Necessary Condition Analysis (NCA)

Professor Jan Dul, founder of NCA, welcomes you and starts off with a quick introduction of necessity logic and Necessary Condition Analysis (NCA). The first week will explain necessity logic, why it is important and how it is different from other sorts of logic such as Boolean and additive logic. Furthermore, the basics of NCA and its benefits are explained. We invite you to go through the videos and readings to improve your understanding of necessity logic and NCA.

In week 2 you will be guided through the process of setting up an NCA study. First, you will deep dive into the formulation of necessary condition hypotheses that can be analyzed with NCA. Next, general research practices of sampling and measurement will be discussed. After this week you will be able to start conducting research with NCA.

In this module, you will examine how an NCA is ran in R, a programming language for statistical computing and graphics. Key elements will be explained such as the identification of the empty spaces in scatter plots. Once you can run an NCA in R, it is important to be able to interpret the results of the analysis, such as the effect size and the p-value. This week will also provide you an opportunity to practice with NCA.

After finishing the first three weeks of this MOOC, you are now able to conduct an NCA. Crucial to every research method is getting across the message of your research. This module will therefore explain how you can convincingly report the results of your NCA study and reflect on the strengths and the weaknesses of the method.