Spss package 22

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SPSS package 22 is a statistical software tool developed by IBM. It provides a comprehensive set of features for data analysis, including data manipulation, statistical modeling, and visualization. The package is designed to help users analyze and interpret complex data from a variety of sources.

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Statistical Package for Social Science version 22.0 (SPSS, SPSS 22.0 statistics package SPSS Statistics version 22.0 software package SPSS 22.0 software package for Windows Statistical Package for the Social Sciences (SPSS) 22.0 Statistical Package for the Social Sciences (SPSS) Statistics for Windows, version 22.0 Statistical Package for Social Sciences (SPSS) 22.0 Statistical Package for Social Sciences (SPSS) version 22.0 SPSS package for Windows, version 22.0 Statistical Package for the Social Sciences (SPSS) software version 22.0

8 protocols using spss package 22

Statistical Analyses of Behavioral Data

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Statistical Analyses were performed using the Statistical Package for the Social Sciences (IBM SPSS Package 22). All data was analyzed using mixed effects models with subject as a random effect and task-dependent factors as fixed effects. Effect sizes were calculated for all pairwise comparisons using Hedge’s g (g) (Hedges, 1981 (link)). The normality of the dependent variables and the residuals from the model were tested using the Shapiro–Wilk test. If the residuals were not normal, the variables were transformed. All RT data, as well as speed and annulus traced in circle tracing, were transformed by log₁₀ before any statistical analyses were carried out. The number correct measure in the emotion recognition task was transformed using a boxcox transformation.
For each task the Pearson’s correlation coefficient (r) was calculated for the mean of each dependent variable for each subject for sessions 1–2 and sessions 2–3 to determine the test–retest reliability of each measure. For the measures that did not meet normality assumptions (p < 0.05 for the Shapiro–Wilk test) Spearman correlations were calculated. All reported p-values are two-tailed unless stated otherwise.

Palmer C.E., Langbehn D., Tabrizi S.J, & Papoutsi M. (2018). Test–Retest Reliability of Measures Commonly Used to Measure Striatal Dysfunction across Multiple Testing Sessions: A Longitudinal Study. Frontiers in Psychology, 8, 2363.

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Chewing Habits and HPV Infection

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Data were analyzed for association using the SPSS package 22 (IBM, Rochester, USA). HR-HPV status and p16 positivity were checked for significance with chewing habits overall and chewing substances (betel quid, gutka, and areca nut) by logistic regression. Odds ratios and their respective 95% confidence intervals (CI) were estimated using univariate and multivariate logistic regression, keeping this variable as the dependent variable. A cutoff p-value<0.2 was chosen in univariate regression to perform multivariate regression analysis [16 (link)]. An insignificant p-value of chi-square in Hosmer and Lemeshow Test validated the goodness-of-fit of the model. The Chi-square test was used to study the correlation between HR-HPV positivity by PCR and IHC p16. All analyses were set as two-sided, and a p<0.05 was considered significant.

Anwar N., Chundriger Q., Awan S., Moatter T., Ali T.S., Abdul Rasheed M, & Pervez S. (2024). Prevalence of high-risk human papillomavirus in oral squamous cell carcinoma with or without chewing habits. PLOS ONE, 19(5), e0300354.

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Assessing GRS Performance in Survival Analysis

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To assess the performance of the GRS in the data sets, receiver operating characteristic (ROC) curves were used. Survival analysis was performed with Kaplan-Meier curves and log-rank test. Cutoff values to define high and low GRS values were calculated using the value with the highest Youden index and differed for tumor and liver. Univariate association with survival was determined using Cox regression. The significant variables with P values < .05 were included in a multivariable Cox regression. Statistics were performed using SPSS package 22 (IBM).

Dekervel J., Popovic D., van Malenstein H., Windmolders P., Heylen L., Libbrecht L., Bulle A., De Moor B., Van Cutsem E., Nevens F., Verslype C, & van Pelt J. (2016). A Global Risk Score (GRS) to Simultaneously Predict Early and Late Tumor Recurrence Risk after Resection of Hepatocellular Carcinoma. Translational Oncology, 9(2), 139-146.

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Pain Assessment in Clinical Trials

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Collected data were subject to statistical analysis performed with the use of SPSS package 22.0 (SPSS Inc., Chicago IL, USA). The clinically significant VAS difference of 14 mm was accepted for sample size calculations. In the pilot study, the standard deviation of 24 h dynamic VAS was 22.5 mm. Using these data, we determined that we would need 86 patients to achieve 90% power with 5% alpha. Each group should have included a minimum of 43 patients. We assumed that 10% would be lost to follow-up. Data are presented as the mean ± standard deviation, mean (CI), median (interquartile range), or number (%). Groups with normally distributed data were compared with an independent t-test, non-normally distributed data were compared with the Mann–Whiney U test, and categorical variables were compared with the χ² test or Fisher’s exact test. Changes in the pain severity in both groups were analyzed using a general linear model (GLM) with repeated measures. P values were not adjusted for multiple comparisons and should be interpreted cautiously. P <0.05 was considered statistically significant.

