As the area of Software Engineering (SE) matures the role of human factors in software development is commonly recognized as important. Increasingly we see empirical studies that investigate the connection between, for example, personalities and preferences, attitudes or performances of software engineers. Statistical analysis holds a key role by providing the means for uncovering associations between various facets of human factors and behavioral effects on projects and outcomes. Traditional statistical techniques tend to explore and interpret the multidimensional personality and behavioral data from an "average-point" perspective, targeting central trends. This paper introduces a methodology with statistical tools that can provide a new and different perspective for this type of SE data. It seeks the boundaries of a psychometric dataset and discovers reference or "benchmark" personalities, the archetypal personalities. Then, the method examines the placement of all individuals in the dataset in relation to the archetypes. Furthermore, the SE preference characteristics, or generally, any other types of behavioral SE data, are analyzed with respect to the archetypes. As a case to exemplify the methodology we analyze personality and project preference data from 276 master level SE students and compare to previous "average-point" statistical analysis of the same data. We also discuss how Archetypal Analysis, the heart of the proposed methodology, combined with multi-correspondence analysis might be of general use in empirical SE.