    Cursus   MANBPRA205A  Categorie     Voertaal   Engels  Aangeboden door   Radboud Universiteit; Faculteit der Managementwetenschappen; Bachelor Politicologie;  Docenten     Collegejaar   2022   Periode   3  (30012023 t/m 09042023) 
 Aanvangsblok   3  
 Onderwijsvorm   voltijd  
 Opmerking     Inschrijven via OSIRIS   Ja  Inschrijven voor bijvakkers   Ja  Voorinschrijving   Nee  Wachtlijst   Nee  Plaatsingsprocedure    
     
After completing the course, you will:
 have gained insight into the use and theory of regression analysis in the context of theorytesting political science research
 know how to interpret (simple and multiple) linear and logistic regression models
 know how to interpret these models when they include continuous and categorical predictor variables
 know how to interpret these models when they include nonlinear effects (through the inclusion of polynomial, exponential and logarithmic functions)
 know how to interpret these models when they are used for moderation and mediation analysis
 have gained insight into the benefits and limitations of regression analysis for making inferential statements, including statements about causality
 understand how these limitations are related to uncertainty in (frequentist) inferential statistics in general, and to the assumptions associated with regression models in particular
 have practical knowledge of the application of different types of regression analysis and related data challenges such as dealing with outliers and collinearity
 know how to practically prepare your data and execute various types of regression analysis in scientifically reproducable manner with syntax commands
 be able to interpret the computer output of the relevant statistics
 know how to present your findings in clear and wellstructured tables



Regression analysis is an incredibly widely used method of inquiry in many scientific fields, including political science. Flip through the issues of many major scientific journals, and you will commonly encounter some type of regression analysis as the method of choice in the articles. Even scientific studies that do not use regression methods often still require some understanding of regression methods. This is not only true for other statistical methods, but also for many socalled qualitative research methods. For instance, strategies for caseselection in case study research are sometimes justified by regressionbased reasoning. Other qualitative research methods are sometimes justified explicitly as alternatives to regression analysis, and thus require a good understanding of the strengths and weaknesses of this method. In short, it is nowadays virtually impossible to become a scientist in our field without a good understanding of regression analysis. The method is also widely used in applied research and is frequently is employed as a basis for policy advise, and hence of great importance to professionals concerned with policy choices and policy evaluation.
Political Science Research Methods II (PSRM II) addresses the use of linear and logistic regression analysis in theorydriven research. It teaches you how regression analysis works, how it can be used to tackle substantive research questions, but also what its limitations are. In this way, the course builds on the knowledge and skills that you have acquired in your previous introductory methods and statistics courses. You will apply the knowledge and skills learned in PSRM II directly in the course Project 3: Democracy and Representation.



BA2/Premasters. Intermediate level.


For bachelor students: statistics and methods courses taught in the first year, in particular OIMA, OIMB and Project 1.
For premaster students: statistics and methods courses taught in your previous studies and in the first semester of the premaster, in particular Statistics.
Students who lack appropriate foreknowledge should be aware that they will be unlikely to pass PSRM II.


The final grade is determined by a written exam held during exam week; the passing of weekly assignments is a prerequisite for passing the course.
The written exam consists of multiple choice questions and is graded with chancecorrection. It is not allowed to use the book, notes or other additional resources during the written exam. It is allowed to use a simple scientific calculator and a dictionary.
There are 7 mandatory, individual assignments. Each assignment will be graded as either 'pass' or 'fail'. In order to pass the course, it is necessary to have passed at least 6 of these 7 assignments by the end of the course. There will be 1 retake opportunity for each of the 7 assignments. Additionally, there will be 1 'wildcard' second retake opportunity, which can be used for 1 of the first 5 assignments. All relevant deadlines for the assignments and retake opportunities will be listed in the course manual. The results of the assignments will be communicated through Brightspace.
For students that have passed at least 6 of the 7 assignments by the end of the course, the final grade will be determined by the written exam, with a grade >=5.5 representing a passing grade. Students that have not passed at least 6 of the 7 assignments by the end of the course do not pass the course.
The final grade will be registered in Osiris.
Partial results from previous years are not valid.


 


   Verplicht materiaalBoekField, A. Discovering statistics using SPSS. Los Angeles: Sage (5th Edition)
or
Field, A. Discovering statistics using SPSS. Los Angeles: Sage (4th Edition) 
ISBN  :   9781526419521 
Titel  :   Discovering statistics using SPSS 
Auteur  :   Field, A. 
Uitgever  :   Sage 
Druk  :   5 


Aanbevolen materiaalBoekISBN  :   9781483333434 
Titel  :   How to Use SPSS Syntax., An Overview of Common Commands 
Auteur  :   Manfred te Grotenhuis and Chris Vischer 
Uitgever  :   Sage Publications Inc 
Druk  :   1 


WerkvormenComputerpracticumWerkvormtype   Computerpracticum 
 HoorcollegeWerkvormtype   Hoorcollege 
Opmerkingno weblectures

 ToetsenFinal gradeWeging   1 
Toetsvorm   Multiple choice 
Gelegenheden   Blok 3, Blok 4 


  
 
 