In this dissertation, we focused on two alternative approaches to evaluate the hypothesis of interest more directly, i.e. informative hypothesis testing and model selection using order-restricted information criteria. These approaches have shown to be more ‘powerful’ than NHST. The main implication is the possibility to reduce costs. Data collection in the social and behavioral sciences is usually the most expensive part of conducting research. Since the outcome of this dissertation ensures that researchers can use smaller samples, the costs of data collection can be reduced. In addition, researchers who are dealing with inevitable small samples in particular may benefit from these alternative approaches. Finally, we hope that this dissertation gives applied researchers a push to employ more
informative hypotheses

Vanbrabant, L. (2018). Reduction in sample size by order restrictions. (doctoral dissertation)

Promotor: Prof. Dr. Yves Rosseel
Co-Promotor: Dr. Rens van de Schoot

PhD Student

The topic of my PhD project is sample-size reduction by order constraints. Many researchers are familiar with the power gain in the context of the one-sided t-test.

Yves Rosseel
Professor of Data Analysis and developer of Lavaan
Yves is the author of lavaan, an R-package for latent variable analysis. In 2013, Rens and Yves supervised a research master student, Leonard Vanbrabant, to add constrained statistical inference to the package lavaan. After this successful collaboration Leonard was hired as PhD-student, and they obtained funding from the Belgium Reseach Institute to continue working on the project which resulted in the R package Restriktor.
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