Statistical package for the social sciences version 19.0 for windows

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Statistical Package for the Social Sciences (SPSS) version 19.0 for Windows is a software package used for statistical analysis. It provides tools for data management, analysis, and presentation. SPSS version 19.0 supports a wide range of statistical procedures, including descriptive statistics, bivariate statistics, prediction for numerical outcomes, and prediction for identifying groups.

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Version 19.0 (6 citations) 19.0 for Windows (3 citations)

6 protocols using statistical package for the social sciences version 19.0 for windows

Statistical Analysis of Study Data

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A required sample size of 161 participants with a confidence level of 95% and a confidence interval of 5% was calculated assuming a PQR rate of 12% in our study population. In the experimental time-frame, our sample size was expected to exceed this figure.
Descriptive analysis of variables was used to summarize the data. Ordinal and continuous data without normal distribution, based on the Kolmogorov-Smirnov test for normality of the underlying population, are presented as the median and interquartile range. The Mann–Whitney U-test, Chi-square, or Fisher's exact test were used for comparisons. Differences were considered statistically significant when p < 0.05. The data were analyzed using the Statistical Package for the Social Sciences version 19.0 for Windows (SPSS Inc., Chicago, IL, USA).

Ferraz S.M., Moreira J.P., Mendes L.C., Amaral T.M., Andrade A.R., Santos A.R, & Abelha F.J. (2018). Evaluation of the quality of recovery and the postoperative health status after elective surgery. Brazilian Journal of Anesthesiology, 68(6), 577-583.

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Statistical Analysis of Demographic Factors

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Data were analyzed using the Statistical Package for the Social Sciences, version 19.0, for Windows (SPSS Inc., Chicago, IL, USA). Tests of normality and descriptive statistics were performed for all outcome variables as a function of age and gender. Differences in the mean values between the groups for all normally distributed measures were examined using an independent-samples t-test. The measures that did not meet the normal distribution assumptions were compared using the nonparametric Mann–Whitney U test. Statistical significance was set at p ≤ 0.05. Lastly, to analyze the relationship among all variables, Spearman’s correlation coefficients were calculated. The strength of the relationship was set at r < 0.25 indicating a weak effect, r = 0.25–0.5 a moderate effect, r = 0.5–0.75 a strong effect, and r > 0.75 a very strong effect [49 ].

Coradinho H., Melo F., Almeida G., Veiga G., Marmeleira J., Teulings H.L, & Matias A.R. (2023). Relationship between Product and Process Characteristics of Handwriting Skills of Children in the Second Grade of Elementary School. Children, 10(3), 445.

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Analyzing PACG Patients' Psychometric Profiles

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The Statistical Package for the Social Sciences version 19.0 for Windows (SPSS Inc., Chicago, USA) was used to analyze all the data. The results of EPQ and GDT between PACG patients and healthy controls were compared by independent sample t-test for continuous variables and Chi-square test or Fisher's exact test for categorical variables. Correlation relationships were analyzed using Pearson's correlations. A two-sided test with P < 0.05 was considered statistically significant.

Pu H., Wang Y., Wei Q., Ma H.J., Hu P.P., Li S.L., Pang H.B., Tian Y.H, & Wang K. (2017). Decision-making Impairments in Primary Angle-closure Glaucoma Patients. Chinese Medical Journal, 130(12), 1424-1428.

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Predictive Value of Plasma NGAL in Cardiovascular Events

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Statistical analyses were conducted using the Statistical Package for the Social Sciences, Version 19.0 for Windows (SPSS, Chicago, IL, USA). Data are presented as medians and interquartile ranges (IQR). The relationship between the plasma NGAL and other baseline valuables was studied by linear regression analysis. Multivariate regression model including variables with P < 0.05 in the individual model was used to determine which covariates were independently associated with log plasma NGAL levels. Differences in event-free survival by quartile of plasma NGAL were examined using the Kaplan-Meier method and compared using a log-rank test. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated for each factor with Cox proportional hazards analysis. To identify independent predictors of CV events, all baseline variables with P < 0.05 in the univariate analysis were entered into a multivariate model. In addition, to assess if CV events could be predicted more accurately after the addition of plasma NGAL to the model, the C-index for the receiver-operating characteristic (ROC) curves was calculated using a logistic multivariate model. Differences were considered statistically significant at P < 0.05.

Hasegawa M., Ishii J., Kitagawa F., Takahashi H., Sugiyama K., Tada M., Kanayama K., Takahashi K., Hayashi H., Koide S., Nakai S., Ozaki Y, & Yuzawa Y. (2016). Plasma Neutrophil Gelatinase-Associated Lipocalin as a Predictor of Cardiovascular Events in Patients with Chronic Kidney Disease. BioMed Research International, 2016, 8761475.

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Statistical Analysis of Liver Surgery Outcomes

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The computer program "Statistical Package for The Social Sciences" version 19.0 for Windows (SPSS, Inc, Chicago, IL, USA) was used for statistical analysis, while propensity score matching (PSM) was conducted by R (https://www.r-project.org/). The propensity score model of the presence of HS was constructed via stepwise variable selection into a multivariable logistic regression model. Candidate variables included all variables significantly associated with hepatectomy rate from MDT via univariable analysis, with a threshold of P < 0.20 required for initial inclusion and P < 0.10 required to remain in the model.21 (link) The categorical parameters were compared using two-sided Pearson’s χ² test, Fisher’s exact test, as appropriate. Summary statistics on time-to-event variables were calculated according to the Kaplan–Meier method. Difference in prognosis was assessed by Cox regression analysis. All comparisons were two-tailed, and P < 0.05 was considered statistically significant.

Jian M., Chang W., Ren L., Liu T., Chen Y., Wei Y., Lin Q., Xu J, & Qin X. (2019). Predictive And Prognostic Value Of Hepatic Steatosis In Conversion Therapy For Colorectal Liver-limited Metastases: A Propensity Score Matching Analysis. Cancer Management and Research, 11, 8315-8326.

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Statistical Analysis of Experimental Data

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The normality of the data was assessed using Kolmogorov–Smirnov tests. We expressed data in mean ± standard deviation for normally distributed data and median (interquartile range) for nonnormally distributed data. The mean difference between the two groups was tested using the Student's t-test or Mann–Whitney U-test based on data distribution both before and after the intervention period. Effect of intervention was analyzed using paired Student's t-test for dependent samples test or Wilcoxon signed-rank test based on data distribution. We used the Statistical Package for the Social Sciences version 19.0 for Windows (SPSS, Chicago, IL, USA). P <.050 was set as statistically significant.

Subramanian S.K., Sripad V.D., Dharmalingam A., Guhan V.N., Kalidoss V.K., Gautam N., Shankaralingappa A., Rajendran R, & Mohiuddin S.G. (2023). Effect of 4-Week Heartfulness Meditation on Stress Scores, Sleep Quality, and Oxidative and Inflammatory Biochemical Parameters in COVID-19 Patients after Completion of Standard Treatment – A Randomized Controlled Trial. International Journal of Yoga, 15(3), 195-204.

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