Applied Missing Data Analysis

By S. Eberhart. 2016

4,089 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5288488f5938bf6893eb4947c15a28fdd3318697

How researchers see this paper

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

Citations in the corpus

Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement (2015, methodology): “lihood method (Enders, 2010) to estimate missing data.”
How to handle missing data: A comparison of different approaches (2015, unknown): “There are already many textbooks and tutorial papers on how to deal with missingness (Enders, 2010; Hallgren & Witkiewitz, 2013; Jelicˇic´, Phelps, & Lerner, 2009; Schafer & Graham, 2002).”
Job burnout: The contribution of emotional stability and emotional self‐efficacy beliefs (2018, unknown): “Moreover, the Missing Completely At Random (MCAR) Little’s test (Enders, 2010) confirmed that the data satisfied the assumption of being MCAR (v(2) = 32.”
Stability, change, and implications of students’ motivation profiles: A latent transition analysis (2017, methodology): “Models were estimated using Mplus 7.31 (Muthén & Muthén, 2015) robust maximum likelihood estimator (MLR) in conjunction with Full Information Maximum Likelihood (FIML) to handle missing data (Enders, 2010).”
Social Support and Classroom Management Are Related to Secondary Students’ General School Adjustment: A Multilevel Structural Equation Model Using Student and Teacher Ratings (2018, methodology): “To avoid listwise deletion, missing data were handled using full information maximum likelihood estimation (Enders, 2010).”
Determining Power and Sample Size for Simple and Complex Mediation Models (2017, background): “Moreover, missing data are pervasive in psychological research and reduce statistical power in addition to other potentially harmful consequences (Enders, 2010).”
On the joys of missing data. (2014, methodology): “The scale scores were used from the first measurement period in order not to overwhelm the FIML estimator with auxiliary variables, yet to inform the process as much as possible (see Enders, 2010, regarding the use of auxiliary variables in FIML and imputation models).”
The reciprocal relations between self-concept, motivation and achievement: juxtaposing academic self-concept and achievement goal orientations for mathematics success (2014, background): “Furthermore, FIML has been shown to outperform traditional missing data approaches even when data are not MAR (see Enders, 2010).”
Disproportionate Use of Lethal Force in Policing Is Associated With Regional Racial Biases of Residents (2018, methodology): “To compensate, all analyses were repeated using multiple imputation, the best currently available missing data approach (Enders, 2010).”
Effects of Monetary Versus Environmental Information Framing: Implications for Long-Term Pro-Environmental Behavior and Intrinsic Motivation (2018, unknown): “1012 Environment and Behavior 50(9) missing at random (NMAR), full information MLR would still be recommended as one of the most robust procedures for handling missing data (Enders, 2010).”
A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality (2016, methodology): “Full information robust maximum likelihood (FIML) estimation was used to handle the small amount of missing data at the item level (M = 0.646%; Enders, 2010; Graham, 2009).”
Family Business Succession: What’s Motivation Got to Do With It? (2019, methodology): “The fact that we still obtained a good diversity of businesses in terms of location (relative to population spread), size, age, and industry and that we used all the available data despite attrition (Enders, 2010) allowed us to get the most out of those who participated.”
Doubly Latent Multilevel Analyses of Classroom Climate: An Illustration (2014, methodology): “Similarly, it was possible to use a larger sample than that used in the original Fast and colleagues (2010) study because we relied on Full information maximum likelihood—rather than a quasilistwise deletion strategy—to handle missing data (Enders, 2010; Little & Rubin, 1987; Schafer, 1997).”
Multiple-Group Analysis for Structural Equation Modeling With Dependent Samples (2015, methodology): “These estimates can be computed using the expectation maximization algorithm (Enders, 2010).”
Relationships Between Self-Determination and Postschool Outcomes for Youth With Disabilities (2015, unknown): “Missing data can be classified as either ignorable or nonignorable (Enders, 2010).”
An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (2015, background): “Thus, these missing data fully correspond to missingcompletely-at-random assumptions of modern missing data techniques (Enders, 2010).”
Perceived mastery climate, felt trust, and knowledge sharing (2018, methodology): “data sets, and the resultant model parameters were pooled using Rubin’s combination rules (Enders, 2010).”