https://studiegids.vu.nl/en/courses/2024-2025/S_QDAKnowledge and Understanding. The student has acquired knowledge and understanding of: (1) The mathematical way that linear regression works; (2) Complex research designs that include regression, mediation, and moderation; Application. The student has acquired the competences to: (3) Test the assumptions of linear regression; (4) Perform and interpret linear regression models, including models with interaction effects; (5) Test research designs with one mediation or one moderation effect with the use of linear regression; (6) Perform linear regression with the use of the R-software; Making judgements. The student is able to: (7) Evaluate the results of published studies with research designs that include mediation or moderation; Communication. The student has acquired the skills to: (8) Present results of quantitative research that tests mediation or moderation effects in a scientific way.The goal of this course is to provide students with an adequate background in the methods of quantitative analyses of social science data. Students with a background in qualitative data analysis will focus on learning how to apply multiple regression techniques to analysing quantitative data. The course deepens their knowledge and understanding outcomes on methodology, specifically basic knowledge of and insight into quantitative methods and the basic skills to apply statistical methods to analyse data. Students learn to present research results and interpretations of data in a clear manner. This course has a levelling up function in the research master program. In the core courses of the program an advanced level of knowledge in quantitative methods is required. Therefore, this course is actually a crash-course of quantitative methods that will allow students with limited experience in quantitative methods to catch up with the rest and successfully follow the program. The course focusses on teaching multiple linear regression in the context of testing common research designs such as mediation and moderation. In addition, in the context of the linear model, students will be confronted with the common issues that emerge in the analysis of small and large datasets.Most lectures and course materials will be online on Canvas. There are two types of lectures: conceptual and software ones. Conceptual lectures focus on the understanding of the methods, while the software lectures focus on how to execute the data analysis in R. There will be two in-person sessions per week. We will start sessions with a summary of the week’s online lecture and address conceptual and software doubts. And allow time to discuss the applications of data analysis in research. Students will be required to post on Canvas two questions the day before the in-person sessions, related to the lectures material or possible extensions for broader application on research. There will be a weekly assignment that students should prepare and upload to Canvas.The assessment will include a combination of four written assignments (counting each for 25%) that the students have to submit throughout the course. The weekly assignments will be submitted through Canvas. The assessment criteria and the description of the assignment can be also found on Canvas.Darlington, R. B., & Hayes, A. F. (2017). Regression Analysis and Linear Models. Concepts, Applications, and Implementation. New York, NY, US: The Guilford Press. Fairchild, A. J., & McQuillin, S. D. (2010). Evaluating mediation and moderation effects in school psychology: A presentation of methods and review of current practice. Journal of School Psychology, 48(1), 53–84. https://doi.org/10.1016/j.jsp.2009.09.001 MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation Analysis. Annual Review of Psychology, 58(1), 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542First-year students of the research master Social Sciences for a Digital Society that have insufficient background in quantitative methods.This course focuses on advanced topics on linear regression. Before you start, make sure that you have knowledge about basic statistics, and more specifically: descriptive statistics, hypothesis testing and the basics of linear regression. The entry level will be assessed with a diagnostic test at the beginning of the course. Students that lack (part) of this knowledge are referred by the lecturer to material that will allow them to accommodate their deficiencies.