Data are presented as median [interquartile range; IQR; Q25-Q75]. Nominal data are presented as numbers (%). For nominal data, a Chi-squared test (Pearson or Fisher´s exact test) was used to compare differences between groups. Non-parametric Kruskal-Wallis test was used to compare differences between groups for continuous data, with subsequent post-hoc analyses (Mann-Whitney U test). Correlations were assessed by Point-Biserial correlation coefficient for dichotomous variables (r, with p-value). An r-value of ≥0.30 (positive or negative value) was interpreted as a correlation (0.30 – 0.7 = moderate correlation; >0.70 = strong correlation). Linear regression analyses were performed to analyse effects of nominal factors on QuickDASH score (unstandardized B [95% CI]; p-value). A linear regression analysis was performed to investigate the effect of another hand surgical procedure or surgery for another nerve entrapment performed at the same time as UNE surgery on QuickDASH results. All regressions were adjusted for age, sex and diabetes. A p-value <0.05 was considered statistically significant. IBM® SPSS® Statistics, version 26, 2019 (IBM Inc., Chicago, IL) was used for all calculations. Each treated arm was analysed as a separate case and statistical entity.
Spss statistics version 26 2019
Manufactured by IBM
Sourced in United States
SPSS Statistics version 26 is a comprehensive statistical software package released by IBM in 2019. It provides advanced analytical capabilities for data management, analysis, and presentation. The core function of SPSS Statistics is to enable users to perform a wide range of statistical tests and analyses on their data.
Automatically generated - may contain errors
Lab products found in correlation
11 protocols using spss statistics version 26 2019
1
Ulnar Nerve Entrapment (UNE) Surgery Outcomes
2
Mortality Risk Factors Analysis
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Power analysis was done with G*Power® Version 3.1 (Heinrich-Heine University of Dusseldorf, Germany) and showed a sample size of n=28 for a power of 0.95 with an alpha error of 0.05 and an average effect size of 0.3. The post hoc analysis showed for our study sample size with 124 patients a power of 1.0 with an average effect size of 0.3 and an alpha error of 0.05. Further statistical analysis were performed using Microsoft® Office Excel® for Mac 2019 (Microsoft Corporation, Redmond, Washington, USA) and IBM® SPSS® Statistics Version 26 2019 (IBM Corporation, Armonk, New York, USA). The data are presented as mean and standard deviation or as absolute numbers and percentage. Categorial data were testet using the Chi-square test or Fisher´s exact test. Continouous data were compared using Student´s t test or Mann- Whitney U test. All significant univariate trends were entered into a multivariate (binary logistic) analysis to identify risk factors for mortality. Significance was set at p≤0.05.
3
Statistical Analysis Protocols for Research
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Statistical analysis was performed using Microsoft Office Excel for Mac 2019 (Microsoft Corporation) and IBM SPSS Statistics Version 26 2019 (IBM Corporation). The data is presented as mean and standard deviation or as absolute numbers and percentage. Categorial data was tested using the chi-square test or Fisher’s exact test. Continuous data was compared using Student’s t test or Mann-Whitney U test. Significance was set at P ≤ .05.
4
Iodine Status Assessment Protocol
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Data analyses were performed using IBM SPSS statistics version 26, 2019 (Armonk, New York, NY, USA). Normality was assessed using the Q-Q test and histograms, where p < 0.05 was considered not normal in the Shapiro-Wilk test. Non-normally distributed data is presented as a median with IQR (25–75) and normally distributed data is presented as a mean ± SD. Categorical data was presented as a number and compared between groups using Chi square tests. Mann Whitney U tests were used to compare continuous nonparametric data between groups and paired t-tests were used to compare parametric continuous data between groups. Spearman’s test was used to assess correlations between continuous data and UIC. Independent samples Kruskal Wallis tests were used to compare differences in UIC between participants with specific food consumption from various categories. p < 0.05 was considered significantly different, unless otherwise specified.
The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12619001546145) and Ethics approval was obtained from the University of South Australia Human Research Ethics Committee (Application No: 202118).
The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN 12619001546145) and Ethics approval was obtained from the University of South Australia Human Research Ethics Committee (Application No: 202118).
