The divergence point analysis using bootstrapping (DPA-B) has become an increasingly popular approach for analysing visual world paradigm data. However, several researcher degrees of freedom must be considered, and the impact of these decisions has not been systematically in-vestigated. We examine the influence of five degrees of freedom: (1) region of interest size, (2) inclusion/exclusion of incorrectly answered trials, (3) bin size, (4) significance window length, and (5) bootstrap iteration number. We illustrate these effects using two datasets collected from human participants. Because this data does not have a known divergence time, we additionally present a publicly available data simulator that allows users to specify a true divergence time, thus allowing us to more directly assess the impact of these degrees of freedom on DPA-B outcomes. Across both empirical and simulated data, we found that bin size and significance window length exerted the strongest influence, with 50 ms bins and a 200 ms significance window length producing the most stable and interpretable results. Additionally, we found that the DPA-B tends to overestimate divergence times; however, this bias was consistent across conditions, preserving its utility for cross condition comparisons. Based on our findings, we offer empirically grounded recommenda-tions for DPA-B implementation and emphasise the importance of transparent reporting, coupled with post-hoc flexibility tailored to individual datasets. Together, these results provide practical guidance to improve the quality, reproducibility, and comparability of research employing DPA-B.