Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-23T10:47:17.785Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantitative Approaches for Analyzing Longitudinal Data in Second Language Research

Published online by Cambridge University Press:  22 December 2014

Khaled Barkaoui
Get access Rights & Permissions [Opens in a new window]

Abstract

This article discusses methods used in second language (L2) research to analyze quantitative longitudinal data. Longitudinal studies are experimental and nonexperimental studies that collect repeated measures of the same variable(s) from the same participant(s) at two or more time points. Three challenging areas in longitudinal L2 research are first discussed: study design, measurement, and data analysis and modeling. Next, various traditional and recent quantitative approaches for analyzing longitudinal data are discussed, including difference or gain scores, repeated measures univariate and multivariate analysis of variance (RM ANOVA, MANOVA), multilevel modeling (MLM), autoregressive models and latent growth curve modeling (LGCM) within the structural equation modeling (SEM) framework, item response theory (IRT), single-case research designs and time series analysis (TSA), and event history analysis (EHA). Longitudinal L2 studies published in the last 10 years are reviewed to identify trends and patterns in the use of different quantitative approaches for analyzing longitudinal L2 data, describe best data analysis practices in such research, and provide recommendations for future longitudinal L2 studies. It is argued that, when feasible and appropriate, recent approaches (e.g., MLM, LGCM) have several conceptual, methodological, and practical advantages and can stimulate the development and empirical examination of more complex questions and models concerning L2 development over time than is possible with traditional techniques.

Type
Research Article
Information
Annual Review of Applied Linguistics , Volume 34 , March 2014 , pp. 65 - 101
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

ANNOTATED BIBLIOGRAPHY

Laursen, B., Little, T. D., & Card, N. A. (Eds.). (2012). Handbook of developmental research methods. New York, NY: Guilford.

The 41 chapters in this volume cover a wide range of considerations and issues in longitudinal research, including research design, measurement, data collection strategies, and various traditional and modern techniques for longitudinal data analysis, which are described in accessible, nontechnical language and illustrated using real examples. This is a valuable resource for anyone studying change over time.

Menard, S. (Ed.). (2008). Handbook of longitudinal research: Design, measurement, and analysis. New York, NY: Academic Press.

This comprehensive handbook discusses various conceptual, methodological, and practical considerations and issues concerning research design, measurement, data missingness and attrition, experimental research, and quantitative and qualitative data analysis in longitudinal research. This is a valuable resource for both novice and experienced researchers engaged in longitudinal research.

Neuman, S. B., & McCormick, S. (Eds.). (1995). Single-subject experimental research: Applications for literacy. Newark, DL: International Reading Association.

The eight chapters in this volume provide guidelines for designing, implementing, and reporting single-case studies, describe various single-case research designs, and discuss the collection and analysis of qualitative and quantitative data in single-case research. Several examples of single-case literacy studies are described.

Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.). (2012). Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences. New York, NY: Routledge.

This 11-chapter volume provides accessible discussions and examples of various approaches to quantitative longitudinal data analysis, including ANOVA, MLM, SEM, and EHA. Each chapter discusses the conceptual, methodological, and statistical aspects of each data analysis approach and relevant software for data analysis, and provides a list of recommended readings and examples of studies that used the approach under focus. Other chapters in the volume discuss practices, considerations, and issues concerning study design, sampling, measurement, missing data, mediation analysis, and the analysis of categorical data in longitudinal research.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford, UK: Oxford University Press.

Singer and Willett provide a comprehensive discussion of various considerations and issues in the measurement and modeling of change and growth over time. The first part of the book provides an accessible discussion of the conceptual, statistical, and practical aspects of MLM and LGCM in the context of analyzing longitudinal data to study change over time. The second part focuses on EHA. Both parts include numerous step-by-step examples of longitudinal data analysis.

Weinfurt, K. P. (2000). Repeated measures analyses: ANOVA, ANCOVA, and HLM. In Grimm, L. G. & Yarnold, P. R. (Eds.), Reading and understanding more multivariate statistics (pp. 317–361). Washington, DC: American Psychology Association.

This chapter provides very accessible discussion, comparisons, and examples concerning the use of gain scores, RM ANOVA, MANOVA, ANCOVA, and MLM for longitudinal data analysis. The chapter also provides guidelines concerning the use of RM ANOVA, MANOVA, ANCOVA, and ANOVA on gain scores in experimental and quasi-experimental studies with two measurement occasions (i.e., pretest and posttest).

