Applying Structural Equation Modeling to Second-language (L2) Research: Key Concepts and Fundamental Reconsiderations
Subject Areas : آموزش زبان انگلیسی
1 - Islamic Azad University, Fereshtegaan International Branch
Keywords: structural equation modeling, multivariate data analysis, L2 research, quantitative research methods, advanced statistics,
Abstract :
As conceptual models of language learning, use, and processing mature, it is both natural and necessary for the statistical models we apply to follow suit. One statistical approach with great potential in the field Applied Linguistics and L2 studies is structural equation modeling (SEM). SEM is introduced in this paper as a powerful and highly flexible family of analyses. In doing so, the paper outlines (a) the types of variables and possible modeled relationships that SEM is equipped to address and (b) statistical considerations for applying SEM in L2 research, and (c) a number of additional and key considerations for those interested in delving deeper into SEM (e.g., goodness of fit indices, model modification procedures, etc.). This paper also describes the potential of SEM to contribute to construct validation (e.g., convergent and discriminant validity). Throughout the paper, a plethora of examples pertaining to applications of SEM in L2 research are provided.
Bachman, L. F. (1990). Fundamental considerations in language testing. Oxford: Oxford University Press.
Bandalos, D. L., Finney, S. J. (2019). Factor analysis: Exploratory and confirmatory. In G. R. Hancock, R. O. Mueller, & L. M. Stapleton (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 98-122). New York, NY: Routledge.
Bryant, F. B. (2000). Assessing the validity of measurement. In L. Grimm & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 99-146). Washington, DC, US: American Psychological Association.
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3th ed.). New York, NY: Taylor & Francis.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105. https://doi.org/10.1037/h0046016
Cronbach, L. J., & Meehl, P. C. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative vs. reflective indicators
in measure development: Does the choice of indicators matter? British Journal of Management, 13, 263-282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61, 1203-1218. https://doi.org/10.1016/j.jbusres.2008.01.009
de Jong, N. H., Steinel, M. P., Florijn, A. F., Schoonen, R., & Hulstijn, J. H. (2012). Facets of speaking proficiency. Studies in Second Language Acquisition, 34(1), 5-34. https://doi.org/10.1017/S0272263111000489
Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 56-83. https://doi.org/10.1080/10705519909540119
Fulcher, G., & Davidson, F. (2007). Language testing and assessment: An advanced resource book. New York, NY: Routledge.
Gass, S., Loewen, S., & Plonsky, L. (2021). Coming of age: the past, present, and future of quantitative SLA research. Language Teaching, 54(2), 245-258. https://doi.org/10.1017/S0261444819000430
Ghanbar, H., & Rezvani, R. (2023). Structural Equation Modeling in L2 Research: A Systematic Review. International Journal of Language Testing, 13(Special Issue), 79-108. 10.22034/ijlt.2023.381619.1224
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs, NJ: Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed). London: SAGE Publication.
Heene, M., Hilbert, S., Freudenthaler, H. H., & Bühner, M. (2012). Sensitivity of SEM fit indexes with respect to violations of uncorrelated errors. Structural Equation Modeling, 19(1), 36-50. https://doi.org/10.1080/10705511.2012.634710
Henry, A., & Cliffordson, C. (2013), Motivation, Gender, and Possible Selves. Language Learning, 63, 271-295. https://doi.org/10.1111/lang.12009
Hiver, P., & Al‐Hoorie, A. H. (2019). Reexamining the role of vision in second language motivation: A preregistered conceptual replication of You, Dörnyei, and Csizér (2016). Language Learning. https://doi.org/10.1111/lang.12371
Hu, G. (2005). Contextual influences on instructional practices: A Chinese case for an ecological Approach to ELT. TESOL Quarterly, 39(4), 635-660. https://doi.org/10.2307/3588525
Hu, L.T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A multidisciplinary Journal, 6, 1-55. https://doi.org/10.1080/10705519909540118
In'nami, Y., & Koizumi, R. (2011). Structural equation modeling in language testing and learning research: A review. Language Assessment Quarterly, 8(3), 250-276. https://doi.org/10.1080/15434303.2011.582203
Janssen, C., Segers, E., McQueen, J. M., & Verhoeven, L. (2017). Transfer from implicit to explicit phonological abilities in first and second language learners. Bilingualism: Language and Cognition, 20(4), 795-812. https://doi.org/10.1017/S1366728916000523
Kenny, D. A., & Milan, S. (2012). Identification: A nontechnical discussion of a technical issue. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 145-163). New York: The Guilford Press.
