All statistical analyses were performed using the SPSS 19.0 version (SPSS Inc., Chicago, IL, USA) and GraphPad 5.0 software. Each experiment was performed in triplicate. All values for experimental results are expressed as the mean ± SEM. The statistical significance of differences between independent groups was determined by one-way analysis of variance (ANOVA) or t-test. A two-sided P value < 0.05 was considered statistically significant.
Spss 19.0 version
Manufactured by IBM
Sourced in United States
SPSS 19.0 is a statistical software package designed for data analysis and management. It provides a range of tools for data manipulation, statistical modeling, and visualizing results. The software is used by researchers, analysts, and organizations to gain insights from their data.
Automatically generated - may contain errors
Lab products found in correlation
69 protocols using spss 19.0 version
1
Statistical Analysis of Experimental Data
2
Analyzing Internal Consistency and Validity
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SPSS (19.0 version) was used to gather statistical analysis in order to examine internal consistency and concurrent validity. Additionally, the coefficient of reliability of Cronbach’s alpha was employed for the main scale and the three subscales. Pearson’s bivariate correlations determined the concurrent validity within symptoms, burnout indicators, and coping. The multivariate kurtosis pointed to the non-normality of the obtained data (Mardia coefficient = 735.4). Consequently, Generalized Least Squares (GLS) was used as estimation method.
3
Statistical Analysis of Experimental Data
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The data processing in this study was analyzed by SPSS 19.0 version statistical software, some measurement data were expressed by the mean ± standard deviation ( ± s), and the count data were expressed by the percentage (%). Pairwise comparison used analysis of variance. P < 0.05 indicated that the difference was significant.
4
Statistical Analysis of Experimental Data
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The data was processed by SPSS19.0 version statistical software, the measurement data were expressed by the mean ± standard deviation (‾x ± s), and the count data were expressed by the percentage (%). One-way analysis of variance was used for pairwise comparison. The difference was statistically significant at P < 0.05.
5
Statistical Analysis of Experimental Data
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The data was processed by SPSS19.0 version statistical software, the measurement data were expressed by the mean ± standard deviation ( ), and the count data were expressed by the percentage (%). One-way analysis of variance was used for pairwise comparison. The difference was statistically significant at P < 0.05.
6
Cardiovascular Risk Factors Analysis
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Data are expressed as mean (s.d. ), except for IGF1, insulin, HOMAIR and calcium scores expressed as median (interquartile range). Student's t-test was used for variables with normal distribution, and Mann–Whitney U-test for variables without Gaussian distribution (IGF1, insulin, HOMAIR and calcium scores). Fisher's exact test was used to analyze classification of CAC sores and percentage of previous smoking, high BP and diabetes. Statistical analysis was performed using the statistical software SPSS 19.0 version. P values under 0.05 were considered significant.
7
Genetic Factors in Peri-Implant Health
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The statistical analysis of the data was carried out with the statistical package SPSS 19.0 version (SPSS Inc., Chicago, IL, USA).
Differences regarding the distribution of participants, according to gender and smoking were sought by applying the z-test for proportions adjusted with Bonferroni corrections and differences between the two groups regarding mean age were sought by applying the Kruskal -Wallis test.
For clinical parameters, indicators of Descriptive Statistics were used, such as mean and standard deviation for each group, with the patient as the observational unit. Differences in clinical parameters for full-mouth periodontal recordings and investigated implants respectively, were sought by applying the Kruskal-Wallis test.
Differences in the prevalence of investigated genes were also sought between the group with healthy implants and peri-implantitis, by applying the z-test for proportions adjusted with Bonferroni corrections.
Statistical significance was set for all comparisons at the 0.05 level.
Differences regarding the distribution of participants, according to gender and smoking were sought by applying the z-test for proportions adjusted with Bonferroni corrections and differences between the two groups regarding mean age were sought by applying the Kruskal -Wallis test.
For clinical parameters, indicators of Descriptive Statistics were used, such as mean and standard deviation for each group, with the patient as the observational unit. Differences in clinical parameters for full-mouth periodontal recordings and investigated implants respectively, were sought by applying the Kruskal-Wallis test.
Differences in the prevalence of investigated genes were also sought between the group with healthy implants and peri-implantitis, by applying the z-test for proportions adjusted with Bonferroni corrections.
Statistical significance was set for all comparisons at the 0.05 level.
8
Prevalence and Factors of Musculoskeletal Disorders among Nurses
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The data was imported and analyzed using SPSS 19.0 version software. We used algorithms for descriptive statistics (percentage, mean, median, standard deviation, and so forth) to describe the sociological characteristics of the participants. The prevalence of MSDs was calculated as the percentage of nurses who developed symptoms of MSDs in at least one of the nine positions on the body (showed in the Nordic Questionnaire). We used chi-squared test when comparing two percentages and t-test when comparing two means. Multiple logistic regressions were used to analyze and identify factors associated with MSDs. The models were checked for fitness using Hosmer-Lemeshow's goodness-of-fit test. All analyses were carried out in the Faculty of Public Health, Haiphong University of Medicine and Pharmacy. The level of significance was set at a p-value of less than 0.05.
9
Statistical Analysis of Experimental Data
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The data in this study was analyzed by SPSS19.0 version statistical software, the measurement data was expressed as the mean ± standard deviation ( ), and the count data was expressed as the percentage (%). Pairwise comparison adopted one-way analysis of variance. The difference was statistically significant at P < 0.05.
10
Neurocognitive Outcome Analysis
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The data processing in this study was analyzed by SPSS 19.0 version statistical software, the measurement data were expressed as mean ± standard deviation , and the count data were expressed in percentage (%). The basic data of the EG and the CG were compared with the independent t test. The FA values of each brain area and ADC value were compared and analyzed with the one-way analysis of variance in the EG before and after the treatment and in the CG. The difference was statistically significant at P < 0.05.
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