title: Multilevel multidimensional item response model with a multilevel latent covariate.
abstract: In a pre-test-post-test cluster randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores, ignoring measurement error, are used as response variable and covariate, respectively, to estimate the intervention effect. However, these test scores are frequently subject to measurement error, and statistical inferences based on the model ignoring measurement error can yield a biased estimate of the intervention effect. When multiple domains exist in test data, it is sometimes more informative to detect the intervention effect for each domain than for the entire test. This paper presents applications of the multilevel multidimensional item response model with measurement error adjustments in a response variable and a covariate to estimate the intervention effect for each domain.
authors: Vanderbilt University, Nashville, Tennessee, USA.
authors: Sun-Joo
authors: SJ
authors: Cho
authors: University of Kentucky, Lexington, Kentucky, USA.
authors: Brian
authors: B
authors: Bottge
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000379:methods*; D015233:Models, Statistical; D058011:Multifactor Dimensionality Reduction; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: measurement error; multidimensional item response model; multilevel model