This paper defines and promotes the qualities of a "bottom-up" approach to single-case research (SCR) data analysis. Although "top-down" models, for example, multi-level or hierarchical linear models, are gaining momentum and have much to offer, interventionists should be cautious about analyses that are not easily understood, are not governed by a "wide lens" visual analysis, do not yield intuitive results, and remove the analysis process from the interventionist, who alone has intimate understanding of the design logic and resulting data patterns. "Bottom-up" analysis possesses benefits which fit well with SCR, including applicability to designs with few data points and few phases, customization of analyses based on design and data idiosyncrasies, conformation with visual analysis, and directly meaningful effect sizes. Examples are provided to illustrate these benefits of bottom-up analyses.