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Timothy Hayes

Showing results (1-10 of 41) with videos related to

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Behavior Research Methods|March 30, 2021
R-squared change in structural equation models with latent variables and missing dataTimothy Hayes
Archives of Pathology & Laboratory Medicine|November 8, 2002
Dysfibrinogenemia and thrombosisTimothy Hayes
Psychological Methods|February 8, 2024
Individual-level probabilities and cluster-level proportions: Toward interpretable level 2 estimates in unconflated multilevel models for binary outcomesTimothy Hayes
Educational and Psychological Measurement|January 15, 2020
Factor Score Regression in the Presence of Correlated Unique FactorsTimothy Hayes, Satoshi Usami
Bulletin (Hospital for Joint Diseases (New York, N.Y.))|May 26, 2004
Metasynchronous bilateral Achilles tendon ruptureTimothy Hayes, Damian McClelland, Nicola Maffulli
Behavior Research Methods|September 9, 2024
Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test)Timothy Hayes, Amanda N Baraldi, Stefany Coxe
Multivariate Behavioral Research|January 30, 2019
A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance MatricesCraig K Enders, Timothy Hayes, Han Du
Multivariate Behavioral Research|December 31, 2015
On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between VariablesSatoshi Usami, Timothy Hayes, John J McArdle
Prevention Science : the Official Journal of the Society for Prevention Research|July 3, 2026
Tutorial: Using Random Forest Analysis to Identify Auxiliary Variables of Missing DataStefany Coxe, Amanda N Baraldi, Timothy Hayes
Psychology and Aging|September 22, 2015
Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulationsTimothy Hayes, Satoshi Usami, Ross Jacobucci, et al.
Pageof 5

Showing results (1-10 of 41) with videos related to

Sort By:
Pageof 5
Behavior Research Methods|March 30, 2021
R-squared change in structural equation models with latent variables and missing dataTimothy Hayes
Archives of Pathology & Laboratory Medicine|November 8, 2002
Dysfibrinogenemia and thrombosisTimothy Hayes
Psychological Methods|February 8, 2024
Individual-level probabilities and cluster-level proportions: Toward interpretable level 2 estimates in unconflated multilevel models for binary outcomesTimothy Hayes
Educational and Psychological Measurement|January 15, 2020
Factor Score Regression in the Presence of Correlated Unique FactorsTimothy Hayes, Satoshi Usami
Bulletin (Hospital for Joint Diseases (New York, N.Y.))|May 26, 2004
Metasynchronous bilateral Achilles tendon ruptureTimothy Hayes, Damian McClelland, Nicola Maffulli
Behavior Research Methods|September 9, 2024
Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test)Timothy Hayes, Amanda N Baraldi, Stefany Coxe
Multivariate Behavioral Research|January 30, 2019
A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance MatricesCraig K Enders, Timothy Hayes, Han Du
Multivariate Behavioral Research|December 31, 2015
On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between VariablesSatoshi Usami, Timothy Hayes, John J McArdle
Prevention Science : the Official Journal of the Society for Prevention Research|July 3, 2026
Tutorial: Using Random Forest Analysis to Identify Auxiliary Variables of Missing DataStefany Coxe, Amanda N Baraldi, Timothy Hayes
Psychology and Aging|September 22, 2015
Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulationsTimothy Hayes, Satoshi Usami, Ross Jacobucci, et al.
Pageof 5