Tag data
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.
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?
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.
Tidy Data
In this post, we'll go over Hadley Wickham's three principles for ensuring tidy data.