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Handling Missing Data

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Modern Statistical Methods for HCI

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

This chapter provides an overview of the topic of missing data. We introduce the main types of missing data that can occur in practice and discuss the practical consequences of each of these types for general data analysis. We then describe general and practical solutions to the problem of missing data, discussing common but flawed approaches as well as more powerful approaches such as multiple imputation, which is an approach to dealing with missing data that is suitable for many—although not all—situations. Finally, we consider the topic of missing data as part of statistical inference more generally, and how it can be handled in both maximum likelihood and Bayesian approaches to inference.

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Notes

  1. 1.

    Editor note: In this section the authors discuss Bayesian methods. These have not yet been covered in the previous chapters. The basic ideas behind Bayesian inference are introduced in Chap. 8. We recommend those readers who are totally unaware of Bayesian methods to read Chap. 8 before proceeding.

  2. 2.

    Continuing with the notation introducted in Sect. 4.2.1, here we will denote the fully observed variables in our data by x, the partially observed variables by \(y = y^{\text {obs}}, y^{\text {obs}}\), and we will index the missing variables in y by the I. We can also assume that any or all of x, y and I may be multivariate arrays.

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Author information

Authors and Affiliations

  1. Division of Psychology, Nottingham Trent University, Nottingham, UK

    Thom Baguley & Mark Andrews

Authors
  1. Thom Baguley
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  2. Mark Andrews
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Corresponding author

Correspondence to Thom Baguley .

Editor information

Editors and Affiliations

  1. Moray School of Education, Edinburgh University, Edinburgh, United Kingdom

    Judy Robertson

  2. Donders Centre for Cognition, Radboud University Nijmegen, Tilburg, The Netherlands

    Maurits Kaptein

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© 2016 Springer International Publishing Switzerland

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Baguley, T., Andrews, M. (2016). Handling Missing Data. In: Robertson, J., Kaptein, M. (eds) Modern Statistical Methods for HCI. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-26633-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-26633-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26631-2

  • Online ISBN: 978-3-319-26633-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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