A factored regression model for composite scores with item-level missing data.

Alacam, Egamaria; Enders, Craig K.; Du, Huiqin; Keller, Brian T.

doi:10.1037/met0000584

Cited by 2 publications

(1 citation statement)

References 80 publications

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“…For example, in multi-item scales, it has been recommended that items from the same scale should be combined into scale scores (e.g., scale means; Eekhout et al, 2018;Gottschall et al, 2012;Vera & Enders, 2021). Combining items into scale scores simplifies the imputation model by effectively placing constraints on the parameters of the fully-specified model in accordance with the suspected scale structure (see also Alacam et al, 2023). If the underlying items are incomplete, the composite scores can be iteratively updated to reflect the most recent imputations of the underlying variables by incorporating passive imputation steps (for additional details, see Royston, 2005;van Buuren & Groothuis-Oudshoorn, 2011).…”

Section: Composite Scoresmentioning

confidence: 99%

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares

Grund,

Lüdtke,

Robitzsch

2023

Preprint

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Multiple imputation (MI) is one of the most popular methods for handling missing data in psychological research. However, many imputation approaches are poorly equipped to handle large numbers of variables, which are a common sight in studies that employ questionnaires to assess psychological constructs. In such a case, conventional imputation approaches often become unstable and require that the imputation model be simplified, for example, by removing variables or combining them into composite scores. In this article, we propose an alternative method that extends the fully conditional specification (FCS) approach to MI with dimension reduction techniques such as partial least squares (PLS). To evaluate this approach, we conducted a series of simulation studies, in which we compared it with other approaches that were based on variable selection, composite scores, or dimension reduction through principal components analysis (PCA). Our findings indicate that this novel approach can provide accurate results even in challenging scenarios, where other approaches fail to do so. Finally, we also illustrate the use of this method in real data and discuss the implications of our findings for practice.

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Section: Composite Scoresmentioning

confidence: 99%

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares

Grund,

Lüdtke,

Robitzsch

2023

Preprint

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Handling missing data in longitudinal clinical trials: three examples from the pediatric psychology literature

Peugh,

Mara

2024

Journal of Pediatric Psychology

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Researchers by default tend to choose complex models when analyzing nonindependent response variable data, this may be particularly applicable in the analysis of longitudinal trial data, possibly due to the ability of such models to easily address missing data by default. Both maximum-likelihood (ML) estimation and multiple imputation (MI) are well-known to be acceptable methods for handling missing data, but much of the recently published quantitative literature has addressed questions regarding the research designs and circumstances under which one should be chosen over the other. The purpose of this article is threefold. First, to clearly define the assumptions underlying three common longitudinal trial data analysis models for continuous dependent variable data: repeated measures analysis of covariance (RM-ANCOVA), generalized estimating equation (GEE), and a longitudinal linear mixed model (LLMM). Second, to clarify when ML or MI should be chosen, and to introduce researchers to an easy-to-use, empirically well-validated, and freely available missing data multiple imputation program: BLIMP. Third, to show how missing longitudinal trial data can be handled in the three data analysis models using three popular statistical analysis software packages (SPSS, Stata, and R) while keeping the published quantitative research in mind.

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A factored regression model for composite scores with item-level missing data.

Cited by 2 publications

References 80 publications

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares

Handling missing data in longitudinal clinical trials: three examples from the pediatric psychology literature

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