Zumbo, B. D. (1999). The simple difference score as an inherently poor measure of change: Some reality, much mythology. In Thompson, B. (Ed.), Advances in social science methodology (Vol. 5, pp. 269–304). Bingley, UK: JAI Press.

This chapter provides a detailed and accessible discussion of the supposed unreliability and invalidity of gain scores; challenges these criticisms; and provides advice and detailed guidelines concerning whether, when, and how to use gain scores when analyzing data from two occasions as well as how to address the limitations of gain scores. The chapter also discusses and makes recommendations concerning the use of RM ANOVA, ANCOVA, and ANOVA on gain scores in experimental and quasi-experimental studies with two measurement occasions.

REFERENCES

Allison, P. A. (1990). Change scores as dependent variables in regression analysis. Sociological Methodology, 20, 93–114.Google Scholar
Bae, J., & Lee, Y. (2012). Evaluating the development of children's writing ability in an EFL context. Language Assessment Quarterly, 9, 348–374.Google Scholar
Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In Nesselroade, J. R. & Baltes, P. B. (Eds.), Longitudinal research in the study of behavior and development (pp. 1–39). New York, NY: Academic Press.Google Scholar
Barkaoui, K. (2013). An introduction to multilevel modeling in language assessment research. Language Assessment Quarterly, 10, 241–273.Google Scholar
Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single case experimental designs: Strategies for studying behaviour changes (3rd ed.). New York, NY: Pearson.Google Scholar
Bast, J., & Reitsma, P. (1997). Matthew effects in reading: A comparison of latent growth curve models and simplex models with structured means. Multivariate Behavioral Research, 32, 135–167.CrossRefGoogle Scholar
Bates, J. E., & Novosad, C. (2005). Measurement of individual difference constructs in child development, or taking aim at moving targets. In Teti, D. M. (Ed.), Handbook of research methods in developmental science (pp. 103–122). Malden, MA: Blackwell.Google Scholar
Bijeleveld, G. C. J. H., van der Kamp, L. J. T., Mooijaart, A., van der Kloot, W. A., van der Leeden, R., & ven der Burg, E. (1998). Longitudinal data analysis: Designs, models, and methods. Thousand Oaks, CA: Sage.Google Scholar
Bitchener, J., & Knoch, U. (2008). The value of written corrective feedback for migrant and international students. Language Teaching Research, 12, 409–431.Google Scholar
Bitchener, J., & Knoch, U. (2010a). The contribution of written corrective feedback to language development: A ten month investigation. Applied Linguistics, 31, 193–214.Google Scholar
Bitchener, J., & Knoch, U. (2010b). Raising the linguistic accuracy level of advanced L2 writers with written corrective feedback. Journal of Second Language Writing, 19, 207–217.Google Scholar
Bloomfield, A., Ross, S., Masters, M., Gynther, K., & O’Connell, S. (2014). How does foreign language proficiency change over time? Results of data mining official test records. In Connor-Linton, J. & Amoroso, L. W. (Eds.), Measured language: Quantitative studies of acquisition, assessment, and variation (pp. 199–212). Washington, DC: Georgetown University Press.Google Scholar
Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods and Research, 32, 336–383.Google Scholar
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley-Interscience.Google Scholar
Bollen, K. A., & Zimmer, C. (2010). An overview of the autoregressive latent trajectory (ALT) model. In van Montfort, K., Oud, J. H. L., & Satorra, A. (Eds.), Longitudinal research with latent variables (pp. 153–176). Amsterdam, The Netherlands: Springer.Google Scholar
Bontempo, D. E., Grouzet, F. M. E., & Hofer, S. M. (2012). Measurement issues in the analysis of within-person change. In Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 97–142). New York, NY: Routledge.Google Scholar
Bornstein, M. H., Hah, C., Haynes, O. M., Manian, N., & Tamis-LeMonda, C. S. (2005). New research methods in developmental science: Applications and illustrations. In Teti, D. M. (Ed.), Handbook of research methods in developmental science (pp. 509–533). Malden, MA: Blackwell.Google Scholar
