On the joys of missing data.
By T. Little, Terrence D. Jorgensen, Kyle M Lang, E. Moore. Journal of Pediatric Psychology, 2014
646 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Pediatrics, Perinatology and Child Health, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/82ca7586303122e58cff9c3c13d1088ad952cd84
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In Management Science and Operations Research
- Levi K. Shiverdecker
- Gilbert R. Parra
- Pui‐wa Lei
- Natalie A. Williams
- K. Berlin
- Cheng-Hsien Li
- Thomas A. Schmitt
- Yanyun Yang
In Pediatrics, Perinatology and Child Health
In Statistics and Probability
Citations in the corpus
- An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. (2014, methodology): “These data were used to calculate age- and sex-specific body mass index (BMI) scores using tables provided by the Centers for Disease Control and Prevention/National Center for Health Statistics (CDC, 2010).”
- Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data (2020, methodology): “(Muthén & Asparouhov, 2002b), which is designed to protect against misspecification of the likelihood (Muthén, 1998–2004, p. 32) and is recommended for small and medium samples (Muthén & Asparouhov, 2002b). Moreover, MLR incorporates a chi-squared correction that is equivalent to the Yuan and Bentle”
- Minority Stress Among Transgender Adolescents: The Role of Peer Victimization, School Belonging, and Ethnicity (2018, neutral): “As such, including age as an auxiliary variable (i.e., covariate) in the model should recover potential biases in parameter estimation (Little et al. 2013).”