[Identification and treatment of missing data].
Zhong Nan Da Xue Xue Bao Yi Xue Ban
; 38(12): 1289-94, 2013 Dec.
Article
in Zh
| MEDLINE
| ID: mdl-24384956
ABSTRACT
Missing data plagues almost all surveys and researches. The occurrence of missing data will cause losses of original sample information and undermine the validity of the research results to some extents, so researchers should attach great importance to this problem. In this article, we introduced 3 kinds of missingness mechanism, namely missing completely at random, missing at random, and not missing at random. We summarized some common approaches to deal with missing data, including deletion, weighting approach, imputation and parameter likelihood method. Since these methods had its pros and cons, we should carefully select the proper way to handle missing data according to the missingness mechanism.
Full text:
1
Database:
MEDLINE
Main subject:
Data Collection
/
Data Interpretation, Statistical
Type of study:
Diagnostic_studies
Language:
Zh
Year:
2013
Type:
Article