spss statistics for windows v 26, by IBM - Product details

1

Factors Influencing Contraceptive Use Effectiveness

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Descriptive statistics are given as frequencies and percentages. The main outcome was the division of the study population into three groups according to the effectiveness of contraception (i.e., high-performing methods, either low-performing methods or both effective and ineffective methods, and not using contraception). The Pearson chi-square test was used to compare these groups in relation to demographic, social and related to sexuality or reproductive health factors. When interpreting results presented in larger contingency tables, we analysed adjusted standardized residuals, where the absolute value > 1.96 corresponds to a significance of p < 0.05. In the multivariate analysis, the multinomial logistic regression model was estimated, with no users as the reference category. Goodness-of-fit was estimated by calculating Nagelkerke pseudo R², where the value above 0.7 is recommended [18 (link)]. Regression analysis results are presented as adjusted odds ratios and 95% confidence intervals (CI).
The statistical software IBM SPSS Statistics for Windows, v. 26.0. (IBM Corp., Armonk, NY, USA) was used for analysis.

Izdebski Z., Wąż K., Warzecha D., Mazur J, & Wielgoś M. (2022). Factors Associated with Uptake of Effective and Ineffective Contraceptives among Polish Women during the First Period of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 19(19), 12748.

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2

Comprehensive Statistical Analysis of Experimental Data

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Statistical analyses were performed using IBM SPSS Statistics for Windows v.26.0 (IBM Corp., Armonk, NY, USA). The Shapiro-Wilk normality test was used to test the normality of all quantitative variables. Continuous variables with normal distribution were presented as mean± standard deviation (SD) and compared by Student’s t-test. The relationship between categorical variables was assessed using Chi-square test or Fisher’s exact test. A two-tailed P value lower than 0.05 was considered statistically significant.

Qu J., Liu X, & Lv X. (2021). Pulmonary Mucormycosis as the Leading Clinical Type of Mucormycosis in Western China. Frontiers in Cellular and Infection Microbiology, 11, 770551.

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3

Abutment Microgap Analysis

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Data were analyzed using a statistical software program (IBM SPSS Statistics for Windows, v.26.0; IBM Corp., Armonk, NY, USA). For the descriptive analysis, the mean, median, and standard deviation values per group were analyzed. The normality of the data distribution was evaluated using the Shapiro–Wilk test (p < 0.05). The data were not normally distributed. Therefore, the microgap was analyzed using the Mann–Whitney U test between the transepithelial abutment and Ti-base abutment groups. The confidence level was 95%, so p-values less than 0.05 were considered statistically significant.

Cascos R., Celemín-Viñuela A., Mory-Rubiños N., Gómez-Polo C., Ortega R., Agustín-Panadero R, & Gómez-Polo M. (2023). Influence of the Use of Transepithelial Abutments vs. Titanium Base Abutments on Microgap Formation at the Dental Implant–Abutment Interface: An In Vitro Study. Materials, 16(19), 6532.

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4

Statistical Analysis of Study Variables

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Data were entered into Epi Info 7, and statistical analyses were performed using IBM SPSS Statistics for Windows v. 26.0 (IBM).
Descriptive statistics were used to summarize the study variables. All variables were summarized using frequencies and proportions. The categorical data were compared using the chi-square test. Logistic regression models, such as bivariable and multivariable logistic regressions, were employed to determine the associations between variables. We assessed the independence, linearity, and normality tests before considering the multivariable regression analysis to ensure that our model was normally and randomly distributed. The odds ratio was determined along with the 95% CI, and those variables with a p-value <0.2 in the bivariable analysis were subjected to multivariable analysis to identify the independently associated variables. To account for the differences between the control and study groups, we used confounders related to demographic characteristics during the multivariable-adjusted odds ratio analysis. A p-value < 0.05 in the multivariable analysis was considered a statistically significant association.

Zealiyas K., Teshome S., Berhe N., Amogne W., Haile A.F., Abate E., Yimer G., Weigel C., Ahmed E.H., Abebe T, & Baiocchi R. (2023). The Burden of Epstein–Barr Virus (EBV) and Its Determinants among Adult HIV-Positive Individuals in Ethiopia. Viruses, 15(8), 1743.

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5

Descriptive Statistical Analysis of LDS

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All descriptive analyses were performed using GraphPad Prism V.8.0.2 for Windows (GraphPad Software, La Jolla, California, USA) and IBM SPSS Statistics for Windows, V.26.0 (IBM, Armonk, New York, USA). The descriptive statistics consisted of univariate analyses for all LDS subject and included mean, SD and range, unless otherwise specified. As this was a descriptive study, no formal statistical comparisons were made across phenotypic subcategories.

Jani P., Nguyen Q.C., Almpani K., Keyvanfar C., Mishra R., Liberton D., Orzechowski P., Frischmeyer-Guerrerio P.A., Duverger O, & Lee J.S. (2020). Severity of oro-dental anomalies in Loeys-Dietz syndrome segregates by gene mutation. Journal of Medical Genetics, 57(10), 699-707.

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6

Prevalence and Factors of Delivery Inertia

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To estimate the prevalence of DI, the number and frequency of inertia cases were calculated for the total and by sex. To evaluate the patient profile according to their DI in each category of qualitative variables, double-entry tables were made by applying the Chi-Square statistical test.
Prevalence ratios (PRs) and 95% confidence intervals (95% CIs) of inertia at each level of the explanatory variables were estimated using multivariate Poisson regression models with robust variance (27 (link)), differentiating by sex. A stepwise variable selection procedure was performed, based on the Akaike information criterion (AIC). The multicollinearity of the variables in the construction of the models was studied. The goodness-of-fit likelihood ratio test (LRT), AIC value, and receiver operating characteristic (ROC) area of each model were performed. To avoid the multiplicity problem due to the analysis by subgroups due to sex/gender, the type I error was adjusted by the Bonferroni method to 0.025. The analyses were performed using IBM SPSS Statistics for Windows, v. 26.0 (IBM Corporation, Armonk, NY, United States) and R software, v. 4.0.2 (R Core Team, Vienna, Austria).

