Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/95816
Title: Defining and Operationalizing Propositional Complexity into Idea Units: Effects of Mode, Discourse Type, Task Type and Task Complexity
Author: Parra Paños, Laura
Director: Gilabert Guerrero, Roger
Keywords: Llengua i ensenyament
Multilingüisme
Adquisició d'una segona llengua
Tesis de màster
Language and education
Multilingualism
Second language acquisition
Masters theses
Issue Date: Jul-2015
Abstract: Propositional complexity is a dimension of L2 performance that refers to the amount of information that a person conveys in a given message and, according to Ellis and Barkhuizen (2005), it can be measured in terms of idea units (IUs). This study does not only aim at developing some guidelines as to how to segment oral and written data into IUs in order to operationalize a measurement of propositional complexity, but it also aims at investigating the relative impact of mode, discourse type, task type and task complexity on participants’ production of IUs. In order to achieve these objectives, the study analysed data that was generated by participants out of performing tasks that differed in mode, discourse type, task type and task complexity. After segmenting this data following the guidelines that were designed, it was considered that the guidelines might constitute a reliable means of operationalizing propositional complexity, as a considerably high agreement between raters was obtained. As regards the relative influence of mode discourse type, task type and task complexity on the number of IUs conveyed, after conducting a standard and a hierarchical multiple regression, the results showed that 38.5% of the variability in production of IUs can be significantly explained by these independent variables and that all of these variables made a significant unique contribution on the number of IUs that can be produced. Nonetheless, the amount of variance in the dependent variable explained by each of the predictors was different. In the standard regression, mode appeared to be the best predictor, uniquely explaining 9.9% of the variance in production of IUs, while the rest of the predictors independently explained between 1.7 and 4.4% of the variance. In the hierarchical regression, nonetheless, the results (which were supported by further analyses that were conducted), indicated that both mode and discourse type were the best predictors of number of IUs conveyed. Task type and task complexity were found to account for a similar amount of variance to the one obtained in the standard regression.
Note: Màster de Lingüística Aplicada i Adquisició de Llengües en Contextos Multilingües, Departament de Filologia Anglesa i Alemanya, Universitat de Barcelona, Curs: 2015, Tutor: Roger Gilabert Guerrero
URI: http://hdl.handle.net/2445/95816
Appears in Collections:Màster Oficial - Lingüística Aplicada i Adquisició de Llengües en Contextos Multilingües

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