An introduction to modern missing data analyses.
By Amanda N. Baraldi, Craig K. Enders. Journal of School Psychology, 2010
1,470 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2eddfa4d27dbadc94a0e248592347e85b5d5e4d5
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In Artificial Intelligence
- Alexandre J. S. Morin
- Benjamin Nagengast
- Andrew S. Belasco
- A. Etard
- Sophie Morrill
- E. W. G. Moore
- Eva Caprara
- E. Buyse
In Sociology and Political Science
- Benjamin Nagengast
- Alexandre J. S. Morin
- Herbert W. Marsh
- F. Worrell
- G. Caprara
- Andrew S. Belasco
- A. Etard
- J. Lazo
In Statistics and Probability
- Alexandre J. S. Morin
- Benjamin Nagengast
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- A. Etard
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Citations in the corpus
- How does school climate impact academic achievement? An examination of social identity processes (2017, methodology): “Missing data imputation is the standard technique for SEM and provides less biased estimation of the coefficients, compared to deleting cases with missing data (Baraldi & Enders, 2010; Little & Rubin, 2014; Peugh, & Enders, 2004).”
- Teachers' knowledge, attitudes, and supportive behaviors toward LGBT students: Relationship to Gay-Straight Alliances, antibullying policy, and teacher training (2016, background): “%) indicating that the overall data distribution would not be affected by imputing values for missing data (Baraldi & Enders, 2010).”
- A survey on missing data in machine learning (2021, background): “statistical and machine learning imputation techniques such as mean, regression, K nearest neighbor, ensemble based etc, have been proposed in the literature to handle missing values [9, 10].”
- Doubly Latent Multilevel Analyses of Classroom Climate: An Illustration (2014, background): “…been argued to be the norm in school-based studies conducted in countries with mandatory education, in which the most common source of attrition is student mobility (e.g., Baraldi & Enders, 2010; Enders, Dietz, Montague, & Dixon, 2006; Graham, Hofer, Donaldson, MacKinnon, & Schafer, 1997).”
- Creating College Opportunity: School Counselors and Their Influence on Postsecondary Enrollment (2013, methodology): “As such, FIML is increasingly becoming a preferred method of choice for handling missing data (Allison 2012; Baraldi and Enders 2010; Raykov and Marcoulides 2010) and it is the missing data approach that I chose to adopt in this study.”
- Child engagement in the transition to school: Contributions of self-regulation, teacher–child relationships and classroom climate (2015, positive): “This approach has been shown to be effective and more powerful than traditional ones (Baraldi & Enders, 2010).”
- Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, methodology): “In this example, we will highlight how to utilize FIML with Mplus, which can often address individuals’ needs with smaller data sets and the use of informative auxiliary variables (Baraldi & Enders, 2010; Howard, Rhemtulla, & Little, 2015).”
- Global gaps in trait data for terrestrial vertebrates (2020, methodology): “Assessing the amount of missing data in addition to the so-called “missingness mechanism” (whether missing data are missing at random, as opposed to there being systematic biases in the way missing values are distributed; see Baraldi & Enders, 2010) is an important prerequisite.”
- Rebutting Existing Misconceptions About Multiple Imputation as a Method for Handling Missing Data (2019, background): “However, under NMAR, listwise deletion will yield biased results as well (Baraldi & Enders, 2010, pp. 8, 21), except in the cases described by White and Carlin (2010) and by Vach (1994), where listwise deletion may outperform multiple imputation.”