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Quantitative Approaches for Analyzing Longitudinal Data in Second Language Research

Published online by Cambridge University Press:  22 December 2014

Khaled Barkaoui
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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 

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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.

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