Busk, P. L., & Marascuilo, L. A. (1992). Statistical analysis in single-case research: Issues, procedures, and recommendations, with applications to multiple behaviors. In Kratochwill, T. R. & Levin, J. R. (Eds.), Single-case research design and analysis: New directions for psychology and education (pp. 159–185). Hillsdale, NJ: Erlbaum.Google Scholar
Collins, L. M. (1991). Measurement in longitudinal research. In Collins, L. A. & Horn, J. L. (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 137–148). Washington, DC: American Psychological Association.Google Scholar
Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528.Google Scholar
Cronbach, L. J., & Furby, L. (1970). How we should measure “change”: Or should we? Psychological Bulletin, 74, 68–80.Google Scholar
Crossley, S. A., Salsbury, T., & McNamara, D. S. (2009). Measuring L2 lexical growth using hypernymic relationships. Language Learning, 59, 307–334.Google Scholar
Crossley, S. A., Salsbury, T., & McNamara, D. S. (2010). The development of polysemy and frequency use in English second language speakers. Language Learning, 60, 573–605.Google Scholar
Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529–569.Google Scholar
Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In Collins, L. M. & Sayer, A. G. (Eds.), New methods for the analysis of change (pp. 107–135). Washington, DC: American Psychological Association.Google Scholar
Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11, 121–136.Google Scholar
Dale, P. S., Harlaar, N., & Plomin, R. (2012). Nature and nurture in school-based second language achievement. Language Learning, 62, 28–48.Google Scholar
de Vaus, D. (2001). Research design in social research. Los Angeles, CA: Sage.Google Scholar
Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). New York, NY: Psychology Press.Google Scholar
Edwards, M. C., & Wirth, R. J. (2009). Measurement and the study of change. Research in Human Development, 6, 74–96.Google Scholar
Elder, C., & O’Loughlin, K. (2003). Investigating the relationship between intensive English language study and band score gain on IELTS. In IELTS Research Reports (Vol. 4, pp. 207–254). Manchester, UK: IELTS Australia and British Council.Google Scholar
Embretson, S. E., & Poggio, J. (2012). The impact of scaling and measurement methods on individual differences in growth. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (pp. 82–108). New York, NY: Guilford.Google Scholar
Endler, L. C., & Bond, T. G. (2006). Tracking cognitive development with the Rasch model. In Liu, X. & Boone, W. (Eds.), Applications of Rasch measurement in science education (pp. 74–109). Maple Grove, MN: JAM Press.Google Scholar
Feng, D., Cong, Z., & Silverstein, M. (2012). Missing data and attrition. In Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 71–96). New York, NY: Routledge.Google Scholar
Fischer, G. H. (1976). Some probabilistic models for measuring change. In DeGruijter, D. N. M. & van der Kamp, L. J. T. (Eds.), Advances in psychological and educational measurement (pp. 97–110). New York, NY: Wiley.Google Scholar
Gilmore, A. (2011). “I prefer not text”: Developing Japanese learners’ communicative competence with authentic materials. Language Learning, 61, 786–819.Google Scholar
Green, A. (2005). EAP study recommendations and score gains on the IELTS academic writing test. Assessing Writing, 10, 44–60.Google Scholar
Grimm, K. J., & Ram, N. (2012). Growth curve modeling from a structural equation modeling perspective. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (pp. 411–431). New York, NY: Guilford.Google Scholar
Gustafsson, J. E. (2010). Longitudinal designs. In Creemers, B. P. M., Kyrikides, L., & Sammons, P. (Eds.), Methodological advances in educational effectiveness research (pp. 77–101). New York, NY: Routledge.Google Scholar
Gutierrez-Clellen, V., Simon-Cereijido, G., & Sweet, M. (2012). Predictors of second language acquisition in Latino children with specific language impairment. American Journal of Speech-Language Pathology, 21, 64–77.Google Scholar
Hartmann, D. P. (2005). Assessing growth in longitudinal investigations: Selected measurement and design issues. In Teti, D. M. (Ed.), Handbook of research methods in developmental science (pp. 319–339). Malden, MA: Blackwell.Google Scholar
