Search results
Results from the WOW.Com Content Network
The elaboration likelihood model (ELM) of persuasion [1] is a dual process theory describing the change of attitudes. The ELM was developed by Richard E. Petty and John Cacioppo in 1980. [ 2 ] The model aims to explain different ways of processing stimuli, why they are used, and their outcomes on attitude change.
Ron Sun proposed a dual-process model of learning (both implicit learning and explicit learning). The model (named CLARION) re-interpreted voluminous behavioral data in psychological studies of implicit learning and skill acquisition in general. The resulting theory is two-level and interactive, based on the idea of the interaction of one-shot ...
The elaboration likelihood model is similar in concept to and shares many ideas with other dual processing models, such as the heuristic-systematic model of information processing. [27] In the elaboration likelihood model, cognitive processing is the central route and affective/emotion processing is often associated with the peripheral route. [28]
Elaboration likelihood model – emphasizes the two routes of persuasion – central (cognitive arguments) and peripheral (emotional influence). Social impact theory - emphasizes the number, strength, and immediacy of the people trying to influence a person to change their mind.
Progressive elaboration, a process for creating a work breakdown structure in project management; Conceptual elaboration, the Buddhist concept of conceptual proliferation; Elaboration likelihood model, a psychological theory on the change of attitudes; Elaboration principle, a process of recruiting new members into a group
The heuristic-systematic model of information processing (HSM) is a widely recognized [citation needed] model by Shelly Chaiken that attempts to explain how people receive and process persuasive messages. [1] The model states that individuals can process messages in one of two ways: heuristically or systematically. Systematic processing entails ...
A likelihood region is the set of all values of θ whose relative likelihood is greater than or equal to a given threshold. In terms of percentages, a p % likelihood region for θ is defined to be [16] [18] [21] {: ()}.
Likelihood ratio statistic: [19] Used to test the null hypothesis that a model has perfect model fit. It should be applied to models with an increasing number of factors until the result is nonsignificant, indicating that the model is not rejected as good model fit of the population.