Missing Data Analysis
Missing Data Analysis is a methodological framework within statistical data analysis that specifically addresses the identification, characterization, and imputation of absent or unobserved data values in a dataset, distinguishing itself by its focus on maintaining data integrity and minimizing bias in inference across diverse data structures such as cross-sectional, longitudinal, time series, and experimental designs.
3K
Publications
406.4K
Citations
6.9K
Authors
2.3K
Institutions
Publications per year
2017–2026
904
3K
Missing data: Our view of the state of the art.
Joseph L. Schafer, J. A. Graham · Psychological Methods · 2002 · 10.9K citations
Donald B. Rubin · Biometrika · 1976 · 9.7K citations
1–5 of 3K
6.9K
35
h-index
London School of Hygiene & Tropical Medicine, United Kingdom
42 publications · 14.4K citations
32
h-index
30
h-index
29
h-index
29
h-index
1–5 of 6.9K
2.3K
London School of Hygiene & Tropical Medicine
London, United Kingdom
43 h-index
120 publications
28.3K citations
Harvard University
Cambridge, United States
39 h-index
148 publications
164.7K citations
University College London
London, United Kingdom
37 h-index
102 publications
25.3K citations
University of California, Los Angeles
Los Angeles, United States
36 h-index
110 publications
22.3K citations
MRC Clinical Trials Unit at UCL
London, United Kingdom
34 h-index
57 publications
26K citations
University of Michigan
Ann Arbor, United States
33 h-index
111 publications
13.3K citations
MRC Biostatistics Unit
Cambridge, United Kingdom
31 h-index
55 publications
25.4K citations
Murdoch Children's Research Institute
Melbourne, Australia
27 h-index
78 publications
16.1K citations
1–8 of 2.3K
137 publications · 45.1K citations
62
h-index
146 publications · 21.5K citations
53
h-index
79 publications · 5K citations
40
h-index
51 publications · 7.9K citations
34
h-index
32 publications · 7.9K citations
29
h-index
1–5