Mice: Multivariate Imputation by Chained Equations in R

The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in the cases studied. The present paper reviews and compares the approaches. JM and FCS were applied to pubertal development data of 3801 Dutch girls that had missing data on menarche (two categories), breast development (five categories) and pubic hair development (six stages). Imputations for these data were created under two models: a multivariate normal model with rounding and a conditionally specified discrete model. The JM approach introduced biases in the reference curves, whereas FCS did not. The paper concludes that FCS is a useful and easily applied flexible alternative to JM when no convenient and realistic joint distribution can be specified.

The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densities can be incompatible, and therefore the stationary distribution to which the Gibbs sampler attempts to converge may not exist. This study investigates practical consequences of this problem by means of simulation. Missing data are created under four different missing data mechanisms. Attention is given to the statistical behavior under compatible and incompatible models. The results indicate that multiple imputation produces essentially unbiased estimates with appropriate coverage in the simple cases investigated, even for the incompatible models. Of particular interest is that these results were produced using only five Gibbs iterations starting from a simple draw from observed marginal distributions. It thus appears that, despite the theoretical weaknesses, the actual performance of conditional model specification for multivariate imputation can be quite good, and therefore deserves further study.

The purpose of the current study is to develop a generic and short questionnaire to measure work performance at the individual level – the Individual Work Performance Questionnaire (IWPQ). The IWPQ was based on a four-dimensional conceptual framework, in which individual work performance consisted of task performance, contextual performance, adaptive performance, and counterproductive work behavior. After pilot-testing, the 47-item IWPQ was field-tested amongst a representative sample of 1,181 Dutch blue, pink, and white collar workers. Factor analysis was used to examine whether the four-dimensional conceptual framework could be confirmed. Rasch analysis was used to examine the functioning of the items in more detail. Finally, it was examined whether generic scales could be constructed. A generic, three-dimensional conceptual framework was identified, in which individual work performance consisted of task performance, contextual performance, and counterproductive work behavior. Generic, short scales could be constructed that fitted the Rasch model. A generic, short questionnaire can be used to measure individual work performance across occupational sectors. In future versions of the IWPQ, more difficult items should be added to improve discriminative ability at the high ranges of the scale. This study shows that, using Rasch analysis, a generic and short questionnaire can be used to measure individual work performance. © 2013, Emerald Group Publishing Limite