Data were analyzed using Stata 13.0 (Stata Corp., College Station, TX, USA) and Statistical Package for Social Sciences v.20 (SPSS 20). First, we tested normality of distribution of our numerical variables. Descriptive statistics such as means, SDs, and frequencies were used to describe the primary variables. Pearson correlations were examined to assess zero-order correlations. Multivariable linear regression models were used to predict the outcome of HbA1c as a function of perceived racial discrimination. The initial full model included as predictors age, SES, diabetes duration, and insulin. Coefficients were not significant for diabetes duration and insulin (p > 0.05) and were dropped from the final model. The final model included age, gender, and perceived discrimination as predictors of HbA1c. Model 1 only included main effects, and Model 2 included an interaction between gender and perceived discrimination. In secondary analyses, we examined the relationship between discrimination and HbA1c within subsamples of Black men and Black women. Unstandardized beta coefficients, 95% confidence intervals (CI), and p-values were reported. A p-value of 0.05 or less was interpreted as statistically significant.
Statistical package for social sciences v 20
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Statistical Package for Social Sciences v.20 is a comprehensive statistical software suite used for data analysis, data management, and data documentation. It provides a wide range of statistical and graphical techniques to analyze and present data. The core function of SPSS is to enable users to perform complex statistical analyses on data.
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17 protocols using statistical package for social sciences v 20
1
Examining the Impact of Perceived Racial Discrimination on HbA1c Levels
2
Comparative Analysis of Student Responses
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The data collected were coded and analyzed using Statistical Package for Social Sciences v. 20 (SPSS Inc. Chicago, IL, USA). Individual student responses were compared between groups using frequencies and Chi-square analysis. Matched analysis was conducted using a Wilcox Signed Rank test. A two-tailed p-value of <0.05 was considered statistically significant.
3
Vertical Force Analysis in Drill Maneuvers
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Force data were averaged over the three trials (stamping leg for the drill manoeuvres), then normalised to BW, and are reported as multiples of BW or BW/s. Data were analysed using a two-way analysis of variance to determine the effect of foot drill experience by different groups (TRAINED women, TRAINED men, UNTRAINED men), marching and drill manoeuvre on the dependent variables of peak vertical impact force, peak vertical loading rate and peak tibial impact acceleration. Differences were located using Bonferroni t tests. Statistical analysis was performed using Statistical Package for Social Sciences (V.20, SPSS Inc, Chicago, Illinois, USA). Data are reported as mean (SD) and 95% confidence limits. An α level of p<0.05 was used to determine statistical significance.
4
Supplement Consumption Patterns in Athletes
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The Kolmogorov–Smirnov test was applied to check whether the variables had a normal distribution and the Levene test was used to verify homoscedasticity. For the descriptive analysis, the mean and standard deviation (M ± SD) were used. An ANOVA analysis was performed according to sex (male-female), category (senior-base), and positions (goalkeeper, defense, midfielder, and forward) to analyze the differences in the SS consumed from the different categories determined by the AIS. For supplements that were consumed by more than 10% of the sample, a chi-square test (χ2) was performed to verify possible differences according to sex, category, and positions. The level of statistical significance was set at p < 0.05. Statistical analysis was performed with the Statistical Package for Social Sciences v.20 (SPSS).
5
Statistical Analysis of Continuous and Categorical Data
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Continuous variables are presented as median and interquartile range (IQR) and categorical variables are presented as frequencies with percentages. One-sample Kolmogorov-Smirnov test were used to test continuous data for normal distribution. Pearson Chi-square tests were used to test dichotomous parameters. P-values < 0.05 were considered significant. Statistical Package for Social Sciences V.20 (SPSS Inc., Chicago IL, USA) was used.
6
HbA1c Diagnostic Accuracy for Diabetes
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Statistical analysis was performed using Statistical Package for Social Sciences V.20. Variables were expressed as median (IQR) or proportions. Kruskal-Wallis H non-parametric test was used to compare continuous variables and χ2 test for categorical variables. With OGTT test as the gold standard, receiver operating characteristic curve (ROC) was used for the multiclass variable to define area under the curve (AUC) of HbAlc for diagnosing diabetes and prediabetes. ROC curve was used with OGTT (categorical variable) as state variable and HbA1c (continuous variable) as test variable. The cut-off for HbA1c was noted where sensitivity and specificity were optimal. Data were divided with respect to age and gender for both diabetics and prediabetics. The optimal HbA1c cut-off value was selected where sum of sensitivity and specificity was maximum. Statistical significance was determined as a two-sided p<0.05.
