A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores.

Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.

A Note on the Usefulness of Constrained Fourth-Order Latent Differential Equation Models in the Case of Small T.

Constrained fourth-order latent differential equation (FOLDE) models have been proposed (e.g., Boker et al.

Institutional strategies related to test-taking behavior in low stakes assessment.

Low stakes assessment without grading the performance of students in educational systems has received increasing attention in recent years. It is used in formative assessments to guide the learning process as well as in large-scales assessments to monitor educational programs. Yet, such assessments suffer from high variation in students' test-taking effort.

Bayesian continuous-time Rasch models.

Continuous-time modeling offers a flexible approach to analyze longitudinal data from designs with unequally spaced measurement occasions. Measurement models are popular tools in psychological research to control for measurement error. The objective of the present article is to introduce the continuous-time Rasch model, a combination of the Rasch model and a continuous-time dynamic model.

Testing Students with Special Educational Needs in Large-Scale Assessments - Psychometric Properties of Test Scores and Associations with Test Taking Behavior.

Assessing competencies of students with special educational needs in learning (SEN-L) poses a challenge for large-scale assessments (LSAs). For students with SEN-L, the available competence tests may fail to yield test scores of high psychometric quality, which are-at the same time-measurement invariant to test scores of general education students. We investigated whether we can identify a subgroup of students with SEN-L, for which measurement invariant competence measures of adequate psychometric quality may be obtained with tests available in LSAs.