The data obtained from the present study were compiled systematically, transformed from the WHO pro forma to a computer and a master table preparation was done using Microsoft-Excel sheet. Statistical Package for Social Science version 20 (IBM, Armonk, NY, USA), was used for statistical analysis and data were compared using appropriate statistical tests to find out the statistical significance of the obtained results; at 5% level of significance, i.e., P < 0.05 was considered as statistically significant. Gender-wise distribution (dentition status score, bleeding score, pocket score, loss of attachment score, treatment need score); comparison between study and control group was done by Unpaired “t”-test. Comparison of dentition status score, bleeding score, pocket score, loss of attachment score, and treatment needs score among three groups with thyroid dysfunction was done by ANOVA test. Post hoc Tukey test was used for pair-wise comparisons.
Statistical package for social science version 20
Manufactured by IBM
Sourced in United States
The Statistical Package for Social Science (SPSS) version 20.0 is a software application designed for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and visualization. SPSS 20.0 is capable of handling a wide range of data types and offers a user-friendly interface for conducting various statistical tests and procedures.
Automatically generated - may contain errors
54 protocols using statistical package for social science version 20
1
Oral Health Assessment of Thyroid Dysfunction
2
Statistical Analysis of Social Data
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Data were analyzed using the computerized program; Statistical Package for Social Science, version 20, (IBM, SPSS, Illinois, Chicago, USA). Results were presented as tables and figures. Chi-square test was used for cross tabulation at 0.05 margins of error (P value). Descriptive statistics (frequency tables, median, histogram, means, standard deviation [SD]) and inferential statistics (Chi-square test, and logistic regression test) were performed.
3
Statistical Analysis of Social Data
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Data were recorded and analysed using the Statistical Package for Social Science, version 20 (IBM. Chicago, Illinois, USA). Descriptive analyses were performed using frequencies and percentages. For the bivariate analysis chi-square tests were performed to evaluate the categorical variables; the level of significance was set at p < 0.05 and 95% confidence intervals (95% CI). Estimates were presented as Odds Ratio (OR) and 95% confidence Interval (CI).
4
Antimicrobial Resistance Phenotype-Genotype Validation
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Data were imputed and validated in Excel 2016. Descriptive statistics were
computed for the multiple antibiotic resistance index. Agreement between the
values of antimicrobial resistance phenotypes and their corresponding genotypes
was established by κ value (coefficient of agreement) according to Jeamsripong
et al.63
Chi-square and Fisher’s exact test were used to test association as
appropriate in every case. p < 0.05 was taken as
statistically significant. Statistical Package for Social Science Version 20
(IBM, Santa Barbara, California, USA) was used.
computed for the multiple antibiotic resistance index. Agreement between the
values of antimicrobial resistance phenotypes and their corresponding genotypes
was established by κ value (coefficient of agreement) according to Jeamsripong
et al.63
Chi-square and Fisher’s exact test were used to test association as
appropriate in every case. p < 0.05 was taken as
statistically significant. Statistical Package for Social Science Version 20
(IBM, Santa Barbara, California, USA) was used.
5
Analyzing Social Science Data
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Data were entered, cleaned and coded by Microsoft excel 2007 and analyzed by Statistical
Package for Social Science, version 20 (IBM. Chicago Illinois, USA. The level of significance was set at p-value <0.05 and 95% confidence intervals.
Package for Social Science, version 20 (IBM. Chicago Illinois, USA. The level of significance was set at p-value <0.05 and 95% confidence intervals.
6
Spinal Alignment and Disability Assessment
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Clinical and radiographic measurements were analyzed by IBM Statistical Package for Social Science version 20. A comparison of radiographic parameters was analyzed using dependent T-test or Wilcoxon test. Comparison of clinical outcome between pre-surgical, 6 months, and 12 months measurements was analyzed using the Friedman test followed with the Wilcoxon test as posthoc analysis. Correlation between ODI score and spinopelvic parameters was analyzed using the Spearman test.
7
Disability Prevalence Estimation Protocol
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The data on all screen-positive cases were analyzed using Statistical Package for Social Science version 20 (IBM). Descriptive statistics was applied to present the demographic and other characteristics of disability screen-positive cases. The prevalence of disability was estimated by generating the CIs for the proportion of the population. The following expression was used for the estimation of CI:
where p“p” is sample proportion.
8
Thyroid Dysfunction Endocrinology Center Protocol
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For the present individuals suffering from thyroid dysfunction visiting endocrinology center were selected randomly.
Sample size estimation was calculated using priori test in Statistical Package for Social Science version 20 (IBM, Armonk, NY, United States of America). It was based on the data obtained from the available literature of previous studies.[3 ]
Sample size estimation was calculated using priori test in Statistical Package for Social Science version 20 (IBM, Armonk, NY, United States of America). It was based on the data obtained from the available literature of previous studies.[3 ]
9
Exploring Developmental Disabilities Risk Factors
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Data were analysed using the Statistical Package for Social Science version 20 (IBM, Armonk, NY). A summary of the data was presented using simple percentages and frequency. The associations between DDs and the selected risk factors, i.e. a history of perinatal asphyxia, neonatal jaundice and prematurity, were determined using the χ2 test. The level of significance was set at p<0.05.
10
Hematological Parameter Comparison Study
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The collected data was entered into Epi-data software (Version: 3.0.4), then cleaned and exported into a Statistical Package for Social Science version 20 (IBM Corp., Armonk, NY, USA) for analysis. Descriptive statistics such as frequencies and percentages were used to summarize the data. The Kolmogorov–Smirnov test and histogram were used to check the normal distribution of data. The values of selected hematological parameters were presented with median and interquartile range (IQR). Non-parametric mann–Whitney U tes was used to compare median difference in hematological parameters among groups. The data was presented using tables, charts, and graphs. Bivariable and multivariable logistic regression analysis was done. Crude odds ratio (COR) and adjusted odds ratio (AOR) with a 95% confidence interval (CI) were used to observe the strength of the association between the predictors and the outcome. The backward selection method was used to select the important variables. The variables with a p value of < 0.25 in the bivariable logistic regression analysis were fitted into the multivariable logistic regression analysis. The model of fitness was checked by Hosmer and Lemeshow’s goodness-of-fit statistic. A p value of less than 0.05 was considered statistically significant in any condition.
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