Introduction to Statistical Methods for Data Analysis
In this course, statistical methods are introduced that can be used in all kinds of research problems encountered in health, behavioral and clinical science.
For example, how can we analyze data that is meant to evaluate the effect of
- a traumatic event on well-being of women? Does this effect differ from that of men?
- a certain life habit on the risk of developing some specific disease?
The focus is on statistical concepts and techniques that play a role in summarizing and describing observed variables and relationships between variables, as well as generalizing the results for a larger group of people than the observed group. The first theme of this course is to summarize the observed data. The second theme is the testing concept. The third theme pertains to various basic statistical techniques that are used to analyse observed data.
Some best practice statistical methods will be introduced and are considered as standard methods to deal with the above stated questions.
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Important learning goals in this course are:
- Knowledge of descriptive statistics (including frequency, average, median, standard deviation, cross-classified table among others)
- Knowledge of the principles of inferential statistics, such as population distribution, sample distribution, sampling distribution, central limit theorem, hypothesis testing, p-value, and confidence interval
- Knowledge of the basic principles and concepts of elementary statistical techniques (including t-test, chi-square test, and simple linear regression)
- Knowledge of the differences and similarities between the various basic techniques (such as a t-test and simple linear regression)
- Ability to perform a simple test (t-test, chi-square test) with SPSS
- Ability to perform a simple linear regression analysis with SPSS
- Ability to interpret adequately the results of the learned statistical analysis in view of the research question and, in doing so, to provide critical comments.