sow944 - Quantitative Research Methods
| Module label | Quantitative Research Methods |
| Module code | sow944 |
| Credit points | 6.0 KP |
| Workload | 180 h |
| Institute directory | Department of Social Sciences |
| Applicability of the module |
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| Responsible persons |
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| Skills to be acquired in this module | After completing this module, students will have a working knowledge of a range of research designs and statistical methods that are commonly applied in social science research. Students will thus be able to understand the methods used in a wide range of quantitative empirical research articles. This entails not only an understanding of the achievements of these studies but also to comprehend their potential limitations. Overall, thus, students will be able to critically discuss existing research and to generate ideas for improving on existing research designs and analyses. This also entails that students will be able to recognize the limitations of existing research with regard to claims about causal relationships between variables, an important skill not only for assessing the contribution of research for scientific advancement but also for evaluating its usefulness for interventions and policies. In this class, students will also learn to master the statistical software and programming language R (with RStudio) that allows them to conduct a wide range of data analytics tasks. After successfully completing the class, students will know how to import and export data into/from R, conduct common data management and visualization tasks, specify, run and evaluate a range of basic and advanced statistical models, and write up and prepare their results for presentation and publication. |
| Module contents | The two seminars combine lecture-style presentations of research designs and statistical methods with applied lab sesssion in which students work on hands-on exercises that help them achieve the necessary data analytics skills for this class. At the beginning, students get introduced to the statistical software and programming language R (with RStudio) that is used throughout this class. We will then have a few (hands-on) sessions on data import/export, data management, and visualization. Then, after briefly reviewing the basic linear regression framework and its extensions, we will build on it by introducing generalized linear regression models that allow to extend the regression approach to dependent variables that are not metrically scaled (e.g. count, logistic, ordinal logistic, and multinomial regression). We will then further open the regression toolbox by covering regression models for multilevel data including random and fixed effects models. We will discuss limitations of these models with regard to causal analysis and explore ways to improve their causal validity, both by means of other research designs and improved specification of statistical models. Finally, we will cover how to present and write-up statistical results and how to produce publication-ready output from statistical models. |
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| Language of instruction | English |
| Duration (semesters) | 1 Semester |
| Module frequency | Wintersemester (1. FS) und Sommersemester (2. FS) |
| Module capacity | unlimited |
| Type of course | Comment | SWS | Frequency | Workload of compulsory attendance |
|---|---|---|---|---|
| Lecture | -- | 0 | ||
| Seminar | 4 | SuSe and WiSe | 56 | |
| Total module attendance time | 56 h | |||
| Examination | Prüfungszeiten | Type of examination |
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| Final exam of module | Portfolio consisting of 2 partial performances |
