Published online by Cambridge University Press: 22 December 2014
In this chapter, I review a statistical method for hypothesis or theory testing called structural equation modeling (SEM). First, I describe what a model of second language acquisition (SLA) is. I do this so anyone, even those new to the field of applied linguistics, can understand the basic concepts underlying SEM; that is, SEM researchers first articulate a model of SLA, then get empirical data from the real world that operationalize the variables in the model. Researchers use an SEM program to test the model on the data (to see if the model fits the data; if the model is plausible in relation to the learning context of the people from whom the data were collected). After explaining the basics of SEM, I provide a review of 39 applied linguistics studies that have been published in the last five years (between 2008 and 2013) and that present at least one SEM analysis as part of the results. I discuss four problematic areas related to the use of SEM that I believe these 39 studies highlighted: (a) sample size, (b) model presentation, (c) reliability, and (d) the number of Likert-scale points. I conclude with possible solutions for the four problem areas and outline future directions.
Kieffer, M. J., & Lesaux, N. K. (2012). Direct and indirect roles of morphological awareness in the English reading comprehension of native English, Spanish, Filipino, and Vietnamese speakers. Language Learning, 62, 1170–1204. doi:10.1111/j.1467–9922.2012.00722.x
Kieffer and Lesaux investigated how derivational morphological awareness impacts English reading comprehension in sixth-grade students (n = 952) in southern California. The students came from different language backgrounds: native English, Spanish-speaking language minority, and Filipino-speaking language minority. The data were collected from the students over the course of two testing periods during their sixth-grade year. The authors tested their proposed model on all four language-background groups. The writing is clear, and the figures and tables in which the data are presented are well crafted and easy to understand. These points in combination with excellent, multimodel theory-testing make this article a standout among the recent SLA SEM articles.
Miglietta, A., & Tartaglia, S. (2009). The influence of length of stay, linguistic competence, and media exposure in immigrants’ adaptation. Cross-Cultural Research, 43, 46–61. doi:10.1177/1069397108326289
Miglietta and Tartaglia studied 576 immigrants in Italy (196 from Romania, 179 from North Africa, and 201 from Latin America) to better understand what made them emotionally attached (acculturated) to life in Italy and what did not. The authors surveyed the individuals to find out the following information about them: how long they had been in Italy (i.e., their length of residency); their proficiency in Italian; how much they used Italian with friends and family; how much mass Italian media they consumed, and how much they knew about satellite TV programs from their homelands. The writing is this article is straightforward, and the data are well presented. Both of these help render this an exemplary SLA SEM study.
Rivers, D. J. (2010). National identification and intercultural relations in foreign language learning. Language and Intercultural Communication, 10, 318–336. doi:10.1080/14708477.2010.502234
Rivers investigated Japanese university students’ attitudes toward learning English. He did this by surveying 377 of them regarding their levels of internationalism, patriotism, and Japanese nationalism. He also surveyed them in terms of how vital they considered English-speaking nations and whether they believed English speakers have intercultural appeal. This article is reader friendly and interesting. Rivers's work represents well how researchers in SLA using SEM should go about describing and reporting their research.