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Abstract
High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense's Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data. Imputation by fully conditional specification (FCS) is often considered the state of the art for such datasets due to its generality and flexibility. However, FCS procedures contain a theoretical flaw that is exposed by HRBS data—HRBS imputations created with FCS are shown to diverge across iterations of Markov Chain Monte Carlo. Imputation by joint modeling lacks this flaw; however, current joint modeling procedures are neither general nor flexible enough to handle HRBS data. As such, we introduce an algorithm that efficiently and flexibly applies multiple imputation by joint modeling in data of general structures. This procedure draws imputations from a latent joint multivariate normal model that underpins the generally structured data and models the latent data via a sequence of conditional linear models, the predictors of which can be specified by the user. We perform rigorous evaluations of HRBS imputations created with the new algorithm and show that they are convergent and of high quality. Lastly, simulations verify that the proposed method performs well compared to existing algorithms including FCS.

We propose a new multiple imputation technique for imputing squares. Current methods yield either unbiased regression estimates or preserve data relations. No method, however, seems to deliver both, which limits researchers in the implementation of regression analysis in the presence of missing data. Besides, current methods only work under a missing completely at random (MCAR) mechanism. Our method for imputing squares uses a polynomial combination. The proposed method yields both unbiased regression estimates, while preserving the quadratic relations in the data for both missing at random and MCAR mechanisms.

'Die Messung des Haushaltseinkommens ist besonders stark vom Problem des item-nonresponse betroffen. So beträgt der Anteil der fehlenden Werte im ALLBUS 1996 trotz Nachfrage mit Einkommenskategorien 26 (Westdeutschland) bzw. 19 Prozent (Ostdeutschland). Beim Eurobarometer 1992 liegt der Ausfall zwischen 6 und fast 50 Prozent. In der Forschungspraxis wird der Datenausfall oft entweder ignoriert oder durch einfache Imputationen, d.h. durch Zuweisung von Mittelwerten korrigiert. Diese Vorgehensweisen werden von Rubin (1987) in Frage gestellt, der statt dessen eine multiple Imputation befürwortet. Mit dieser Methode werden die fehlenden Werte mehrfach rekonstruiert, wobei jedesmal die Unsicherheit der auf einer Regressionsgleichung basierenden Einkommenschätzung berücksichtigt wird. Das vorliegende Papier untersucht am Beispiel der Eurobarometerdaten von 1992, welche Folgen eine einfache versus multiple Rekonstruktion der fehlenden Einkommenswerte für die Schätzung des Einkommenseffektes auf die Lebenszufriedenheit hat.' (Autorenreferat)

[EN] Here we introduce a graphical user-friendly interface to deal with missing values called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing missing values, following missing completely at random patterns, exploiting the relationships among variables. In this way, principal component anal- ysis (PCA) models are fitted iteratively to impute the missing data until convergence. Different methods, using PCA internally, are included in the toolbox: trimmed scores regression (TSR), known data regres- sion (KDR), KDR with principal component regression (KDR-PCR), KDR with partial least squares regression (KDR-PLS), projection to the model plane (PMP), iterative algorithm (IA), modified nonlinear iterative partial least squares regression algorithm (NIPALS) and data augmentation (DA). MDI Toolbox presents a general procedure to impute missing data, thus can be used to infer PCA models with missing data, to estimate the covariance structure of incomplete data matrices, or to impute the missing values as a preprocessing step of other methodologies. ; Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02 and DPI2014-55276-C5-1 R, and the Spanish Ministry of Economy and Competitiveness through grant ECO2013-43353-R. ; Folch Fortuny, A.; Arteaga Moreno, FJ.; Ferrer, A. (2016). Missing Data Imputation Toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems. 154:93-100. https://doi.org/10.1016/j.chemolab.2016.03.019 ; S ; 93 ; 100 ; 154

Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation ofhowmultiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examination focuses on research in the major subfield of comparative and international political economy (CIPE) as an illustrative example. Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent five-year period inInternational OrganizationandWorld Politics, two of the leading subfield journals in CIPE. The outcome is striking: in almost half of the studies, key results "disappear" (by conventional statistical standards) when reanalyzed.

Political scientists increasingly recognize that multiple imputation represents a superior strategy for analyzing missing data to the widely used method of listwise deletion. However, there has been little systematic investigation of how multiple imputation affects existing empirical knowledge in the discipline. This article presents the first large-scale examination of the empirical effects of substituting multiple imputation for listwise deletion in political science. The examination focuses on research in the major subfield of comparative and international political economy (CIPE) as an illustrative example. Specifically, I use multiple imputation to reanalyze the results of almost every quantitative CIPE study published during a recent five-year period in International Organization and World Politics, two of the leading subfield journals in CIPE. The outcome is striking: in almost half of the studies, key results "disappear" (by conventional statistical standards) when reanalyzed.