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Missing Data Methods

  • Kristian Kleinke
  • Jost Reinecke
  • Daniel Salfrán
  • Martin Spiess
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

In this chapter missing data procedures and techniques are reviewed and discussed. Among them are both, ad-hoc methods but also more sophisticated techniques including maximum likelihood estimation, weighting and imputation. We discuss pros and cons of the different approaches and techniques, and give practical advice which procedure might be suited best in a given scenario because valid inferences in applied research can only be expected based on informed decisions. A conclusion of this chapter will be that there is not the one method or technique that works best under every possible scenario.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kristian Kleinke
    • 1
  • Jost Reinecke
    • 2
  • Daniel Salfrán
    • 3
  • Martin Spiess
    • 3
  1. 1.Department of Education Studies and PsychologyUniversity of SiegenSiegenGermany
  2. 2.Faculty of SociologyUniversity of BielefeldBielefeldGermany
  3. 3.University of HamburgInstitute of PsychologyHamburgGermany

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