156 related articles for article (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
[TBL] [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
[TBL] [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; 52(1):12-30. PubMed ID: 27834491
[TBL] [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
[TBL] [Abstract][Full Text] [Related]
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; 50(5):504-19. PubMed ID: 26610249
[TBL] [Abstract][Full Text] [Related]
6. Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood.
Chen L; Savalei V
Front Psychol; 2021; 12():667802. PubMed ID: 34512436
[TBL] [Abstract][Full Text] [Related]
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; 18(1):87. PubMed ID: 30153796
[TBL] [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; 54(3):1063-1077. PubMed ID: 34545537
[TBL] [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; 10():7. PubMed ID: 20085642
[TBL] [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; 8():767. PubMed ID: 28588523
[TBL] [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; 54(2):922-940. PubMed ID: 34357540
[TBL] [Abstract][Full Text] [Related]
12. New computations for RMSEA and CFI following FIML and TS estimation with missing data.
Zhang X; Savalei V
Psychol Methods; 2023 Apr; 28(2):263-283. PubMed ID: 35007107
[TBL] [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; 67(3):335-42. PubMed ID: 24291505
[TBL] [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; 22(3):426-449. PubMed ID: 27709974
[TBL] [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; 20(2):287-300. PubMed ID: 20882358
[TBL] [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; 8(3):206-13. PubMed ID: 17549635
[TBL] [Abstract][Full Text] [Related]
17. Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques.
Schminkey DL; von Oertzen T; Bullock L
Res Nurs Health; 2016 Aug; 39(4):286-97. PubMed ID: 27176912
[TBL] [Abstract][Full Text] [Related]
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; 17():341. PubMed ID: 27450066
[TBL] [Abstract][Full Text] [Related]
19. PROMIS Global Health item nonresponse: is it better to impute missing item responses before computing T-scores?
Thompson NR; Katzan IL; Honomichl RD; Lapin BR
Qual Life Res; 2020 Feb; 29(2):537-546. PubMed ID: 31630291
[TBL] [Abstract][Full Text] [Related]
20. The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.
Enders CK
Psychol Methods; 2001 Dec; 6(4):352-70. PubMed ID: 11778677
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
