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We examined the feasibility and results of a multilevel multidimensional nominal response model (ML-MNRM) for measuring both substantive constructs and extreme response style (ERS) across countries. The ML-MNRM considers within-country clustering while allowing overall item slopes to vary across items and examination of whether certain items were more prone to ERS. We applied this model to survey items from TALIS 2013. Results indicated that self-efficacy items were more likely to trigger ERS compared to need for professional development, and the betweencountry relationships among constructs can change due to ERS. Simulations assessed the estimation approach and found adequate recovery of model parameters and factor scores. We stress the importance of additional validity studies to improve the cross-cultural comparability of substantive constructs.

The primary distinguishing feature of computerized adaptive testing (CAT) is that sets of items that are administered to examinees are specifically selected for each examinee during the test process. This implies that examinees who respond differently to items on a test will get different sets of test items. However, if an item bank is small, if there are strict content constraints, or if there is strong exposure control, there might not be much adaptation during the test. Examinees who perform differently might get many of the same test items. The research reported here recommends some statistical indicators of how much adaptation is taking place and shows how these indicators vary for different kinds of adaptive test designs. Guidelines are provided for the values of the statistics indicating that a CAT is strongly adaptive.

Recent years have seen a dramatic increase in item response models for measuring response styles on Likert-type items. These model-based approaches stand in contrast to traditional sum-score-based methods where researchers count the number of times that participants selected certain response options. The multidimensional nominal response model (MNRM) offers a flexible model-based approach that may be intuitive to those familiar with sum score approaches. This paper presents a tutorial on the model along with code for estimating it using three different software packages: flexMIRT ® , mirt, and Mplus. We focus on specification and interpretation of response functions. In addition, we provide analytical details on how sum score to scale score conversion can be done with the MNRM. In the context of a real data example, three different scoring approaches are then compared. This example illustrates how sum-score-based approaches can sometimes yield scores that are confounded with substantive content. We expect that the current paper will facilitate further investigations as to whether different substantive conclusions are reached under alternative approaches to measuring response styles.

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