Statistics in (Business) Psychology

An introductory course for students to learn everything they need to carry out research projects independently—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 the convention, we divide the content into descriptive statistics and inferential statistics. The content builds on itself, so it is advisable to work through it from top to bottom. If you are already in the middle of writing your thesis, however, it is of course perfectly fine to look up individual methods directly.

Descriptive Statistics

Descriptive statistics is about describing datasets. That may not sound like much at first—but it is the foundation for everything that follows.

Fundamentals of statistics and data collection

These materials provide the foundations of statistics. They also cover the relevant fundamentals of data collection. Good data are assumed in the remainder.

  1. Relevance of statistics in psychology and work psychology
  2. The Structure of the Statistical Data Table
  3. Key Terms
  4. Measurement and Operationalization for Descriptive Statistics | Earlier Version
  5. Data Collection for Quantitative Research
  6. Sample and Population in Statistics

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 introduces fundamental 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

More 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 ???? cover more advanced content.

Working with Statistical Software and Data Management

This content provides the basics of 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)
R Syntax Exam Exercises (Easy)
Exam Exercises: R Syntax (Intermediate)

Exploratory Data Analysis in R and SPSS

These materials introduce 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 R base
  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)
Exploration Exam Questions (Easy)
Exam Exercises: Exploratory Analysis (Intermediate)

Testing Difference Hypotheses I

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)
t-test exam question

Testing Difference Hypotheses II: Analysis of Variance

Further analytical methods for testing difference hypotheses are presented here.

  1. Basics
  2. One-Way ANOVA
  3. Further methods
Exercise: ANOVA (basic)
ANOVA exam question

Testing hypotheses about relationships

This section presents analytical methods for testing hypotheses about relationships.

  1. Basics
  2. Correlation and rank correlation
  3. Simple and Multiple Linear Regression
  4. Cross-Lagged Panel Model
  5. Path Analysis in R
  6. Confirmatory Factor Analysis in R ????
Exam Question: Relationship

More topics

  1. Introduction to Bayesian Statistics

Relevant online courses

  1. Statistics with R

Hi, Dominik here.

I love teaching statistics and research design! If you’re currently working on your bachelor’s or master’s thesis and feel unsure about statistics, methods, or simply the whole structure, I’m here to help.

With my experience from countless supervised theses and seven teaching awards, I know exactly how to explain difficult topics simply. Whether you want to write an outstanding thesis or simply want to complete your degree, I’ll guide you through the process so that you can submit your thesis with confidence and a strong result.

Dominik E. Froehlich