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  • Benelux / Low Contries Studies  (2)
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  • 1
    In: Second Language Research, SAGE Publications
    Abstract: As Chinese shows both satellite- and verb-framed properties (Slobin, 2004; Talmy, 2012, 2016), it provides a unique lens through which to observe the extent of first-language (L1) typological influence in second language (L2) acquisition of motion expressions. This study has dual purposes. First, it extends Wu’s (2016) investigation on motion expressions produced by 80 L1 satellite-framed English learners of L2 Chinese to include newly collected data produced by L1 verb-framed speakers, a sample comprised of 41 L1 Japanese learners of Chinese and 40 Japanese native speakers. Second, it synthesizes the data from both studies and comprehensively examines factors that have been proposed to affect development of L2 thinking-for-speaking (TFS) patterns. The results show that development of L2 TFS is best predicted by learners’ L1 type, but the effect is mitigated by L2 proficiency. While the L1 English learners outperform L1 Japanese learners in their development of target-like L2 Chinese TFS, learners with limited L2 proficiency in both groups tend to adopt verb-framed strategies to express only the core path information of a motion event and leave out the manner details. Analysis of L1 Japanese learners’ oral narratives in L1 Japanese and L2 Chinese also shows that reverse L2-to-L1 transfer is less likely to happen when learning a typologically closer L2 that requires minimal restructuring of their L1 TFS.
    Type of Medium: Online Resource
    ISSN: 0267-6583 , 1477-0326
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2023712-1
    SSG: 7,11
    SSG: 5,3
    SSG: 7,23
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Statistica Neerlandica
    In: Statistica Neerlandica, Wiley
    Abstract: Estimation of the average treatment effect is one of the crucial problems in clinical trials for two or multiple treatments. The covariate‐adaptive randomization methods are often applied to balance treatment assignments across prognostic factors in clinical trials, such as the minimization and stratified permuted blocks method. We propose a model‐free estimator of average treatment effects under covariate‐adaptive randomization methods, which is least square adjustment for the estimator of outcome models. The proposed estimator is not only applicable to the case of binary treatment, but also can be extended to the case of multiple treatment. The proposed estimator is consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator and Ye's estimator are comparable, and it performs better than Bugni's estimator when the outcome model is linear. The proposed estimator has some advantages over targeted maximum likelihood estimator, Bugni's estimator and Ye's estimator in terms of the standard error and root mean squared error when the outcome model is nonlinear. The proposed estimator is stable for the from of outcome model. Finally, we apply the proposed methodology to a dataset that studies the causal effect promotional videos mode on the school‐age children's educational attainment in Peru.
    Type of Medium: Online Resource
    ISSN: 0039-0402 , 1467-9574
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1466958-4
    SSG: 7,23
    Location Call Number Limitation Availability
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