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Cílem této publikace je představení stále populárnější metody počítačového adaptivního testování (CAT) v oblasti kinantropologie. Přizpůsobením testu úrovni jednotlivých respondentů nabízí CAT řadu teoretických a metodologických výhod, které mohou výrazně ovlivnit testování teoretických konstruktů. První část monografie se věnuje historickým, teoretických a koncepčním základům CAT včetně popisu Teorie odpovědi na položku (IRT). Praktická aplikace CAT je posléze ve druhé části hodnocena pomocí Monte-Carlo simulací na příkladu Physical Self-Description Questionnaire – nástroje, který je široce používán k hodnocení tělesného sebepojetí v oblasti psychologie a psychologie sportu.

\"This book concerns adaptive tests of significance, which are statistical tests that use the data to modify the test procedures. The modification is used to reduce the influence of outliers. These adaptive tests are attractive because they are often more powerful than traditional tests, and they are also quite practical since they can be performed quickly on a computer using R code or a SAS macro. This comprehensive book on adaptive tests can be used by students and researchers alike who are not familiar with adaptive methods. Chapter 1 provides a gentle introduction to the topic, and Chapter 2 presents a description of the basic tools that are used throughout the book. In Chapters 3, 4, and 5, the basic adaptive testing methods are developed, and Chapters 6 and 7 contain many applications of these tests. Chapters 8 and 9 concern adaptive multivariate tests with multivariate regression models, while the rest of the book concerns adaptive rank tests, adaptive confidence intervals, and adaptive correlations. The adaptive tests described in this book have the following properties: the level of significance is maintained at or near [alpha]; they are more powerful than the traditional test, sometimes much more powerful, if the error distribution is long-tailed or skewed; and there is little power loss compared to the traditional tests if the error distribution is normal. Additional topical coverage includes: smoothing and normalizing methods; two-sample adaptive tests; permutation tests with linear models; adaptive tests in linear models; application of adaptive tests; analysis of paired data; adaptive multivariate tests; analysis of repeated measures data; rank-based approaches to testing; adaptive confidence intervals; and adaptive correlation\"--

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Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT also facilitates evaluation of differential item functioning, inclusion of items with different response formats in the same scale, and assessment of person fit and is ideally suited for implementing computer adaptive testing. Finally, IRT methods can be helpful in developing better health outcome measures and in assessing change over time. These issues are reviewed, along with a discussion of some of the methodological and practical challenges in applying IRT methods.

Modern computer technology has opened up several new possibilities for optimizing the administration of educational and psychological tests. In computer adaptive testing (CAT), tests are automatically tailored to the proficiency level of the individual examinees. Currently, nearly all large-scale testing programs in the western world are already adaptive or in the process of becoming so. Written by active CAT researchers from Europe and North America, the chapters offer a comprehensive introduction to the latest developments in the theory and practice of CAT. The book can be used both as a basic reference on the state of the art in CAT and a valuable resource in graduate courses on test theory. The theoretical chapters in this book cover such topics as item selection and ability estimation, item pool development and maintenance, item calibration and model fit, and testlet-based adaptive testing. The practical chapters describe the operational aspects of existing large-scale CAT programs.

As perfectly summarised by Ida Lawrence, “Testing is growing by leaps and bounds across the world. There is a realization that a nation’s well-being depends crucially on the educational achievement of its population. Valid tests are an essential tool to evaluate a nation’s educational standing and to implement efficacious educational reforms. Because tests consume time that otherwise could be devoted to instruction, it is important to devise tests that are efficient. Doing so requires a careful balancing of the contributions of technology, psychometrics, test design, and the learning sciences. Computer adaptive multistage testing (MSCAT) fits the bill extraordinarily well; unlike other forms of adaptive testing, it can be adapted to educational surveys and student testing. Research in this area will be an evidence that the methodologies and underlying technology that surround MSCAT have reached maturity and that there is a growing acceptance by the field of this type of test design” (from the Foreword to Y. Duanli, A. A. von Davier, & L. Charles (Eds.), Computerized multistage testing: theory and application ). This state-of-the-art paper aims to present an overview of differential item functioning (DIF) in MSCAT using three-parameter logistic item response theory (IRT), offering suggestions to implement it in practice with a hope to motivate testing and assessment researchers and practitioners to initiate projects in this under-practiced area by helping them to better understand some of the relevant technical concepts.

