What question did this study set out to answer?

The aim is to guide users in deciding whether to use multiple imputation (MI) or full information maximum likelihood (FIML) for handling missing survey data.

March 21, 2026Open Access

Survey Data: Missing Cases [Slide deck]

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The aim is to guide users in deciding whether to use multiple imputation (MI) or full information maximum likelihood (FIML) for handling missing survey data.
Step-by-step analysis on choosing between MI and FIML
Detection methods for identifying missing cases
Guidelines for handling and reporting missing data
Improved understanding of missing data techniques
Clear criteria for deciding on MI vs. FIML
Enhanced ability to report missing cases accurately

Implication

Presentation details steps to choose MI or FIML for missing cases in survey data, highlighting essential strategies.

Abstract

This presentation covers a missing case study - step by step, how to decide between MI or FIML, and how to detect, handle, and report missing cases. This presentation is the sixth part of a set of presentations on the topic of "Tools and Workflows (Survey Data)". Presentation available after free registration: https://elearning.gesis.org/course/section.php?id=1252#module-3351

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Cite This Study

Knopf et al. (2025) studied this question.

synapsesocial.com/papers/69be38126e48c4981c678326 https://doi.org/10.5281/zenodo.19049278

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Implication

Presentation details steps to choose MI or FIML for missing cases in survey data, highlighting essential strategies.

Survey Data: Missing Cases [Slide deck]

Key Points

Implication

Abstract

Discussion

Cite This Study

Survey Data: Missing Cases [Slide deck]

Key Points

Implication

Abstract

Discussion

Cite This Study

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