Category Thesis
Support for bachelor’s and master’s students navigating every phase of their thesis — from first idea to final submission.
Measurement and operationalization for descriptive statistics
Measurability is a fundamental concept in statistics, especially in the social sciences such as psychology. In many cases, we try to quantify theoretical constructs such as intelligence, motivation, or stress. This raises the question: How can we transform these abstract concepts into measurable data?
Sample and population in statistics
When we talk about statistics, we often come across the terms “sample” and “population” (or entire population). These two terms are essential for understanding data analysis, particularly in psychology and work psychology. Let’s clearly distinguish these two terms from each other and also discuss the element that connects them: the sampling procedure.
Distributions in Statistics
A distribution describes how the values of a random variable are distributed or arranged. Understanding distributions is crucial because they provide insights into the characteristics of datasets and help us make predictions about future observations. There are different types of distributions, each with unique characteristics and applications. In this article, I explain some of the most important distributions and provide practical examples to deepen your understanding.
Statistical Software in Psychology
If you are wondering which statistics software is best for you, there is no single perfect answer. It depends on what you want to do, how deeply you want to delve into the subject, and which tools you feel comfortable using. Here are four of the most relevant statistics programs you should know about—without ranking them, because each has its own strengths and weaknesses.
Standardization in statistics
In statistics, standardization plays an important role when it comes to bringing different data onto a comparable basis. This method is particularly helpful when data have different means and standard deviations.
Visualization in Statistics
Data visualization is a central part of statistical analysis. It helps present complex relationships in a simple way and identify patterns or anomalies in the data. Especially in the age of data science, the ability to visualize data effectively is a valuable skill. In this article, we explore some of the most important techniques for visualizing statistical data and show how to implement them in R.
Questionnaire construction for quantitative research
A questionnaire is a standardized collection of items (questions) designed to measure a latent construct that cannot be observed directly. Examples of such constructs include attitudes, opinions, and abilities. A similar example is written examinations, in which standardized procedures are used to assess knowledge or competence.
Causality
Causality is one of the most fundamental concepts in science: it describes how causes lead to effects. For example, if you throw a stone into the water, it causes the waves that form. Causality plays a particularly important role in statistics, as we often examine relationships between variables to determine whether and how a change in one variable affects another.
Measures of association for discrete variables
In statistics, we often encounter discrete (categorical) variables, such as gender, educational level, or response categories in a survey. To analyze the relationship between such variables, we use measures of association specifically designed for discrete data. In this blog post, you will learn which measures are available, how they work, and how to calculate them in R.
Measures of association for metric variables
In statistics, we often want to examine the relationship between two metric variables (e.g., height and weight). Various measures can be used to quantify this dependence. The best-known are the Bravais–Pearson correlation coefficient and Spearman’s rank correlation coefficient. In this blog post, I’ll show you how these measures work, when to use them, and how to calculate them in R.