Score-based tests for parameter instability in ordinal factor models

Abstract We present a novel approach for computing model scores for ordinal factor models, that is, graded response models (GRMs) fitted with a limited information (LI) estimator. The method makes it possible to compute score-based tests for parameter instability for ordinal factor models. This way, rapid execution of numerous parameter instability tests for multidimensional item response theory (MIRT) models is facilitated. We present a comparative analysis of the performance of the proposed score-based tests for ordinal factor models in comparison to tests for GRMs fitted with a full information (FI) estimator. The new method has a good Type I error rate, high power and is computationally faster than FI estimation. We further illustrate that the proposed method works well with complex models in real data applications. The method is implemented in the lavaan package in R

Cut off from new competition: Threat of entry and quality of primary care

We study how the threat of entry affects service quantity and quality of general practitioners (GPs). We leverage Germany’s needs-based primary care planning system, in which the likelihood of new GPs reduces by 20 percentage points when primary care coverage exceeds a cut-off. We compile novel data covering all German primary care regions and up to 30,000 GP-level observations from 2014 to 2019. Reduced threat of entry lowers patient satisfaction for incumbent GPs without nearby competitors but not in areas with competitors. We find no effects on working hours or quality measures at the regional level including hospitalizations and mortality

Change my mind: The impact of feedback in online self-assessments for study orientation on change in motivation of prospective students

High dropout rates at universities, often caused by false expectations and a lack of motivation, pose a serious problem in higher education. Online self-assessments (OSAs) assess expectations regarding a field of study (major) and provide feedback on the reality of the major, thus pointing out expectation-reality discrepancies as well as helping prospective students choose a major. Based on cognitive dissonance theory, pointing out expectation-reality discrepancies should be related to changes in motivation for the major (expectancies for success, subjective values, intention to choose a major) and this relationship should be strengthened by feedback. Past research has shown that OSAs can correct expectations and that expectation-reality discrepancies are related to motivation but has not investigated the role of feedback for this process. Therefore, we extend past research by examining whether the positive relationships between expectation-reality discrepancies and changes in motivation for a major are stronger for prospective students who receive feedback on their expectation-reality discrepancies than for prospective students who do not receive feedback after the assessment. We conducted a field experiment in which 234 prospective students were randomly assigned to one of two groups (EG1 = OSA including feedback; EG2 = OSA without feedback). As hypothesized larger expectation-reality discrepancies were associated with larger changes in motivation for a major (expectancies for success, subjective values, intention to choose a major). Beyond that, we found a moderation effect of the feedback condition showing that the positive relationships between expectation-reality discrepancies and expectancies for success were stronger when prospective students received feedback (vs. no feedback). As feedback only showed effects beyond expectation-reality discrepancies in one of the considered outcomes, both the development of assessment and feedback should be targeted to optimize the effectiveness of OSAs