Vorlesungsverzeichnis

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Start > Masterprogramm Management (M.A./M.Sc.) -> Management & Finance & Accounting -> Research Methods

Lehrveranstaltungen

Quantitative Research Methods (#1für M&SAaF) (Übung)

Dozent/in: Huu Tam Nguyen

Termin:
 14-täglich | Dienstag | 08:00 - 10:00 | 12.04.2022 - 08.07.2022 | C 14.001 Seminarraum

Inhalt: - Stata software - Descriptive statistics - Ordinary least squares (OLS) regression - Econometric analysis for longitudinal, cross-sectional, and basic panel data

Quantitative Research Methods (#2 für M&E) (Übung)

Dozent/in: Ossama Elshiewy

Termin:
 Einzeltermin | Fr, 03.06.2022, 12:00 - Fr, 03.06.2022, 18:00 | C 14.027 Seminarraum
Einzeltermin | Fr, 01.07.2022, 12:00 - Fr, 01.07.2022, 18:00 | C 14.027 Seminarraum

Inhalt: Exercise sessions using SPSS __Date__Content___________________ June 3 Introduction to SPSS June 3 Linear models Part 1: ANOVA June 3 Linear models Part 2: OLS July 1 Mediation-models July 1 Moderated-Mediation-models

Quantitative Research Methods (für M&E) (Vorlesung)

Dozent/in: Ossama Elshiewy

Termin:
 14-täglich | Dienstag | 10:15 - 13:45 | 12.04.2022 - 08.07.2022 | C HS 4

Inhalt: LECTURE FORMAT The lectures will be held in presence at the Leuphana campus - To facilitate exchange among participants - To allow immediate queries w.r.t. to the lecture content. LECTURE CONTENT --- Block A: Building a Quantitative Research Model 1. Theory utilization and research models (“conceptualization”) 2. Experimental designs and variable measurement (“operationalization”) --- Block B: Analyzing a Quantitative Research Model 3. Linear Models (ANOVA and OLS) 4. Moderation- and Mediation Models --- EXERCISES Apply the concepts and models from the lecture to real-world research models and data. Both models and data will be available, but students are welcome to discuss own projects.

Quantitative Research Methods (für M&SAaF) (Vorlesung)

Dozent/in: Christoph Wegener

Termin:
 wöchentlich | Dienstag | 10:15 - 11:45 | 04.04.2022 - 08.07.2022 | C 14.204 Seminarraum

Inhalt: Main topics and learning objectives: Core element of the course is to learn how to use databases and statistical software in order to enable students to conduct their own empirical analyses. At the same time, students learn important statistical methods and practice them by applying the learned concepts to practical examples. The lecture is supplemented with presentations and discussions of the methodological background of current research projects.