Many-Facet Partial Credit Rasch Model Analysis of Dörnyei’s L2 Motivational Self System
Motivation to learn a second language has been a significant predictor of variable success in L2 ultimate attainment. One prominent L2 motivation theory in SLA is Dörnyei’s L2 Motivational Self System (L2MSS) in which learners’ motivation is comprised of Ideal L2 Self, Ought-to L2 Self, and L2 Learning Experience, validated through their correlation with one’s Intended Effort towards learning the target language. This paper aims to address two gaps in the L2 motivation literature. First is methodological: measures of motivation are often collected through questionnaires designed to probe the construct in question and analyzed using descriptive statistics or factor analysis. However, descriptive and inferential statistics often rely on interval data when motivation measures are likely to be ordinal. In this regard, the use of the Rasch model as a measure of motivation affords researchers a powerful means of comparing the motivation levels of participants as objective measures. Second, L2 motivation has been theorized as being dynamic and subject to changes throughout one’s L2 learning experience (Dörnyei, 2010). This study examines a motivation questionnaire based on L2MSS and uses a partial credit model to analyze the interaction between length of study and learners’ motivation as defined by the components of L2MSS. Ninety-one ESL students were given a 24-item motivation questionnaire. The result of the analysis showed that the most important aspect of L2MSS was the learners’ learning experience. The best method of analysis for the Rasch model was to subdivide the questionnaire according to each of Dörnyei’s subcomponents (i.e., Ideal L2 Self, Ought-to L2 Self, L2 Learning Experience, and Intended Effort) instead of analyzing the data as a single dimension of L2MSS. Finally, the findings showed no significant interaction between the length of study and learners’ motivation. The analysis of overfit and underfit items and persons provides the advantage of using the Rasch model not found in structural equation models, factor analysis, inferential or descriptive statistics.
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