IBM SPSS vs. 25.0 software was used for statistical analysis. We calculated descriptive statistics for the entire sample. The chi-square test was used for categorical data analysis and group comparison. Continuous data were analyzed by the Kruskal-Wallis test. The association was considered statistically significant when p-values were less than 0.05.
Spss vs 25
Manufactured by IBM
Sourced in United States
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. It provides a variety of data manipulation and analysis capabilities. The program has a graphical user interface and can be used to perform a variety of statistical procedures, including analysis of variance, factor analysis, and regression analysis.
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Lab products found in correlation
10 protocols using spss vs 25
1
Statistical Analysis of Sample Data
2
Comparative Analysis of Gene Expression
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Clinical features of the samples are shown as: relative frequency of each category, 50th percentiles (5–95th), and non-normal and average scale ± SD, as appropriate. For the comparative analysis, a t-Student, chi-squared, Fisher exact, and a Kruskal–Wallis test were used to analyse differences in means and proportions of gene expression between studied groups. The statistical analysis was carried out with the SPSS vs. 25.0 software (IBM Corp, Armonk, NY, USA).
Bestkeeper software (Technical University of Munich, Germany) [29 (link)], NormFinder algorithm (Aarhus University Hospital, Denmark) [30 (link)] and the direct comparison method between means and standard deviations of the relative gene expression between different samples tissues were used. p-values < 0.05 were considered as statistically significant.
Bestkeeper software (Technical University of Munich, Germany) [29 (link)], NormFinder algorithm (Aarhus University Hospital, Denmark) [30 (link)] and the direct comparison method between means and standard deviations of the relative gene expression between different samples tissues were used. p-values < 0.05 were considered as statistically significant.
3
Breeding Stage Effects on Bird Song
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We used generalized linear mixed models to analyze effects of resident status, breeding stage, and the interaction between resident status and breeding stage (fixed effects) on song output (our serial measurement), with individual identity as a random factor. Prior to the start of the experiment, one resident bird was excluded from sampling because its behavior appeared to be affected by an infection. Three additional birds were excluded from our analyses because they died in captivity from unknown illnesses, leaving 10 birds in each treatment. We only included the prebreeding and breeding stages in the analysis because the majority of birds in both treatments did not sing during the nonbreeding stage.
In order to normalize the distribution, we square root transformed song counts for each individual before the analysis. Statistical analysis was performed in SPSS vs 25 (IBM), and two‐tailed tests were used for all analyses, with a significance level of p = .05. All measures of variability are indicated by “±” standard error. Figures were made using R 3.6.0 (R Core Team,2019 ).
In order to normalize the distribution, we square root transformed song counts for each individual before the analysis. Statistical analysis was performed in SPSS vs 25 (IBM), and two‐tailed tests were used for all analyses, with a significance level of p = .05. All measures of variability are indicated by “±” standard error. Figures were made using R 3.6.0 (R Core Team,
4
Statistical Analysis of Numerical and Categorical Data
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Data management and statistical analysis were performed using SPSS vs. 25 (IBM, Armonk, New York, United States). Numerical data were summarized as the means and standard deviations or medians and ranges. Categorical data were summarized as numbers and percentages. Numerical data were compared using the independent t-test, while categorical data were compared using the Chi-square test. One-way analysis of variance test was used to compare more than two groups as regards the quantitative variable. Correlation analysis was performed using Spearman's correlation. All P values were two-sided. P values < 0.05 were considered significant.
5
Echocardiographic Predictors of Atrial Thrombus
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Data management and statistical analysis were done using SPSS vs. 25 (IBM, Armonk, New York, United States). Numerical data were summarized as means and standard deviations or medians and IQR. Categorical data were summarized as numbers and percentages. Numerical data were compared using independent t-test or Mann Whitney U test for normally and non-normally distributed variables respectively. Normality was assessed using normality tests and direct data visualization methods. Categorical data were compared using Chi-square test. Correlation analysis was done between tissue Doppler, speckle tracking parameters, and trans-esophageal parameters using Pearson's and Spearman's correlation (when appropriate). “r" is the correlation coefficient. It ranges from −1 to +1. −1 indicates a strong negative correlation. +1 indicates strong positive correlation while 0 indicates no correlation. ROC analysis was done for trans-esophageal, tissue Doppler and speckle tracking parameters for prediction of LAA thrombus or SEC. Area under curve (AUC) with 95% confidence interval and diagnostic indices were calculated. Multivariate logistic regression analysis was done for the prediction of LAA thrombus or SEC. Odds ratio with 95% confidence interval was calculated for predictors. All P values were two sided. P values less than 0.05 were considered significant.
6
Evaluating Lichen Planus Severity
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Data management and statistical analysis were done using SPSS vs. 25. (IBM, Armonk, New York, United states). Numerical data were summarized as means and standard deviations or medians and ranges. Categorical data were summarized as numbers and percentages. Comparisons between cases and controls were done using Mann Whitney U test for numerical data. Categorical data were compared using Chi-square test or Fisher's exact test. Comparisons between oral and cutaneous lesions were done using independent t test or Mann Whitney U test for normally and non-normally distributed numerical variables, respectively. Categorical data were compared between oral and cutaneous lesions using Chi-square test or Fisher's exact test if appropriate. SSCCAII was compared as regard different parameters within cases (either OLP or cutaneous LP) using Mann Whitney U test. It was also compared between different morphologies using Kruskal Wallis test. Post hoc was done and all post hoc were Bonferroni adjusted. Correlation analysis was done between SSCCAII and other parameters using Spearman's correlation. “r” is the correlation coefficient.
7
Serum IL-17 Analysis in Disease
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Data management and statistical analysis were carried out using SPSS vs.25 (IBM, Armonk, New York, United states). Numerical data was summarized as standard deviations and means or ranges and medians. Comparisons between cases and controls were carried out by independent t-test for numerical data. While categorical data was compared using Chi-square or Fisher's exact test. Serum levels of IL-17 were compared within cases group as regard different study parameters using Independent t-test or Mann Whitney U-test and were compared as regard disease severity and activity using Kruskal Wallis test.
8
Structural Validation of a Model
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SPSS vs25 was used to determine the respondents’ descriptive characteristics, conduct the independent sample t-test, and assess the study dimensions’ reliability using Cronbach’s alpha scores. Due to the complexity of the suggested model, the current study explored its structural properties using confirmatory factor analysis (CFA) and structural equation modeling (SEM) with AMOS vs20.
9
Investigating Social Anxiety Factors
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Data was analyzed in SPSS vs25. Descriptive statistics such as mean and standard deviation were used to describe social anxiety, social support, emotion dysregulation and dispositional mindfulness. Total scores and subscale scores for each of the primary measures (social anxiety, social support, emotional dysregulation and dispositional mindfulness) were computed by first reverse-scoring appropriate items, then averaging the relevant items for each subscale or total score. Social anxiety as the criterion variable, to examine the associations, correlations was examined first. Bonferroni adjustment of significance levels was applied for multiple comparisons/multiple correlation analysis (Bonferroni-corrected significance level: 0.05/18 = 0.0027). Then regression models were fit to test the unique associations of social support, emotion dysregulation and dispositional mindfulness with social anxiety. Age and sex were included as control variables in the regression models.
10
Comprehensive Statistical Analysis of Biomedical Data
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Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) vs.25. Mean ±SD, t test, Mann Whitney test, Chi square test, Pearson test, Kruskal Wallis test, ROC and AUC, were used.
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