Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods. Issue 2 (22nd January 2023)

• Record Type: Journal Article
• Authors: Ayilara, Olawale F.; Sajobi, Tolulope T.; Barclay, Ruth; Jafari Jozani, Mohammad; Lix, Lisa M.
• Is Part Of: Journal of statistical computation and simulation. Volume 93:Issue 2(2023)
• Page Start: 211
• Page End: 230
• Publication Date: 2023-01-22
• Subjects: Longitudinal change -- latent variable -- proportional odds model -- missing data -- matrix factorization; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2022.2098499
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Estimates of longitudinal change in the parameters of latent (i.e. unobserved) variables, including means, are affected by non-response on the items or indicators of the latent variable. This study used Monte Carlo simulation and a numeric example to compare four ordinal item non-response methods: non-negative matrix factorization (NNMF), multiple imputation with conditional proportional odds model (POM), full information maximum likelihood (FIML) and complete-case analysis, when estimating the longitudinal change in latent variable means. The mean squared error for the NNMF method was more than 40% lower than for the FIML and POM methods when the latent variable correlations over time were strong, percentage of missing data was 25% or more, and sample size was large. The NNMF method is a promising method to address item non-response. It is relatively efficient when sample size is large, and the percentage of missing data is high but has limitations under other data-analytic conditions.