Spss version 21.0 statistical software for windows

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SPSS version 21.0 is a statistical software application for Windows that provides tools for data analysis, data management, and data documentation. The software offers a range of statistical procedures to help users analyze and interpret data.

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8 protocols using spss version 21.0 statistical software for windows

Maternal Health Behavior and Information Sources

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All statistical analyses were performed using IBM SPSS Statistical Software version 21.0 for Windows. Background variables and information sources are presented as frequencies, percentages, or means with standard deviation.
For the purpose of analysis, we divided the participants into 3 groups based on the information source perceived to mostly impact maternal health behavior:
Whether a woman had gained weight below, within, or above the GWG guidelines was calculated using mean recommended weight gain in the first trimester (1.5 kg), adding the mean recommended number of grams per week multiplied by the number of weeks the woman was pregnant above the first trimester [16 ]. Good nutritional habits and compliance with nutritional guidelines was defined as a score ≥7 on an 11-point scale. The relationship between information sources and selected variables, including health behaviors and sociodemographic variables, was assessed by logistic regression, linear regression, or chi-square as appropriate. The level of statistical significance was set at P<.05.

Dalhaug E.M, & Haakstad L.A. (2019). What the Health? Information Sources and Maternal Lifestyle Behaviors. Interactive Journal of Medical Research, 8(3), e10355.

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Performance Predictors in Gymnasts

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The characteristics of the participants are described with proportions for categorical variables and with mean and standard deviation values for continuous variables. To test differences in training, body composition, energy and macronutrients contribution, sleep characterization and precompetitive anxiety of gymnasts separated by performance groups, t-tests were applied. Spearman correlation coefficient was used to determine associations between categorical and continuous variables; due to the number of subjects evaluated, the significance level used was 1% (p < .01). Bivariate correlations were run on continuous measures of demographics, body composition, dietary intake, sleep, anxiety and performance. Regression analysis using an automatic linear regression model, aimed to improve model accuracy, was used in order to evaluate predictors of performance prior to competition. Thus, regression standardized predicted values and residuals were computed iteratively, assuming linear models, and adding variables considered significant by correlation analysis using the forward stepwise multiple regression method. The significance level was 5% (p < .05). Data were analysed using IBM SPSS statistical software version 21.0 for Windows (New York, USA).

Silva M.R, & Paiva T. (2016). Poor precompetitive sleep habits, nutrients' deficiencies, inappropriate body composition and athletic performance in elite gymnasts. European journal of sport science, 16(6).

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Evaluating Mortality Predictors in Patients

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Statistical analyses of the data were performed using SPSS for windows version 21.0 statistical software (SPSS Inc., Chicago, IL, USA). The categorical variables in the three groups were analyzed using chi-square test. In this study, parametric quantitative data were presented as the mean value and standard deviation and quantitative characteristics were described as the number and percentage for each category, for binary characteristics. There was also a 95% confidence interval (CI). Continuous variables were analyzed by one-way ANOVA. Receiver operating characteristic (ROC) curve was used to determine the cut-off point value of PTLC. The area under the ROC curve (AUC) showed this value. The value on the ROC curve was used to find out the maximum value of sensitivity and specificity as the cut-off point value. The percentages of sensitivity and specificity, as well as values of odds ratio (OR) with CI 95% were presented for the cut-off point value. Multivariate logistic regression analysis was used to determine the independent predictors of mortality. In this study, P Value ≤ 0.05 was considered statistically significant.

Aghdaii N., Ferasatkish R., Mohammadzadeh Jouryabi A, & Hamidi S.H. (2014). Significance of Preoperative Total Lymphocyte Count as a Prognostic Criterion in Adult Cardiac Surgery. Anesthesiology and Pain Medicine, 4(3), e20331.

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Statistical Analysis of Clinical Study

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Statistical analysis was performed using SPSS for Windows version 21.0 statistical software (SPSS Inc., Chicago, IL, USA). Based on previous studies on the field, a sample size of 36 patients (power = 0.80, alfa = 0.05) would be sufficient, when 1% standard deviation difference is considered significant. Continuous variables were tested for normal distribution by the Kolmogorov-Smirnov test. Normally distributed continuous variables were expressed in mean ± standard deviation (SD) and were compared using the independent samples t-test. Non-normally distributed variables were expressed in median (minimum-maximum) and were compared using the Mann-Whitney U test. Categorical variables were expressed in number and frequency and were compared using the Chi-square test or Fisher's exact test. A P value of <0.05 was considered statistically significant.

