Outcomes in economic evaluations, such as health utilities and costs, are products of multiple variables, often requiring complete item responses to questionnaires. Therefore, missing data are very common in cost-effectiveness analyses. Multiple imputations (MI) are predominately recommended and...

Summary We introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first...

Background: Maternal and neonatal mortality is high in South Asia. Recent studies have identifed factors such as adolescent female fertility, healthcare spending is reducing maternal and neonatal mortality. The objective of this study is to examine the efect of adolescent female fertility and...

This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of...

This paper argues that, when using a large database, organizational researchers would benefit from the use of specific multivariate exploratory data analysis (MEDA) before performing statistical modelling. Issues such as the representativeness of the database across domains (countries or...

Zur Beurteilung des sozialen und wirtschaftlichen Lebensstandards in der Europäischen Union (EU) strebt die amtliche Statistik an, Einkommen und Konsumausgaben privater Haushalte gemeinsam zu betrachten. Hierfür existiert derzeit keine einheitliche Datengrundlage. Die Einkommensangaben sind...

Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher's complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a formal...

This study assesses five approaches for imputing missing values. The evaluated methods include Singular Value Decomposition Imputation (svdPCA), Bayesian imputation (bPCA), Probabilistic imputation (pPCA), Non-Linear Iterative Partial Least squares imputation (nipalsPCA) and Local Least Square...

We study the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable structural systems when the regressors are mismeasured. We control for the endogeneity by making use of covariates as control variables; this ensures conditional...