Classical Methods in Data Analysis

This course starts with the basic applications of biostatistics in the analysis of medical research data. 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.

At the end of this course the participant

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

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.

4 weeks, fulltime (face to face)

Please note that this course is part of an existing program within the Graduate School of Life Sciences.