Missing Data is an academic concept in the domain of study design and causal analysis that refers to the absence of observations for certain variables or units in a dataset, distinguishing it from complete data structures by introducing potential bias and inefficiency in statistical inference due to incomplete information under a specific missingness mechanism.
2.4K
Publications
352.6K
Citations
5.1K
Authors
1.8K
Institutions
Publications per year
2017–2026
652
Publications
2.4K
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 2.4K
Authors
5.1K
- RJ
33
h-index
- DB
29
h-index
- CK
23
h-index
- GM
23
h-index
- IR
22
h-index
1–5 of 5.1K
Institutions
1.8K
Harvard University
Cambridge, United States
36 h-index
127 publications
165.5K citations
University of California, Los Angeles
Los Angeles, United States
31 h-index
74 publications
19.6K citations
London School of Hygiene & Tropical Medicine
London, United Kingdom
30 h-index
70 publications
21.8K citations
University of Michigan
Ann Arbor, United States
26 h-index
59 publications
8.1K citations
University College London
London, United Kingdom
25 h-index
59 publications
10.4K citations
Pennsylvania State University
State College, United States
24 h-index
43 publications
28.9K citations
University of Washington
Seattle, United States
23 h-index
53 publications
4.9K citations
MRC Biostatistics Unit
Cambridge, United Kingdom
22 h-index
41 publications
12K citations
1–8 of 1.8K
Venues
- Journal of the American Statistical Association
106 publications · 43.1K citations
49
h-index
- Biometrics
60 publications · 6K citations
36
h-index
- Statistics in Medicine
75 publications · 4.9K citations
34
h-index
- Structural Equation Modeling A Multidisciplinary Journal
SEM · 35 publications · 8.6K citations
28
h-index
- Psychological Methods
32 publications · 19.8K citations
27
h-index
1–5