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When an aqueous drop contacts an immiscible oil film, it displays complex interfacial dynamics. When the spreading factor is positive, upon contact, the oil spreads onto the drop's liquid–air interface, first forming a liquid bridge whose curvature drives an apparent drop spreading motion and later engulfs the drop. We study this flow using both three-phase lattice Boltzmann simulations based on the conservative phase field model, and experiments. Inertially and viscously limited dynamics are explored using the Ohnesorge number $Oh$ and the ratio between the film height $$H$$ and the initial drop radius $$R$$ . Both regimes show that the radial growth of the liquid bridge $$r$$ is fairly insensitive to the film height $$H$$ , and scales with time $$T$$ as $$r\sim T^{1/2}$$ for $$Oh\ll 1$$ , and as $$r\sim T^{2/5}$$ for $$Oh\gg 1$$ . For $$Oh\gg 1$$ , we show experimentally that this immiscible liquid bridge growth is analogous with the miscible drop–film coalescence case. Contrary to the growth of the liquid bridge, however, we find that the late-time engulfment dynamics and final interface profiles are significantly affected by the ratio $H/R$ . 

This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a wide range of conditions, including number of response categories, sample size, percent of missingness, and degree of model misfit. Results indicate that both PD and MI yield parameter estimates similar to those from analysis of complete data under conditions where the data are missing completely at random (MCAR). When the data are missing at random (MAR), PD parameter estimates are shown to be severely biased across parameter combinations in the study. When the percentage of missingness is less than 50%, MI yields parameter estimates that are similar to results from complete data. However, the fit indices (i.e., χ², RMSEA, and WRMR) yield estimates that suggested a worse fit than results observed in complete data. We recommend that applied researchers use MI when fitting ordinal factor models with missing data. We further recommend interpreting model fit based on the TLI and CFI incremental fit indices.