Contrasting case-wise deletion with multiple imputation and latent variable approaches to dealing with missing observations in count regression models

Publication date: Available online 17 August 2019Source: Analytic Methods in Accident ResearchAuthor(s): Amir Pooyan Afghari, Simon Washington, Carlo Prato, Md Mazharul HaqueAbstractMissing data can lead to biased and inefficient parameter estimates in statistical models, depending on the missing data mechanism. Count regression models are no exception, with missing data leading to incorrect inferences about the effects of explanatory variables. A convenient approach for dealing with missing data is to remove observations with incomplete records prior to the analysis - often referred to as case-wise deletion. Removing incomplete records, however, reduces the sample size, increases standard errors and, if data are not missing completely at random, produces biased parameter estimates. A more complex approach is multiple imputation, which provides an estimate of the modelling uncertainty created by the data ‘missing-ness’, as distinct from the natural variation in the data. However, multiple imputation produces biased parameter estimates if the probability of missing data is related to the observed data - or is endogenous. Latent variable modelling has recently been introduced as an alternative approach for dealing with missing data, but it comes at a high computational cost and complexity.Despite fairly extensive methodological advancements in statistical literature, case-wise deletion is commonly employed to deal with missing data in statistical models of transport, while ...

Source: Analytic Methods in Accident Research - August 17, 2019 Category: Accident Prevention Source Type: research