Vrije Universiteit Amsterdam (VU) • Psychologie

Grade/cijfer: 9.3. This is a well-structured and clear summary of the Psychopharmacology course at VU. It explains everything in an organized and easy-to-understand way, for example by using tables and structured layouts throughout. I studied with this summary for two days before the exam and got a 9.3. I follow the Dutch track but studied and took the exam in English, which I highly recommend if you also attend the lectures. This way, all the terms match what is covered in class, so you do...

This is a summary of week 1-7 for the course Biological and Cognitive Psychology from VU. It contains all learning goals worked-out as prepared by the course itself, based on the the Physiology of Behaviour, GE by Carlson and Birkett (13th ed), the course syllabus about cognitive psychology, and my own lecture notes. This 45-page summary contains everything you need to know. Studying this summary got me a grade of 9.6!

There are class notes and a summary of lectures—also some practice questions for the exam.

This document provides a detailed breakdown of ANOVA, hypothesis testing, and post-hoc comparisons, focusing on: 1. Understanding ANOVA ️ When to use ANOVA (Comparing >2 Means in Different Groups) ️ How ANOVA Works (Between-Group vs. Within-Group Differences) ️ F-Test for Comparing Group Means & Interpreting F-Values 2. Hypothesis Testing & Effect Size ️ Null & Alternative Hypotheses for ANOVA ️ Explained Variation Using Eta Squared (η²) – Like R² in Regression ️ ...

This document provides a detailed breakdown of model selection procedures, assumptions, and regression diagnostics, focusing on: 1. Model Selection in Regression Analysis ️ Exploratory vs. Explanatory Approaches (Hypothesis-Driven vs. Data-Driven) ️ Backward Elimination, Forward Selection & Stepwise Regression ️ Pitfalls of Automated Model Selection (Cross-Validation & Overfitting) 2. Checking Assumptions in Multiple Regression ️ Random Sampling & Representativeness ️ Linear...

This document provides a detailed breakdown of interaction effects in multiple regression, focusing on: 1. Understanding Interaction Effects (Moderation) in Regression ️ Why interaction terms are used in multiple regression ️ How to interpret models with interaction effects ️ When the association between two variables depends on a third variable 2. Hypothesis Testing for Interaction Effects ️ Global F-Test for Overall Model Significance ️ t-Test for Interaction Coefficients ...

This document provides a detailed breakdown of partial effects in multiple regression, focusing on: 1. Understanding Partial Effects in Multiple Regression ️ Why b-coefficients alone can’t determine effect strength ️ Standardized Regression Coefficients (b)* ️ Squared Partial Correlation (R²_partial) & Its Interpretation ️ R-Squared Change (ΔR²) for Unique Predictor Contribution 2. Hypothesis Testing for Individual Predictors ️ t-Tests for b-Coefficients (Significance T...

Multiple Regression Lecture Notes (Advanced Regression Analysis) A complete guide to multiple regression for predicting outcomes using multiple variables ️ Constructing & Interpreting Multiple Regression Models ️ Bivariate vs. Multiple Regression (Statistical Control, Shared Variance, Adjusted R²) ️ Regression Coefficients & Interaction Effects ️ Hypothesis Testing (F-tests, t-tests, p-values, Confidence Intervals) ️ Explained Variation (TSS, SSE, RSS) & Predictive Power of Mod...

Linear Regression Lecture Notes (Regression Analysis & Hypothesis Testing) Master linear regression with step-by-step explanations & real-world applications ️ Constructing & Interpreting Simple Regression Models ️ Hypothesis Testing & Statistical Significance (t-Tests, F-Tests, p-Values) ️ Explained Variation (R², Sum of Squares, Residuals) ️ Assumptions of Linear Regression (Homoscedasticity, Normality, Linearity) ️ SPSS Output & Interpretation (ANOVA, Model Summary, Coefficie...

Multivariate Association Lecture Notes (Advanced Statistics) Deep dive into complex statistical relationships & causality analysis ️ Understanding Correlation vs. Causation (Spurious Associations, Simpson’s Paradox) ️ Multivariate Analysis (Controlling for Third Variables, Mediation & Moderation) ️ Causal Inference & Experimental Design (Randomized Controlled Trials) ️ Regression Modeling & Interaction Effects ️ Real-World Applications in Social Sciences & Research This set...