Some Final Lessons From Jack Block

Personality is a system, and its manifestations in thought, emotion, and behavior are dauntingly complex. The challenge for personality psychology is, as Jack Block (this issue) says in the target article, to develop a “model or point of view or productive principles that can . generate sufficient ‘surface complexity arising out of deep simplicity”’ (p. 22). Easier said than done. Near the end of the article, Block offers some tantalizing hints as to what such a model might look like, and along the way dispenses a number of prescriptions for how personality research ought to proceed, and too often does not. These positive prescriptions might get lost, for some readers, among the various critiques that Block offers. So let me list (and paraphrase) a few of them. 1. Do not be afraid to study many attributes of personality. It is well known that Jack Block was no fan of the Big Five, and his critiques were extensive. However, perhaps because he raised so many criticisms, exactly what troubled him so deeply about the popularity of the Five Factor Approach (FFA; the term Block uses in the article) may have been less well understood. I believe a key consideration was a fear that by becoming the “personality rubric for our time” (p. 4) and establishing itself as the foundation of everything from developmental psychology to clinical psychology to studies of individual differences in brain function, the FFA would foreclose understanding of many aspects of personality that—it is widely acknowledged—the FFA simply misses, and more generally promote a nonthoughtful attitude toward the selection of the personality attributes addressed by research. In my own work, I have almost always included measures of the Big Five, and I long suspected that Block did not really approve, though he never said this to me directly. But perhaps I was forgiven because I also almost never stopped there—my research usually includes the 100 wide-ranging items of Block’s own California Q-set (CAQ), and many other variables as well (see, e.g., Fast & Funder, 2008, 2010; Funder & Colvin, 1991). Block also, in this target article, promotes Goldberg’s “IPIP” effort to make extremely large numbers of personality measures and personality measurement items freely available through the internet, promoting the casting of a wide net over personality (Goldberg et al., 2006). Block once told me that a reviewer had complained about one of his articles that he had analyzed “too many variables”—he retorted that the Emperor of Austria had once complained to Mozart that he used “too many notes. ” Of course, one problem with studying many personality variables at once is the possibility of capitalizing on chance, and in particular the fact that it is impossible to calculate the probability that, for example, 20 out of the 100 CAQ items would be correlated with a behavior or other outcome at better than p < . 05. In an article that was decades before its time, Block (1960) presented a computer-based randomization procedure to evaluate such a probability; it lay dormant and almost unused until Ryne Sherman, a graduate student in my lab, updated and developed a method for employment with modern computer hardware and software and extended it to further, new uses (Sherman & Funder, 2009). It is now easy to assess the degree to which the results of a large number of analyses might capitalize on chance. We need no longer be afraid of “too many notes. ” 2. Personality is not a list of traits, it is a system. This principle is another basis of Block’s misgivings about the Big Five—even though, in complete fairness, McCrae and Costa’s (2008) Five Factor Theory arguably is a system. Beyond the FFA critique, Block’s view of personality as a coherent system is the basis of his not entirely concealed sympathy for Freud and psychoanalytic theorizing, and for his more directly expressed admiration for the attempts by McAdams and his colleagues to understand individual lives (McAdams & Pals, 2006). 1 The ideal for this system would be to start with a truly basic dimension of individual difference and, in