학술논문

Handling Planned and Unplanned Missing Data in a Longitudinal Study
Document Type
article
Source
Tutorials in Quantitative Methods for Psychology, Vol 19, Iss 2, Pp 123-135 (2023)
Subject
missing data
unplanned missingness
planned missingness
full information maximum likelihood
multiple imputation.
Psychology
BF1-990
Language
English
French
ISSN
1913-4126
Abstract
While analyzing data, researchers are often faced with missing values. This is especially common in longitudinal studies in which participants might skip assessments. Unwanted missing data can introduce bias in the results and should thus be handled appropriately. However, researchers can sometimes want to include missing values in their data collection design to reduce its length and cost, a method called ``planned missingness.'' This paper review the recommended practices for handling both planned and unplanned missing data, with a focus on longitudinal studies. The current guidelines suggest to either use Full Information Maximum Likelihood or Multiple Imputation. Those techniques are illustrated with R code in the context of a longitudinal study with a representative Canadian sample on the psychological impacts of the COVID-19 pandemic.