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Course module: 200300125
200300125
Theory Construction and Statistical Modeling
Course infoSchedule
Course code200300125
ECTS Credits7.5
Category / Level3 (Bachelor Advanced)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Social Sciences; Methods and Statistics;
Contact persondr. C.J. van Lissa
E-mailc.j.vanlissa@uu.nl
Lecturers
Lecturer
dr. C.J. van Lissa
Other courses by this lecturer
Contactperson for the course
dr. C.J. van Lissa
Other courses by this lecturer
Teaching period
1  (31/08/2020 to 06/11/2020)
Teaching period in which the course begins
1
Time slotC: MON-afternoon, TUE-afternoon,THU-morning
Study mode
Full-time
RemarkLanguage used: English.
Limited number of participants!
Enrolment periodfrom 02/06/2020 up to and including 28/06/2020
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 17/08/2020 up to and including 18/08/2020
Waiting listNo
Aims
Translating social scientific theories into models. Analyzing models using R and lavaan.

Relation between assessment and objective
The test(s) consist(s) of three parts. One part consists of multiple research questions for which the student has to choose and conduct the correct analysis. Another part requires the student to make correct interpretations of the output of analyses (e.g., output from R and lavaan). Finally, one part is a multiple choice (TRUE or FALSE) test of general knowledge about statistical modeling.
Content
Note: This course can also be taken as part of the honours programme (and then includes additional work and guidance). Contact the course coordinator if you are an honours student.
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.
Assumed knowledge: Anova, regression and correlation. Participants should be familiar with a statistical program, such as SPSS or R or STATA.

Statistics are a tool test whether a theory can be rejected or not. However, social scientific theories are often more complex than the basic relationships that can be tested in SPSS. This course introduces Structural Equation Modeling: a flexible, intuitive technique that will enable you to represent entire theories and their assumptions, and test them on empirical data. Structural Equation Modeling combines factor analysis – tapping into theoretical concepts based on multiple measured indicators – with multiple regression models. It is used to examine whether theoretical constructs are adequately measured, and to test complex theories. We will discuss, among others, the following topics: 1) How can I test whether questions measure what they intend to measure? 2) How to test complicated models (mediation and path models)? 3) Do theoretical models differ across populations or sub-groups in the population?
You will cycle through all phases of social scientific research: Translating a social scientific theory into statistical models, and analyzing those models based on empirical data (provided at the beginning of the course). Finally, you will learn to interpret and report your results.
Analyses are conducted in the statistical software R, and the structural equation modeling package lavaan. No prior knowledge of R is required; this course can serve as a basic introduction to R. We focus on the specific techniques covered in this course. R is a free, open source program, which can be used just as easily for basic t-tests and correlations, as for cutting-edge analyses such as Structural Equation Modeling or machine learning.
Competencies
-
Entry requirements
You must meet the following requirements
  • At least 1 of the courses below must have been passed
    • Methods and statistics 1 (201600305)
    • ALPO: MTS2 (201600335)
    • ALPO: MTS3 (201700365)
    • ADS: Fundamental techniques (201900026)
    • Missing Data Theory and Causal Effects (201500130)
    • ADS: Applied data analysis (201900027)
    • Advanced research methods and statistics (201900054)
    • Practicum data-analyses (200300022)
    • MTS3: Context Developmental Psychology (200300065)
    • 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)
    • 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)
Prerequisite knowledge
Anova and regression analysis. Participants should be familiar with SPSS or any other statistical software (R, SAS, etc).
Required materials
Items
Articles available through Blackboard.
Instructional formats
Computer practical

Class session preparation
Perform computer exercises. Students obtain a dataset and an exercise which brings some of the theoretical aspects from the lectures or literature into practice.

Contribution to group work
We expect a cooperative and proactive attitude of the students.

Lectures

Class session preparation
To prepare class meetings students have to read literature.

Tests
Subtest A
Test weight50
Minimum grade5.5

Subtest B
Test weight50
Minimum grade5.5

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