A latent class selection model for nonignorably missing data

Jung, Hyekyung; Schafer, Joseph L.; Seo, Byungtae

doi:10.1016/j.csda.2010.07.002

Cited by 12 publications

(14 citation statements)

References 22 publications

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“…Bacci and Bartolucci (2015) defined such models where η is also categorical, η and ξ are conditionally independent given X and the non-response model may depend on both η and ξ. Jung et al (2011) also used a categorical ξ but combined with a model for the joint distribution of Y C which is not itself a latent variable model.…”

Section: Previous Literature On Latent Response Propensity Modelsmentioning

confidence: 99%

Latent Variable Modelling with Non-Ignorable Item Non-Response: Multigroup Response Propensity Models for Cross-National Analysis

Kuha

Katsikatsou

Moustaki

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary When missing data are produced by a non‐ignorable non‐response mechanism, analysis of the observed data should include a model for the probabilities of responding. We propose such models for non‐response in survey questions which are treated as measures of latent constructs and analysed by using latent variable models. The non‐response models that we describe include additional latent variables (latent response propensities) which determine the response probabilities. We argue that this model should be specified as flexibly as possible, and we propose models where the response propensity is a categorical variable (a latent response class). This can be combined with any latent variable model for the survey items, and an association between the latent variables measured by the items and the latent response propensities then implies a model with non‐ignorable non‐response. We consider in particular such models for the analysis of data from cross‐national surveys, where the non‐response model may also vary across the countries. The models are applied to data on welfare attitudes in 29 countries in the European Social Survey.

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Section: Previous Literature On Latent Response Propensity Modelsmentioning

confidence: 99%

Latent Variable Modelling with Non-Ignorable Item Non-Response: Multigroup Response Propensity Models for Cross-National Analysis

Kuha

Katsikatsou

Moustaki

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…The use of sufficiently complex imputation models, such as the Gaussian copula model (Hollenbach et al, 2018), mixture models (Murray and Reiter, 2016), or latent class models (Vermunt et al, 2008;Si and Reiter, 2013) are advantageous to minimize possible distributional misspecifications for MAR data. Appropriate imputation models can also treat specific deviations from MAR (missing not at random; MNAR; Harel and Schafer, 2009;Jung et al, 2011;Kano and Takai, 2011;Zhang and Reiser, 2015;Bartolucci et al, 2018;Kuha et al, 2018;Pohl and Becker, 2020).…”

Section: Discussionmentioning

confidence: 99%

Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

Robitzsch

2020

Front. Educ.

189

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“…Several authors have previously proposed approaches for handling latent ignorable missingness in specific joint modelling settings (Jung 2007;Yang et al 2008;Lu, Zhang &Lubke 2011), andHarel (2003) proposes a non-iterative imputation approach for dealing with general latent-dependent missingness under a joint model. These methods, however, all rely on the prior specification of a joint model for the complete data.…”

Section: Discussionmentioning

confidence: 99%

Sequential imputation for models with latent variables assuming latent ignorability

Beesley

Taylor

Little

2019

Aus NZ J of Statistics

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Models that involve an outcome variable, covariates, and latent variables are frequently the target for estimation and inference. The presence of missing covariate or outcome data presents a challenge, particularly when missingness depends on the latent variables. This missingness mechanism is called latent ignorable or latent missing at random and is a generalisation of missing at random. Several authors have previously proposed approaches for handling latent ignorable missingness, but these methods rely on prior specification of the joint distribution for the complete data. In practice, specifying the joint distribution can be difficult and/or restrictive. We develop a novel sequential imputation procedure for imputing covariate and outcome data for models with latent variables under latent ignorable missingness. The proposed method does not require a joint model; rather, we use results under a joint model to inform imputation with less restrictive modelling assumptions. We discuss identifiability and convergence-related issues, and simulation results are presented in several modelling settings. The method is motivated and illustrated by a study of head and neck cancer recurrence. Imputing missing data for models with latent variables under latent-dependent missingness without specifying a full joint model.

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A latent class selection model for nonignorably missing data

Cited by 12 publications

References 22 publications

Latent Variable Modelling with Non-Ignorable Item Non-Response: Multigroup Response Propensity Models for Cross-National Analysis

Latent Variable Modelling with Non-Ignorable Item Non-Response: Multigroup Response Propensity Models for Cross-National Analysis

Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

Sequential imputation for models with latent variables assuming latent ignorability

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