Henry, K. L., & Slater, M. D. (2008). Assessing change and intraindividual variation: Longitudinal multilevel and structural equation modeling. In Hayes, A. F., Slater, M. D., & Snyder, L. B. (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 55–87). Los Angeles, CA: Sage.Google Scholar
Hertzog, C., & Nesselroade, J. R. (2003). Assessing psychological change in adulthood: An overview of methodological issues. Psychology and Aging, 18, 639–657.Google Scholar
Ho, H. Z., O’Farrell, L., Hoong, S., & You, S. (2006). Developmental research: Theory, method, design, and statistical analysis. In Green, J. L., Camilli, G., & Elmore, P. B. (Eds.), Handbook of complementary methods in education research (pp. 207–225). Mahwah, NJ: Erlbaum.Google Scholar
Hofer, S. M., Thorvaldsson, V., & Piccinin, A. M. (2012). Foundational issues of design and measurement in developmental research. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (pp. 3–16). New York, NY: Guilford.Google Scholar
Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.Google Scholar
Huttenlocher, J., Haight, W., Bryk, A., Seltzer, M., & Lyons, T. (1991). Early vocabulary growth: Relation to language input and gender. Developmental Psychology, 27, 236–248.Google Scholar
Ishida, M. (2004). Effects of recasts on the acquisition of the aspectual form -te i-(ru) by learners of Japanese as a foreign language. Language Learning, 54, 311–394.Google Scholar
Jones, R. N. (2012). Latent growth curve models. In Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 271–290). New York, NY: Routledge.Google Scholar
Kamil, M. L. (1995). Statistical analysis procedures for single-subject designs. In Neuman, S. B. & McCormick, S. (Eds.), Single-subject experimental research: Applications for literacy (pp. 84–103). Newark, DL: International Reading Association.Google Scholar
Kieffer, M. (2011). Converting trajectories: Reading growth in language minority learners and their classmates, kindergarten to grade 8. American Educational Research Journal, 48, 1187–1225.Google Scholar
Kieffer, M. (2012). Early oral language and later reading development in Spanish-speaking English language learners: Evidence from a nine-year longitudinal study. Journal of Applied Developmental Psychology, 33, 146–157.Google Scholar
Kieffer, M. J., & Lesaux, N. K. (2012a). Development of morphological awareness and vocabulary knowledge in Spanish-speaking language minority learners: A parallel process latent growth curve model. Applied Psycholinguistics, 33, 23–54.Google Scholar
Kieffer, M. J., & Lesaux, N. K. (2012b). Effects of academic language instruction on relational and syntactic aspects of morphological awareness for sixth graders from linguistically diverse backgrounds. The Elementary School Journal, 112, 519–545.Google Scholar
Kozaki, Y., & Ross, S. J. (2011). Contextual dynamics in foreign language learning motivation. Language Learning, 61, 1328–1354.Google Scholar
Krishef, C. H. (1991). Fundamental approaches to single subject design and analysis. Malabar, FL: Krieger.Google Scholar
Kruk, R. S., & Reynolds, K. A. A. (2012). French immersion experience and reading skill development in at-risk readers. Journal of Child Language, 39, 580–610.Google Scholar
Laursen, B., Little, T. D., & Card, N. A. (Eds.). (2012). Handbook of developmental research methods. New York, NY: Guilford.Google Scholar
Lervag, A., & Aukrust, V. G. (2010). Vocabulary knowledge is a critical determinant of the difference in reading comprehension growth between first and second language learners. Journal of Child Psychology and Psychiatry, 51, 612–620.Google Scholar
Lissitz, R. W., Doran, H., Schafer, W. D., & Willhoft, J. (2006). Growth modeling, value added modeling, and linking: An introduction. In Lissitz, R. W. (Ed.), Longitudinal and value added models of student performance (pp. 1–46). Maple Grove, MN: JAM Press.Google Scholar
Little, R. J. A., & Rubin, R. D. (1987). Statistical analysis with missing data. New York, NY: Wiley.Google Scholar
Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: Guilford.Google Scholar
Llosa, L. (2012). Assessing English language learners’ progress: Longitudinal invariance of a standard-based classroom assessment of English proficiency. Language Assessment Quarterly, 9, 331–347.Google Scholar
