Handling Missing Data by Maximum Likelihood

By P. Allison. 2012

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f2de0d551978821a6ac64f85d084fd19e0c1947d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Contributions of Academic Language, Perspective Taking, and Complex Reasoning to Deep Reading Comprehension (2016, methodology): “Full maximum likelihood estimation was used in order to handle missing data in the outcome variable (Allison, 2012).”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “If the data is not missing at random (NMAR) then the missing data mechanism has to be modelled [113,114], i.”
Creating College Opportunity: School Counselors and Their Influence on Postsecondary Enrollment (2013, methodology): “As such, FIML is increasingly becoming a preferred method of choice for handling missing data (Allison 2012; Baraldi and Enders 2010; Raykov and Marcoulides 2010) and it is the missing data approach that I chose to adopt in this study.”
Experimental Effects of Word Generation on Vocabulary, Academic Language, Perspective Taking, and Reading Comprehension in High-Poverty Schools (2019, methodology): “In addition, the full information maximum likelihood (FIML) estimation available in MPlus efficiently handles missing data without multiple imputation, allowing us to maximize power and minimize bias due to missing information (Allison, 2012).”
Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, unknown): “multi-rate sampling, etc [100]–[306].”
Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “FIML is often considered to be very userfriendly because missing data are handled directly during the estimation of the analysis model without requiring any additional steps to be taken by the user (e.g., Allison, 2012; Graham, 2009).”