Sea Change in Social Science Research? [I am considering adding these topics]

Downfall of NHST
Numbers Must Mean Something
Replication = Exact Replication
Experimentation and Causation
Qualitative vs. Quantitative Illusion


Why Observation Oriented Modeling is needed

Writing in 1936 Johnson stated, "Those data should be measured which can be measured; those which cannot be measured should be treated otherwise. Much remains to be discovered in scientific methodology about valid treatment and adequate and economic description of non-measurable facts." (p. 351). We would quibble with "Those data should be measured..." and change it to "Those attributes should be measured...", but the point is essentially valid; namely, psychologists in the early 1900s had not demonstrated the continuous quantitative structure of the attributes they were studying. Without continuous quantitative structure, the application of t-tests, ANOVA, least squares regression, factor analysis, etc. to psychological data is dubious, at best. Newer methods like SEM and multilevel modeling are no more valid because psychologists have not to date demonstrated they are measuring the attributes they hold dear (e.g., intelligence, depression, anxiety, the Big Five personality traits, etc.). In this sense psychology has failed to progress as a science because it has failed to understand the difference between quantity and quality as distinguishable modes of being. None of these facts are secrets, and a good place for the reader to begin is with Joel Michell's Measurement in psychology: Critical history of a methodological concept (1999, Cambridge University Press).

Summarizing the measurement dilemma in psychology Paul Barrett (2003) argues that psychology can only move forward if it (1) demonstrates the continuous quantitative structure of its cherished attributes, (2) develops non-quantitative techniques, or (3) behaves in a more brutally honest fashion regarding the serious limitations of current methods, treating them as -- at best -- crude approximations of attributes. In Observation Oriented Modeling, we have chosen the second route in developing a philosophy, body of techniques, and software that do not depend on assumed continuous quantities. OOM also completely breaks away from the abstract and convoluted Null Hypothesis Significance Testing paradigm that has stymied psychological research for some 70 years. NHST is furthermore a product of positivistic thinking in the late 1800s and early 1900s, and it is time to let it all go. Countless authors have discussed these and other serious issues, and a few classic and recent examples follow:

It is our hope that by reading these chapters and articles you will gain an appreciation for the deep-seated need for a new approach like Observation Oriented Modeling with its seven principles:

  • The primacy of observations
  • Aggregation often leads to obfuscation
  • Outliers are people too
  • View the world through the lens of an integrated model
  • The primacy of accuracy and repeatability
  • Estimate population parameters in their proper, limited role in psychology 

Observation Oriented Modeling invokes Aristotle's notion of final cause which Thomas Aquinas considered the most important species of cause. Are psychological theories that invoke final cause possible? We believe they are, and the best example is Joseph Rychlak's   Logical Learning Theory which he tested over several decades of research. We also recommend Rychlak's books: The Psychology of Rigorous Humanism, Introduction to Personality and Psychotherapy, and In Defense of Human Consciousness. In Rigorous Humanism professor Rychlak provides a tabled history of Aristotle's four causes (material, formal, efficient, and final) in philosophy and in science. Our work with Vladimir Lefebvre's algebraic model of cognition also leads us to think that it may be workable in final cause models. It certainly provides a formal cause model of the cognition involved in binary decision tasks. Lastly, Bill Powers'  Perceptual Control Theory, which posits that "behavior is goal directed and purposeful, not mechanical and responsive", invokes the notion of final cause and offers a promising framework for the development of the types of integrated models advocated in the OOM book. Because integrated models are causal models, efficient cause, which is allied with the randomized controlled experiment, as well as formal and material causes are all treated in OOM. Aristotle made it clear that all four causes are needed to understand nature.