A comparison of FIML-versus multiple-imputation-based methods to test measurement invariance with incomplete ordinal variables
Y Liu, S Sriutaisuk - Structural Equation Modeling: A …, 2021 - Taylor & Francis
… have missing data. We focus on multi-parameter tests, in line with the standard procedure
of … First, we provide a review of the challenges raised by missing responses on ordinal …
of … First, we provide a review of the challenges raised by missing responses on ordinal …
A multiple imputation score test for model modification in structural equation models.
M Mansolf, TD Jorgensen, CK Enders - Psychological methods, 2020 - psycnet.apa.org
… Second, we provide a brief review of, and rationale for, multiple imputation in the SEM context.
Third, we describe the score test for multiply imputed data. To ensure that the exposition is …
Third, we describe the score test for multiply imputed data. To ensure that the exposition is …
[PDF][PDF] Multiple imputation of missing data in longitudinal organizational research: A step-by-step guide
S Grund, GM Weiher, YZ Varol - 2024 - osf.io
… missing data in occupational and organizational research. However, state-of-the-art methods
such as multiple imputation (… Multilevel multiple imputation: A review and evaluation of joint …
such as multiple imputation (… Multilevel multiple imputation: A review and evaluation of joint …
[图书][B] Multiple imputation of missing data in practice: Basic theory and analysis strategies
Y He, G Zhang, CH Hsu - 2021 - taylorfrancis.com
… about multiple imputation in … statistical concepts of missing data problems and the associated
terminology. It focuses on how to address missing data problems using multiple imputation. …
terminology. It focuses on how to address missing data problems using multiple imputation. …
THIS ARTICLE HAS BEEN RETRACTED: Much Ado About Nothing: Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance
JR van Ginkel, PM Kroonenberg - Methodology, 2020 - meth.psychopen.eu
… Even though multiple imputation does not seem to be a … tests for multi-parameter estimates
in multiply imputed datasets. … the likelihood of imputed dataset m evaluated at the average set …
in multiply imputed datasets. … the likelihood of imputed dataset m evaluated at the average set …
[HTML][HTML] Much Ado About Nothing: Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance
JR van Ginkel, PM Kroonenberg - 2020 - meth.psychopen.eu
… statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and
compared these statistics … a likelihood-ratio test of imputed dataset m but now evaluated at the …
compared these statistics … a likelihood-ratio test of imputed dataset m but now evaluated at the …
Pooling methods for likelihood ratio tests in multiply imputed data sets.
S Grund, O Lüdtke, A Robitzsch - Psychological Methods, 2023 - psycnet.apa.org
… imputed data is more difficult than with complete data, and several different methods have
been developed for this task. In this article, we evaluate … Multiple imputation (MI) is one of the …
been developed for this task. In this article, we evaluate … Multiple imputation (MI) is one of the …
WEARCON: wearable home monitoring in children with asthma reveals a strong association with hospital based assessment of asthma control
MR van der Kamp, EC Klaver, BJ Thio… - BMC medical informatics …, 2020 - Springer
… of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the
… missing data was handled using the multiple imputation regression method. Missing data …
… missing data was handled using the multiple imputation regression method. Missing data …
Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
… ” missing data methods such as full information maximum likelihood and multiple imputation
were becoming a … non-Gaussian continuous outcomes: A simulation assessment. Journal of …
were becoming a … non-Gaussian continuous outcomes: A simulation assessment. Journal of …
A novel stochastic process diffusion model for wind turbines condition monitoring and fault identification with multi-parameter information fusion
Q Yao, H Bing, G Zhu, L Xiang, A Hu - Mechanical Systems and Signal …, 2024 - Elsevier
… conditional on the available data for imputation purposes. Unlike current … data. This
divergence computation, performed across multiple target parameters, culminates in an evaluation …
divergence computation, performed across multiple target parameters, culminates in an evaluation …