Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
[HTML][HTML] Religious/spiritual beliefs and behaviours and study participation in a prospective cohort study (ALSPAC) in Southwest England
Background Longitudinal studies are key to understanding risk factors for health, well-being,
and disease, yet associations may be biased if study invitation and participation are non …
and disease, yet associations may be biased if study invitation and participation are non …
An investigation of factored regression missing data methods for multilevel models with cross-level interactions
BT Keller, CK Enders - Multivariate Behavioral Research, 2023 - Taylor & Francis
A growing body of literature has focused on missing data methods that factorize the joint
distribution into a part representing the analysis model of interest and a part representing the …
distribution into a part representing the analysis model of interest and a part representing the …
[PDF][PDF] A tutorial for handling suspected missing not at random data in longitudinal clinical trials.
JL Peugh, MD Toland, H Harrison - The Quantitative Methods for …, 2023 - tqmp.org
Missing data in longitudinal randomized clinical trials, even if assumed to be missing at
random (MAR), can result in biased parameter estimates and incorrect treatment …
random (MAR), can result in biased parameter estimates and incorrect treatment …
Distributionally weighted least squares in structural equation modeling.
H Du, PM Bentler - Psychological Methods, 2022 - psycnet.apa.org
Abstract n real data analysis with structural equation modeling, data are unlikely to be
exactly normally distributed. If we ignore the non-normality reality, the parameter estimates …
exactly normally distributed. If we ignore the non-normality reality, the parameter estimates …
Compatibility in imputation specification
Missing data such as data missing at random (MAR) are unavoidable in real data and have
the potential to undermine the validity of research results. Multiple imputation is one of the …
the potential to undermine the validity of research results. Multiple imputation is one of the …
A self‐censoring model for multivariate nonignorable nonmonotone missing data
We introduce an itemwise modeling approach called “self‐censoring” for multivariate
nonignorable nonmonotone missing data, where the missingness process of each outcome …
nonignorable nonmonotone missing data, where the missingness process of each outcome …
Measurement invariant but non-normal treatment responses in guided internet psychotherapies for depressive and generalized anxiety disorders
Assessment of treatment response in psychotherapies can be undermined by lack of
longitudinal measurement invariance (LMI) in symptom self-report inventories, by …
longitudinal measurement invariance (LMI) in symptom self-report inventories, by …
Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials
A Staudt, J Freyer-Adam, T Ittermann, C Meyer… - BMC Medical Research …, 2022 - Springer
Background Missing data are ubiquitous in randomised controlled trials. Although sensitivity
analyses for different missing data mechanisms (missing at random vs. missing not at …
analyses for different missing data mechanisms (missing at random vs. missing not at …
A factored regression model for composite scores with item-level missing data.
Composite scores are an exceptionally important psychometric tool for behavioral science
research applications. A prototypical example occurs with self-report data, where …
research applications. A prototypical example occurs with self-report data, where …