The spring semester 2021 will take place online until further notice. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

Ralph Hansmann: Catalogue data in Autumn Semester 2016

Name PD Dr. Ralph Hansmann
ETH Zürich, CHN K 76.2
Universitätstrasse 16
8092 Zürich
DepartmentEnvironmental Systems Science

701-0721-00LPsychology3 credits2VR. Hansmann, C. Keller, M. Siegrist
AbstractThis course provides an introduction to psychological research and modelling, focusing on cognitive psychology and the psychological experiment. Participants learn to formulate problems for psychological investigation and apply basic forms of psychological experiment.
ObjectiveStudents are able to
- describe the areas, concepts, theories, methods and findings of psychology.
- differentate scientific psychology from "everyday" psychology.
- structure the conclusions and significance of an experiment. according to a theory of psychology.
- formulate a problem for psychological investigation.
- apply basic forms of psychological experiment.
ContentEinführung in die psychologische Forschung und Modellbildung unter besonderer Berücksichtigung der kognitiven Psychologie und des psychologischen Experiments. Themen sind u.a.: Wahrnehmung; Lernen und Entwicklung; Denken und Problemlösen; Kognitive Sozialpsychologie; Risiko und Entscheidung.
701-1541-00LMultivariate Methods
One of the lectures 701-1541-00 (autumn semester) OR 752-2110-00 (spring semester) are highly recommended for students in Environmental Sciences with the Major Environmental systems and Policy.
3 credits2V + 1UR. Hansmann
AbstractThe course teaches multivariate statistical methods such as linear regression, analysis of variance, cluster analysis, factor analysis and logistic regression.
ObjectiveUpon completion of this course, the student should have acquired:
(1) Knowledge on the foundations of several methods of multivariate data analysis, along with the conditions under which their use is appropriate
(2) Skill in the estimation, specification and diagnostics of the various models
(3) Hands-on experience with those methods through the use of appropriate software and actual data sets in the PC lab
ContentThe course will begin with an introduction to multivariate methods such as analysis of variance and multiple linear regression, where a metric dependent variable is "explained" by two or more independent variables. Then two methods for structuring complex data, cluster analysis and factor analysis will be covered. In the last part, procedures for the analysis of relationships involving dichotomous or polytomous dependent variables (e.g., the choice of a mode of transportation) will be discussed.
LiteratureWill be announced at the beginning of the course.