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Multilevel multidimensional item response model with a multilevel latent covariate
Author(s) -
Cho SunJoo,
Bottge Brian
Publication year - 2015
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12051
Subject(s) - covariate , statistics , multilevel model , test (biology) , econometrics , item response theory , type i and type ii errors , mathematics , latent variable , psychometrics , paleontology , biology
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.