Imputed spatial data: Cautions arising from response and covariate imputation measurement error

2020 ◽  
pp. 100419 ◽  
Author(s):  
Daniel A. Griffith ◽  
Yan-Ting Liau
Keyword(s):  
Measurement Error ◽  
Spatial Data

scholarly journals   Bayesian adjustment for measurement error in an offset variable in a Poisson regression model

2021 ◽  
pp. 1471082X2110080
Author(s):  
Kangjie Zhang ◽  
Juxin Liu ◽  
Yang Liu ◽  
Peng Zhang ◽  
Raymond J. Carroll
Keyword(s):  
Measurement Error ◽  
Random Effects ◽  
Spatial Data ◽  
Poisson Regression ◽  
Measurement Errors ◽  
Error Model ◽  
County Level ◽  
Measurement Error Model ◽  
The Usa ◽  
Car Crashes

Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county level teen car crash counts. We develop a measurement error model to account for the fact that the total teenage population in the county level is used as a proxy for the teenage driver population. To the best of our knowledge, there is no existing literature that considers adjustment for measurement error in an offset variable. Furthermore, limited work has addressed the measurement errors in the context of spatial data. In our modelling, a Berkson measurement error model with spatial random effects is applied to adjust for the error-prone offset variable in a Bayesian paradigm. The Bayesian Markov chain Monte Carlo (MCMC) sampling is implemented in rstan. To assess the consequence of adjusting for measurement error, we compared two models with and without adjustment for measurement error. We found the effect of a time indicator becomes less significant with the measurement-error adjustment. It leads to our conclusion that the reduced number of teen drivers can help explain, to some extent, the effectiveness of GDL.

Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke
Keyword(s):  
Measurement Error ◽  
Computerized Adaptive Testing ◽  
Test Procedure ◽  
Adaptive Testing ◽  
Parameter Estimates ◽  
Simulation Studies ◽  
Computerized Adaptive Test ◽  
Item Banks ◽  
Item Parameters ◽  
General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

Measurement error and person-specific reliability in multilevel autoregressive modeling.

2019 ◽  
Vol 24 (1) ◽  
pp. 70-91 ◽  
Author(s):  
Noémi K. Schuurman ◽  
Ellen L. Hamaker
Keyword(s):  
Measurement Error ◽  
Autoregressive Modeling
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