The problem of missing data seriously complicates the statistical analysis of data and simply ignoring it is not a good strategy. In this course students get acquainted with missing data theory and causal effects, they will learn that these fields are related, and they will learn how to solve the missingness problem by means of imputation (filling in the values) and by means of clever research designs that allow for missing values without influencing the validity of the research results. Students will learn how to solve basic missing data problems by designing, performing, interpreting, and evaluating analyses on incomplete data. This course makes students better equipped for a further career (e.g. junior researcher or research assistant) or education in research, such as a (research) Master program, or a PhD.
At the end of this course, students are able to:
1. apply and interpret the basic methodological and statistical concepts that are associated with doing causal and/or inferential research when not all collected data are observed;
a. explain concepts from inferential statistics, such as correlation and regression.
b. make an informed choice for research designs that minimize missing data problems.
c. apply and explain the choice for techniques to investigate missing values.
d. apply and explain the concept of multiple imputation.
e. interpret statistical software output and report software output.
f. explain and conceptualize causal inference and its relation to missing data theory.
g. perform the different steps in solving basic missing data problems and report on these steps.
2. apply and interpret important techniques in missing data theory and the theory of causal effects;
a. perform, interpret and evaluate quantitative (causal) analyses on incomplete data with statistical software.
b. perform causal analyses in statistical software.
Relation between assessment and objective
In this course, skills and knowledge are evaluated on three separate occasions:
1. With the exam the knowledge from methodological and statistical concepts is evaluated (learning goals 1a, 1d, 1f), as well as the application of these concepts to research scenarios (learning goals 1b and 1c). During the exam students need to interpret statistical software output (learning goal 1e).
2. With the practical test it is tested if the student has sufficient skills to solve basic missing data problems and execute quantitative analyses on multiply imputed data sets (learning goals 2a and 2b).
3. The research project focuses on applying the newly gained knowledge and skills with respect to solving a basic missing data problem and reporting on the steps taken to obtain a solution (learning goal 1g).
