Nationally Mandated Testing for Accountability: English Language Learners in the US

Chalhoub‐Deville, Micheline; Deville, Craig

doi:10.1002/9780470694138.ch36

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…As a result, a simple group-based nominal or ordinal membership coding for ELL or non-ELL status is insufficient for any research to appropriately characterize the relationship between language proficiency and math achievement. To overcome this limitation, it is better to use a direct language measurement so that difference in language proficiency at the individual level can be taken into consideration (Chalhoub-Deville & Deville, 2008; Francis & Rivera, 2007). Our research uses high-quality longitudinal data and a vertically scaled reading score that benefits not only as a direct measure of language proficiency but also as a continuous measure available for all subjects, both ELLs and non-ELLs, across time.…”

Section: Literature Reviewmentioning

confidence: 99%

Differential and long-term language impact on math

Chen

Chalhoub‐Deville

2016

Language Testing

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Literature provides consistent evidence that there is a strong relationship between language proficiency and math achievement. However, research results show conflicts supporting either an increasing or a decreasing longitudinal relationship between the two. This study explored a longitudinal data and adopted quantile regression analyses to overcome several limitations in past research. The goal of the study is to detect more accurate and richer information on the longterm relationship between language and math, taking into consideration the socioeconomic status, gender, and ethnicity background at the same time. Results confirmed a persistent relationship between math achievement and all the factors explored. More importantly, it revealed that the strength of the relationship between language and math differed for students with various abilities both within and across grades. Model comparison suggests that language demand contributes to the achievement gap between ELLs and non-ELLs in math. There also seems to be a disadvantage for the geographically isolated group in academic achievement. Interpretation and implications for teaching and assessment are discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Differential and long-term language impact on math

Chen

Chalhoub‐Deville

2016

Language Testing

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“…In a similar vein, Chalhoub-Deville (2008) referring to the case of the No Child Left Behind legislation in the United States, argued that those engaged with large-scale educational initiatives have a duty to anticipate and attempt to influence the effect that they exert. In this respect she suggested that language planners and policy advisers on assessment should look to the literature on “social impact assessment” (SIA) for guidance (see her article in this volume).…”

Section: Language Policy and The Cefrmentioning

confidence: 99%

European Language Policy: Assessment, Learning, and the Cefr

Jones¹,

Saville²

2009

Ann Rev Appl Linguist

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This article describes how language policy is formed at a European level, focusing on the Common European Framework of Reference for Language (CEFR). The CEFR's prominent role in assessment has led to criticisms of its adequacy as a model for learning and fears that it is being used as an instrument of centralization and harmonization. First, we argue for studying the CEFR's effect on language policy as a case of impact, as this concept is understood within language assessment. We refer to experience with Asset Languages, developed as part of the United Kingdom's national languages strategy. Second, we agree with many commentators who insist on the framework's “flexible and context-amenable” nature. If use of the CEFR is made prescriptive and closed, it indeed becomes a straitjacket. What is needed is engagement with the complexity of specific contexts. We introduce the European Survey on Language Competences, a European Union (EU) initiative scheduled for 2011, which will further raise the profile of the CEFR as an assessment framework. This project should contribute to achieving comparability of measures and standards across languages. At the same time it underlines the need to develop contextualized, practical ways of realizing the CEFR's potential as a framework for teaching and learning.

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“…Students who face negative social, economic, and environmental impacts tend to be the ones to have the lower scores and tend to be the ones with the most difficult adjustment to school life. Unfortunately, there is ample evidence that these students tend to be the ones with lower test scores, but also lower attendance and higher dropout rates, making them more likely to have missing data (e.g., Chalhoub-Deville & Deville, 2008; Kieffer, Lesaux, Rivera, & Francis, 2009; J. Lee, 2010; Shin, Davison, Long, Chan, & Heistad, 2013; Wagner, Newman, Cameto, & Levine, 2006; Wang & Lin, 2005; Wright & Li, 2008).…”

Section: Methodsmentioning

confidence: 99%

Maximum likelihood versus multiple imputation for missing data in small longitudinal samples with nonnormality.

Shin

Davison

Long

2017

Psychological Methods

103

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The study examined the performance of maximum likelihood (ML) and multiple imputation (MI) procedures for missing data in longitudinal research when fitting latent growth models. A Monte Carlo simulation study was conducted with conditions of small sample size, intermittent missing data, and nonnormality. The results indicated that ML tended to display slightly smaller degrees of bias than MI across missing completely at random (MCAR) and missing at random (MAR) conditions. Although specification of prior information in the MI imputation-posterior (I-P) phase influenced the performance of MI, especially with nonnormal small samples and missing not at random (MNAR), the impact of this tight specification was not dramatic. Several corrected ML test statistics showed proper rejections rates across research designs, whereas posterior predictive p values for MI methods were more likely to be influenced by distribution shape and yielded higher rejection rates in MCAR and MAR than in MNAR. In conclusion, ML appears to be preferable to MI in research conditions with small missing samples and multivariate nonnormality whether or not strong prior information for the I-P phase of MI analysis is available. (PsycINFO Database Record

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Nationally Mandated Testing for Accountability: English Language Learners in the US

Cited by 11 publications

References 11 publications

Differential and long-term language impact on math

Differential and long-term language impact on math

European Language Policy: Assessment, Learning, and the Cefr

Maximum likelihood versus multiple imputation for missing data in small longitudinal samples with nonnormality.

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