We present an expanded training set of salt-marsh foraminifera for reconstructing Holocene relative sea-level change from 12 sites in New Jersey that represent varied physiographic environments. Seven groups of foraminifera are recognized, including four high- or transitional-marsh assemblages and a low-salinity assemblage. A weighted-averaging transfer function trained on this dataset was applied to a dated core from Barnegat Bay to reconstruct sea level with uncertainties of ± 14% of tidal range. We evaluate the transfer function using seven tests. (1) Leave-one-site-out cross validation suggests that training sets of salt-marsh foraminifera are robust to spatial autocorrelation caused by sampling along transects. (2) Segment-wise analysis shows that the transfer function performs best at densely sampled elevations and overall estimates of model performance are over optimistic. (3) Dissimilarity and (4) non-metric multi-dimensional scaling evaluated the analogy between modern and core samples. The closest modern analogues for core samples were drawn from six sites demonstrating the necessity of a multi-site training set. (5) Goodness-of-fit statistics assessed the validity of reconstructions. (6) The transfer function failed a test of significance because of the unusual properties of some cores selected for sea-level reconstruction. (7) Agreement between reconstructed sea level and tide-gauge measurements demonstrates the transfer function's utility.