III. MISSING DATA: WHAT TO DO WITH OR WITHOUT THEM

By K. Widaman. 2006

536 citations recorded by the source corpus.

Subfields: Forestry, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c900e3a05f38933bbdaff307e94e8d7d4c7c56ce

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Citations in the corpus

Shared Book Reading and Head Start Preschoolers' Vocabulary Learning: The Role of Book-Related Discussion and Curricular Connections (2012, result): “Thus, we considered these spring observation data to be missing completely at random and, consistent with best practices in the field (see Acock, 2005; Widaman, 2006), retained these two teachers in the study, with composite scores reflecting only their book-reading practices in the fall.”
The New Friends Vignettes: Measuring parental psychological control that confers risk for anxious adjustment in preschoolers (2009, methodology): “Therefore, EM (expectation maximization) imputation was used in EQS, based on the complete data set, to impute missing values (Tabachnick & Fidell, 2001; Widaman, 2006).”
Threshold analysis of association between child care quality and child outcomes for low-income children in pre-kindergarten programs (2010, methodology): “Using the multiple imputation approach (Rubin, 1976, 1987; Schafer, 1997; Schafer & Graham, 2002; Widaman, 2006), missing data are estimated interatively based on assuming data were missing at random.”
Young girls’ arithmetic and spatial skills: The distal and proximal roles of family socioeconomics and home learning experiences (2012, methodology): “Best practice recommendations for SEM with oderate amounts of missing data (e.g., Enders & Bandalos, 2001; idaman, 2006) are that full information maximum likelihood (i.e., IML) estimation be used, if variables related to missingness can e included in the analysis; in the present study, all of the…”
Girls’ Spatial Skills and Arithmetic Strategies in First Grade as Predictors of Fifth-Grade Analytical Math Reasoning (2017, background): “This has been shown to produce unbiased estimates and valid model fit information (in comparison to listwise deletion and other imputation techniques) even with moderate amounts of missing data (Enders, 2001; Enders & Bandalos, 2001; Hartley & Hocking, 1971; Little, Jorgensen, Lang, & Moore, 2013; W”
The Reciprocal Links between School Engagement, Youth Problem Behaviors, and School Dropout during Adolescence (2013, methodology): “FIML should adequately account for missing data that may not be missing at random in this study sample, since the predictors of missingness were included in the model (Widaman, 2006).”
Adolescent Triangulation into Parental Conflicts: Longitudinal Implications for Appraisals and Adolescent-Parent Relations. (2010, background): “Wiley does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Wiley. minimize bias in the estimates (Widaman, 2006).”
Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, background): “Some methodologists have characterized maximum likelihood estimation as “implicit imputation” because it does not produce a filled-in data set (Widaman, 2006).”