Lord, F. M. (1963). Elementary models for measuring change. In Harris, W. C. (Ed.), Problems in measuring change (pp. 21–38). Madison, WI: University of Wisconsin Press.Google Scholar
Luke, D. A. (2008). Multilevel growth curve analysis for quantitative outcomes. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 545–564). New York, NY: Academic Press.Google Scholar
Lundstrom, K., & Baker, W. (2009). To give is better than to receive: The benefits of peer review to the reviewer's own writing. Journal of Second Language Writing, 18, 30–43.Google Scholar
Lyster, R., & Izquierdo, J. (2009). Prompts versus recasts in dyadic interaction. Language Learning, 59, 453–498.Google Scholar
Maas, C. J., & Hox, J. J. (2005). Sufficient sample size for multilevel modeling. Methodology, 1, 86–92.Google Scholar
Matsumura, S. (2003). Modeling the relationships among interlanguage pragmatic development, L2 proficiency, and exposure to L2. Applied Linguistics, 24, 465–491.Google Scholar
McCall, M., & Hauser, C. (2007). Item response theory and longitudinal modeling: The real world is less complicated than we fear. In Lissitz, R. W. (Ed.), Assessing and modeling cognitive development in schools (pp. 143–174). Maple Grove, MN: JAM Press.Google Scholar
McCormick, S. (1995). What is single-subject experimental research? In Neuman, S. B. & McCormick, S. (Eds.), Single-subject experimental research: Applications for literacy (pp. 1–31). Newark, DL: International Reading Association.Google Scholar
Mellenbergh, G. J. (1999). A note on simple gain score precision. Applied Psychological Measurement, 23, 87–89.Google Scholar
Mellow, J. D. (2008). The emergence of complex syntax: A longitudinal study of the ESL development of dependence resolution. Lingua, 118, 499–521.Google Scholar
Mellow, J. D. (2013). Time series. In Chapelle, C. A. (Ed.), The encyclopedia of applied linguistics. Malden, MA: Blackwell.Google Scholar
Mellow, J. D., Reeder, K., & Forster, E. (1996). Using time-series research designs to investigate the effects of instructing on SLA. Studies in Second Language Acquisition, 18, 325–350.Google Scholar
Menard, S. (2002). Longitudinal research (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
Menard, S. (2008a). A brief introduction to time series analysis. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 579–600). New York, NY: Academic Press.Google Scholar
Menard, S. (2008b). Introduction: Longitudinal research design and analysis. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 3–12). New York, NY: Academic Press.Google Scholar
Millsap, R. E., & Cham, H. (2012). Investigating factorial invariance in longitudinal data. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (pp. 109–126). New York, NY: Guilford.Google Scholar
Newsom, J. T. (2012). Basic longitudinal analysis approaches for continuous and categorical variables. In Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 143–179). New York, NY: Routledge.Google Scholar
Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.). (2012). Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences. New York, NY: Routledge.Google Scholar
Nock, M. K., Michel, B. D., & Photos, V. I. (2007). Single-case research designs. In McKay, D. (Ed.), Handbook of research methods in abnormal and clinical psychology (pp. 337–350). Thousand Oaks, CA: Sage.Google Scholar
Ortega, L., & Iberri-Shea, G. (2005). Longitudinal research in second language acquisition: Recent trends and future directions. Annual Review of Applied Linguistics, 25, 26–45.Google Scholar
Ortega, L., & Byrnes, H. (2008a). The longitudinal study of advanced L2 capacities: An introduction. In Ortega, L. & Byrnes, H (Eds.), The longitudinal study of advanced L2 capacities (pp. 3–20). New York, NY: Routledge.Google Scholar
Ortega, L., & Byrnes, H. (2008b). Theorizing advancedness, setting up the longitudinal research agenda. In Ortega, L. & Byrnes, H. (Eds.), The longitudinal study of advanced L2 capacities (pp. 281–300). New York, NY: Routledge.Google Scholar
Pitts, S. C., Prost, J. H., & Winters, J. J. (2005). Quasi-experimental designs in developmental research: Design and analysis considerations. In Teti, D. M. (Ed.), Handbook of research methods in developmental science (pp. 80–100). Malden, MA: Blackwell.Google Scholar
Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent growth curve modeling. Los Angeles, CA: Sage.Google Scholar
Rees, J., & Klapper, J. (2008). Issues in the quantitative longitudinal measurement of second language progress in the study abroad context. In Ortega, L. & Byrnes, H. (Eds.), The longitudinal study of advanced L2 capacities (pp. 89–105). New York, NY: Routledge.Google Scholar
Revesz, A. (2012). Working memory and the observed effectiveness of recasts on different L2 outcome measures. Language Learning, 62, 93–132.Google Scholar
Roberts, J. S., & Ma, Q. (2006). IRT models for the assessment of change across repeated measurements. In Lissitz, R. W., (Ed.), Longitudinal and value added models of student performance (pp. 100–129). Maple Grove, MN: JAM Press.Google Scholar
Rogosa, D. (1995). Myths and methods: “Myths about longitudinal research” plus supplemental questions. In Gottma, J. M. (Ed.), The analysis of change (pp. 3–66). Mahwah, NJ: Erlbaum.Google Scholar
Ross, S. J. (2005). The impact of assessment method on foreign language proficiency growth. Applied Linguistics, 26, 317–342.Google Scholar
Ross, S. (2014). Subjectivity and efficiency in language assessment: Explorations of a compensatory rating approach. In Connor-Linton, J. & Amoroso, L. W. (Eds.), Measured language: Quantitative studies of acquisition, assessment, and variation (pp. 51–62). Washington, DC: Georgetown University Press.Google Scholar
Ruspini, E. (2002). Introduction to longitudinal research. London, UK: Routledge.Google Scholar
Salsbury, T., Crossley, S. A., & McNamara, D. S. (2011). Psycholinguistic word information in second language oral discourse. Second Language Research, 27, 343–360.CrossRefGoogle Scholar
Sato, M., & Lyster, R. (2012). Peer interaction and corrective feedback for accuracy and fluency development: Monitoring, practice, and proceduralization. Studies in Second Language Acquisition, 34, 591–626.Google Scholar
Schoonen, R., van Gelderen, A., Stoel, R. D., Hulstijn, J., & de Glopper, K. (2011). Modeling the development of L1 and EFL writing proficiency of secondary school students. Language Learning, 61, 31–79.Google Scholar
Selig, J. P., & Little, T. D. (2012). Autoregressive and cross-lagged panel analysis for longitudinal data. In Laursen, B., Little, T. D., & Card, N. A. (Eds.), Handbook of developmental research methods (pp. 265–278). New York, NY: Guilford.Google Scholar
Serrano, R., Llanes, À., & Tragant, E. (2011). Analyzing the effect of context of second language learning: Domestic intensive and semi-intensive courses vs. study abroad in Europe. System, 39, 133–143.Google Scholar
Shaw, B. A., & Liang, J. (2012). Growth models with multiple regression. In Newsom, J. T., Jones, R. N., & Hofer, S. M. (Eds.), Longitudinal data analysis: A practical guide for researchers in aging, health, and social sciences (pp. 217–242). New York, NY: Routledge.Google Scholar
Shneyderman, A., & Abella, R. (2009). The effects of the extended foreign language programs on Spanish-language proficiency and academic achievement in English. Bilingual Research Journal, 32, 241–259.Google Scholar
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford, UK: Oxford University Press.Google Scholar
Skiba, R., Dittmar, N., & Bressem, J. (2008). Planning, collecting, exploring, and archiving longitudinal L2 data. In Ortega, L. & Byrnes, H (Eds.), The longitudinal study of advanced L2 capacities (pp. 21–88). New York, NY: Routledge.Google Scholar
Steele, F. (2008). Multilevel models for longitudinal data. Journal of Royal Statistical Society, 171, 5–19.Google Scholar
Stoolmiller, M. (2008). Latent growth curve models. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 523–544). New York, NY: Academic Press.Google Scholar
Storch, N. (2009). The impact of studying in a second language (L2) medium university on the development of L2 writing. Journal of Second Language Writing, 18, 103–118.Google Scholar
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). New York, NY: Pearson.Google Scholar
Tanguma, J. (1999). Analyzing repeated measures designs using univariate and multivariate methods: A primer. In Thompson, B. (Ed.), Advances in social science methodology: Vol. 5 (pp. 233–250). Bingley, UK: JAI Press.Google Scholar
