In order to report the quantitative and qualitative variables, mean/standard deviation and percentage of classes were used, respectively. Following the confirmation of the normality of continuous variables using the Kolmogorov-Smirnov test, the t-test was used to compare the quantitative variables. The Chi-square test or Fisher exact test was used to determine the association between the qualitative variables. To analyze the data, SPSS version 20 software was used (statistical analysis was performed using the SPSS version 20 software). A P value ≤ 0.05 was considered statistically significant.
Spss version 20
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SPSS version 20 is a statistical software package developed by IBM. It provides a range of data analysis and management tools. The core function of SPSS version 20 is to assist users in conducting statistical analysis on data.
Automatically generated - may contain errors
Lab products found in correlation
Epidata version 3, by IBM (144 mentions)
Epi info version 7, by IBM (74 mentions)
Prism 5, by GraphPad (66 mentions)
Prism 6, by GraphPad (61 mentions)
Sas version 9, by SAS Institute (45 mentions)
Epi info version 3, by IBM (43 mentions)
Prism 8, by GraphPad (39 mentions)
Prism 7, by GraphPad (31 mentions)
Spss statistics for windows version 20, by IBM (25 mentions)
Epidata version 4, by IBM (18 mentions)
R version 3, by R Project for Statistical Computing (15 mentions)
Spss statistics for window, by IBM (14 mentions)
Sas 9, by SAS Institute (11 mentions)
Prism version 8, by GraphPad (11 mentions)
Stata version 12, by StataCorp (11 mentions)
Prism version 6, by GraphPad (9 mentions)
Spss statistics v20, by IBM (9 mentions)
Spss statistic, by IBM (8 mentions)
Epidata software version 3, by IBM (8 mentions)
Excel 2007, by IBM (8 mentions)
10 763 protocols using spss version 20
1
Statistical Analysis of Quantitative and Qualitative Variables
2
Visual Functional Connectivity Analysis in Diabetic Retinopathy
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Variables of clinical features of the three groups were analyzed by SPSS version 20.0 software (SPSS Inc, Chicago, Illinois, USA) using Chi-square test and independent-samples t-test. A p value less than 0.05 was considered statistically significant. The sample size was calculated by G-power version 3.1.9.2.
One-sample t-test was used to evaluate intragroup patterns of z-values functional connectivity (zFC) maps by applying SPM8 software (voxel-level: p<0.01). One-way ANOVA was performed to assess the significant zFC maps of V1 differences among three groups. Two-sample two-tailed t-test was conducted to investigate the group differences of the zFC maps and the pairs of zFC maps between V1 and vision-related regions applying the Gaussian random field (GRF) method, which was performed to correct for multiple comparisons and regressed covariates of sex and age applying the SPM 8 software (voxel-level: p<0.01, GRF correction, cluster-level: p<0.05).
Pearson correlation coefficient was conducted to evaluate the relationships between the zFC values of different brain regions and clinical features in DR applying SPSS version 20.0 software.
One-sample t-test was used to evaluate intragroup patterns of z-values functional connectivity (zFC) maps by applying SPM8 software (voxel-level: p<0.01). One-way ANOVA was performed to assess the significant zFC maps of V1 differences among three groups. Two-sample two-tailed t-test was conducted to investigate the group differences of the zFC maps and the pairs of zFC maps between V1 and vision-related regions applying the Gaussian random field (GRF) method, which was performed to correct for multiple comparisons and regressed covariates of sex and age applying the SPM 8 software (voxel-level: p<0.01, GRF correction, cluster-level: p<0.05).
Pearson correlation coefficient was conducted to evaluate the relationships between the zFC values of different brain regions and clinical features in DR applying SPSS version 20.0 software.
