The influence of personality on important life outcomes has been widely recognized for thousands of years (Condon, 2014), and the difficulty of its measurement has been vexing for many decades (Galton, 1884; Cattell, 1945; Goldberg, 1981; Ackerman, 2018). The challenge with objective measurement stems from the need for massive amounts of data to account for dynamic interplay between variations in thousands of narrow dispositional traits (aka individual differences in behavior) and the ever-evolving contextual factors inherent to modern living. It is a prototypical “big data” problem. Despite this, dozens of ambitious social scientists have posited a diverse array of personality assessment models. Many of these are heavily imbued with theory, nearly all are focused solely on one domain of personality (e.g., very broad dispositional traits or vocational interests) to the exclusion of others (e.g., cognitive abilities, values, or less generalizable maladaptive behaviors), and most have been derived based on surprisingly small samples drawn from populations that have come to be known as "WEIRD" (Henrich et al., 2010). Simply put, there is widespread need for models that are empirically-grounded in more (and more representative) data.In this manuscript, I demonstrate that it is possible to address the shortcomings of extant theory-driven approaches by combining recent innovations from outside of personality research to empirically derive personality assessment models. This is done by administering a large pool of widely-used public domain items from the International Personality Item Pool (Goldberg et al., 1999) to three large online samples (N > 125,000) using a planned missingness design (Revelle et al., 2016). While the existing "best practices" for developing personality assessment models tends towards several iterative rounds of data collection and analysis guided by theory culminating in publication of only the final product, I have endeavored to make a highly detailed record of all steps followed during the development of the SAPA Personality Inventory in order to encourage feedback regarding critical analytic decisions. This has unfortunately resulted in the production of a book-length manuscript but I hope that this transparency will serve to minimize (even if it does not eliminate) the influence of bias.January 10, 2018: This manuscript remains subject to review and further revision. I welcome additional feedback (by email). I will note the status of these revisions as they occur.