Sequential imputation for models with latent variables assuming latent ignorability

Beesley, Lauren J.; Taylor, Jeremy M. G.; Little, Roderick J. A.

doi:10.1111/anzs.12264

Cited by 9 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Latent ignorability [ 19 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] is one of the weakest nonignorable missingness mechanisms. Latent ignorability weakens the assumption of ignorability for MAR data.…”

Section: Statistical Models For Handling Missing Item Responsesmentioning

confidence: 99%

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

Robitzsch

2021

EJIHPE

View full text Add to dashboard Cite

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

show abstract

Section: Statistical Models For Handling Missing Item Responsesmentioning

confidence: 99%

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

Robitzsch

2021

EJIHPE

View full text Add to dashboard Cite

show abstract

“…Unlike other models already existing in the literature, such as Bacci and Bartolucci [22] and Beesley et al [32], the proposed model accounts for missingness and allows for this missingness to be non-random by depending on levels of the latent class of interest. The estimated probabilities of class membership of the "Response Propensity" latent variable are affected by class membership of the "Access to Knowledge Sources" latent variable making the missingness nonignorable.…”

Section: Discussionmentioning

confidence: 99%

A Latent Class Model for Multivariate Binary Data Subject to Missingness

Zakaria¹,

Hafez²,

Gad³

2021

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent's propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).

show abstract

“…Latent ignorability (LI; [21][22][23][24][25][26][27]) is one of the weakest nonignorable missingness mechanisms. LI weakens the assumption of ignorability for MAR data.…”

Section: Mislevy-wu Modelmentioning

confidence: 99%

Regularized Mislevy-Wu Model for Handling Nonignorable Missing Item Responses

Robitzsch

2023

Information

View full text Add to dashboard Cite

Missing item responses are frequently found in educational large-scale assessment studies. In this article, the Mislevy-Wu item response model is applied for handling nonignorable missing item responses. This model allows that the missingness of an item depends on the item itself and a further latent variable. However, with low to moderate amounts of missing item responses, model parameters for the missingness mechanism are difficult to estimate. Hence, regularized estimation using a fused ridge penalty is applied to the Mislevy-Wu model to stabilize estimation. The fused ridge penalty function is separately defined for multiple-choice and constructed response items because previous research indicated that the missingness mechanisms strongly differed for the two item types. In a simulation study, it turned out that regularized estimation improves the stability of item parameter estimation. The method is also illustrated using international data from the progress in international reading literacy study (PIRLS) 2011 data.

show abstract

Sequential imputation for models with latent variables assuming latent ignorability

Cited by 9 publications

References 33 publications

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

A Latent Class Model for Multivariate Binary Data Subject to Missingness

Regularized Mislevy-Wu Model for Handling Nonignorable Missing Item Responses

Contact Info

Product

Resources

About