Standard

Missing data in multilevel research. / Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander.

The handbook of multilevel theory, measurement, and analysis. Hrsg. / Stephen E. Humphrey; James M. LeBreton. Washington, DC : American Psychological Association, 2019. S. 365-386.

Publikation: ForschungBeiträge in Sammelwerken

Harvard

Grund, S, Lüdtke, O & Robitzsch, A 2019, Missing data in multilevel research. in SE Humphrey & JM LeBreton (Hrsg.), The handbook of multilevel theory, measurement, and analysis. American Psychological Association, Washington, DC, S. 365-386.

APA

Grund, S., Lüdtke, O., & Robitzsch, A. (2019). Missing data in multilevel research. in S. E. Humphrey, & J. M. LeBreton (Hrsg.), The handbook of multilevel theory, measurement, and analysis (S. 365-386). Washington, DC: American Psychological Association.

Vancouver

Grund S, Lüdtke O, Robitzsch A. Missing data in multilevel research. in Humphrey SE, LeBreton JM, Hrsg., The handbook of multilevel theory, measurement, and analysis. Washington, DC: American Psychological Association. 2019. S. 365-386.

BibTeX

@inbook{169e44bf50fe49a7a0774bdea1723aad,
title = "Missing data in multilevel research",
abstract = "Multilevel research is often faced with missing data. Over the past years, powerful methods such as multiple imputation (MI) and maximum likelihood estimation (ML) have become available for the treatment of incomplete data. In this chapter, we provide a general introduction to the problem of missing data, and we discuss the theory and application of these methods as well as their individual strengths and weaknesses. We offer guidance on how ML and MI may be used for an effective treatment of missing values in multilevel research and what role the multilevel structure may play in the treatment of incomplete data. Finally, we provide results from a computer simulation study as well as an empirical example that illustrates the use of these methods in multilevel analyses.",
author = "Simon Grund and Oliver Lüdtke and Alexander Robitzsch",
year = "2019",
isbn = "978-1-4338-3001-3",
pages = "365--386",
editor = "Humphrey, {Stephen E.} and LeBreton, {James M.}",
booktitle = "The handbook of multilevel theory, measurement, and analysis",
publisher = "American Psychological Association",

}

RIS

TY - CHAP

T1 - Missing data in multilevel research

AU - Grund,Simon

AU - Lüdtke,Oliver

AU - Robitzsch,Alexander

PY - 2019

Y1 - 2019

N2 - Multilevel research is often faced with missing data. Over the past years, powerful methods such as multiple imputation (MI) and maximum likelihood estimation (ML) have become available for the treatment of incomplete data. In this chapter, we provide a general introduction to the problem of missing data, and we discuss the theory and application of these methods as well as their individual strengths and weaknesses. We offer guidance on how ML and MI may be used for an effective treatment of missing values in multilevel research and what role the multilevel structure may play in the treatment of incomplete data. Finally, we provide results from a computer simulation study as well as an empirical example that illustrates the use of these methods in multilevel analyses.

AB - Multilevel research is often faced with missing data. Over the past years, powerful methods such as multiple imputation (MI) and maximum likelihood estimation (ML) have become available for the treatment of incomplete data. In this chapter, we provide a general introduction to the problem of missing data, and we discuss the theory and application of these methods as well as their individual strengths and weaknesses. We offer guidance on how ML and MI may be used for an effective treatment of missing values in multilevel research and what role the multilevel structure may play in the treatment of incomplete data. Finally, we provide results from a computer simulation study as well as an empirical example that illustrates the use of these methods in multilevel analyses.

M3 - Contributions to collected editions/anthologies

SN - 978-1-4338-3001-3

SP - 365

EP - 386

BT - The handbook of multilevel theory, measurement, and analysis

PB - American Psychological Association

ER -

ID: 801462