of categorical count indexes was tested by the χ² tests or Fisher's exact probability method. The hypothesis testing used two-sided testing to obtain test statistics and their corresponding p values, and p < 0.05 was considered as the standard of significant difference.

Ke L., Chen L., Yaling Y., Can G., Jun L, & Chuan Z. (2022). Investigation on the Pathological Mechanism of Frequent Exacerbators With Chronic Obstructive Pulmonary Disease Based on the Characteristics of Respiratory Flora. Frontiers in Medicine, 8, 816802.

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Risk Factors for Depression in Medical Staff

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SPSS 22.0 statistical analysis software was used for all analyses. Proportions or mean values with standard deviations were used to describe basic demographic data, relative risk factors, and PCL-C, SAS, and SDS scores. A univariate logistic regression model was then used to select significant factors (P < .05) that were then used in the multivariate logistic regression analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to analyze the risk factors. Independent associations between the risk factors and the SAS and SDS scores were also explored using single linear regression. Multiple linear regression was used to detect the risk factors related to serious depression of medical staff. All results were considered significant when P < .05 (2-sided).

Zhou C., Shi L., Gao L., Liu W., Chen Z., Tong X., Xu W., Peng B., Zhao Y, & Fan L. (2018). Determinate factors of mental health status in Chinese medical staff: A cross-sectional study. Medicine, 97(10), e0113.

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Recurrence Risk Factors in Acute Ischemic Stroke

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SPSS 22.0 statistical analysis software was used for data analysis. Continuous variables were tested for normality using the Kolmogorov‒Smirnov test. Normally distributed measures were expressed as the mean±standard deviation, t tests were used for comparisons between two groups, and ANOVA was used for comparisons between multiple groups. The time to first recurrence in patients who underwent intravenous thrombolysis for AIS was analyzed by the Kaplan‒Meier method. Stroke recurrence rates were compared between patients with and without AD. The association between stroke recurrence and potential factors in patients who underwent intravenous thrombolysis for AIS was assessed by the Cox proportional risk model with and without adjustment for the prespecified factors mentioned above. Additionally, the hazard ratios (HRs) and 95% confidence intervals (CIs) of the associations were estimated. A two-sided P value of <0.05 was used as the threshold for statistical significance for all tests. Figures were generated using PowerPoint and GraphPad Prism software (version 8.0).

Cheng Y., Wang Q., Niu G, & Luo C. (2023). Correlation of Atherosclerotic Dyslipidemia with Long-Term Stroke Recurrence in Patients Undergoing Intravenous Thrombolysis for Acute Ischemic Stroke. International Journal of General Medicine, 16, 1621-1629.

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