Statistics in (Business) Psychology

A basic course for students to learn everything needed to independently carry out research projects—for example, a bachelor’s or master’s thesis. Completely open and free of charge.

Descriptive
Inference

Here you will learn everything you need to write a quantitative bachelor’s or master’s thesis in psychology, business psychology, or the social sciences.

Following this convention, we organize the content according to descriptive statistics and inferential statistics. The topics build on one another, so it is advisable to work through them from top to bottom. However, if you are already in the middle of writing your thesis, it is of course perfectly fine to look up individual methods directly.

Descriptive Statistics

Descriptive statistics are concerned with describing datasets. At first, that may not sound like much—but it is the foundation for everything that follows.

Foundations of Statistics and Data Collection

These materials cover the basics of statistics. They also provide the relevant foundations for data collection. The material that follows assumes high-quality data.

  1. Relevance of Statistics in Psychology and Occupational Psychology
  2. The Structure of the Statistical Data Table
  3. Important Terms
  4. Measurement and Operationalization for Descriptive Statistics | Earlier Version
  5. Data Collection for Quantitative Research
  6. Sample and Population in Statistics
Exercise: Descriptive Statistics 1

Univariate analysis

This section presents basic analyses and evaluation methods.

  1. Statistical software
  2. Measures of central tendency in descriptive statistics
  3. Measures of dispersion in descriptive statistics
  4. Random variables
  5. Distribution
  6. Standardization
  7. Visualization

Theoretical foundations of relationships and inference

This section covers preparatory material for inferential statistics.

  1. Causality
  2. Estimation
  3. Hypothesis testing

Relationships between multiple variables

This section presents basic analyses and methods for evaluating relationships.

  1. Measures of association for nominal (discrete) variables
  2. Measures of association for ordinal variables
  3. Measures of association for metric variables

Further topics

Further topics that are not necessarily at the core of the course but are nevertheless often relevant.

  1. Informed Consent in Research
  2. Reporting in Your Thesis
  3. Probability Basics
  4. Checking for Multicollinearity
  5. Levene Test | Earlier Version
  6. Checking Normality with the Shapiro-Wilk Test
  7. Preparing for a Statistics Exam

Inferential Statistics

Lessons marked with 🧠 describe more advanced content.

Working with Statistical Software and Data Management

This content provides an introduction to working with statistical software.

  1. Choosing Statistical Software (e.g. for your thesis)
  2. The Importance of Data Management
R
  1. Basic Use of R (RStudio)
  2. Data Management in R
SPSS
  1. Basic Use of SPSS
  2. Data Management in SPSS
Exercise: Data management (easy)
Exercise: Data management (intermediate)
Exercise: Data Management (difficult)
Exam Questions on R Syntax (Easy)
R Syntax Exam Exercises (Intermediate)

Exploratory Data Analysis in R and SPSS

This content teaches exploratory data analysis using various software applications.

  1. Measures of central tendency in descriptive statistics (Review)
  2. Measures of dispersion in descriptive statistics (Review)
  3. Exploratory factor analysis
R
  1. Descriptive Statistics in R
  2. Visualization with base R
  3. Visualization with R ggplot2 🧠
  4. The psych Package for R 🧠
  5. Exploratory Factor Analysis in R 🧠
  6. Operators in R
SPSS
  1. Descriptive Statistics in SPSS
  2. Visualization with SPSS
  3. Exploratory Factor Analysis in SPSS
Exercise: Explore data (intermediate)
Exercise: Explore data (advanced)
Exercise: Interpreting EFA (intermediate)
Exploratory Analysis Exam Questions (Easy)
Exam Questions: Exploratory Analysis (Intermediate)

Testing hypotheses about differences I: t-Tests

This section presents analytical methods for testing hypotheses about differences.

  1. Basics
  2. t-tests
Exercise: t-test (simple)
Exercise: t-test (intermediate)
Exercise: t-test (difficult)
Exam question: t-test

Testing hypotheses about differences II: Analysis of Variance

This section presents analytical methods for testing hypotheses about differences.

  1. Basics
  2. One-Way Analysis of Variance (ANOVA)
  3. Further Analyses
Exercise: ANOVA (easy)
Test ANOVA

Testing Relationships

This section presents analytical methods for testing hypotheses about relationships.

  1. Basics
  2. Correlaton and Rank-Correlation
  3. Simple and Multiple Linear Regression
  4. Cross-Lagged Panel Model
  5. Pathanalyse in R
Klausuraufgabe Zusammenhang

Further topics

  1. Introductino to Bayesian Statistics