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Advances in Psychological Science    2014, Vol. 22 Issue (6) : 1025-1035     DOI: 10.3724/SP.J.1042.2014.01025
Research Methods |
Planned Missing Data Design: Through Intended Missing Data Make Research More Effective
WANG Mengcheng;YE Haosheng
(Center for Psychology and Brain Science; Department of Psychology of Education College, Guangzhou University, Guangzhou 510006, China)
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Missing data is a very common phenomenon in social science research. Among most existing statistical analysis about missing data, Full-information maximum likelihood estimation and multiple imputation are recommended as most common approach at present for handling missing data. Planned missing data designs use special research design which create completely random missing data, and then employ modern missing data estimation techniques to get unbiased parameters and maximize statistical power in the process. Planned missing data designs can be used in cross-section survey to increase questionnaire items or reduce respondent burden. In longitudinal survey to shorten measure occasion, moreover, enrich validities. There are two types of planned missing data designs: 3-form design and two-method measurement design.

Keywords missing data      planned missing data designs      full-information maximum likelihood estimation      multiple imputation      3-form design      two methods measurement design     
Corresponding Authors: YE Haosheng   
Issue Date: 15 June 2014
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WANG Mengcheng
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WANG Mengcheng,YE Haosheng. Planned Missing Data Design: Through Intended Missing Data Make Research More Effective[J]. Advances in Psychological Science, 2014, 22(6): 1025-1035.
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[1] YE Sujing; TANG Wenqing; ZHANG Minqiang; CAO Weicong. Techniques for Missing Data in Longitudinal Studies and Its Application[J]. Advances in Psychological Science, 2014, 22(12): 1985-1994.
[2] MU Shou-Kuan;ZHOU Wei. Handling Missing Data: Expectation-Maximization Algorithm and Markov Chain Monte Carlo Algorithm[J]. , 2011, 19(7): 1083-1090.
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