Classical Methods in Data Analysis (online)

Topics are: types of data, location and variability measures, samples and populations, distributions, confidence intervals, hypothesis testing, comparing two or more means or proportions (parametric and non-parametric methods), and relationships between two variables (correlation, simple linear regression). The course also includes an extensive discussion of the multiple linear regression model. This is an ideal course for anyone who wishes to further his/her medical education by getting a better understanding of data analysis.

By the end of the course you will…

• have insight in the √n law and its consequences for sample size
• have insight in the general principles of decision procedures (“testing”), and be able to apply these procedures in practice using common statistical packages (SPSS, R)
• understand the principles of the following statistical analysis techniques: Student T tests (1-sample, 2-sample and paired), Analysis of Variance (1-way and 2-way ANOVA), Simple and multiple linear regression analysis, 1-sample, 2-sample and paired proportion tests (χ 2 test for goodness-of-fit, Pearson’s χ 2 test and McNemar’s χ 2 test)
• know in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
• be able to apply these techniques using common statistical packages (SPSS, R)
• have insight in the Kolmogorov Smirnov test (normal distribution) and the Fisher test for equality of variances and be able to apply these tests in practice using common statistical packages (SPSS, R)
• understand the results obtained with these techniques, and be able to apply these results in practice (e.g. in answering a study questions
• be familiar with the terms ‘explained variance’ and multi-collinearity
• understand the principles of model reduction in regression analysis
• understand the basic principles of the technique of logistic regression analysis
• be able to choose the appropriate non-parametric technique to be applied in case of non-normally distributed data, and understand the principles of these methods.

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

12 weeks, 14 hours per week

Even though you can manage your own time our courses are not intended as individual education. We offer personalized online learning with lots of interaction with peer students, the E-moderator and lecturers. Flexibility from students, a positive attitude towards teachers and peers and the willingness to learn together and help each other is invaluable to our courses. To experience maximum interaction, we advise you to log on several times per week.

Note that the starting dates of courses, interim deadlines, and dates of exams are fixed, but you can choose when and where you want to watch web lectures and work on assignments. The e-moderator of the course will inform you about the beginning of the course and about deadlines during the course.

The average required study workload for the courses of MSc Epidemiology Postgraduate Online is 14 hours per week. You will need this time to study, to keep up with the assignments and course material.