To understand how human experiences emerge, change and are interlinked requires understanding which conditions are necessary for thoughts, feelings and behaviors. Linear models which are currently the default for statistical analyses in psychological research do not allow to approach this question directly. Necessary conditional analysis as recently introduced by Dul (2016) can supplement linear models to better understand the necessary conditions for various human experiences. The aim of this work is to familiarize researchers in the area of psychological science with this data-analytic approach. To that end, I illustrate the concept of necessary conditions and separate it from sufficient conditions before I outline the central ideas behind necessary condition analysis. Using an empirical example in the theoretical framework of Self-Determination Theory, I then illustrate step by step, how a necessary conditions analysis can be conducted in R and how the results of this analysis can be interpreted. Limitations of this approach and future directions for the development of necessary condition analysis are discussed.
▲ Professor, George Washington University
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