Course module SOW-SCS130 Credits (ECTS) 3 Category MA (Master) Language of instruction English Offered by Radboud University; Faculty of Social Sciences; Social and Cultural Sciences; Lecturer(s) Coordinator prof. dr. R.N. Eisinga Other course modules lecturer Lecturer prof. dr. R.N. Eisinga Other course modules lecturer Contactperson for the course prof. dr. R.N. Eisinga Other course modules lecturer Examiner prof. dr. R.N. Eisinga Other course modules lecturer Academic year 2020 Period PER1   (01/09/2020 to 01/11/2020) Starting block PER1 Course mode full-time Remark Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course. Remarks - Registration using OSIRIS Yes Course open to students from other faculties No Pre-registration No Waiting list No Placement procedure -

	Aims Knowledge: Students obtain up-to-date statistical knowledge and understanding of linear and logistic regression models for the analysis of continuous and binary dependent variables. Skills: Students obtain skills in analyzing continuous and binary responses in multiple regression. They learn how to communicate the results of regression models to a specialist and non–specialist audience. Attitudes: Students are able to look at the analysis of multivariate data with a more sophisticated and critical attitude.  Content In a series of five sessions students gain insight in both theory and practice of linear and logistic regression analysis. Each session consists of a lecture providing background and statistical theory, followed by a computer lab training in which data are to be analyzed and findings interpreted and communicated. The course ends with a combined lab and written exam testing both theoretical knowledge and practical skills. Content: 1    Introduction regression models        lab exercises and assignment: SPSS, simple regression, statistical hypothesis testing 2    Multiple regression model       lab exercises and assignment: multiple linear regression analysis 3    Regression model with nominal predictor variables        lab exercises and assignment: dummy variable regression analysis 4    Regression model with interaction variables       lab exercises and assignment: interactions effects in multiple regression 5    Logistic regression model       lab exercises and assignment: logistic regression analysis Level Presumed foreknowledge Test information Specifics
	Required materials Literature Lewis-Beck, M. S. (1980).Quantitative Applications in the Social Sciences: Applied regression : SAGE Publications Ltd doi: 10.4135/9781412983440 Literature Hardy, M. A. (1993). Quantitative Applications in the Social Sciences: Regression with dummy variables : SAGE Publications Ltd doi: 10.4135/9781412985628 Literature Jaccard, J. & Turrisi, R. (2003).Quantitative Applications in the Social Sciences: Interaction effects in multiple regression : SAGE Publications Ltd doi: 10.4135/9781412984522 Literature Pampel, F. C. (2000). Quantitative Applications in the Social Sciences: Logistic regression : SAGE Publications Ltd doi: 10.4135/9781412984805 Literature The “little green books” mentioned above are available as e-book from the Radboud University Library and may be downloaded as pdf file Instructional modes Lecture Attendance Mandatory Yes Remark Lectures and computer lab exercises Tests Examination Test weight 1 Test type Exam Opportunities Block PER1, Block PER2 Assignments Test weight 0 Test type Assignment Opportunities Block PER1