Purpose In the Patient-Reported Outcomes Measurement Information System (PROMIS), seven domains (Physical Function, Anxiety, Depression, Fatigue, Sleep Disturbance, Social Function, and Pain Interference) are packaged together as profiles. Each of these domains can also be assessed using computer adaptive tests (CATs) or short forms (SFs) of varying length (e.g., 4, 6, and 8 items). We compared the accuracy and number of items administrated of CAT versus each SF. Methods PROMIS instruments are scored using item response theory (IRT) with graded response model and reported as T scores (mean = 50, SD = 10). We simulated 10,000 subjects from the normal distribution with mean 60 for symptom scales and 40 for function scales, and standard deviation 10 in each domain. We considered a subject’s score to be accurate when the standard error (SE) was less than 3.0. We recorded range of accurate scores (accurate range) and the number of items administrated. Results The average number of items administrated in CAT was 4.7 across all domains. The accurate range was wider for CAT compared to all SFs in each domain. CAT was notably better at extending the accurate range into very poor health for Fatigue, Physical Function, and Pain Interference. Most SFs provided reasonably wide accurate range. Conclusions Relative to SFs, CATs provided the widest accurate range, with slightly more items than SF4 and less than SF6 and SF8. Most SFs, especially longer ones, provided reasonably wide accurate range.

In summary, readers are encouraged to read carefully the IRT articles in this special issue. They provide theoretical as well as practical arguments in favor of using IRT models in the health outcomes measurement field. At the same time, readers should be aware that the IRT field is complex and software is limited and, in my judgment, not very user friendly (although some packages are better than others). In addition, sample sizes will often need to be larger than in classical measurement, at least with the more general IRT models, and applications are rarely straightforward. Considerable practical experience is needed to ensure successful applications of IRT in the development and validation of instruments for health outcomes measurement.

Objectives. To assess how personal social network characteristics moderated mental health declines during the COVID-19 pandemic in emerging adults compared with other age groups. Methods. The Person to Person Health Interview Study, a representative, probability-based cohort study (n = 2485) in Indiana, collected data through face-to-face (baseline) and phone (follow-up) interviews before and during the pandemic. We used survey-weighted growth curve models to examine network effects on computer-adaptive testing measures of depression and anxiety severity. Results. Respondents reported significantly increased depression and anxiety in 2021, which returned almost to baseline levels for most age groups by 2022 ( P < .001). Stronger ties to others and more interconnected ties were significantly associated with lower depression (B = −0.112 [ P < .05]; B = −0.086 [ P < .001]) and anxiety (B = −0.101 [ P < .05]; B = −0.063 [ P < .01]) severity across the pandemic. Interaction models revealed disproportionate protective effects of network characteristics on depression (B = −0.456 [ P < .001]; B = −0.268 [ P < .001]) and anxiety (B = −0.388 [ P < .001]; B = −0.284 [ P < .001]) for emerging adults. Conclusions. Cohesive and affectively strong personal networks promote resiliency to common mental health challenges during periods of crisis, particularly for emerging adults whose social roles and relationships were disrupted during a critical period of development. ( Am J Public Health. 2024;114(S3):S258–S267. https://doi.org/10.2105/AJPH.2023.307426 )

We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguistic models to estimate item difficulty directly for computer-adaptive testing. This alleviates the need for expensive pilot testing with human subjects. We used these methods to develop an online proficiency exam called the Duolingo English Test, and demonstrate that its scores align significantly with other high-stakes English assessments. Furthermore, our approach produces test scores that are highly reliable, while generating item banks large enough to satisfy security requirements.