3
Comprehensive Statistical Analysis of Biological Data
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Statistical analysis was done using the Student t-test and SPSS version 20 for the most of data. For the tumor incidence data, statistical analysis was done using the one-way ANOVA with post hoc Tukey test for multiple comparisons (SPSS version 20). The IMAGE J software (http:rsb.info.nih.gov/ij/index.html ) was used for the densitometry analysis of protein bands whilst the Bead Studio 2.1 and GENE SPRING 7.0 were used for microarray analysis. Most of the data represent the ± SD of triplicate measurements. The quantitative PCR data represent mean value with ± SEM of the triplicate measurements.
4
Comparing BRCA1/2 Variant Characteristics
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Comparison of clinical characteristics between patients with and without BRCA1/2 variants (non-carriers) was performed using two-tailed t-tests, Pearson Chi-Square tests, or Fisher’s exact tests as appropriate (SPSS version 20; SPSS, Chicago, IL). Missing data was not included in the analysis. Survival analyses were performed using univariate and multivariate Cox proportional hazards regression models (SPSS version 20). The log-rank test and Kaplan-Meier plots were used to visualize survival characteristics (STATA version 12; StataCorp, College Station, TX). A two-sided test P values < 0.05 were considered as statistically significant.
5
Anthropometric Indices and Air Pollution
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Data were entered into EPIDATA database and analyzed using SPSS version 20 software. Categorical data were analyzed using chi square tests, odds ratios and their 95% confidence intervals.
Z-scores of the anthropometric indices were obtained from the WHO Anthro software [14 ]. Anthropometric indices were compared between different categories of sociodemographic variables and exposure status using the independent sample t-test. Significant variables were then included in multiple linear regression analyses. Exposure levels were compared using Mann Whitney U statistic.
The association between z-scores of anthropometric indices and air pollution levels (PM2.5, CO and CO2) were assessed using the Pearson product moment correlation coefficient.
All analyses were done using SPSS version 20 [15 ].
Z-scores of the anthropometric indices were obtained from the WHO Anthro software [14 ]. Anthropometric indices were compared between different categories of sociodemographic variables and exposure status using the independent sample t-test. Significant variables were then included in multiple linear regression analyses. Exposure levels were compared using Mann Whitney U statistic.
The association between z-scores of anthropometric indices and air pollution levels (PM2.5, CO and CO2) were assessed using the Pearson product moment correlation coefficient.
All analyses were done using SPSS version 20 [15 ].
6
Demographic and Clinical Characteristics Analysis
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The demographic and clinical characteristics of the included patients were taken into
account. The clinical data included liver function tests (ALT and AST) and lipid profile (serum
cholesterol and TG) at the baseline and a mean 6-month duration. Continuous variables were
compared using the independent t-test and nominal variables by using the
χ2 and Fisher’s exact test using SPSS version 2.0.
Values of p≤ 0.05 were considered significant. Continuous variables
were compared using the independent t-test and nominal variables by using the
χ2 and Fisher’s exact test using SPSS version 2.0.
Values of p ≤ 0.05 were considered significant.
account. The clinical data included liver function tests (ALT and AST) and lipid profile (serum
cholesterol and TG) at the baseline and a mean 6-month duration. Continuous variables were
compared using the independent t-test and nominal variables by using the
χ2 and Fisher’s exact test using SPSS version 2.0.
Values of p≤ 0.05 were considered significant. Continuous variables
were compared using the independent t-test and nominal variables by using the
χ2 and Fisher’s exact test using SPSS version 2.0.
Values of p ≤ 0.05 were considered significant.
7
Molecular Mechanisms of Myopia Pathogenesis
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As only 12 guinea pigs were included in FDM and control group, the normal distribution test was conducted in the variables by Kolmogorov-Smirnov test (SPSS version 20, Chicago, USA). The biometric parameters of SE, axial length, corneal curvature, mRNA expression, and Western blot protein are presented as the mean ± standard deviation or median (interquartile range). Measurements of the right eye in the FDM and control group were compared by independent t test or Mann-Whitney U test (SPSS version 20). Comparison of the relative GJD2 mRNA level and Cx36 amount in the right eye retina in FDM and control group were also conducted by paired t test. A significant level of P < 0.05 was used.
