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Journal Abstract Search

159 related items for PubMed ID: 32333330

  • 1. Two-stage maximum likelihood approach for item-level missing data in regression.
    Chen L, Savalei V, Rhemtulla M.
    Behav Res Methods; 2020 Dec; 52(6):2306-2323. PubMed ID: 32333330
    [Abstract] [Full Text] [Related]

  • 2. Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level.
    Savalei V, Rhemtulla M.
    J Educ Behav Stat; 2017 Aug; 42(4):405-431. PubMed ID: 29276371
    [Abstract] [Full Text] [Related]

  • 3. Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators.
    Cham H, Reshetnyak E, Rosenfeld B, Breitbart W.
    Multivariate Behav Res; 2017 Aug; 52(1):12-30. PubMed ID: 27834491
    [Abstract] [Full Text] [Related]

  • 4. A comparison of full information maximum likelihood and multiple imputation in structural equation modeling with missing data.
    Lee T, Shi D.
    Psychol Methods; 2021 Aug; 26(4):466-485. PubMed ID: 33507765
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  • 5. Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation.
    Mazza GL, Enders CK, Ruehlman LS.
    Multivariate Behav Res; 2015 Aug; 50(5):504-19. PubMed ID: 26610249
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  • 7. Multiple imputation for patient reported outcome measures in randomised controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level.
    Rombach I, Gray AM, Jenkinson C, Murray DW, Rivero-Arias O.
    BMC Med Res Methodol; 2018 Aug 28; 18(1):87. PubMed ID: 30153796
    [Abstract] [Full Text] [Related]

  • 8. Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data.
    Lim AJ, Cheung MW.
    Behav Res Methods; 2022 Jun 28; 54(3):1063-1077. PubMed ID: 34545537
    [Abstract] [Full Text] [Related]

  • 9. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.
    Marshall A, Altman DG, Royston P, Holder RL.
    BMC Med Res Methodol; 2010 Jan 19; 10():7. PubMed ID: 20085642
    [Abstract] [Full Text] [Related]

  • 10. Normal Theory GLS Estimator for Missing Data: An Application to Item-Level Missing Data and a Comparison to Two-Stage ML.
    Savalei V, Rhemtulla M.
    Front Psychol; 2017 Jan 19; 8():767. PubMed ID: 28588523
    [Abstract] [Full Text] [Related]

  • 11. Combining proration and full information maximum likelihood in handling missing data in Likert scale items: A hybrid approach.
    Wu W, Gu F, Fukui S.
    Behav Res Methods; 2022 Apr 19; 54(2):922-940. PubMed ID: 34357540
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  • 12. New computations for RMSEA and CFI following FIML and TS estimation with missing data.
    Zhang X, Savalei V.
    Psychol Methods; 2023 Apr 19; 28(2):263-283. PubMed ID: 35007107
    [Abstract] [Full Text] [Related]

  • 13. Missing data in a multi-item instrument were best handled by multiple imputation at the item score level.
    Eekhout I, de Vet HC, Twisk JW, Brand JP, de Boer MR, Heymans MW.
    J Clin Epidemiol; 2014 Mar 19; 67(3):335-42. PubMed ID: 24291505
    [Abstract] [Full Text] [Related]

  • 14. Maximum likelihood versus multiple imputation for missing data in small longitudinal samples with nonnormality.
    Shin T, Davison ML, Long JD.
    Psychol Methods; 2017 Sep 19; 22(3):426-449. PubMed ID: 27709974
    [Abstract] [Full Text] [Related]

  • 15. Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey.
    Peyre H, Leplège A, Coste J.
    Qual Life Res; 2011 Mar 19; 20(2):287-300. PubMed ID: 20882358
    [Abstract] [Full Text] [Related]

  • 16. How many imputations are really needed? Some practical clarifications of multiple imputation theory.
    Graham JW, Olchowski AE, Gilreath TD.
    Prev Sci; 2007 Sep 19; 8(3):206-13. PubMed ID: 17549635
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  • 18. Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
    Mukaka M, White SA, Terlouw DJ, Mwapasa V, Kalilani-Phiri L, Faragher EB.
    Trials; 2016 Jul 22; 17():341. PubMed ID: 27450066
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