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Weighting Procedures and the Cluster Forming Algorithm for Delete-k Jackknife Variance Estimation for Institutional Surveys ICT

Qian, Jiahe
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
Weighting, Estimated Variance, Horvitz-Thompson Estimator, Information and Communication Technology (ICT) Literacy


The estimation of the variance from survey data uses the delete-k jackknife resampling replicate (JRR) approach, which can be adapted for variant institutional sampling designs and for dissimilarity in institute conditions. To form clusters of k cases, a merge-dilute algorithm is proposed. The algorithm merges the cases of different groups into a queue and then allocates the cases of the queue to form homogeneous clusters of required sizes. The new algorithm is applied to the ICT sample from an institute taking the 2004 fall trial assessment.

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