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

Nonresponse in panel studies can lead to a substantial loss in data qualitydue to its potential to introduce bias and distort survey estimates. Recentwork investigates the usage of machine learning to predict nonresponse inadvance, such that predicted nonresponse propensities can be used to inform thedata collection process. However, predicting nonresponse in panel studiesrequires accounting for the longitudinal data structure in terms of modelbuilding, tuning, and evaluation. This study proposes a longitudinal frameworkfor predicting nonresponse with machine learning and multiple panel waves andillustrates its application. With respect to model building, this approachutilizes information from multiple waves by introducing features that aggregateprevious (non)response patterns. Concerning model tuning and evaluation,temporal cross-validation is employed by iterating through pairs of panel wavessuch that the training and test sets move in time. Implementing this approachwith data from a German probability-based mixed-mode panel shows thataggregating information over multiple panel waves can be used to buildprediction models with competitive and robust performance over all test waves.

A Longitudinal Framework for Predicting Nonresponse in Panel Surveys