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The CLASSI-N Method for the Study of Sequential Processes

机译:研究顺序过程的CLASSI-N方法

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摘要

In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the relationship between these mediating variables and the response. Recently, Ceulemans and Van Mechelen (Psychometrika 73(1):107-124, 2008) addressed these challenges by developing the CLASSI model. A major drawback of CLASSI is that it requires information about the same set of stimuli for all participants (i. e., crossed data), whereas recently a number of data gathering techniques have been proposed in which the set of stimuli differs across participants, yielding nested data. Therefore we present the CLASSI-N model, which extends the CLASSI model to nested data. A simulated annealing algorithm is proposed. The results of a simulation study are discussed as well as an application to data concerning depression.
机译:在许多心理学研究领域,通过推测一个顺序过程来解释刺激-反应曲线,在该过程中,一些变量在刺激和反应之间介导。绘制顺序过程的图表通常是一项复杂的任务,因为(1)可能存在许多可能的中介变量,并且(2)这些中介变量与响应之间的关系可能会出现个体差异。最近,Ceulemans和Van Mechelen(Psychometrika 73(1):107-124,2008)通过开发CLASSI模型解决了这些挑战。 CLASSI的主要缺点是,它要求所有参与者都具有与同一组刺激有关的信息(即交叉数据),而最近提出了许多数据收集技术,其中各组参与者之间的刺激不同,从而产生嵌套数据。因此,我们提出了CLASSI-N模型,该模型将CLASSI模型扩展到嵌套数据。提出了一种模拟退火算法。讨论了模拟研究的结果以及对抑郁症数据的应用。

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