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The course “Statistic Foundation for Academicians and Researchers in Medical Science” is designed to equip researchers and academicians in different disciplines of Medical Science to be an expert in the concept of statistical analysis. There is no pre-requisite required for this course. The course will start from a statistical base, then introduce the analytical software and its usage format, then move to the basic analytics required for Medical research with examples. SPSS and Excel are the software used for analysis. This course will equip the researcher to select the analytical tool based on the structure of data, and teach the complete foundational requirement in statistical do’s and do not's. This will enable researchers to face the analytical section with confidence. The course will be a perfect foundation for advanced data analytics, hypothesis testing, and data mining courses.
After this course researchers can able to design quality research with good analytical background.
This section is the base for any statistical analysis in Medical Science. This first module will provide the foundation of variables and types of data. This will enable researchers to select analytical tools based on data type and proceed in a scientific way. This section also provides a piece of better knowledge on how to categorize variables based on the data type
This module will equip the researchers to use the analytical software SPSS, Excel and R. R is an open-source analytical platform while SPSS and excel need the payment for accessibility. Trail version of SPSS will be downloaded from the IBM website.
This module will start with a strong basic introduction to statistics. Starting from the central tendency, then move to the range, quartiles, variance, and standard deviation. Considering any statistical model this statistical concept will be the base. At least one of the statistical concepts mentioned above will be included in every statistical analysis as a base. This module will equip the researcher to know the structure of data.
In research after data collection, the major problem faced by the researchers is the unstructured or raw data. Using the data in the same form for analytics will be catastrophic. There are assumptions for every statistical tool and violation of these assumptions result in faulty output. This module will teach detecting problems with data, following assumptions, and explaining the remedies for those issues. Major topics covered are checking normality, outliers, missing variables, nonengaged respondents, skewness issues, and remedies.
The final section will teach with real Medical Science data. This module covers the tools like frequency table, cross-tabulation, correlation, and simple regression using the statistical software R, Excel, and SPSS. This section fulfills all the requirements for basic research analytics. However, IIRADM strongly suggests taking the course of Hypothesis testing which will be a perfect pair of analytical courses required for basic research. Including hypotheses in analytics will increase the standard of research whether it is for Undergraduate, Postgraduate, or Doctoral courses.