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Following a brief introduction to the IRT framework, we emphasize its major advantages and explore potential applications in various research areas. The main part of this tutorial provides a comprehensive, step-by-step guide to Monte Carlo simulation-based sample-size estimation in IRT, which is essential for obtaining precise estimates of item and person parameters, structural effects, and model fit. Accurate a priori sample-size estimation is also crucial for effective study planning, especially in preregistration and registered reports. We highlight 10 key decisions, organized into four areas: (a) determining the data-generation model,(b) defining the test design and the process of missing values,(c) selecting the IRT model and parameters of interest, and (d) setting up and running the Monte Carlo simulation. The procedure is illustrated with examples from educational, personality, and clinical psychology. An extensively annotated and easily customizable syntax is available in an online repository.  \nKeywords  \nitem-response theory, sample-size estimation, study planning, reproducibility, open data, open materials  \nReceived 7/24/24; Revision accepted 12/20/24  \nThe measurement of psychological attributes provides the foundation of research on individual differences inhuman cognition, personality, and clinical symptoms. Before a study can address substantive research questions, for example, on risk factors associated with depression or the effectiveness of intervention programs to improve adolescents’ mental health, it is necessary to accurately estimate the relevant psychological characteristics. Despite this foundational importance, aspects of psychological measurement, including construct coverage or content validity, are often neglected (Clifton, 2020; Steger et al. , 2023), sometimes resulting in a “measurement schmeasurement attitude”(Flake & Fried, 2020, p. 459) . Appropriate measurement models for estimating trait scores are rarely given detailed attention; instead, researchers often use statistical methods of classical test theory implemented in standard statistical software without evaluating whether the implied response process is suitable for the observed item responses.  \nItem-response theory (IRT) provides a comprehensive framework for developing, evaluating, and refining  \npsychological measures . Particularly, when combined with modern assessment designs, such as domain sampling (Markus & Borsboom, 2013), multimatrix booklet designs (Gonzalez & Rutkowski, 2010), or adaptive measurements (Magis et al. , 2017), IRT can lead to more reliable and valid measurements that comprehensively cover the construct of interest. Despite its welldocumented advantages, IRT is largely confined to specific areas of psychology, such as educational assessment and personnel selection. One reason for the limited use of IRT may be the challenge posed by its larger sample-size requirements, especially in complex measurement designs. A priori sample-size planning, therefore, plays a crucial role in the wider adoption of IRT models. By determining the required sam","cbCaijahOwWIAnQ0","https://ap.wps.com/l/cbCaijahOwWIAnQ0","pdf",493839,13,"English","# Sample-Size Planning in Item-Response Theory - A Tutorial\n## A Short Recap on IRT Modeling\n## Monte Carlo Simulation-Based Sample-Size Estimation\n## Key Decisions for Study Planning\n## Applications and Annotated R Syntax","[{\"question\":\"Why is a priori sample-size planning important in IRT studies?\",\"answer\":\"Because required sample sizes affect the precision of item and person parameter estimates, structural effects, and model fit. It also helps avoid biased estimates and reduced generalizability, supporting preregistration and registered reports.\"},{\"question\":\"What does the tutorial focus on in estimating sample size for IRT?\",\"answer\":\"A comprehensive step-by-step guide for Monte Carlo simulation-based sample-size estimation in IRT. It targets accurate estimation of item and person parameters, structural effects, and model fit.\"},{\"question\":\"Which decisions are highlighted for simulation-based sample-size estimation?\",\"answer\":\"The tutorial organizes 10 key decisions into four areas: the data-generation model, test design and missing values, the chosen IRT model and parameters of interest, and the Monte Carlo simulation setup and running process.\"}]","Sample-Size Planning in Item-Response Theory - A Tutorial | PDF",33]