7
Evaluating Mentoring Workshop Impacts
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Data were collected via the online survey platform Research Electronic Data Capture (RedCap) and quantitative data were analyzed using Statistical Package for Social Sciences V.20 (SPSS; IBM Corp. 2017 ) For analyzing knowledge acquisition from each workshop, a degree of correctness score was created by coding the correct answers for each question within each workshop (10 questions per workshop) and generating an average pre-workshop score and a post-workshop score. Eight paired sample t-tests were then conducted to examine if knowledge increased from before and after each of the four mentee and four mentor workshops, respectively. The Bonferroni correction was applied to paired sample t tests to control for multiple comparisons. Data related to participant feedback on mentoring workshops were analyzed using descriptive statistics, frequencies, and a one-sample t-test also using SPSS.
Qualitative data were analyzed using thematic analysis (Braun and Clarke 2006 (link)). Investigator triangulation (Denzin 1973 ) was achieved with two authors who independently coded the data into themes and sub-themes. Discussions were held between the coders to share findings, and address discrepancies. Consensus was attained, and a third coder reviewed the final analysis. If disagreements remained or arose, a fourth coder would have been involved as per the study protocol.
Qualitative data were analyzed using thematic analysis (Braun and Clarke 2006 (link)). Investigator triangulation (Denzin 1973 ) was achieved with two authors who independently coded the data into themes and sub-themes. Discussions were held between the coders to share findings, and address discrepancies. Consensus was attained, and a third coder reviewed the final analysis. If disagreements remained or arose, a fourth coder would have been involved as per the study protocol.
8
Statistical Analysis Methodology for Clinical Study
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Continuous variables are presented as means AE standard deviations and were compared using the Student t test or the Mann-Whitney U test, as appropriate. The Kruskal-Wallis test was used when more than 2 measurements were compared. Only available values were included in the analysis. Categorical variables are presented as frequency (%) and were compared using Pearson's c 2 test when the expected frequency count for each contingency table's cell was at least 5. Otherwise, a Fisher exact test was used. Statistical significance was set at a 0.05. Postoperative aortic gradients and aortic valvular area data were missing in less than 1% of patients. Whenever a value for a given variable was unavailable, the patient was not included in the analysis to avoid any nonresponse bias. Statistical analyses were performed using Statistical Package for Social Sciences v20 (IBM, Armonk, NY).
9
Statistical Analysis of Quantitative Data
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The IBM Statistical Package for Social Sciences v 20 was used to analyze the results (SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov test was used to determine that the quantitative data had a normal distribution. Non-parametric tests (Kruskal-Wallis test and Mann Whitney U test with Bonferroni correction) were used on data with a questionably normal distribution, whereas parametric tests (one-way ANOVA test, posthoc Tukey test, Tamhane's T2 test) were used on data with a normal distribution. The Chi-square test was used to compare predicted and observed values. The correlation coefficients of Pearson (r) and Spearman (rs) were used to assess the relationships between variables. Statistical significance was applied to all variations with a chance likelihood of 0.05 or less.
10
Characterizing Coronary Heart Disease Prevalence
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Patient having unconfirmed diagnosis of CHD or diagnosed case of Acquired Heart Disease.
Our aim was to collect at least data of 1000 cases of CHD in our defined study period of two months covering around 20 OPD days, using a properly designed data sheet. The required information was entered in the sheet by interviewing the study participants and from their hospital record. Duplication of the data was avoided by entering the hospital registration number. Total 1100 subjects were enrolled and data sheets were filled, of which only 1003 were analyzable. Collected data was entered and analyzed by using Statistical Package for Social Sciences v 20.0 (SPSS, Inc., Chicago, IL, USA). Descriptive statistics including frequencies, mean and percentages are calculated.
Our aim was to collect at least data of 1000 cases of CHD in our defined study period of two months covering around 20 OPD days, using a properly designed data sheet. The required information was entered in the sheet by interviewing the study participants and from their hospital record. Duplication of the data was avoided by entering the hospital registration number. Total 1100 subjects were enrolled and data sheets were filled, of which only 1003 were analyzable. Collected data was entered and analyzed by using Statistical Package for Social Sciences v 20.0 (SPSS, Inc., Chicago, IL, USA). Descriptive statistics including frequencies, mean and percentages are calculated.
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