Longitudinal and multigroup modeling with missing data.

By W. Wothke. 2000

360 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/51d78245cb7e2537c8bd9e4cd1b7fb05e08ad424

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Citations in the corpus

The Rainbow Project: Enhancing the SAT through assessments of analytical, practical, and creative skills (2006, methodology): “That said, examples of structural equation models using FIML estimation presented in the literature feature samples with up to 80% incomplete data with generally good results for model fit and parameter estimation (e.g., McArdle & Hamagami, 1992; Wothke, 2000).”
Context and leadership: An examination of the nine-factor full-range leadership theory using the Multifactor Leadership Questionnaire. (2003, methodology): “, listwise or pairwise deletion) and generally provides unbiased parameter estimates (Arbuckle, 1996; Wothke, 2000).”
Why Does Parents' Involvement Enhance Children's Achievement? The Role of Parent-Oriented Motivation. (2012, methodology): “…utilizes full information maximum likelihood (FIML) estimation in the presence of missing data; FIML provides more reliable standard errors to handle missing data under a wide range of conditions than does either listwise and pairwise deletion or mean imputation (Arbuckle, 1996; Wothke, 2000).”
THE FAMILY → LOW SELF-CONTROL → DEVIANCE (2007, background): “This approach, which is considered the current state of the art on handling missing data, is more efficient and introduces less bias than all other missing data procedures (e.g., listwise deletion or mean imputation; Schafer & Graham, 2002; Wothke, 2000).”
Graphical Models for Inference with Missing Data (2013, background): “Unless data are missing completely at random, listwise deletion can bias the outcome [30].”
Attrition in developmental psychology (2017, background): “…on a probability density function to iteratively maximize the likelihood of the estimates employing, ‘‘ . . . all of the information of the observed data, including mean and variance for the missing portions of a variable, given the observed portion(s) of other variables,’’ (Wothke, 2000, p. 3).”
Creating a Vision of Creativity: The First 25 Years (2006, methodology): “All missing data in the sample were managed using the full-information maximum likelihood (FIML) technique (see McArdle, 1994; Wothke, 2000).”
High antenatal maternal anxiety is related to ADHD symptoms, externalizing problems, and anxiety in 8- and 9-year-olds. (2004, methodology): “Imputation makes more efficient use of the observed dataFno cases are sacrificedFand the apparently complete data set can be analyzed by standard methods, whereas case deletion may induce bias (Schafer & Graham, 2002; Wothke, 2000).”
A validity and reliability study of the coping self-efficacy scale. (2006, methodology): “Simulation studies have shown FIML typically outperforms common ad hoc missing data-handling methods, such as listwise deletion, across a wide variety of data analysis scenarios (Arbuckle, 1996; Wothke, 2000).”
A Comparison of Maximum-Likelihood and Asymptotically Distribution-Free Methods of Treating Incomplete Nonnormal Data (2003, methodology): “Wothke (2000) compared the abilities of four methods (full information ML, mean imputation, pairwise deletion, and listwise deletion) to reproduce parameter values in a growth curve model involving four measurements per subject.”
The Developmental Relationship Between Language and Low Early Numeracy Skills Throughout Kindergarten (2014, methodology): “With the application of full infor mation maximum likelihood within struc tural equating modeling, all available scores of the missing children were included in the analyses (Wothke, 2000).”
The Measurement Equivalence of Web-Based and Paper-and-Pencil Measures of Transformational Leadership (2006, methodology): “FIML is superior to other imputation techniques as it gives unbiased estimates of means, variances, and other parameters (Arbuckle, 1996; Byrne, 2001; Wothke, 2000).”
Multivariate Time Series Imputation with Generative Adversarial Networks (2018, methodology): “Deletion based methods erase all observations/records with missed values, including Listwise deletion [45] and Pairwise deletion [35].”
Development of Life Satisfaction in Old Age: Another View on the "Paradox'' (2006, methodology): “Compared to these procedures, FIML proved to be more efficient even if MCAR holds (Arbuckle, 1996; Wothke, 2000; Enders and Bandalos, 2001).”
Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement (2007, methodology): “FIML uses all available data to generate maximum likelihood-based sufficient statistics for the covariance matrix and mean vector, is an effective way to handle incomplete data, and is available in most SEM packages (Wothke, 2000).”