Longitudinal Modeling with Randomly and Systematically Missing Data: A Simulation of Ad Hoc, Maximum Likelihood, and Multiple Imputation Techniques

By Daniel A. Newman. 2003

651 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/908bd8ec7851ac6b505a0ce8c37295abe89f38c8

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Missing Data Imputation versus Full Information Maximum Likelihood with Second-Level Dependencies (2011, methodology): “Other approaches such as pairwise deletion, full information maximum likelihood (FIML), hot-deck imputation, mean imputation, and similar response pattern imputation have all been studied with regard to their performance in estimating unbiased estimates from both MCAR and MAR data ( Brown, 1994 ; E”
Someone to Look Up To (2013, methodology): “The EM technique assumed a distribution for the partially missing data and based its inferences on the likelihood under that distribution (Newman, 2003, 2009).”
Conceptualization and measurement of temporal focus : the subjective experience of the past, present, and future (2009, methodology): “Thus, to retain all respondents and provide the sample size needed to test our sizeable factor models, missing data were handled using the Full Information Maximum Likelihood (FIML) approach (Enders, 2001; Enders & Bandalos, 2001; Newman, 2003).”
Why Do Firms Bribe? (2010, methodology): “We imputed missing values, around 30% of sample, with new values by multiple imputation (MI) procedures (Newman 2003, Royston 2005).”
Does Social Connectedness Promote a Greater Sense of Well-Being in Adolescence Over Time? (2012, methodology): “Studies indicate that even if data are not missing at random, FIML is still a more effective way of handling missing data than traditional procedures, such as list wise deletion or single- or two-way imputation (Enders, 2001; Newman, 2003).”
Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science: Evidence from Qatar (2012, methodology): “Given that more than 5% of the studentand school-level cases had one or more missing values, I employed multiple imputation (MI; Little & Rubin, 2002; Rubin, 1987; Schafer, 1997) to avoid the underestimation of standard errors (Newman, 2003; Peugh & Enders, 2004).”
Trajectories of psychological need satisfaction from early to late adolescence as a predictor of adjustment in school (2014, methodology): “…over simple imputation procedure such as mean substitution, median substitution, regression substitution, or maximum likelihood strategies (e.g., Expectation Maximization), which underestimate parameters’ standard errors and provide possibly instable parameter estimations (e.g., Newman, 2003).”
The “Quick Start Guide” for Conducting and Publishing Longitudinal Research (2011, background): “going to happen despite herculean efforts to avoid it. There are many excellent introductions (e.g., Anseel et al. 2010; Newman 2003; Palmer and Royall 2010) and detailed overviews (e.g., Allison 2001) of missing data.”
Employee Job Search (2012, neutral): “At worst, these nonrandom missing data could bias conclusions drawn from these studies (Newman, 2003).”
Adolescent loneliness and social anxiety (2016, methodology): “FIML method was used because it produces less biased estimates than other methods such as imputing the sample mean or dropping cases for data missing within and across waves (Newman, 2003).”
The Role of Team Identification in the Dissimilarity-Conflict Relationship (2006, methodology): “Newman (2003) has conducted a Monte Carlo analysis which demonstrates the efficacy of FIML over the traditional approaches of listwise or pairwise deletion.”
A review of techniques for treating missing data in OM survey research (2005, background): “001 statistics (Aldrin and Damsleth, 1989; Stinebrickner, 1999) and psychometrics (Brown, 1983; Fichman and Cummings, 2003; Newman, 2003) have paid more attention to the issue.”
Attrition in developmental psychology (2017, background): “The interested reader is referred to other simulation studies that convey the detrimental impact of attrition reporting on parameter estimation (e.g., Demirtas & Schafer, 2003; Newman, 2003; Van Buuren, Brand, Groothuis-Oudshoorn, & Rubin, 2006).”