Missing Data in Educational Research: A Review of Reporting Practices and Suggestions for Improvement

By J. Peugh, Craig K. Enders. 2004

1,144 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2929bccec2c4e30425d92f2988a06cf7c52f005a

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Citations in the corpus

An Empirical Investigation of Professional Development Effects on Literacy Instruction Using Daily Logs (2007, methodology): “Peugh and Enders (2004) advocated for this approach to missing data, because listwise deletion is robust only when one assumes that data are missing completely at random.”
Missing data techniques for multilevel data: implications of model misspecification (2011, background): “In addition, modern MDTs may be applied under less restrictive statistical assumptions [1,18].”
An introduction to modern missing data analyses. (2010, methodology): “Despite recommendations from APA and an extensive methodological literature on the subject, these rather archaic techniques are still quite common in published research studies (Peugh & Enders, 2004; Bodner, 2006; Wood, Whitec & Thompson, 2004).”
University Instructors’ Achievement Goals for Teaching (2019, methodology): “Missing Values Missing data ( 0.9% for each item) were imputed using the expectation-maximization algorithm (Peugh & Enders, 2004).”
Trajectories of psychological need satisfaction from early to late adolescence as a predictor of adjustment in school (2014, methodology): “Because longitudinal research inevitably entails missing data, we imputed the data to correct for loss of statistical power, as well as diminished precision of and biases in estimates that result from pairwise and listwise deletion (see Acock, 2005; Peugh & Enders, 2004; Schafer & Graham, 2002).”
Students’ Sense of Belonging at School in 41 Countries (2016, background): “Markov Chain Monte Carlo multiple imputation estimates the values of the missing data, which addresses these missing data issues more effectively than deletion, mean substitution, or simple imputation (Peugh & Enders, 2004).”
Creating College Opportunity: School Counselors and Their Influence on Postsecondary Enrollment (2013, methodology): “10 Put more explicitly, and to utilize an example from Peugh and Enders (2004), suppose our objective is to estimate a covariance matrix and vector of means used to provide regression estimates.”
Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. (2007, methodology): “Statisticians strongly recommend the use of imputation, rather than the traditional procedure of omitting participants with missing data, because it leads to more precise and unbiased conclusions (Peugh & Enders, 2004; Schafer & Graham, 2002).”
Relations between Teachers’ Goal Orientations, Their Instructional Practices and Students’ Motivation (2013, methodology): “We imputed missing values due to the partially incomplete design and item non-response (less than 10% for all teacher and student items) using the expectation-maximization algorithm (see Peugh & Enders, 2004).”
The Work Tasks Motivation Scale for Teachers (WTMST) (2008, background): “Indeed, the American Psychological Association Task Force on Statistical Inferences discourages the use of listwise deletion (see Peugh & Enders, 2004).”