Early Home Activities and Oral Language Skills in Middle Childhood: A Quantile Analysis

Abstract

Oral language development is a key outcome of elementary school, and it is important to identify factors that predict it most effectively. Commonly researchers use ordinary least squares regression with conclusions restricted to average performance conditional on relevant covariates. Quantile regression offers a more sophisticated alternative. Using data of 17,687 children from the United Kingdom's Millennium Cohort Study, we compared ordinary least squares and quantile models with language development (verbal similarities) at 11 years as the outcome. Gender had more of an effect at the top of the distribution, whereas poverty, early language, and reading to the child had a greater effect at the bottom. The picture for TV watching was more mixed. The results are discussed in terms of the provision of universal and targeted interventions.

The identification of speech and language problems in elementary school: Diagnosis and co-occurring needs

“…At age 11, vocabulary levels at five years failed to explain any unique variance once reading was accounted for, as would be predicted given the intertwined relationship between language and reading. Consistent with previous research (Dockrell, Ricketts, & Lindsay, 2012;Law, Rush, King, Westrupp & Reilly, 2017), being socially disadvantaged predicted the identification of SLN, as did gender. However, these demographic variables were mediated by cognitive and behavioural factors as neither at seven or 11 years were they significant once these factors were accounted for.…”

What Quantile Regression Does and Doesn't Do: A Commentary on Petscher and Logan (2014)

“…Contributions citing PL (e.g., Law, Rush, King, Westrupp, & Reilly, 2018;McIlraith, 2018;Simzar, Martinez, Rutherford, Domina, & Conley, 2015;Tighe & Schatschneider, 2016) show that readers are indeed misled and copy PL's misconceptions about the QRM. However, these misconceptions are by no means limited to PL and the related literature but seem widespread in the social sciences (see, e.g., Budig & Hodges, 2010, and the commentary by Killewald & Bearak, 2014).…”

Warnock 40 Years on: The Development of Special Educational Needs Since the Warnock Report and Implications for the Future

“…The use of big data has increased. In addition to large scale cohort studies (Dockrell and Hurry, 2018;Law et al, 2018) the DfE's National Pupil Database has provided an important resource which has led to studies of the total English state school population including the relationship between SEN and demographic factors, for example ethnicity (Strand and Lindsay, 2009;Lindsay and Strand, 2016;Strand and Lindorff, 2018).…”