A general approach for imputation of non-normal continuous data based on copula transformation

Lun, Zhixin; Khattree, Ravindra

doi:10.1080/03610918.2022.2025839

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Non‐normality is discussed in section 3.3 of van Buuren (2018) with several references. Readers may also take guidance from Lüdtke et al (2020), Lun and Khattree (2022) and Lee and Carlin (2010).…”

Section: A Worked Example: Developmental Predictors Of Working Memorymentioning

confidence: 99%

Best practices for addressing missing data through multiple imputation

Woods

Gerasimova

Dusen

et al. 2023

Infant and Child Development

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A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputationThe foundation for this paper was created during a 'hackathon' session occurring on 23 June 2021, at the annual virtual meeting of the Society for Improving Psychological Science. We invited anyone interested in the topic to attend, welcoming both experts and those with little experience addressing missing data in their research, specifically welcoming participation from those who were not sure how to address the missing data they experienced.Decisional guidelines for analyzing the type and extent of missing data were then crowdsourced and curated during this hackathon, resulting in a missing data and multiple imputation decision tree (Woods et al., 2021, available at https://doi.org/10.31234/osf.io/mdw5r) and a companion infographic (Woods & Schmidt, 2021, available at https://miro.com/app/board/o9_J18JGJQk=/). We also created multiple imputation coding templates for several prominent software languages (Stata, Mplus, R, SPSS, SAS and Blimp). All hackathon materials and coding templates are available at https://osf.io/j3f8m/.

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