A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys

Yajuan Si, Roderick J. A. Little, Ya Mo and Nell Sedransk
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Roderick J. A. Little: University of Michigan
Ya Mo: Boise State University
Nell Sedransk: National Institute of Statistical Sciences

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 3, 271-295

Abstract: Nonresponse bias is a widely prevalent problem for data on education. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011. A key step is the construction of indices of nonresponse bias based on proxy pattern-mixture models for survey variables of interest. A novel feature is to characterize the strength of evidence about nonresponse bias contained in these indices, based on the strength of the relationship between the characteristics in the nonresponse adjustment and the key survey variables. Our NRBA improves the existing methods by incorporating both missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software.

Keywords: nonresponse bias analysis (NRBA); missing not at random (MNAR); strong predictors; proxy pattern-mixture model; sensitivity analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:3:p:271-295

DOI: 10.3102/10769986221141074

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