Dataset Information

Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

ABSTRACT: When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed.

SUBMITTER: Kwok OM

PROVIDER: S-EPMC5996051 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

Kwok Oi-Man OM Lai Mark Hok-Chio MH Tong Fuhui F Lara-Alecio Rafael R Irby Beverly B Yoon Myeongsun M Yeh Yu-Chen YC

Frontiers in psychology 20180605

When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not o ...[more]

PMID: 29922193

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Project description:IntroductionThe Better Start Literacy Approach is an example of a multi-tiered system of support (MTSS) to facilitate children's early literacy success. It is set within a strengths-based and culturally responsive framework for literacy teaching and is being implemented in over 800 English medium schools across New Zealand. This report focuses on how children identified at school entry as English Language Learners (ELL) responded to the Better Start Literacy Approach during their first year at school.MethodUsing a matched control design, the growth in phoneme awareness, phoneme-grapheme knowledge, and oral narrative skills for 1,853 ELL was compared to a cohort of 1,853 non-ELL. The cohorts were matched for ethnicity (mostly Asian, 46% and Pacific, 26%), age (M = 65 months), gender (53% male), and socioeconomic deprivation index (82% in areas of mid to high deprivation).ResultsData analyses indicated similar positive growth rates for ELL and non-ELL from baseline to the first monitoring assessment following 10 weeks of Tier 1 (universal/class level) teaching. Despite demonstrating lower phoneme awareness skills at baseline, following 10 weeks of teaching, the ELL cohort performed similarly to non-ELL in non-word reading and spelling tasks. Predictors of growth analyses indicated that ELL from areas of low socioeconomic deprivation, who used a greater number of different words in their English story retells at the baseline assessment, and females made the most growth in their phonic and phoneme awareness development. Following the 10-week monitoring assessment, 11% of the ELL and 13% of the non-ELL cohorts received supplementary Tier 2 (targeted small group) teaching. At the next monitoring assessment (20 weeks post baseline assessment) the ELL cohort showed accelerated growth in listening comprehension, phoneme-grapheme matching and phoneme blending skills, catching up to their non-ELL peers.DiscussionDespite limitations of the dataset available, it provides one of the few insights into the response of ELL to Tier 1 and Tier 2 teaching in their first year at school. The data suggest that the Better Start Literacy Approach, which includes high-quality professional learning and development for teachers, literacy specialists, and speech-language therapists, is an effective approach toward developing foundational literacy skills for ELL. The important role speech-language therapists have in collaborating with class teachers to support children's early literacy success within a MTSS framework is discussed.

Project description:Interpreting polygenic overlap between ADHD and both literacy-related and language-related impairments is challenging as genetic associations might be influenced by indirectly shared genetic factors. Here, we investigate genetic overlap between polygenic ADHD risk and multiple literacy-related and/or language-related abilities (LRAs), as assessed in UK children (N ≤ 5919), accounting for genetically predictable educational attainment (EA). Genome-wide summary statistics on clinical ADHD and years of schooling were obtained from large consortia (N ≤ 326,041). Our findings show that ADHD-polygenic scores (ADHD-PGS) were inversely associated with LRAs in ALSPAC, most consistently with reading-related abilities, and explained ≤1.6% phenotypic variation. These polygenic links were then dissected into both ADHD effects shared with and independent of EA, using multivariable regressions (MVR). Conditional on EA, polygenic ADHD risk remained associated with multiple reading and/or spelling abilities, phonemic awareness and verbal intelligence, but not listening comprehension and non-word repetition. Using conservative ADHD-instruments (P-threshold < 5 × 10-8), this corresponded, for example, to a 0.35 SD decrease in pooled reading performance per log-odds in ADHD-liability (P = 9.2 × 10-5). Using subthreshold ADHD-instruments (P-threshold < 0.0015), these effects became smaller, with a 0.03 SD decrease per log-odds in ADHD risk (P = 1.4 × 10-6), although the predictive accuracy increased. However, polygenic ADHD-effects shared with EA were of equal strength and at least equal magnitude compared to those independent of EA, for all LRAs studied, and detectable using subthreshold instruments. Thus, ADHD-related polygenic links with LRAs are to a large extent due to shared genetic effects with EA, although there is evidence for an ADHD-specific association profile, independent of EA, that primarily involves literacy-related impairments.

Dataset Information

Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

Publications

Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

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