“Occam's razor (also spelled Ockham's razor or Ocham's razor; Latin: novacula Occami) is the problem-solving principle that recommends searching for explanations constructed with the smallest possible set of elements. It is also known as the principle of parsimony or the law of parsimony (Latin: lex parsimoniae)”
In the context of fitting structural CFA models to intelligence test data, it can be summarized as “given two models with similar fit to the data, the simpler model is preferred” (Kline, 2011, p. 102).” The law of parsimony is frequently invoked in research articles when an investigator is faced with competing factor models regarding the underlying structure of a cognitive ability test battery. However, when complex human behavior is involved, especially something as complex as human intelligence and the brain, it is possible that Occam’s razor might interfer with a thourough understanding of human intelligence and test batteries designed to measure intelligence. The following quote2note has stuck with me as an important reminder that when faced with alternative and more complex statistical CFA models, these models should not be summarily dismissed based only on the parsimony principle. As stated by Stankov, Boyle, and Cattell (1995)
“while we acknowledge the principle of parsimony and endorse it whenever applicable, the evidence points to relative complexity rather than simplicity…the insistence on parsimony at all costs can lead to bad science” (p. 16).
Stankov, L., Boyle, G. J., & Cattell, R. B. (1995). Models and paradigms in personality and intelligence research. In D. Saklofske & M. Zeidner (Eds.), International handbook of personality and intelligence (pp. 15–43). New York, NY: Plenum Press.
