Module label | Research methods II - Statistical Learning |
Module code | psy112 |
Credit points | 6.0 KP |
Workload | 180 h |
Institute directory | Department of Psychology |
Applicability of the module |
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Responsible persons |
Hildebrandt, Andrea (Module responsibility)
Hildebrandt, Andrea (Authorized examiners)
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Prerequisites | Enrolment in Master's programme Neurocognitive Psychology. |
Skills to be acquired in this module | Goals of module: Building upon the basic knowledge in multivariate statistical modeling covered in psy111, after completion of this module students will know how to deal with big data to address empirical questions in neurocognitive psychology. They will be able to solve prediction and classification problems to the realm of basic and applied statistical/machine learning purposes. Furthermore, students will understand the specifics of applied research and the statistical modeling of noisy, longitudinal data. Competencies: ++ interdisciplinary kowledge & thinking ++ statistics & scientific programming ++ data presentation & discussion + independent research + scientific literature ++ ethics / good scientific practice / professional behavior ++ critical & analytical thinking ++ scientific communication skills + group work |
Module contents | Part 1: Statistical / machine learning methods
Part 2: Statistical / machine learning methods with R (voluntary seminar)
Part 3: Evaluation research (seminar)
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Reader's advisory | |
Links | |
Language of instruction | English |
Duration (semesters) | 1 Semester |
Module frequency | The module will start every summer term. |
Module capacity | unlimited |
Modullevel / module level | MM (Mastermodul / Master module) |
Modulart / typ of module | Pflicht / Mandatory |
Lehr-/Lernform / Teaching/Learning method | Part 1: lecture; Parts 2 and 3: seminars; additional tutorials are offered. |
Vorkenntnisse / Previous knowledge | psy 111 Research methods I – Statistical Modeling |
Course type | Comment | SWS | Frequency | Workload of compulsory attendance |
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Lecture | 2 | SuSe | 28 | |
Seminar | R seminar voluntary |
2 | SuSe | 28 |
Tutorial | statistics |
SuSe | 0 | |
Total time of attendance for the module | 56 h |
Examination | Time of examination | Type of examination |
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Final exam of module | end of summer term |
The module will be tested with an oral exam (25 min). Required active participation for gaining credits: attendance of at least 70% in the mandatory seminar (use attendance sheet that will be handed out in the beginning of the term). |