Structural Equation Modeling: Possibilities for Language Learning Researchers

被引:19
|
作者
Hancock, Gregory R. [1 ]
Schoonen, Rob [2 ]
机构
[1] Univ Maryland, College Pk, MD 20742 USA
[2] Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands
关键词
SEM; covariance structure analysis; modeling relations; multi-group comparisons; READING-COMPREHENSION; 2ND-LANGUAGE; KNOWLEDGE; GROWTH; ACHIEVEMENT; PROFICIENCY; ATTITUDES; EXPLICIT; ENGLISH; 1ST;
D O I
10.1111/lang.12116
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Although classical statistical techniques have been a valuable tool in second language (L2) research, L2 research questions have started to grow beyond those techniques' capabilities, and indeed are often limited by them. Questions about how complex constructs relate to each other or to constituent subskills, about longitudinal development in those constructs and factors affecting that development, and about differences among populations in average amounts of complex constructs or in their relations require a broader analytical framework. Fortunately, that of structural equation modeling (SEM), a versatile and ever-expanding family of techniques, is able to accommodate such questions and many more. The current article describes some of the questions that can be addressed by SEM, presents some research examples within the existing L2 literature, and then provides examples of the incredible potential of SEM, cautions in its practice, and resources for further information.
引用
收藏
页码:160 / 184
页数:25
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