
Aims at
 Practical skills in data handling, scale construction and experience with statistical analyses for social science research;
 Knowledge of these assumptions underlying the statistical models (generalized linear models),
 Skills in evaluating whether the assumptions are reasonably met in a research problem,
 Ability to translate between the research problem and statistical models,
 Ability to select appropriate models among the familiar models;
 Ability to report analysis, both in statistical and substantive terms.


The first part of the course involves data handling, scale construction (mainly classical test theory, factor analysis), and the disciplined cautious use of syntax in statistical software (MERM: SPSS; SaSR: Stata). The second part of the course addresses statistical models, mostly from the class of generalized linear models. Models to be discussed:
(1) Linear regression modeling for continuous dependent variables (such as income), focusing on modeling mediation, interactions and nonlinear relations;
(2) Regression models for binary dependent variables (such as whether or not people are employed): logistic and probit regression, emphasizing different types of interpretaions;
(3) Regression models for ordinal dependent variables (e.g., voting intentions measured by a Likert item): ordinal logit.
(4) Regression models for nominal dependent variables with subjectlevel and/or alternativelevel predictors (e.g., the denomination of the school attended by children): multinomial logit and conditional logit models;
(5) Regression models for the timeuntiltheoccurrenceofanevent (e.g., the birth of the first child, the divorce of a marriage, promotion in a career): discrete time survival analysis and Cox regression.
The discussion on these regressiontype models focuses on the substantive interpretation of these nonlineair models. In addition, we discuss numerous general statistical issues such as statistical estimation methods; Wald testing versus likelihood ratio testing; Hausmanstyle tests for model specification; marginal effects, etc. Computer practical for MERM (using SPSS) and SaSR (using Stata) are separate.




 IngangseisenVoorkennisBasic knowledge of multivariate variance and covariance analysis. 
  Verplicht materiaalWordt nader bekendgemaakt 
 WerkvormenComputerpracticum AlgemeenDuring the computer lab (MERM: SPSS, SaSR: Stata), students are invited to work in small groups. Biweekly take home assignments have to be prepared in small groups outside of the computer lab; these are graded. 2 x a week 120 minutes. Voorbereiding bijeenkomstenBefore the meetings, students (re)read the literature and study the lecture notes; they complete the questions in the sample sessions, work occasionally on paperandpencil assignments, and solve assignments for selfstudy for which the solutions are provided. Bijdrage aan groepswerkStudents are expected to actively participate, present the results of their assignments and discuss during the meetings.
 Evaluatie
 Hoor/werkcollege AlgemeenStudents are allowed/invited to work in small groups for preparation of the lectures, and for the selfstudy assignment. 2 x a week 90 minutes. Voorbereiding bijeenkomstenBefore the lecture meetings students read the literature that will be discussed during the meetings and work through the scheduled "sample session(s". Bijdrage aan groepswerkStudents are expected to actively participate, present the results of their assignments and discuss during the meetings.

 ToetsenEindresultaatWeging   100 
Minimum cijfer   5,5 
Beoordeling1. working knowledge of "domains of application" and assumption of the statistical models and techniques discussed in the course. 2. Ability to apply and interpret these models using statistical software. 3. Translating scientific arguments and hypotheses into statistical hypotheses about parameters of suitably specified statistical models. 4. Data handling. 5. Reporting of research results. Aspecten van academische vorming • Academisch denken, werken en handelen • Intellectuele vaardigheden • Materiaal / data verzamelen / produceren • Onderzoeksvaardigheden integraal • Beheersen van methoden en technieken 


 