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.

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.

Much effort has been made to develop models for longitudinal data analysis, but comparably less attention has been paid to the use of individual specific values on latent variables in longitudinal models. In a tutorial style, this article introduces the reader to four common approaches to obtain individual scoresindividual mean score, Bartlett method, regression method, Kalman filterand reviews criteria commonly used to evaluate their performance. By means of simulated data, we mimic realistic scenarios and investigate in how far analytic results on the asymptotic performance of individual scores translate into practical situations. We end this article with a discussion of the use and usefulness of individual scores.

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.

Different methods to obtain individual scores from multiple item latent variable models exist, but their performance under realistic conditions is currently underresearched. We investigate the performance of the regression method, the Bartlett method, the Kalman filter, and the mean score under misspecification in autoregressive panel models. Results from three simulations show different patterns of findings for the mean absolute error, for the correlations between individual scores and the true scores (correlation criterion), and for the coverage in our settings: a) all individual score methods are generally quite robust against the chosen misspecification in the loadings, b) all methods are similarly sensitive to positively skewed as well as leptokurtic response distributions with regard to the correlation criterion, c) only the mean score is not robust against an integrated trend component, and d) coverage for the mean score is consistently below the nominal value.