Missing by Design: Planned Missing-Data Designs in Social Science

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This article presents research designs that employ modern statistical tools to optimize costs and precision of research along with some additional methodological advantages. In planned missing-data designs some parts of information about respondent are purposely not collected. This gives flexibility and opportunity to explore a broad range of solutions with considerably lower cost. Modern statistical tools for coping with missing-data, namely multiple imputation (MI) and maximum likelihood estimation with missing data (ML) are presented. Several missing-data designs are introduced and assessed by Monte Carlo simulation studies. Designs particularly useful in surveys, longitudinal analysis and measurement applications are showed and tested in terms of statistical power and bias reduction. Article shows advantages, opportunities and problems connected with missing-data designs and their application in social science researches.



missing-data, research design, multiple imputation, maximum likelihood, Monte Carlo simulations, statistical power


Ask: Research and Methods. Volume 20, Issue 1 (2011), pp. 81-105