This article reviews recent advances in missing data research using graphical models to
represent multivariate dependencies. We first examine the limitations of traditional …

Purpose This study explored the role of market mavens for young tourists and existential
authenticity for young tourist co-creation experience. It determines intentions to revisit and …

Researchers faced with incomplete data are encouraged to consider whether their data are
'missing completely at random'(MCAR),'missing at random'(MAR) or 'missing not at …

In the past decade, online controlled experimentation, or A/B testing, at scale has proved to
be a significant driver of business innovation. The practice was first pioneered by the …

Missing observations are pervasive throughout empirical research, especially in the social
sciences. Despite multiple approaches to dealing adequately with missing data, many …

Ignorable likelihood (IL) approaches are often used to handle missing data when estimating
a multivariate model, such as a structural equation model. In this case, the likelihood is …

We examine the effect of streaming based on ability levels on individuals' civic participation
throughout their adult life. The hypothesis we test is that ability grouping influences …

A frequent concern in analyzing incomplete multivariate measurements in mixed categorical
and quantitative scales is whether missing completely at random (MCAR) is an appropriate …

What does it mean to say that a quantity is identifiable from the data? Statisticians seem to
agree on a definition in the context of parametric statistical models---roughly, a parameter …

This chapter is aimed at interested readers who want to learn why statistical tests may not be
suitable for rejecting or not rejecting the null hypothesis of missing completely at random …