Pallares-Carratala V., Carratala-Munuera C., Lopez-Pineda A., Quesada J.A., Gil-Guillen V., Orozco-Beltran D., Alfonso-Sanchez J.L., Navarro-Perez J, & Martin-Moreno J.M. (2022). Characterizing Diagnostic Inertia in Arterial Hypertension With a Gender Perspective in Primary Care. Frontiers in Cardiovascular Medicine, 9, 874764.

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7

Abutment Microgap Analysis

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Data were analyzed using a statistical software program (IBM SPSS Statistics for Windows, v.26.0; IBM Corp., Armonk, NY, USA). For the descriptive analysis, the mean, median, and standard deviation values per group were analyzed. The normality of the data distribution was evaluated using the Shapiro–Wilk test (p < 0.05). The data were not normally distributed. Therefore, the microgap was analyzed using the Mann–Whitney U test between the transepithelial abutment and Ti-base abutment groups. The confidence level was 95%, so p-values less than 0.05 were considered statistically significant.

Cascos R., Celemín-Viñuela A., Mory-Rubiños N., Gómez-Polo C., Ortega R., Agustín-Panadero R, & Gómez-Polo M. (2023). Influence of the Use of Transepithelial Abutments vs. Titanium Base Abutments on Microgap Formation at the Dental Implant–Abutment Interface: An In Vitro Study. Materials, 16(19), 6532.

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8

Seasonal Trends in Antiretroviral Therapy

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All the continuous variables were tested for normality with the Shapiro–Wilk test. The correspondence of each parameter was evaluated with a normal or non-normal distribution through the Kolmogorov–Smirnov test. Non-normal variables were described as median values and interquartile range (IQR), and categorical variables as numbers and percentages. Kruskal–Wallis and Mann–Whitney tests were adopted for differences in continuous variables between seasons, considering a statistical significance with a two-sided p-value < 0.05.
Stepwise multivariate logistic regression analyses were performed to predict drug cut-off values (ETV).
All tests were performed with IBM SPSS Statistics for Windows v.26.0 (IBM Corp., Chicago, IL, USA).

Cusato J., Mula J., Palermiti A., Manca A., Antonucci M., Avataneo V., De Vivo E.D., Ianniello A., Calcagno A., Di Perri G., De Nicolò A, & D’Avolio A. (2021). Seasonal Variation of Antiretroviral Drug Exposure during the Year: The Experience of 10 Years of Therapeutic Drug Monitoring. Biomedicines, 9(9), 1202.

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9

Epidural and Oxytocin Impact on Second Stage Labor

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Median duration of the second stage of labour and interquartile ranges were estimated using survival analyses. Caesarean sections and operative vaginal deliveries during the active phase were censored. Women with a caesarean section in the first stage of labour were left censored and women with an active phase of second stage ≥120 minutes were right‐censored and not included in the survival analyses. We considered oxytocin augmentation and epidural analgesia to be mediators, and stratified the analyses of estimated median duration in women with and without epidural analgesia, and with and without oxytocin augmentation.
One minus survival plots were created from Cox regression analyses and stratified into BMI groups. TGCS group 1 and 3 were analysed separately.
We calculated the unadjusted hazard ratio (HR) as an estimate of relative risk of delivery using Cox regression analyses. The normal weight group with BMI of 18.5–24.9 kg/m² was used as the reference group. We also performed analyses adjusted for maternal age. The assumptions of proportional hazards for the Cox regression analyses were checked using log minus log plots. Statistical analyses were performed with IBM SPSS statistics for Windows v.26.0 (IBM Corp.).

Østborg T.B., Sande R.K., Kessler J., Tappert C., von Brandis P, & Eggebø T.M. (2022). Put your weight behind it—Effect of body mass index on the active second stage of labour: A retrospective cohort study. Bjog, 129(13), 2166-2174.

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10

Comparing Fundoplication Outcomes in EA Patients

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SPSS (IBM Statistical Package for the Social Sciences [SPSS] Statistics for Windows, v 26.0 Armonk, NY: IBM Corp) was used for descriptive analyses. Data were noted as median and range or as frequency (%). Patient characteristics, preoperative symptoms, fundoplication indications and results of post-fundoplication symptoms in EA patients were compared with those of non-EA patients, using χ² test in case of ≥10 cases and Fisher’s Exact test in case of <10 patients. Pre- versus post-fundoplication symptoms/complications were calculated using paired t-test. Comparisons of the four abovementioned treatment outcomes between EA patients and non-EA patients were calculated using χ² test for trend. We considered a P < 0.05 as statistically significant.

van Lennep M., Chung E., Jiwane A., Saoji R., Gorter R.R., Benninga M.A., Krishnan U, & van Wijk M.P. (2022). Fundoplication in children with esophageal atresia: preoperative workup and outcome. Diseases of the Esophagus, 35(10), doac006.

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Spss statistics for windows v 26

Lab products found in correlation

60 protocols using spss statistics for windows v 26

Factors Influencing Contraceptive Use Effectiveness

Comprehensive Statistical Analysis of Experimental Data

Abutment Microgap Analysis

Statistical Analysis of Study Variables

Descriptive Statistical Analysis of LDS

Prevalence and Factors of Delivery Inertia

Abutment Microgap Analysis

Seasonal Trends in Antiretroviral Therapy

Epidural and Oxytocin Impact on Second Stage Labor

Comparing Fundoplication Outcomes in EA Patients

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