Category Methods
Practical guidance on research design, methodology, and data analysis for your thesis.
Cramér’s V: Core Idea, Calculation, and Interpretation
Cramér's V is a statistical measure that quantifies the strength of the association between two nominal variables. Unlike the chi-square test, which only tests the significance of an association, Cramér's V provides a standardized measure of its strength.
Contingency Coefficient: Core Idea, Calculation, and Interpretation
The contingency coefficient is a measure used to quantify the strength of the association between two nominal variables in a cross-tabulation. It complements tests such as the chi-square test by expressing the strength of an association independently of the sample size.
Chi-Square Test: Basic Idea, Calculation, and Interpretation
The chi-square test (χ²) is one of the fundamental statistical tools for analyzing relationships between discrete variables. It is frequently used to test hypotheses about distributions and independence.
Odds Ratio: Basic Concept, Calculation, and Interpretation
The odds ratio (OR) is a widely used measure for describing the association between two dichotomous variables. It is used in medicine, psychology, epidemiology, and other disciplines, particularly when analyzing contingency tables.
Phases and Principles of Test Construction
The development of a psychological test is a carefully planned process that takes place in several phases. Each phase is crucial to ensuring that the finished instrument is reliable and valid. In this article, we take a close look at the test construction phases.
Hypothesis Tests: How We Turn Data into Claims
You may have heard of hypothesis testing—a central statistical tool for systematically examining claims or assumptions. But how exactly does it work, and why are hypothesis tests so important?
Three steps to the perfect sample
Defining the right sample and target population in research can be a real challenge. Imagine that you have an exciting research question, but you are unsure whom you should actually survey. Too broad or too specific—both can cause problems.
How to Use the Wilcoxon Test: A Step-by-Step Guide in R and SPSS
The Wilcoxon test is a useful statistical tool that you can use when you want to compare two groups of data. Unlike some other tests, such as the t-test, the Wilcoxon test does not make strict assumptions about the distribution of the data. It is particularly helpful when the data are not normally distributed or when you have small sample sizes.
Introduction to Statistics
In this post, we will focus a little more on statistics and quantitative research.
The Shapiro-Wilk Test: A Practical Guide to Assessing Normality in R, SPSS, JASP, and PSPP
The Shapiro-Wilk test checks whether data are normally distributed. It is an essential tool in statistics and is easy to perform in programs such as R, SPSS, JASP, and PSPP. This guide provides clear instructions for conducting and interpreting the test in different programs, including APA reporting.