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Course module: 201500130
201500130
Missing Data Theory and Causal Effects
Course infoSchedule
Course code201500130
ECTS Credits7.5
Category / Level3 (Bachelor Advanced)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Social Sciences; Methods and Statistics;
Contact persondr. G. Vink
E-mailg.vink@uu.nl
Lecturers
Lecturer
prof. dr. S. van Buuren
Other courses by this lecturer
Lecturer
Docenten van de afdeling/departement(en)
Other courses by this lecturer
Lecturer
dr. G. Vink
Other courses by this lecturer
Contactperson for the course
dr. G. Vink
Other courses by this lecturer
Teaching period
3  (08/02/2021 to 23/04/2021)
Teaching period in which the course begins
3
Time slotC: MON-afternoon, TUE-afternoon,THU-morning
Study mode
Full-time
RemarkLanguage used: English.
Limited number of participants!
Enrolment periodfrom 02/11/2020 up to and including 29/11/2020
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Waiting listNo
Aims
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).
Content
Note: Students who cannot comply with the entrance requirements mentioned below will be asked to provide further information on their eligibility. The course coordinator will decide on their eligibility.

In eight weeks you will learn the basics of missing data theory and causal effects, and the connection to research philosophy. During every lecture we will treat a different theoretical aspect. Following each lecture there will be a computer lab meeting that connects the statistical theory to practice (with statistical software package mice in R), as well as a workgroup meeting wherein you will work on solving your own missing data problem. 

Assumed knowledge
Multivariate statistics. Participants should be familiar with interpreting statistical software output (such as SAS/STATA/SPSS).
Competencies
-
Entry requirements
You must meet the following requirements
  • At least 1 of the courses below must have been passed
    • MTS3: Context Cognitive Psychology (200300076)
    • MTS3: Context Clinical Psychology (200300104)
    • M&S 3:Context social and organisationpsy (200300160)
    • Social problems (200400185)
    • Methods, techniques and statistics 3 (200400233)
    • MTS3:Context Social, Health & Org.Psych. (200400460)
    • MV/PM: Assessment and evaluation (200500044)
    • Methods, techniques and statistics 3 (200600364)
    • Data-analysis (200700054)
    • Methods in Educational Research (200800010)
    • Methods, techniques and statistics 3 (201000398)
    • HC:MTS3:Context Developmental Psychology (201400065)
    • HC:MTS3: Context Soc.&Organisational Psy (201400460)
    • MTS3: Context Cognitive Psychology (201700076)
    • MTS3: Context Clinical Psychology (201700104)
    • Practicum data-analyses (200300022)
    • MTS3: Context Developmental Psychology (200300065)
    • Advanced research methods and statistics (201900398)
    • Advanced RMS for Psychology: SHO Psych. (201900460)
    • Advanced RMS for Psychology: CCA Psych. (201900065)
    • Advanced RMS for Psychology: Exp. Psych. (201900076)
    • Advanced RMS for Psychology: Clin.Psych. (201900104)
    • Advanced research methods and statistics (201900054)
Prerequisite knowledge
Multivariate statistics. Participants should be familiar with interpreting statistical software output (such as SAS/STATA/SPSS).
Required materials
Reader
Reader for the course 'Missing data theory and causal effects'. The reader will be made available at the start of the course by the course coordinator.
Costs of materials:0.00
Book
The book can be studied freely from http://www.stefvanbuuren.name/fimd
ISBN: 9781138588318
Title:Flexible Imputation of Missing Data
Author:Stef Van Buuren
Publisher:Chapman and Hall/CRC
Edition:2
Costs of materials:0.00
Instructional formats
Lecture

Class session preparation
In order to arrive prepared for class meetings students have to read literature and complete take home exercises.

Practicals

Class session preparation
Students need to have finished the previous practical before each practical lab

Work group

Class session preparation
Students have to work in small groups on a missing data problem. Every week students will progress in their abilities and skillset to solve the missing data problem. The workgroup is used to ask questions, practice skills, work on the missing data problem and make group appointments. Each group will give a short presentation about their group’s progress with respect to the missing data problem.

Tests
Assignment(s) 1
Test weight15
Minimum grade5.5

Assessment
The first assignment

Assignment(s) 2
Test weight15
Minimum grade5.5

Assessment
The second assignment

Assignment(s) 3
Test weight30
Minimum grade5.5

Assessment
The final assignment

Practical Work
Test weight0
Minimum grade-

Assessment
Students need to demonstrate that they have learned the necessary practical skills by handing in a sufficient result for EVERY practical

Prelim
Test weight40
Minimum grade5.5

Assessment
The exam consists of TRUE/FALSE statements, multiple choice questions and essay questions.

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