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      The contributions of transcription skills to paper-based and computer-based text composing in the early years

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          Abstract

          Digital tools are an integral part of most writing communities across the globe, enhancing the criticality of gaining a comprehensive understanding of both paper and computer-based writing acquisition and development. The relationships between transcription skills and children’s paper-based writing performance are well documented. Less is known about the relationships between transcription skills and children’s computer-based writing performance. In this study, we examined the unique contributions of transcription skills (i.e., handwriting automaticity, keyboarding automaticity and spelling) in predicting Grade 2 students (N = 544) paper-based and computer-based writing performance (i.e., compositional quality and productivity) after controlling for other student-level factors (i.e., gender, word reading, reading comprehension, and attitudes towards writing) and classroom-level factors (i.e., amount of time teaching handwriting, keyboarding, and spelling). Multilevel modelling showed that, compared to handwriting automaticity, spelling skills accounted for a larger percentage of unique variance in predicting paper-based compositional quality; handwriting automaticity accounted for a larger percentage of unique variance in explaining paper-based compositional productivity. Findings further showed that keyboarding automaticity accounted for a larger percentage of unique variance in students’ computer-based compositional quality and productivity when compared to spelling. Gender and word reading skills were also found to be uniquely related to students’ writing performance across modalities. These findings underscore the need for educators to address and nurture the automaticity of inscription and spelling skills to enhance students' compositional quality and productivity, whether in traditional paperbased or computer-based text composing.

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          Centering predictor variables in cross-sectional multilevel models: a new look at an old issue.

          Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in the context of 2-level MLMs. The authors begin with a basic overview of centering and explore the differences between grand and group mean centering in the context of some prototypical research questions. Empirical analyses of artificial data sets are used to illustrate key points throughout. The article provides a number of practical recommendations designed to facilitate centering decisions in MLM applications. Copyright 2007 APA, all rights reserved.
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            Centering Decisions in Hierarchical Linear Models: Implications for Research in Organizations

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              Complex Sample Data in Structural Equation Modeling

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Reading and Writing
                Read Writ
                Springer Science and Business Media LLC
                0922-4777
                1573-0905
                April 20 2024
                Article
                10.1007/s11145-024-10543-6
                decec523-a4c2-4db0-8081-eae3a882357e
                © 2024

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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