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Full-Text Articles in Psychology
Item Parameter Recovery With And Without The Use Of Priors, Paulius Satkus, Christine E. Demars
Item Parameter Recovery With And Without The Use Of Priors, Paulius Satkus, Christine E. Demars
Department of Graduate Psychology - Faculty Scholarship
Marginal maximum likelihood (MML), a common estimation method for IRT models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for MML estimation. In this study, using samples sizes of 1000 or smaller, not using priors often led to extreme, implausible parameter estimates. Applying prior distributions to the c-parameters alleviated the estimation problems with samples of 1000; priors on both the a-parameters and c-parameters were needed for the samples of …
Differential Motivation In Remote Educational Assessment: Person-Based Filtering Versus Response-Based Filtering, Sarah Alahmadi, Christine E. Demars
Differential Motivation In Remote Educational Assessment: Person-Based Filtering Versus Response-Based Filtering, Sarah Alahmadi, Christine E. Demars
Department of Graduate Psychology - Faculty Scholarship
Large-scale educational assessments are often considered low-stakes, increasing the possibility of confounding true performance level with low motivation. These concerns are amplified in remote testing conditions. To remove the effects of low effort levels in responses observed in remote low-stakes testing, several motivation filtering methods can be used to purify the data. We estimated scores from assessment data collected remotely in Spring 2021 six ways, applying examinee-based filtering methods (filtering examinees based on total time) and response-based filtering methods (filtering responses using the effort-moderated IRT model), varying the thresholds selected to separate solution behavior (SB) responses from rapid-guessing behavior (RGB). …