Taris, T. W. (2000). A primer in longitudinal data analysis. Thousand Oaks, CA: Sage.Google Scholar
Taris, T. W. (2008). Reliability issues in longitudinal research. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 139–152). New York, NY: Academic Press.Google Scholar
Tong, F., Irby, B. J., Lara-Alecio, R., Yoon, M., & Mathes, P. G. (2010). Hispanic English learners’ responses to longitudinal English instructional intervention and the effect of gender: A multilevel analysis. Elementary School Journal, 110, 542–566.Google Scholar
Twisk, J. W. R. (2003). Applied longitudinal data analysis for epidemiology: A practical guide. Cambridge, UK: Cambridge University Press.Google Scholar
Twisk, J. W. R. (2008). Causal inference in longitudinal experimental research. In Menard, S. (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 279–293). New York, NY: Academic Press.Google Scholar
Ullman, J. B., & Bentler, P. M. (2004). Structural equation modeling. In Hardy, M. & Bryman, A. (Eds.), The handbook of data analysis (pp. 431–458). Los Angeles, CA: Sage.Google Scholar
Usborne, E., Caouette, J., Qumaaluk, Q., & Taylor, D. M. (2009). Bilingual education in an Aboriginal context: Examining the transfer of language skills from Inuktitut to English or French. International Journal of Bilingual Education and Bilingualism, 12, 667–684.Google Scholar
Vandergrift, L., & Tafaghodtari, M. H. (2010). Teaching L2 learners how to listen does make a difference: An empirical study. Language Learning, 60, 470–497.Google Scholar
Van Geert, P., & van Dijk, M. (2002). Focus on variability: New tools to study intra-individual variability in developmental data. Infant Behavior and Development, 25, 340–374.Google Scholar
van Gelderen, A., Schoonen, R., Stoel, R., de Glopper, K., & Hulstijn, J. (2007). Development of adolescent reading comprehension in language 1 and language 2: A longitudinal analysis of constituent components. Journal of Educational Psychology, 99, 477–491.CrossRefGoogle Scholar
Verhoeven, L. (1994). Transfer in bilingual development: The linguistic interdependence hypothesis revisited. Language Learning, 44, 381–415.Google Scholar
Verhoeven, L., & Leeuwe, J. (2012). The simple view of second language reading throughout the primary grades. Reading and Writing, 25, 1805–1818.Google Scholar
Wang, W., Wilson, M., & Adams, R. J. (1998). Measuring individual differences in change with multidimensional Rasch models. Journal of Outcome Measurement, 2, 240–265.Google Scholar
Weinfurt, K. P. (2000). Repeated measures analyses: ANOVA, ANCOVA, and HLM. In Grimm, L. G. & Yarnold, P. R. (Eds.), Reading and understanding more multivariate statistics (pp. 317–361). Washington, DC: American Psychology Association.Google Scholar
Willett, J. B. (1988). Questions and answers in the measurement of change. Review of Research in Education, 15, 345–422.Google Scholar
Wolfe, E. W., & Chiu, C. W. T. (1999a). Measuring change across multiple occasions using the Rasch rating scale model. Journal of Outcome Measurement, 3, 360–381.Google Scholar
Wolfe, E. W., & Chiu, C. W. T. (1999b). Measuring pretest-posttest change with a Rasch rating scale model. Journal of Outcome Measurement, 3, 134–161.Google Scholar
Yang, Y., & Lyster, R. (2010). Effects of form-focused practice and feedback on Chinese EFL learners’ acquisition of regular and irregular past tense forms. Studies in Second Language Acquisition, 32, 235–263.Google Scholar
Yanovitzky, I., & VanLear, A. (2008). Time series analysis: Traditional and contemporary approaches. In Hayes, A. F., Slater, M. D., & Snyder, L. B. (Eds.), The Sage sourcebook of advanced data analysis methods for communication research (pp. 89–124). Los Angeles, CA: Sage.Google Scholar
Yasuda, S. (2011). Genre-based tasks in foreign language writing: Developing writers’ genre awareness, linguistic knowledge, and writing competence.Journal of Second Language Writing, 20, 111–133.Google Scholar
Zumbo, B. D. (1999). The simple difference score as an inherently poor measure of change: Some reality, much mythology. In Thompson, B. (Ed.), Advances in social science methodology (Vol. 5, pp. 269–304). Bingley, UK: JAI Press.Google Scholar