Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete dataset that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this paper compares imputation under an unrestricted multivariate normal model (Multiple Imputation) to imputation ...

Multiple imputation has entered mainstream practice for the analysis of incomplete data. We have used it extensively in a large Australian longitudinal cohort study, the Victorian Adolescent Health Cohort Study (1992–2008). Although we have endeavored to follow best practices, there is little published advice on this, and we have not previously examined the extent to which variations in our app...

Top coding of extreme values of variables like income is a common method of statistical disclosure control, but it creates problems for the data analyst. The paper proposes two alternative methods to top coding for statistical disclosure control that are based on multiple imputation. We show in simulation studies that the multiple-imputation methods provide better inferences of the publicly rel...

Recently developed computational methods allow the imputation of human leukocyte antigen (HLA) genes using intergenic single nucleotide polymorphism markers. To improve the imputation accuracy in HLA imputation, it is essential to increase the sample size and the diversity of alleles in the reference panel. Our software, MergeReference, helps achieve this goal by providing a streamlined pipelin...

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of Intelligent Transportation Systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for ...

BACKGROUND Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with such missing values. We evaluated 6 strategies to deal with missing values. METHODS We developed an...

INTRODUCTION For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta-analysis at each threshold. A standard meta-analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, w...

While fusion can be accomplished at multiple levels in a multibiometric system, score level fusion is commonly used as it offers a good trade-off between fusion complexity and data availability. However, missing scores affect the implementation of several biometric fusion rules. While there are several techniques for handling missing data, the imputation scheme which replaces missing values wit...