5
Statistical Analysis of Viral Load
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Statistical analyses were performed using IBM SPSS statistics version 26 (2019) software (IBM, Armonk, NY, USA). Shapiro–Wilk test was applied to quantitative variables for assessing normal distribution. Where normal distribution was assessed, two ways mixed ANOVA were applied when considering repeated measures (within-subject factor: repeated measure; between-subject factor: group). Where quantitative variables were not normally distributed, non-parametric tests were applied (Mann–Whitney´s U-test for independent samples and Wilcoxon test for paired samples). Viral load data were analysed by T-Student’s test for unrelated samples. Percentages were compared by Pearson chi-square test and Fisher´s exact test. A difference was considered statistically significant when p≤ 0.050.
6
Multivariable Analysis of Clinical Outcomes
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Statistical analysis was performed using Microsoft® Office Excel® for Mac 2019 (Microsoft Corporation, Redmond, WA, USA) and IBM® SPSS® Statistics Version 26 2019 (IBM Corporation, Armonk, NY, USA). For categorical variables, frequency counts were computed and presented along with their percentages. For continuous variables, means were computed along with their standard deviations. Bivariable analysis was performed to compare statistical differences of MC (SSI, IF, NP, and death). Chi-square tests or Fisher´s exact tests were used to compare these categorical variables. In the multivariable logistic regression models for MC (including death), MC (excluding death), and death, only variables significant at the p ≤ 0.05 level were included. Significance for final results was set at p ≤ 0.05 a priori.
7
Estimating Ewe Fertility Rates
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Fertility rates per lot were estimated using the percentage of ewes lambing in each lot after CAI. Statistical analyzes were performed using IBM SPSS statistics version 26 (2019) software (IBM, Armonk, NY, USA). Means and SD (standard deviation) were calculated for every farm and treatment group (control, test 1 group, and test 2 group). A two-way, mixed ANOVA (analysis of variance) was run; it considered a between-subject factor (farm) and a within-subject factor as having related groups (paired treatment groups: control vs. test 1 group in the first stage of the study; control vs. test 2 group in second stage of the study). There was homogeneity of variances and covariances (P > 0.05) as assessed by Levene´s test of homogeneity of variances and Box´s M test, respectively. P-values < 0.050 were considered to be statistically significant. When significant effects were detected among farms, pairwise comparisons with Bonferroni's correction were applied.
8
Comparative Analysis of Treatment Outcomes
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Data are presented as numbers (n; %) or median (interquartile range; IQR). Any differences in categorial data were analyzed with χ2-test or Fisher’s exact test and in continuous data (not-normally distributed) with the Mann-Whitney U Test. The significance level was set at a p-value of <0.05. Statistical analyses were performed with IBM SPSS Statistics (version 26; 2019).
All methods were performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki.
All methods were performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki.
9
Statistical Analysis of Age Data
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Subsequently, all the data were recorded in computer programs, such as Microsoft Office Excel 2010 and IBM SPSS statistics version 26 (2019) software (IBM, Armonk, NY, USA), which allowed the statistical study of that data. Age data were presented as mean ± SD; normality of age data was assessed by Shapiro–Wilk’s test. Since the assumption of normality was discarded, comparisons of age among groups were carried out by non-parametric test (Mann–Whitney U test and Kruskal–Wallis test). For comparing percentages, a chi-square test was used. A p-value < 0.05 was considered as statistically significant.
10
Chewing Gum Evaluation for Denture Fit
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Statistical analyses were performed using SPSS software (IBM SPSS Statistics 2019 Version 26, USA). All obtained values were numerical. Descriptive data were expressed as mean, standard deviation, median and the interquartile range. Numeric data were tested for normal distribution using the Koglomorov-Smirnov test. All obtained data were non-parametric; therefore, the Fridman test and Wilcoxon test were used to analyse differences between the data at different follow-up times and baseline. This analysis tested the difference in the values of the obtained parameters measured on the upper and lower side of the chewing gum sample immediately after receiving complete dentures, after three months, and after six months. The analysis of the parameters measured on the upper side of the gum evaluated the adequacy of dental restorations in the upper and lower jaw. The Mann Whitney test was used for intra-individual comparison, between the upper and lower side of the chewing gum sample. Differences were considered significant when p-value was <0.05.
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