Selecting a statistical framework for a behavioral study has profoundly different results than does a linguistically framed research strategy. The linguistic strategy overcomes many limitations inherent in statistical strategies and offers more meaningful results. Inferential statistical studies often discuss how the findings "explain" the results of the study. Seldom mentioned is the fact that statistical explanations occur in terms of the framework of statistical methodology. Statistical explanations do not explain anything in terms of the actual behavior at issue and do not lead to subsequent interventions about the motivated choices for a target group. Linguistic strategies work especially well if the objective is to make a practical difference in behavior as opposed to raising questions for further research in academic circles. Key Words: Motivational Profiling, Motivation, Systems Analysis, Behavioral Engineering, Content Analysis, Measurement Paradigms, Frames, Psycholinguistics, Mechanism of Action, and Behavior Change
Explaining Behavior versus Changing Behavior
Statistical results of various behavioral studies are customarily characterized as "explanations." What does an abstract conceptual explanation really explain when it is describing the results of an irrelevant method using data twice, thrice, or even further removed from the behavioral phenomenon in question? Such explanations are like telling a homeless person that the solution to his or her hunger problem is "economics". Even the homeless person would find such an explanation laughable. The abstraction offers no "explanation" in terms of cause and effect. Researchers obtain merely a conceptual rationale, with no sensory connection or contextual framework with physical reality in order to observe the elements of behavior in question. Separating stimulus from response in statistical design "forces" a fuzzy outcome upon the investigator.
In humans, motivation leading to choices is a perpetual phenomenon. When a researcher wants to know why a certain individual or group of people choose to behave a certain way, statistics don't "explain" the behavior in terms that are useful to a researcher or practitioner who wants to modify or apply the choices of that target individual or group. A statistical explanation is similar to a nametag on a colleague at a convention. It names something, such as a university affiliation or academic rank. The label does not actually explain anything relating to a change mechanism useful to altering the choices of that colleague. A label, a nominalization, a statistic has no explanatory mechanism.
In contrast, linguistically based research strategies identify the mechanisms behind the choices being made by the targeted individual or group of people. Knowing the linguistic mechanisms of choice allows the researcher, choosing methods, or practitioner, choosing outcomes, to modify the effects in the situation at hand. Linguistic "cause-effect" technology, which emerged from early qualitative methods of content analysis, trumps statistical "concept" in most any research setting where outcomes affect human events. To accomplish the change of choice among targeted subjects, the researcher simply utilizes the linguistic mechanisms found in the linguistic research strategy (Yeager, 2003). The cause-effect connection between research findings and the subsequent intervention to change choices actually "explains" how to cause the desired changes. Deliberate change occurs because the unconscious motivation, the linguistic mechanism of action, can be identified and managed toward a specific outcome. There is no more guesswork in behavioral engineering than there is in engineering an aircraft or a bridge. Effective technology presupposes consistent replicability of methods and results.
An Impoverished Statistical Heritage
In comparison to one hundred years of accelerated development in aviation and aerospace technology, the statistically dominated behavioral sciences have progressed embarrassingly little. …