Balci E., Aykut A., Demir A., Vardar K., Karduz G, & Aksu U. (2021). Retrograde Autologous Priming Method Reduces Plasma Free Hemoglobin Level in Aortic Surgery. Annals of Cardiac Anaesthesia, 24(4), 427-433.

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Comparative In Vivo and In Vitro Analysis

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Results are presented as means ± SEM. Statistical analyses were performed using SPSS version 21.0 statistical software for Windows (SPSS Inc., Chicago, IL). For the in vivo studies, unpaired Student's t-test was used to compare two groups. For the in vitro studies, data were analyzed using one-way ANOVA followed by Dunnett's post-hoc tests. A p-value less than 0.05 was considered statistically significant.

Cui P., Li X., Wang X., Feng Y., Lin J.F., Billig H, & Shao R. (2015). Lack of cyclical fluctuations of endometrial GLUT4 expression in women with polycystic ovary syndrome: Evidence for direct regulation of GLUT4 by steroid hormones. BBA Clinical, 4, 85-91.

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Glucose Homeostasis Assessment Protocol

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Results are presented as the means ± SEM. Statistical analyses were performed using SPSS version 21.0 statistical software for Windows (SPSS Inc., Chicago, IL). No statistical method was used to pre-determine animal size. The normal distribution of the data was tested with the Shapiro-Wilk test. Differences between groups were analysed by one-way ANOVA followed by Tukey’s post hoc test for normally distributed data or the Kruskal–Wallis test followed by the Mann–Whitney U-test for skewed data. For the OGTT studies, data were analyzed using one-way ANOVA repeated measures followed by Tukey’s post hoc test. A p-value less than 0.05 was considered statistically significant.

Zhang Y., Sun X., Sun X., Meng F., Hu M., Li X., Li W., Wu X.K., Brännström M., Shao R, & Billig H. (2016). Molecular characterization of insulin resistance and glycolytic metabolism in the rat uterus. Scientific Reports, 6, 30679.

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Glucose Tolerance and Statistical Analysis

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Data are represented as the means ± SEM. Statistical analyses were performed using SPSS version 21.0 statistical software for Windows (SPSS Inc., Chicago, IL). The normal distribution of the data was tested with the Shapiro–Wilk test. Differences between groups were analyzed by one-way ANOVA followed by Tukey's post hoc test for normally distributed data or the Kruskal–Wallis test followed by the Mann–Whitney U test for skewed data. For the OGTT studies, data were analyzed using one-way ANOVA repeated measures followed by Tukey's post hoc test. All p-values < 0.05 were considered statistically significant.

Zhang Y., Hu M., Meng F., Sun X., Xu H., Zhang J., Cui P., Morina N., Li X., Li W., Wu X.K., Brännström M., Shao R, & Billig H. (2017). Metformin Ameliorates Uterine Defects in a Rat Model of Polycystic Ovary Syndrome. EBioMedicine, 18, 157-170.

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Endometrial Hyperplasia and PCOS Evaluation

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Numerical, grouped results are presented as means ± SEM. All data were analyzed using one-way or two-way ANOVA followed by Dunnett's post-hoc tests. For the in vivo study, two-way ANOVA was used to assess the main effects of menstrual cycle phase/endometrial hyperplasia and PCOS and to identify the interactions between them. A p-value less than 0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 21.0 statistical software for Windows (SPSS Inc., Chicago, IL).

Li X., Pishdari B., Cui P., Hu M., Yang H.P., Guo Y.R., Jiang H.Y., Feng Y., Billig H, & Shao R. (2015). Regulation of Androgen Receptor Expression Alters AMPK Phosphorylation in the Endometrium: In Vivo and In Vitro Studies in Women with Polycystic Ovary Syndrome. International Journal of Biological Sciences, 11(12), 1376-1389.

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