Item Imputation Without Specifying Scale Structure

Methodology ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Stef van Buuren
Keyword(s):  
Missing Data ◽  
Conditional Independence ◽  
R Package ◽  
Scale Structure ◽  
Promising Alternative ◽  
Fully Conditional Specification ◽  
Conditional Specification ◽  
Number Of Factors

Imputation of incomplete questionnaire items should preserve the structure among items and the correlations between scales. This paper explores the use of fully conditional specification (FCS) to impute missing data in questionnaire items. FCS is particularly attractive for items because it does not require (1) a specification of the number of factors or classes, (2) a specification of which item belongs to which scale, and (3) assumptions about conditional independence among items. Imputation models can be specified using standard features of the R package MICE 1.16. A limited simulation shows that MICE outperforms two-way imputation with respect to Cronbach’s α and the correlations between scales. We conclude that FCS is a promising alternative for imputing incomplete questionnaire items.

scholarly journals Evaluating FIML And Multiple Imputation In Joint Ordinal-Continuous Measurements Models With Missing Data

2021 ◽  
Author(s):  
Aaron Lim ◽  
Mike W.-L. Cheung
Keyword(s):  
Missing Data ◽  
Least Squares ◽  
Multiple Imputation ◽  
Latent Variable ◽  
Weighted Least Squares ◽  
Low Frequencies ◽  
Full Information Maximum Likelihood ◽  
Fully Conditional Specification ◽  
Conditional Specification ◽  
Almost All

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the performance of four approaches to handling missing data in these models, a joint ordinal-continuous full information maximum likelihood (JOC-FIML) approach and three multiple imputation approaches (fully conditional specification, fully conditional specification with latent variable formulation, and expectation-maximization with bootstrapping) combined with the weighted least squares with mean and variance adjustment (WLSMV) estimator. In a Monte-Carlo simulation, the JOC-FIML approach produced unbiased estimations of factor loadings and standard errors in almost all conditions. Fully conditional specification combined with WLSMV was second best, producing accurate estimates if the sample size was large. We recommend JOC-FIML across most conditions, except when certain ordinal categories have extremely low frequencies as it was less likely to converge. If the sample is large, fully conditional specification combined with weighted-least-squares is recommended when the FIML approach is not feasible (e.g., non-convergence, variables that predict missingness are not of interest to the analysis).

scholarly journals Dealing with missing information on covariates for excess mortality hazard regression models – Making the imputation model compatible with the substantive model

2021 ◽  
Vol 30 (10) ◽  
pp. 2256-2268
Author(s):  
Luís Antunes ◽  
Denisa Mendonça ◽  
Maria José Bento ◽  
Edmund Njeru Njagi ◽  
Aurélien Belot ◽  
...  
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Survival Data ◽  
Regression Models ◽  
Cancer Survival ◽  
Population Based ◽  
Hazard Regression ◽  
The North ◽  
Fully Conditional Specification ◽  
Conditional Specification

Missing data is a common issue in epidemiological databases. Among the different ways of dealing with missing data, multiple imputation has become more available in common statistical software packages. However, the incompatibility between the imputation and substantive model, which can arise when the associations between variables in the substantive model are not taken into account in the imputation models or when the substantive model is itself nonlinear, can lead to invalid inference. Aiming at analysing population-based cancer survival data, we extended the multiple imputation substantive model compatible-fully conditional specification (SMC-FCS) approach, proposed by Bartlett et al. in 2015 to accommodate excess hazard regression models. The proposed approach was compared with the standard fully conditional specification multiple imputation procedure and with the complete-case analysis using a simulation study. The SMC-FCS approach produced unbiased estimates in both scenarios tested, while the fully conditional specification produced biased estimates and poor empirical coverages probabilities. The SMC-FCS algorithm was then used for handling missing data in the evaluation of socioeconomic inequalities in survival from colorectal cancer patients diagnosed in the North Region of Portugal. The analysis using SMC-FCS showed a clearer trend in higher excess hazards for patients coming from more deprived areas. The proposed algorithm was implemented in R software and is presented as Supplementary Material.

scholarly journals Missing Data: A Unified Taxonomy Guided by Conditional Independence

2017 ◽  
Vol 86 (2) ◽  
pp. 189-204 ◽  
Author(s):  
Marco Doretti ◽  
Sara Geneletti ◽  
Elena Stanghellini
Keyword(s):  
Missing Data ◽  
Conditional Independence

scholarly journals MGMM: An R Package for fitting Gaussian Mixture Models on Incomplete Data

2019 ◽  
Author(s):  
Zachary R. McCaw ◽  
Hanna Julienne ◽  
Hugues Aschard
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Model Fitting ◽  
Simulated Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Parameter Estimates ◽  
Cluster Assignment ◽  
Underlying Distribution

AbstractAlthough missing data are prevalent in applications, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that, when the underlying distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than state of the art imputation followed by standard GMM. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty even when the generative distribution is not a GMM. This assessment may be used to identify unassignable observations. MGMM is available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM.

scholarly journals On the use of the not-at-random fully conditional specification (NARFCS) procedure in practice

2018 ◽  
Vol 37 (15) ◽  
pp. 2338-2353 ◽  
Author(s):  
Daniel Mark Tompsett ◽  
Finbarr Leacy ◽  
Margarita Moreno-Betancur ◽  
Jon Heron ◽  
Ian R. White
Keyword(s):  
Fully Conditional Specification ◽  
Conditional Specification
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