English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Longitudinal studies are subject to nonresponse when individuals fail toprovide data for entire waves or particular questions of the survey. We compareapproaches to nonresponse bias analysis (NRBA) in longitudinal studies andillustrate them on the Early Childhood Longitudinal Study, Kindergarten Classof 2010-11 (ECLS-K:2011). Wave nonresponse with attrition often yields amonotone missingness pattern, and the missingness mechanism can be missing atrandom (MAR) or missing not at random (MNAR). We discuss weighting, multipleimputation (MI), incomplete data modeling, and Bayesian approaches to NRBA formonotone patterns. Weighting adjustments are effective when the constructedweights are correlated to the survey outcome of interest. MI allows forvariables with missing values to be included in the imputation model, yieldingpotentially less biased and more efficient estimates. Multilevel models withmaximum likelihood estimation and marginal models estimated using generalizedestimating equations can also handle incomplete longitudinal data. Bayesianmethods introduce prior information and potentially stabilize model estimation.We add offsets in the MAR results to provide sensitivity analyses to assessMNAR deviations. We conduct NRBA for descriptive summaries and analytic modelestimates and find that in the ECLS-K:2011 application, NRBA yields minorchanges to the substantive conclusions. The strength of evidence about our NRBAdepends on the strength of the relationship between the characteristics in thenonresponse adjustment and the key survey outcomes, so the key to a successfulNRBA is to include strong predictors.

Nonresponse Bias Analysis in Longitudinal Studies: A Comparative Review with an Application to the Early Childhood Longitudinal Study