{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rezvan PH"],"funding":["NICHD NIH HHS","NCATS NIH HHS","NIMH NIH HHS"],"pagination":["682-713"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9718541"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(4)"],"pubmed_abstract":["Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to <i>ad-hoc</i> methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which <i>ad-hoc</i> procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts <i>ad-hoc</i> methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of <i>ad-hoc</i> procedures may be muted when missing data percentages are modest."],"journal":["Communications in statistics. Case studies, data analysis and applications"],"pubmed_title":["Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales."],"pmcid":["PMC9718541"],"funding_grant_id":["T32 MH109205","UL1 TR001881","U19 HD089886","P30 MH058107"],"pubmed_authors":["Comulada WS","Rezvan PH","Belin TR","Fernandez MI"],"additional_accession":[]},"is_claimable":false,"name":"Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales.","description":"Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to <i>ad-hoc</i> methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which <i>ad-hoc</i> procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts <i>ad-hoc</i> methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of <i>ad-hoc</i> procedures may be muted when missing data percentages are modest.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-05-28T01:31:43.369Z","creation":"2025-02-19T01:18:07.79Z"},"accession":"S-EPMC9718541","cross_references":{"pubmed":["36467970"],"doi":["10.1080/23737484.2022.2115430"]}}