Data curation and statistical analysis was done using statistical package for social sciences version 21.0, Armonk, NY: IBM Corp [21 ]. Descriptive statistics was computed using mean, percentage and standard deviation. Comparison was estimated using Independent T Test and P Value less than 0.05 was treated statistically significant. Inferential statistics was computed by estimating binary logistic regression and multiple regression analysis.
Statistical package for social sciences version 21
Manufactured by IBM
Sourced in United States
SPSS Statistics 21 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is widely used in the social sciences, as well as in other fields that require quantitative data analysis.
Automatically generated - may contain errors
Lab products found in correlation
189 protocols using statistical package for social sciences version 21
1
Statistical Analysis of Social Data
2
Malocclusion Class and Racial Differences
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Data was analyzed using Statistical Package for Social Sciences version 21.0 (IBM Corp, Armonk, NY, USA). The CA was presented as mean and standard deviation. Shapiro Wilk test was used to check the normality of the data and the data was found to be normal. Inferential statistics were performed using parametric tests of significance since the collected data was normal and continuous in nature. Inferential statistics were performed using one way Analysis of Variance (ANOVA) and repeated measures ANOVA test. One way ANOVA test was used to find out the significance of malocclusion class-wise and racial differences in the CA. Independent t test was used to check the significance of gender-wise differences. Post hoc pairwise comparison was done using post hoc Boneferroni’s test. The level of statistical significance was set at <0.05.
3
Assessing Public Knowledge on Healthcare Topics
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The data collected were analyzed using the Statistical Package for Social Sciences version 21.0 (IBM Corp, Armonk, NY, USA). Quantitative variables were expressed as mean ± standard deviation. Qualitative variables expressed as proportions. The number of correct answers out of 23 was used to grade the knowledge level of the respondents as poor knowledge (0–8 correct answers), some knowledge (9–16 correct answers) and good knowledge (17–23 correct answers). Correlation analysis was used to assess for association between knowledge scores, age and educational level. Analysis of variance (ANOVA) was used to assess if mean knowledge scores differed significantly among the occupational groups. P < 0.05 was considered significant.
4
Statistical Analysis of Continuous and Categorical Data
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Data were subjected to normality distribution, and accordingly continuous data are presented as medians [interquartile range] and/or means ± standard deviation. Comparisons between two groups of continuous variables were performed using the Mann-Whitney U-test. Categorical variables were compared using Chi-square test with Fisher’s exact test and expressed as percentages. Analyses were two-sided and differences with p-values < 0.05 were considered statistically significant. The analyses were carried out using the Statistical Package for Social Sciences version 21.0 (IBM Corp., Armonk, NY, USA).
5
Predictors of Evidence-Based Medicine Adoption
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The survey team used the Statistical Package for Social Sciences, version 21.0 (IBM Corp., Armonk, NY, USA) for analysis. We conducted descriptive statistical analysis to obtain frequency and measure proportions. A chi-square test was applied to identify the association between knowledge and attitude categories with background characteristics of the study population. Finally, logistic regression analysis was performed to identify the predictors of not including EBM in clinical practice. In this method, we analyzed each independent variable with the EBM practice. Next, we adjusted all the study variables: age, gender, physicians’ nationality, qualification, current position, PHC work experience, and EBM training in the past five years. We fixed an alpha (p) value of less than 0.05 as a significant value.
6
Statistical Analysis of Clinical Outcomes
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Statistical analyses were performed using the Statistical Package for Social Sciences version 21.0 for Windows (IBM Corp.; Armonk, NY, USA). The characteristics of the study population were described using descriptive statistics. The Shapiro–Wilk test was used to test whether a continuous variable followed a normal distribution. Continuous data were then presented as median (25th, 75th percentile) and compared with the Mann–Whitney U-test. The categorical variables were presented as numbers (percentages) and compared using the chi-square test. Fisher’s exact test was used in analyses where the chi-square assumptions were not met. Binary logistic regression analyses were performed to evaluate the risk factors for MS and LVH, using the variables that had P <.2 in the univariate analyses. A 2-tailed P value of less than .05 was considered statistically significant.
7
Comparative Statistical Analysis of Social Sciences
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Statistical analyses were performed using Statistical Package for Social Sciences, version 21.0 (IBM Corp, Armonk, NY, USA). Descriptive results were reported as frequencies and percentages. Comparisons between groups were performed using analysis of variance, and the Kruskal-Wallis test was used to test the significance of differences between groups. P-values <0.05 were considered statistically significant.
8
Statistical Analysis of Qualitative and Quantitative Data
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Descriptive statistics for all qualitative variables were represented using frequency and percentage. Comparison among the study groups is made with the help of unpaired
t-test as per results of the normality test. All the quantitative variables were checked for normality assumption and were presented using mean and standard deviation. Association between the groups was assessed using the chi-square test. The association of normally distributed quantitative variables with the groups was measured using one-way analysis of variance (ANOVA) and the association of non-normally distributed quantitative variables with the groups was measured using Kruskal–Wallis test. For all the comparisons, the
p-value of less than 0.05 was considered statistically significant. The data was entered in Excel (Microsoft Corp.) spreadsheet and analysis was done using Statistical Package for Social Sciences version 21.0 (IBM Inc.).
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Descriptive statistics for all qualitative variables were represented using frequency and percentage. Comparison among the study groups is made with the help of unpaired
t-test as per results of the normality test. All the quantitative variables were checked for normality assumption and were presented using mean and standard deviation. Association between the groups was assessed using the chi-square test. The association of normally distributed quantitative variables with the groups was measured using one-way analysis of variance (ANOVA) and the association of non-normally distributed quantitative variables with the groups was measured using Kruskal–Wallis test. For all the comparisons, the
p-value of less than 0.05 was considered statistically significant. The data was entered in Excel (Microsoft Corp.) spreadsheet and analysis was done using Statistical Package for Social Sciences version 21.0 (IBM Inc.).
9
Endodontic Obturation Quality Analysis
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The data analysis was performed in the Statistical Package for Social Sciences version 21.0 (IBM SPSS Statistics, Chicago, USA). The Kappa agreement coefficient was used to describe the intra-examiner agreement for level of obturation. Cronbach's Alpha was used to evaluate the operators' internal consistency for the measurement methods. Data were analyzed by repeated measures logistic regression. The independent variable (level of obturation) was dichotomized as acceptable (adequate cases) or unacceptable (short and overfilled cases). Variables with a p-value ≤ 0.20 in the unadjusted model were included for the adjusted model. Statistical significance was set at 5%.
10
Identifying Brain Regions in PACG Patients
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The ReHo differences in brain regions were saved as regions of interest, and the mean signal values (beta value) of these brain regions were extracted by averaging ReHo values over all voxels using REST software (http://www.resting-fmri.Sourceforge.net ). Next, the mean beta values of these brain regions and behavioral performances were entered into IBM Statistical Package for Social Sciences version 21.0 software (IBM Corporation, Armonk, NY, USA). ROC curve was used to explore the abilities of these brain regions to distinguish the PACG patients from the NCs. Furthermore, Pearson’s linear correlation analysis was used to explore the relationship between these specific brain regions and behavioral performances.
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