8
Factors Influencing Intimate Partner Violence Disclosure
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Data were cleaned and entered using Epi data version 3.1 then exported to SPSS version 20 for analysis. Data were analyzed using SPSS version 20. Descriptive statistics were computed to determine the prevalence of disclosure of intimate partner violence. Binary and multivariate logistic regression analysis was done to see the association between the dependent (disclosure status of intimate partner violence) and independent variables. Binary logistic regression was used to identify variables that are a candidate for multivariate logistic regression analysis at p value < 0.25, and multivariate logistic regression analysis was used to determine the factors that are independently associated with disclosure status of intimate partner violence at p value < 0.05 with a 95% confidence level. Finally, variables with P value < 0.05 were considered statistically significant. Model fitness was assessed through the Hosmer and Lemeshow test (p = 0.087).
For the qualitative part, the data was transcribed and translated from Amharic to the English language by the research team and language teachers independently. Texts were thoroughly read repeatedly to identify thematic areas. Their inductive meanings were extracted and described in narratives thematically. Three of the authors have participated in the thematic analysis. Finally, ideas were triangulated with the quantitative result.
For the qualitative part, the data was transcribed and translated from Amharic to the English language by the research team and language teachers independently. Texts were thoroughly read repeatedly to identify thematic areas. Their inductive meanings were extracted and described in narratives thematically. Three of the authors have participated in the thematic analysis. Finally, ideas were triangulated with the quantitative result.
9
Analysis of Factors Influencing Health Outcomes
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Each completed questionnaire was checked for completeness before data entry manually. Then the data was coded & entered in to a computer by using EPI info version 7 and clean-up was made to check accuracy, consistency. Any error identified was corrected. Finally, data was exported to SPSS version 20 for further clean up, recoding and analysis.
Data analysis was done by using SPSS version 20. Crude and adjusted odds ratios with 95% CI were calculated to determine the strength of association between response variable and predictor variables. P-value less than 0.05 was considered as a level of significance. Descriptive and summary statistics were employed.
Crude odds ratios (COR) with 95% confidence intervals (CI) were calculated using bivariate logistic regression analysis. Variables having p-value of less than or equal to 0.2 (to control the possible confounder) were fitted to multivariate logistic regression so as to assess the presence and strength of association. Hosmer–Lemeshow goodness-of-fit test was used to check the assumption of the model.
Data analysis was done by using SPSS version 20. Crude and adjusted odds ratios with 95% CI were calculated to determine the strength of association between response variable and predictor variables. P-value less than 0.05 was considered as a level of significance. Descriptive and summary statistics were employed.
Crude odds ratios (COR) with 95% confidence intervals (CI) were calculated using bivariate logistic regression analysis. Variables having p-value of less than or equal to 0.2 (to control the possible confounder) were fitted to multivariate logistic regression so as to assess the presence and strength of association. Hosmer–Lemeshow goodness-of-fit test was used to check the assumption of the model.
10
Determinants of Community-Based Health Insurance Dropout
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All returned questionnaire was checked for completeness and consistency of responses. The collected data were cleaned, coded, and entered EPI-data and it was exported to SPSS version 20 for analysis. Data were cleaned and analyzed using SPSS version 20. The dropout rates of the CBHI indicator are dichotomous variables, which were classified as 0 and 1, which do not meet the criteria and meeting the criteria, respectively. Descriptive statistics such as mean, median, frequency, and a number of variables were summarized by using frequency, tables, graphs, and texts. Carry out variable relationship diagnosis, such as goodness of fit test by Hosmer and Lemeshow test, multicollinearity, and sample adequacy test. The variables with p-value ≤0.2 in the Bivariate analysis were fitted to a multivariate logistic regression model to identify their association with the outcome variable. It is reported that a variable with a 95% CI of P<0.05 can be used as a predictor of the CBHI dropout rate.
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