Khany, R., & Tazik, K. (2019). Levels of statistical use in applied linguistics research articles: From 1986-2015. Journal of Quantitative Linguistics, 26, 48-65. https://doi.org/10.1080/09296174.2017.1421498
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: The Guilford Press.
Lei, P., & Wu, Q. (2012). Estimation in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 164-180). New York, NY, US: The Guilford Press.
Llosa, L. (2007). Validating a standards-based classroom assessment of English proficiency: A multitrait-multimethod approach. Language Testing, 24(4), 489-515. https://doi.org/10.1177/0265532207080770
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23, 412-433. doi:10.1037/met0000144
Mueller, R. O., & Hancock, G. R. (2019). Structural equation modeling. In G. R. Hancock, R. O. Mueller, & L. M. Stapleton (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 445-456). New York, NY: Routledge.
Mueller, R. O., & Knapp, T. R. (2019). Reliability and validity. In G. R. Hancock, R. O. Mueller, & L. M. Stapleton (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 397-401). New York, NY: Routledge.
Nunnally, J. C., & Bernstein, I. (1994). Psychometric theory. New York, NY: McGraw-Hill.
Ockey, G. J., & Choi, I. (2015). Structural equation modeling reporting practices for language assessment. Language Assessment Quarterly, 12(3), 305-319.
Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for social sciences (6th ed.). New York, NY: Routledge.
Plonsky, L., & Ghanbar, H. (2018). Multiple regression in L2 research: A methodological synthesis and guide to interpreting R2 values. The Modern Language Journal, 102(4), 713–731. https://doi.org/10.1111/modl.12509
Polat, N., & Cepik, S. (2016). An exploratory factor analysis of the sheltered instruction observation protocol as an evaluation tool to measure teaching effectiveness. TESOL Quarterly, 50(4), 817-843. https://doi.org/10.1002/tesq.248
Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (2nd ed.). Mahwah, NJ: Erlbaum.
Raykov, T., Gabler, S., & Dimitrov. D. M. (2016). Maximal reliability and composite reliability: Examining their difference for multicomponent measuring instruments using latent variable modeling. Structural Equation Modeling, 23(3), 384-391. https://doi.org/10.1080/10705511.2014.966369
Scheele, A. F., Leseman, P. P. M., & Mayo, A. Y. (2010). The home language environment of monolingual and bilingual children and their language proficiency. Applied Psycholinguistics, 31, 117-140. https://doi.org/10.1017/S0142716409990191
Shiotsu, T., & Weir, C. J. (2007). The relative significance of syntactic knowledge and vocabulary breadth in the prediction of reading comprehension test performance. Language Testing, 24(1), 99-128. https://doi.org/10.1177/0265532207071513
Stevens. J. (2009). Applied multivariate statistics for social sciences (5th ed). London: Routledge.
Tabachnik, B. G., & Fidell, L. S. (2019). Using multivariate Statistics (7th ed.). Boston: Allyn and Bacon.
Teng, L. S., Sun, P. P., & Xu, L. (2018). Conceptualizing writing self‐Efficacy in English as a foreign language contexts: Scale validation through structural equation modeling. TESOL Quarterly, 52(4), 911-942. https://doi.org/10.1002/tesq.432
West SG, Taylor AB and Wu W (2012) Model fit and model selection in structural equation modeling. In Hoyle RH (ed.) Handbook of structural equation modeling (pp. 209-231). New York, NY, US: The Guilford Press.
Winke P (2013) An investigation into second language aptitude for advanced Chinese language learning. The Modern Language Journal 97(1): 109-130. https://doi.org/10.1111/j.1540-4781.2013.01428.x
