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. We aimed to identify institutional strategies related to serious test-taking behavior in low stakes assessment to provide medical schools with practical recommendations on how test-taking effort might be increased. First, we identified strategies that were already used by medical schools to increase the serious test-taking behavior on the low stakes Berlin Progress Test (BPT). Strategies which could be assigned to self-determination theory of Ryan and Deci were chosen for analysis. We conducted the study at nine medical schools in Germany and Austria with a total of 108,140 observations in an established low stakes assessment. A generalized linear-mixed effects model was used to assess the association between institutional strategies and the odds that students will take the BPT seriously. Overall, two institutional strategies were found to be positively related to more serious test-taking behavior: discussing low test performance with the mentor and consequences for not participating. Giving choice was negatively related to more serious test-taking behavior. At medical schools that presented the BPT as evaluation, this effect was larger in comparison to medical schools that presented the BPT as assessment.

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. 2020) as alternative to second-order latent differential equation (SOLDE) models to estimate second-order linear differential equation systems such as the damped linear oscillator model. When, however, only a relatively small number of measurement occasions T are available (i.e., [Formula: see text]), the recommendation of which model to use is not clear (Boker et al. 2020). Based on a data set, which consists of [Formula: see text] observations of daily stress for [Formula: see text] individuals, we illustrate that FOLDE can help to choose an embedding dimension, even in the case of a small T. This is of great importance, as parameter estimates depend on the embedding dimension as well as on the latent differential equations model. Consequently, the wavelength as quantity of potential substantive interest may vary considerably. We extend the modeling approaches used in past research by including multiple subjects, by accounting for individual differences in equilibrium, and by including multiple instead of one single observed indicator.

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. In a series of simulations we demonstrate the performance of the proposed model and that ignoring individual unequal time interval lengths, choosing a wrong measurement model, and selecting a wrong analysis strategy results in poor parameter estimates. The newly proposed continuous-time Rasch model overcomes these problems and offers a powerful new approach to longitudinal analysis with dichotomous items. A step-by-step tutorial on how to run a continuous-time Rasch model with the R package ctsem and an illustrative empirical example is given. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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. We furthermore investigated whether differences in test-taking behavior may explain dissatisfying psychometric properties and measurement non-invariance of test scores within LSAs. We relied on person fit indices and mixture distribution models to identify students with SEN-L for whom test scores with satisfactory psychometric properties and measurement invariance may be obtained. We also captured differences in test-taking behavior related to guessing and missing responses. As a result we identified a subgroup of students with SEN-L for whom competence scores of adequate psychometric quality that are measurement invariant to those of general education students were obtained. Concerning test taking behavior, there was a small number of students who unsystematically picked response options. Removing these students from the sample slightly improved item fit. Furthermore, two different patterns of missing responses were identified that explain to some extent problems in the assessments of students with SEN-L.