
Only students who are admitted to one of the premaster progr. BA, COM, ES, EST, PA or PSY are allowed to follow this course.
At the end of the module, students will be able to, in general terms:
 correctly select from a set of the most important univariate, bivariate and multivariate inferential statistical methods to describe and test characteristics of variables and relationships between variables;
 carry out the most important univariate, bivariate and multivariate inferential statistical analyses using R for statistics;
 correctly interpret and report about output of these univariate, bivariate and multivariate inferential statistical analyses.
More specifically students will be able to:
 explain the role and main assumptions of inferential statistics in the process of scientific research and its relationship with descriptive statistics, and know the main concepts used in the context of inferential statistics;
 construct confidence intervals and perform tests for both proportions and means;
 describe and statistically assess the relationship between one independent variable (dichotomous or nominal) and a dependent dichotomous or nominal variable;
 describe a relationship between one independent variable (dichotomous, nominal and scale) and a dependent scale variable using the linear model;
 construct confidence intervals and perform tests in the context of a bivariate relationship between one independent (dichotomous, nominal and scale) variable and a dependent scale variable using the linear model;
 describe a relationship between several independent variables and a dependent scale variable using the linear model (both in the context of addition and in the context of interaction);
 construct confidence intervals and perform tests in the context of several independent variables and a dependent scale variable using the linear model (both in the context of addition and in the context of interaction);
 assess whether the output of a parametric test should lead to adjusting the model (and the test) used and more generally assess whether the data allow using a parametric test to construct confidence intervals and perform tests in the context of a simple and multivariate relationships;
 construct a test for a mean, the difference between means and the association between scale variables when the assumptions for a parametric test are not fulfilled.



In this course the basic notions of data analysis that would allow them to make inferences about populations on the basis of a randomly sampled data set are introduced. The course uses the regression (or ‘linear’) model as the basic skeleton and in this context introduces confidence intervals and tests. In addition, it familiarizes students with the logic and implementation of some nonparametric statistical analyses (methods that do not use a concepts like ‘the mean’ and ‘variance’). Usage of these methods is illustrated using research examples. The software used in both teaching and in the assessment is R for statistics.




 Assumed previous knowledgeIt is assumed students are very well versed in the distinction between units and variables; the measurement levels of variables (dichotomous, nominal, scale); the main ‘statistics’ describing variables (‘mean’, ‘standard deviation’ and ‘variance), the ‘standardization’ of variables, and with the (standardized) normal distribution (and the associated ‘empirical rule’). These topics are covered in the course Research Methods and Descriptive Statistics. 
Bachelor Communication Science 
Bachelor International Business Administration 
Master Educational Science and Technology 
Master Public Administration 
  Required materialsBookAnalysing data using linear models, by Stephanie M. van den Berg (a link to the most recent edition will be provided using a link in the Canvas environment). Additional required reading materials will also be provided via Canvas. 

 Recommended materialsInstructional modesTestsTheory part 1
 Theory part 2
 Assignment


 