Category Thesis
Support for bachelor’s and master’s students navigating every phase of their thesis — from first idea to final submission.
The Kruskal–Wallis Test: How to Conduct Nonparametric Comparisons Between Groups in R, SPSS, JASP, and PSPP
The Kruskal–Wallis test examines differences between more than two independent groups when the data are not normally distributed. This guide provides clear instructions for conducting and interpreting the test in various programs, including APA reporting. An indispensable tool for researchers, analysts, and anyone working with data.
The independent-samples t-test: A practical guide to conducting and interpreting it in R, SPSS, JASP, and PSPP
The t-test for two independent samples compares the means of two groups. This practical guide shows how to conduct and interpret the test in R, SPSS, JASP, and PSPP, including APA-style reporting. It is an indispensable tool for anyone who wants to identify differences between two groups.
Levene’s Test: How to Easily Check and Interpret Homogeneity of Variance
Levene’s test assesses the homogeneity of variance across groups. It can be implemented in various software tools, with the interpretation always based on the p-value. A significant value indicates that the variances differ.
Effective reporting of descriptive statistics in your thesis
In theses, descriptive statistics provide a clear overview of the collected data before more in-depth analyses begin.
Statistics with R
R has become one of the most popular and versatile applications for conducting statistical analyses in recent years. In this course, I will introduce you to the world of R for statistical analysis. This is relevant to you, among other things,…
Levene’s Test: Checking Homogeneity of Variances Made Easy
Levene’s test assesses the homogeneity of variances. It is important for many parametric tests. This article shows how to perform and interpret it in R, SPSS, and JASP.
Key Terms in Empirical Social Research
Empirical social research aims to generate well-founded knowledge about social phenomena through systematic observation and analysis. Key methods such as induction, deduction, and abduction play a central role in deriving insights from observations or testing theories. The choice of research subject and the appropriateness of the methods to that subject—that is, selecting a suitable methodological approach—are crucial for obtaining valid results.
The population and the sample are also essential to the research process and must be selected carefully to produce representative results. A well-known example is the Gallup case from the 1930s, which vividly illustrates the importance of sampling.
Another central aspect is the use of variables, which form the basic building blocks of every study. Independent and dependent variables define which factors are being examined and what effects they have on one another. Correctly formulating and testing hypotheses is also essential for obtaining robust research findings.
Overall, empirical social research provides a solid foundation for understanding social relationships through its methodological diversity and precise data analysis.
Measurement and Levels of Measurement in Statistics
Measurement plays a fundamental role in research. It enables us to capture the properties or characteristics of objects and phenomena quantitatively, making them comparable. However, not all measurements are the same. How data are measured and interpreted depends on the level of measurement. In this blog post, we take a closer look at the different levels of measurement and their significance in research.
The structure of the statistical data table
The structure of a statistical data table is based on three essential concepts: statistical units, variables, and variable values. These three elements form the basis for understanding data structures in statistics and are essential for grasping the data analysis process.
Measures of central tendency in descriptive statistics
In statistics, there are a variety of measures of central tendency that help us describe the center of a data distribution. These measures are essential for understanding the characteristics of a data distribution. The most important ones we will look at today are the arithmetic mean, the median, and the mode.