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

Several techniques exist to assess and reduce nonresponse bias, includingpropensity models, calibration methods, or post-stratification. Theseapproaches can only be applied after the data collection, and assume reliableinformation regarding unit nonresponse patterns for the entire population. Inthis paper, we demonstrate that sample allocation taking into account theexpected response rates (ERR) have advantages in this context. The performanceof ERR allocation is assessed by comparing the variances of estimates obtainedthose arising from a classical allocation proportional to size (PS) and thenapplying post-stratification. The main theoretical tool is asymptoticcalculations using the delta-method, and these are complemented with extensivesimulations. The main finding is that the ERR allocation leads to lowervariances than the PS allocation, when the response rates are correctlyspecified, and also under a wide range of conditions when the response ratescan not be correctly specified in advance.

Reducing Variance with Sample Allocation Based on Expected Response Rates