2014
DOI: 10.7275/p7x7-jb36
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Impact of Violation of the Missing-at-Random Assumption on Full-Information Maximum Likelihood Method in Multidimensional Adaptive Testing

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Cited by 2 publications
(1 citation statement)
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“…Repeated measures analyses were conducted using linear mixed modeling (LMM), enabling inclusion of cases with missing data via restricted maximum likelihood (REML). No significant differences were observed between completers (n = 29) and non-completers (n = 6) on any outcome or demographic variables (Table S2), suggesting data was missing at random, a requirement of LMM (Han & Guo, 2014). An unstructured covariance matrix was assumed.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Repeated measures analyses were conducted using linear mixed modeling (LMM), enabling inclusion of cases with missing data via restricted maximum likelihood (REML). No significant differences were observed between completers (n = 29) and non-completers (n = 6) on any outcome or demographic variables (Table S2), suggesting data was missing at random, a requirement of LMM (Han & Guo, 2014). An unstructured covariance matrix was assumed.…”
Section: Statistical Analysesmentioning
confidence: 99%