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Spss 22 for windows

Manufactured by IBM
Sourced in United States, Japan

SPSS 22 for Windows is a statistical software package designed to analyze and manage data. It provides a range of tools for data manipulation, descriptive statistics, and advanced statistical modeling. SPSS 22 is suitable for a variety of research and analysis applications.

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277 protocols using spss 22 for windows

1

Factor Analysis of Global Indicators

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At the first stage, we ran a FA with principal component analysis (PCA) for factor extraction and Varimax for factor rotation. PCA aimed to extract smaller numbers of more unique global indices as factors instead of single indicators. For easy nomination, we preferred these factors would be more compatible with the World Bank global categorization.
Mortality, population structure, and dynamic indicators were not in included in the FA since they were highly correlated with GFR and MMR (as discussed in the regression model).
Researchers have suggested various methods for selecting the number of factors. Some of these methods are eigenvalues greater than 1, large eigenvalues (without specifying a cut-off point), scree test, examining multiple solutions/interpretability of the solution (including simple structure), a priori number of factors, percentage of variance accounted for, parsimony, parallel, analysis or chi-square test (for maximum likelihood factoring) [20 ]. However, the recommended cut-off points must be treated flexibly in PCA [21 ].
All statistical analyses in the current study were conducted with Microsoft Excel 2013 and SPSS for Windows 22.0 (SPSS Inc., Chicago, IL, USA).
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2

EQ-5D Utility Assessment Protocol

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Statistical analysis was performed using SPSS for Windows 22.0. Continuous variables are presented as mean ± standard deviation, while categorical variables are presented as percentages. Comparative analysis for paired categorical variables (before and after therapy) was performed for every EQ-5D dimension using McNemar and Wilcoxon test. Comparative analysis to compare the EQ-5D utility score and EQ-VAS before and after therapy was performed using Wilcoxon signed rank test and paired T-test, respectively. Consequent analysis was performed to find the correlation between factors that influence difference between utility score of EQ. 5D and EQ-VAS. The analysis was performed using Mann Whitney test due to abnormal data distribution. p value < 0,05 was considered statistically significant.
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3

Regression Analysis with SPSS

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The same parameters used for the ANN procedure were implemented for the regression analysis. The SPSS for Windows 22.0 (SPSS Inc, Chicago, IL) program was hence used. Significant values were calculated from coefficients obtained from the regression analysis. A value of 95% was considered significance level.
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4

Burnout Analysis in Healthcare Professionals

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All statistics were performed with SPSS for Windows 22.0 software package program. Reliability analyses were conducted on the scales. Percentage, frequency, median and standard deviation statistics were used in determining descriptive characteristics. Shapiro–Wilk test was used to test the compatibility for normal distribution and to determine whether the variables are suitable for normal distribution or not.
According to the results, it was determined that the variables did not show normal distribution since emotional exhaustion, depersonalization, personal accomplishment and burnout total scores are p<0.05. When there is no normal distribution, Mann Whitney-U Test will be used for variables with two groups and Kruskal–Wallis-H Test will be used for variables with more than two groups. In addition, the error rate is determined as (ά=0.05) in all tests and the difference between the groups will be considered statistically significant when p<0.05. Comments will be made in light of the tables constructed by analyzing the data.
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5

Statistical Analysis of Research Data

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SPSS for Windows 22.0 software was used for data analysis. The descriptive statistics consisted of percentages, mean values, standard deviations (SD). The inferential statistical analysis consisted of a two-sample independent t-test, paired t-test, and McNemar’s test [42 (link)]. The statistically significant level was set at less than 0.05.
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6

Efficacy and Feasibility of Novel Intervention

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All collected data were analyzed using SPSS for Windows 22.0 (SPSS, Inc., Chicago, IL, USA). We analyzed the full analysis set according to the intention-to-treat approach using the last observation carried forward method. Descriptive statistics are presented as means (M) and SDs for continuous variables and absolute numbers (N) and percentages for categorical variables. Independent t-tests were used for the comparison of between-group differences for continuous variables, and Fisher’s exact test was used for between-group comparisons of categorical data (baseline data). For the primary outcome, preliminary effect sizes and their 95% confidence interval were calculated using Cohen’s d for continuous variables (between-group effect sizes, pooled SD). In addition, exploratory data analysis that calculated appropriate summary measures for empirical distribution was performed, and descriptive two-sided P-values were calculated. Between-group differences—scores at T1 and T2—were analyzed using independent t-tests.
With regard to secondary endpoint, feasibility, acceptability of randomization, and retention rate were reported descriptively. In addition, negative effects of treatment were reported.
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7

Postnatal Growth Comparison: Vaginal vs. C-section

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Data cleaning and analysis were carried out using SPSS for Windows 22.0 (SPSS Inc., Chicago).
The Emergency Nutrition Assessment (ENA) for SMART software (2010 version) was used for the anthropometric data analysis and reported using WHO 2006 growth reference values with Standardized Monitoring and Assessment of Relief and Transitions (SMART) cut-offs.
A comparative analysis was carried out between normal vaginal and caesarean delivery on postnatal child growth. We conducted two-step hierarchical multiple regression analyses to determine independent predictors of height for age of children under two years. Multicollinearity was investigated by using the variance inflation factor (VIF). VIF (the reciprocal of the tolerance statistics) of greater than 5 is generally considered evidence of multicollinearity.
Explanatory variables that were significant at bivariate analysis at a p value of 0.05 or less were fed into the regression model after confirming the absence of multicollinearity between these independent variables.
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8

Statistical Analysis of Research Data

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Obtained data were computerized and evaluated by using SPSS for Windows 22.0 (SPSS, Inc, Chicago, IL). Descriptive statistics are presented as median (range, 25–75%), frequency distribution, and percentage. The Pearson chi-square test was used to evaluate categorical variables. The conformity of the variables to normal distribution was examined by using visual (histogram and probability graphs) and analytical methods (Kolmogorov-Smirnov/Shapiro-Wilk Test). For the variables that were found not to fit the normal distribution, the Mann-Whitney U test was used as a statistical method between 2 independent groups. The Spearman correlation test was used to determine the relationship between the variables. The correlation coefficients between 0 and 0.25 were interpreted as weak, 0.26–0.50 as medium, 0.51–0.75 as strong, and 0.76–1.0 as very strong correlation. Statistical significance level was accepted as P<0.05.
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9

Epidemiological Analysis of Patient Concerns

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Epidata 3.0 was used to establish the database and double-entry was performed by two postgraduates in our research team. All data analyses were carried out in SPSS for Windows 22.0. Descriptive statistics were employed to describe the number, distribution and rank of patients’ RFE and health problems. Student t-test and analysis of variance were used to compare the differences of mean number of RFE or health problems per encounter according to patient characteristics.
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10

Statistical Analysis of Qualitative and Quantitative Data

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We used SPSS software (SPSS for Windows 22.0, SPSS, Chicago, IL) to do some statistical analysis. We used chi-square test for qualitative data and Student t test for quantitative data. P value less than 0.05 indicated significant difference.
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