Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data

By Roderick J. A. Little, Donald B. Rubin. 2002

6,479 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d5db9d5cbd5ab72f4b6b9f0bbd6be1d0c7e5fa95

How researchers see this paper

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

In Artificial Intelligence

1. Victoria Savalei
2. Paul T. von Hippel
3. Craig K. Enders
4. Alexandre J. S. Morin
5. Shinichi Nakagawa
6. Eric B. Laber
7. Geert Molenberghs
8. Tenko Raykov

In Statistics and Probability

1. Victoria Savalei
2. Craig K. Enders
3. Paul T. von Hippel
4. Carl F. Falk
5. Eric B. Laber
6. Alexandre J. S. Morin
7. Peng Ding
8. Herbert W. Marsh

Citations in the corpus

• The effect of time volunteering and charitable donations in later life on psychological wellbeing (2010, methodology): “Missing data were handled using the full information maximum likelihood (FIML) algorithm (Little and Rubin 2002) in Mplus.”
• Multiple imputation using chained equations for missing data in TIMSS: a case study (2013, neutral): “Don’t know responses: As Little and Rubin (2002) point out, deciding what to do with individuals who respond with “don’t know” is especially challenging.”
• Testing the Immediate and Long-Term Efficacy of a Tier 2 Kindergarten Mathematics Intervention (2016, background): “In the present study, we did not believe that attrition or other missing data represented a meaningful departure from the missing-at-random assumption, meaning that missing data did not likely depend on unobserved determinants of the outcomes of interest (Little & Rubin, 2002).”
• Math Shelf: A Randomized Trial of a Prekindergarten Tablet Number Sense Curriculum (2016, methodology): “We chose mixed effects modeling instead of conventional regression or analysis of covariance to better handle missing data and to stay in line with the intention-to-treat principle (Little & Rubin, 2002).”
• Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, methodology): “The simplest method is simply imputing the mean for that variable, although this method is almost never appropriate as it leads to severely underestimated variance (Jamshidian & Mata, 2007; Little & Rubin, 2002).”
• Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula (2011, neutral): “The fact that drop-out need not be explicitly modelled is an example of ignorability as defined for likelihood analyses under MAR (Little and Rubin, 2002).”
• Some Aspects of Measurement Error in Linear Regression of Astronomical Data (2007, methodology): “The measured data likelihood is then found by integrating the likelihood function for the complete data over the unobserved true values (e.g., Little & Rubin 2002; Gelman et al. 2004).”
• How African American Is the Net Black Advantage? Differences in College Attendance Among Immigrant Blacks, Native Blacks, and Whites (2009, methodology): “Consequently, we treated missing data on independent variables via multiple imputation (Acock 2005; Little and Rubin 1987, 2002).6”
• Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies (2004, methodology): “Standard methods (e.g., as in Little & Rubin, 2002; Rubin, 1987) need to be taught, and special methods, for instance, for dealing with missing covariates in propensity score analyses (D’Agostino & Rubin, 1999), also should be taught.”