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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
Lecturer
O. Ryan, MSc
Other courses by this lecturer
Lecturer
dr. L.D.N.V. Wijngaards
Other courses by this lecturer
Teaching period
1  (02/09/2019 to 08/11/2019)
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 03/06/2019 up to and including 30/06/2019
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Waiting listNo
Aims
Learning to translate social scientific theories into models. Learning to analyse models using SPSS and AMOS.

Relation between assessment and objective
The test(s) consist(s) of different parts. To test whether the student learned how to translate social scientific theories in models the student will be given one or multiple research questions for which the student has to choose the correct way of analysis and has to perform the appropriate analysis accordingly. To test whether the student learned how to analyse models the student is expected to make correct interpretations of the output from AMOS and/or SPSS that the student is presented with. To test the general knowledge of the student about statistical modeling in the final part of the test there will be statements of which the student has to indicate whether they are true or false.
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 and regression analysis. Participants should be familiar with SPSS.

In the social sciences statistics are used to test whether a theory can be rejected or not. SPSS is used as a statistical toolkit to do this. SPSS however cannot be used to test many of the theories that are used in psychology, sociology, pedagogical sciences and educational sciences. In this course, you will be introduced to Structural Equation Modeling, a flexible, intuitive technique that will enable you to test almost all possible theories directly using empirical data.
Based upon data that is provided at the introduction of the course, you will cycle through all phases of social scientific research. More specifically you will learn to translate a social scientific theory into a statistical model. You will learn to analyse your data with these models. And finally you will learn to interpret and report your results. Analyses will be executed using the statistical modeling packages SPSS and AMOS. SPSS is a combination of a data-editing and data analysis program. It is possible to build statistical models in SPSS, but for some questions building these models is either cumbersome or impossible. For this we use the computer program AMOS. AMOS analyses data based upon a graphical representation of the model the user is interested in, it is a so-called 'user-friendly' program. In the graph the user can specify regression type models and factor analytic models.
In this course the emphasis is on the methodological aspects of measurement and the testing of 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) Whether statistical models differ for sub-groups in your population.
Entry requirements
At least one of the following course modules must be completed:
- 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.
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
Tests on computer + written assignment
Test weight100
Minimum grade5.5

Assessment
The course will be completed with tests on the computer and a written assignment. The tests consist both of questions about the literature and a practical part in which students analyse a real-life dataset, answer questions and report their results.

Aspects of student academic development
Synthesizing and structuring of information
Intellectual skills
Using information and communication technology ('computer literacy')

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