
Students …
 are able to describe the differences between experimental, quasiexperimental, and correlational research designs, and they are able to infer from research examples which research design was used;
 are able to describe and reproduce in their own words the basic concepts of simple regression. These basic concepts include: linear association, leastsquares estimation, explained variance, Multiple Rsquare, multiple correlation, adjusted Rsquare, raw and standardized regression coefficients, model tests, predicted scores, and residuals;
 are able to explain the differences between zeroorder, partial, and semipartial (part) associations, both from a statistical perspective and a substantive point of view; they are able to link the different associations with real research questions; they are able to reconstruct from a Venn diagram, the zeroorder, partial, and semipartial correlations and multiple Rsquare values for regression models with two continuous predictors;
 are able to execute a (logistic) linear multiple regression analyses (using SPSS) for models including continuous and categorical predictors; both for models with main effects only, and models including interactions between categorical and continuous variable, or two continuous variables;
 are able to correctly interpret practical applications of hierarchical regression analysis; are able to test significance of the Rsquare change using the Ftest (using SPSS and by hand) and are able to correctly interpret the results; are able to design the hierarchical analysis given the research question envisaged;
 are able to choose the appropriate regression analysis given the research question envisaged;
 are able to draw the correct substantive conclusions from results of the regression analyses, within the context of psychological research;
 are able to reproduce the four basic assumptions of multiple regression analyses and they are able to test the tenability of the assumptions in real data;
 are able to apply the following concepts from inferential statistics (as taught in the course Introduction to Statistics) within the context of multiple (logistic) regression analysis: null hypothesis significance testing, onetailed versus twotailed testing, test statistics, pvalues, confidence intervals, Type I and Type II errors, power, precision, statistical versus practical significance, effect sizes;
 know the APA guidelines with respect to reporting the results of (hierarchical) multiple (logistic) regression;
 know the commonly accepted rules of thumb for interpretation of effect sizes in terms of explained variances;
 know the concepts of probabilities, odds and logits; they know the relationship between the three scales; they are able to transform one into another (formulae are provided).



This course includes the following subjects:
 Correlational research designs
 Linear regression analysis: simple and multiple regression analysis, tests of single regression parameters, tests for model comparison, tests for a set of parameters, categorical predictors using dummies, interactions and probing of simple effects when significant interactions are found, model assumptions and testing their tenability in real data.
 Binary logistic regression analysis, using continuous and categorical predictors, including hierarchical logistic regression, and logistic regression with interaction effects.
Bijzonderheden
 Tutorials. We offer tutorials in which the subjects are discussed and where students can practice the matters at hand. Although the tutorials are not compulsory, we do expect students to follow them. They are complementary to the lectures. We also expect active participation of the students in the tutorials. Students are not allowed to attend other tutorials than the one they did subscribe for.
 SPSS lab sessions. Part of this course are two lab sessions in which students complete exercises in SPSS. The SPSS skills will be tested during an exam.
The grading of the course consists of two parts: the course exam (graded 110) and the SPSS practical (graded passfail). A student passes the course and receives the corresponding ects when he/she is graded a 6 or higher in the course exam and a pass in the SPSS practical. Both partial results remain valid after the academic year in which they were obtained.





Written test opportunities 
Description  Test  Block  Opportunity  Date 


Written test opportunities (HIST) 
Description  Test  Block  Opportunity  Date 

SPSS toets / SPSS test  EXAM_02  BLOK 1  1  02122019  Schriftelijk / Written  EXAM_01  BLOK 2  1  09122019  Schriftelijk / Written  EXAM_01  BLOK 2  2  14012020  SPSS toets / SPSS test  EXAM_02  BLOK 1  2  20012020 

  Required materialsLiteratureThe book that is used in this course is a customized edition of the book Applied Statistics by Rebecca Warner. This (cheaper) edition, specifically developed for TSB, has a different title (Introduction to Techniques for Causal Analysis) and author (John Gelissen) and is only available via the study association or via Studystore. You can also still use the original book (Warner, ISBN: 9781412991346; second edition) 
ISBN  :   9781544356112 
Title  :   Custom: Introduction to Techniques for Causal Analysis 
Author  :   John Gelissen 
 HandoutsSlides/Lecture notes (beschikbaar via Canvas). 

 Recommended materialsTestsWritten
 SPSS test


 