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When responding to a writing task, writers spend a significant amount of their time not writing. These periods of physical inactivity, or pauses, during writing provide observable and measurable cues as to when, where, and how long writers halt to plan and/or revise their texts. Consequently, examining writers’ pausing patterns can provide important insights into the cognitive processes that writers employ when composing and the impact of various individual, task, and contextual factors on those processes. This article discusses theory and research on writers’ pausing behavior; how pause analysis can be used to investigate second language (L2) learners’ writing processes; challenges in researching writers’ pausing behavior (e.g., defining pauses); and some strategies to address these challenges. Next, the article illustrates how L2 writers’ pause data can be collected, analyzed, and interpreted, using keystroke logging data from a research project that aimed to examine the effects of task type, L2 proficiency, and keyboarding skills on L2 learners’ writing processes when writing on the computer. The article concludes with a call for more research on L2 writers’ pausing behavior, particularly how L2 writers’ pausing behavior relates to L2 writing outcomes and development across learners, contexts, and time.
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.
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