sow965 - Advanced Statistics (Vollständige Modulbeschreibung)

Modulbezeichnung	Advanced Statistics
Modulkürzel	sow965
Kreditpunkte	6.0 KP
Workload	180 h
Einrichtungsverzeichnis	Institut für Sozialwissenschaften
Verwendbarkeit des Moduls	Master Social Sciences (Master) > Wahlpflichtmodule
Zuständige Personen	Schnettler, Sebastian (Modulverantwortung) Lehrenden, Die im Modul (Prüfungsberechtigt)
Teilnahmevoraussetzungen	sow944
Kompetenzziele	After completing this module, students will be able to: demonstrate a comprehensive knowledge of advanced statistical concepts and techniques; interpret and critically evaluate data, draw meaningful conclusions and make reliable predictions based on the specific advanced statistical method on offer; understand research publications using advanced statistical methods; apply advanced statistical methods in their own empirical projects; utilise software packages (e.g., R) to analyse data using advanced statistical tools; effectively communicate statistical results and implications.
Modulinhalte	The specific contents of this module may alternative between classes offered within this module. Examples for advanced statistics contents that may be offered are causal inference, computational social science methods, machine learning, quantitative text analysis, Bayesian statistics, multilevel models, advanced data visualisation, and advanced statistical programming. This list is not intended to be exhaustive. Further topics may be offered depending on capacities and demand.
Literaturempfehlungen	Best, Henning, und Christof Wolf, Hrsg. 2015. The SAGE handbook of regression analysis and causal inference. Los Angeles [Calif.]: SAGE Reference. Engel, Uwe, Anabel Quan-Haase, Sunny Xun Liu, und Lars Lyberg, Hrsg. 2022. Handbook of computational social science: theory, case studies and ethics. Abingdon, Oxon ; New York, NY: Routledge. Huntington-Klein, Nick (2022): "The effect: an introduction to research design and causality". Boca Raton: CRC Press, Taylor & Francis Group. McElreath, Richard (2022): "Statistical Rethinking. A Bayesian Course with Examples in R and Stan." Boca Raton: CRC Press, Taylor & Francis Group.
Links
Unterrichtssprache	Englisch
Dauer in Semestern	1 Semester
Angebotsrhythmus Modul	Winter- oder Sommersemester (1., 2. oder 3. FS)
Aufnahmekapazität Modul	unbegrenzt
Modulart	Wahlpflicht / Elective
Modullevel	MM (Mastermodul / Master module)

Lehrveranstaltungsform	SWS	Angebotsrhythmus	Workload Präsenz
Vorlesung		--	0
Seminar	2	SoSe oder WiSe	28
Arbeitsgruppe	2	SoSe oder WiSe	28
Präsenzzeit Modul insgesamt			56 h

Prüfung	Prüfungszeiten	Prüfungsform
Gesamtmodul		1 Portfolio Short exam + take-home exercises or Short exam + short research report of own data analytics project or Take-home exercises + short research report of own data analytics project