Simulation study replicating Smid et al. with active learning

Smid et al. (2020) conducted a systematic review to characterize the performance of Bayesian and frequentist estimation for SEM with small sample sizes. After manually screening 5050 studies, only 27 were selected to answer their research question. In the current study, both the Bayesian and logistic regression models found more than 80% of relevant publications after screening only 10% of all publications. They helped to identify 95% of relevant publications after screening about 20% of all publications. In conclusion, relevant publications can be detected much earlier when an active learning model dictates the order in which articles are screened in systematic reviews.

 

Ferdinands, G. (2020 – online). AI-Assisted Systematic Reviewing: Selecting Studies to Compare Bayesian Versus Frequentist SEM for Small Sample Sizes. Multivariate Behavioral Research, DOI:10.1080/00273171.2020.1853501.

Pre-processing Data
Simulation Scripts
Raw Data
Simulation Results
Categories: Scientific Papers, Simulation Studies, Special Use-Cases

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