Multiple Imputation of Multilevel Missing Data
An Introduction to the R Package pan
Bibliographic Data
| ID | 3456836 |
|---|---|
| Authors | Simon Grund (0000-0002-1290-8986, Leibniz Institute for Science and Mathematics Education, corresponding author), Oliver Lüdtke (0000-0001-9744-3059, Leibniz Institute for Science and Mathematics Education), Alexander Robitzsch (0000-0002-8226-3132, Leibniz Institute for Science and Mathematics Education) |
| Year | 2016 |
| Volume | 6 |
| Issue | 4 |
| Publication date | 2016-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2158244016668220 |
| OpenAlex | W4296733573 |
| Language | EN |
| Citations received | 21 |
| References cited | 57 |
The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software
Data mining · Data science · Information retrieval · Machine learning · Missing data · Multilevel model · R package · Computer Science · Health disparities and outcomes · Statistical Methods and Bayesian Inference · Survey Methodology and Nonresponse · Software
Multiple Imputation of Missing Data for Multilevel Models
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Longitudinal impact of early childhood science instruction on 5th grade science achievement
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A comparison of inclusive and restrictive strategies in modern missing data procedures.
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Planned missing data designs in psychological research.
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Inference and missing data
Missing Data Analysis
Centering predictor variables in cross-sectional multilevel models
Inference from Iterative Simulation Using Multiple Sequences
Multiple imputation using chained equations
Mice
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Hierarchical linear models
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| Unique citing works | 21 |
|---|---|
| Citations per year | 2,33 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 21 |