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Measurement error is an inherent part of any test score. This uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners. In this empirical study, we evaluate the impact of several communication formats on the interpretation of measurement accuracy and its influence on the decision-making process in clinical practice. We provided 230 clinical practitioners with score reports in five formats: textual, error bar, violin plot, diamond plot, and quantile dot plot. We found that quantile dot plots significantly increased accuracy in the assessment of measurement uncertainty compared with other formats. However, a direct relation between visualization format and decision quality could not be found. Although traditional confidence intervals and error bars were favored by many participants due to their familiarity, responses revealed several misconceptions that make the suitability of these formats for communicating uncertainty questionable. Our results indicate that new visualization formats can successfully reduce errors in interpretation., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The use of empirical prior information about participants has been shown to substantially improve the efficiency of computerized adaptive tests (CATs) in educational settings. However, it is unclear how these results translate to clinical settings, where small item banks with highly informative polytomous items often lead to very short CATs. We explored the risks and rewards of using prior information in CAT in two simulation studies, rooted in applied clinical examples. In the first simulation, prior precision and bias in the prior location were manipulated independently. Our results show that a precise personalized prior can meaningfully increase CAT efficiency. However, this reward comes with the potential risk of overconfidence in wrong empirical information (i.e., using a precise severely biased prior), which can lead to unnecessarily long tests, or severely biased estimates. The latter risk can be mitigated by setting a minimum number of items that are to be administered during the CAT, or by setting a less precise prior; be it at the expense of canceling out any efficiency gains. The second simulation, with more realistic bias and precision combinations in the empirical prior, places the prevalence of the potential risks in context. With similar estimation bias, an empirical prior reduced CAT test length, compared to a standard normal prior, in 68% of cases, by a median of 20%; while test length increased in only 3% of cases. The use of prior information in CAT seems to be a feasible and simple method to reduce test burden for patients and clinical practitioners alike., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

The current study aims to examine the psychometric properties of the Structured Clinical Interview for the DSM-5 Alternative Model for Personality Disorders Module I (SCID-5-AMPD-I) assessing the Level of Personality Functioning Scale (LPFS) in a heterogeneous sample of 282 nonpsychotic patients. Latent variable models were used to investigate the dimensionality of the LPFS. The results indicate that the LPFS, as assessed by the SCID-5-AMPD-I, can be considered as a unidimensional construct that can be measured reliably across a wide range of the latent trait. Threshold parameters for the 12 indicators of the LPFS increased gradually over the latent scale, indicating that the five LPFS levels were ordered as predicted by the model. In general, the increase of threshold parameters was relatively small for the shift from Level 2 to Level 3. A better distinction among the different severity levels might be obtained by fine-tuning the interview guidelines or the Level 2 indicators themselves.

This study aims at evaluating the psychometric properties of the antisocial personality disorder (ASPD) criteria in a large sample of patients, most of whom had one or more personality disorders (PD). PD diagnoses were assessed by experienced clinicians using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders , 4th edition, Axis II PDs. Analyses were performed within an item response theory framework. Results of the analyses indicated that ASPD is a unidimensional construct that can be measured reliably at the upper range of the latent trait scale. Differential item functioning across gender was restricted to two criteria and had little impact on the latent ASPD trait level. Patients fulfilling both the adult ASPD criteria and the conduct disorder criteria had similar latent trait distributions as patients fulfilling only the adult ASPD criteria. Overall, the ASPD items fit the purpose of a diagnostic instrument well, that is, distinguishing patients with moderate from those with high antisocial personality scores.

It is currently not entirely clear to what degree the research on multidimensional computerized adaptive testing (CAT) conducted in the field of educational testing can be generalized to fields such as health assessment, where CAT design factors differ considerably from those typically used in educational testing. In this study, the impact of a number of important design factors on CAT performance is systematically evaluated, using realistic example item banks for two main scenarios: health assessment (polytomous items, small to medium item bank sizes, high discrimination parameters) and educational testing (dichotomous items, large item banks, small- to medium-sized discrimination parameters). Measurement efficiency is evaluated for both between-item multidimensional CATs and separate unidimensional CATs for each latent dimension. In this study, we focus on fixed-precision (variable-length) CATs because it is both feasible and desirable in health settings, but so far most research regarding CAT has focused on fixed-length testing. This study shows that the benefits associated with fixed-precision multidimensional CAT hold under a wide variety of circumstances., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

As there is currently a marked increase in the use of both unidimensional (UCAT) and multidimensional computerized adaptive testing (MCAT) in psychological and health measurement, the main aim of the present study is to assess the incremental value of using MCAT rather than separate UCATs for each dimension. Simulations are based on empirical data that could be considered typical for health measurement: a large number of dimensions (4), strong correlations among dimensions (.77-.87), and polytomously scored response data. Both variable- ( SE < .316, SE < .387) and fixed-length conditions (total test length of 12, 20, or 32 items) are studied. The item parameters and variance-covariance matrix Φ are estimated with the multidimensional graded response model (GRM). Outcome variables include computerized adaptive test (CAT) length, root mean square error (RMSE), and bias. Both simulated and empirical latent trait distributions are used to sample vectors of true scores. MCATs were generally more efficient (in terms of test length) and more accurate (in terms of RMSE) than their UCAT counterparts. Absolute average bias was highest for variable-length UCATs with termination rule SE < .387. Test length of variable-length MCATs was on average 20% to 25% shorter than test length across separate UCATs. This study showed that there are clear advantages of using MCAT rather than UCAT in a setting typical for health measurement., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.