Lai J., Situ D., Xie M., Yu P., Wang J., Long H, & Lai R. (2021). Continuous Paravertebral Analgesia versus Continuous Epidural Analgesia after Video- Assisted Thoracoscopic Lobectomy for Lung Cancer: A Randomized Controlled Trial. Annals of Thoracic and Cardiovascular Surgery, 27(5), 297-303.

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One-way ANOVA Protocol Evaluation

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This study was conducted on the basis of a completely randomized design, and a one-way ANOVA was used for data analysis using SPSS package 22.0 (SPSS Inc., Chicago, IL, USA). All experimental assessments were performed in triplicate, and data was expressed as the mean ± standard deviation (SD). The comparison of means was carried out by Duncan’s multiple range tests with a statistical significance at p < 0.05.

Zamani A., Khajavi M., Abedian Kenari A., Haghbin Nazarpak M., Solouk A., Esmaeili M, & Gisbert E. (2023). Physicochemical and Biochemical Properties of Trypsin-like Enzyme from Two Sturgeon Species. Animals : an Open Access Journal from MDPI, 13(5), 853.

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Postural Control in Older Adults and Stroke

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Data were screened for normal distribution using the Shapiro–Wilk test. A two by two analysis of variance (ANOVA) was conducted to test the effect of the intervention on the psychophysiological workload, assessed by HRV variables. A one-way ANOVA was conducted to test the subjective fatigue after the intervention task, assessed by the NASA Task Load Index. Two separate multivariate analyses of variance (MANOVA) were conducted with time (pre- vs. post-mental fatigue task), group (older adults, stroke group, and control group), task (single vs. dual-task), and SOT conditions (EOSS, ECSS, EOSSV) as independent variables for each of the dependent postural control measures–jerk of COM and RMS of COM. The significant main effects and interactions were resolved using planned repeated-measures ANOVA and were followed up with posthoc tests. To verify the magnitude of the changes after the intervention, the effect size (ES) was calculated based on Cohen’s d. Effect size is classified as small (d = 0.0–0.20), medium (d = 0.30–0.50), and large (d = 0.50–0.80). The level of statistical significance was set at p ≤ 0.05. Data were analyzed using the SPSS package 22.0 (SPSS Inc., Chicago, IL, USA).

Varas-Diaz G., Kannan L, & Bhatt T. (2020). Effect of Mental Fatigue on Postural Sway in Healthy Older Adults and Stroke Populations. Brain Sciences, 10(6), 388.

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Correlating Fish Properties and Metals

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Excel 2010 (Microsoft, Redmond, Washington DC, USA) and SPSS package 22, (SPSS, Chicago, IL, USA) were used for data processing and analysis. The relationship between the properties of fish and metals were considered significant by the Pearson correlation when p < 0.05. The tables and figures were carried out using OriginPro 8 (Electronic Arts Inc, Redwood, Washington DC, USA) and Coreldraw X7 (Corel, Ottawa, Canada).

Li J., Miao X., Hao Y., Xie Z., Zou S, & Zhou C. (2020). Health Risk Assessment of Metals (Cu, Pb, Zn, Cr, Cd, As, Hg, Se) in Angling Fish with Different Lengths Collected from Liuzhou, China. International Journal of Environmental Research and Public Health, 17(7), 2192.

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Stent Length Measurement Validation

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Categorical variables are presented as frequencies, and continuous variables are reported as median (interquartile range, IQR) and mean ± SD (for normally distributed variables). For the comparisons of continuous variables, Wilcoxon signed-ranks test with exact method and a two-tailed paired-samples t test were used. Correlations between variables are described with the use of Spearman and Pearson correlations. Variables with p < 0.1 were used in the general linear model to obtain the predictors of the differences between the stent length measured by IVUS, E-E length, and the M-L. All statistical tests were two-sided, and p value < 0.05 was considered significant. All analyses were performed using SPSS package 22 (SPSS Inc., Chicago, IL, USA).

Algowhary M., Taha S., Hasan-Ali H, & Matsumura A. (2019). In vivo measurement of stent length by using intravascular ultrasound. The Egyptian Heart Journal, 71, 32.

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