Relevant bibliographies by topics / Rasch model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Rasch model'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Rasch model"

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Otiv, Sunil. "The Rasch Model." Plastic and Reconstructive Surgery 131, no. 2 (2013): 283e—286e. http://dx.doi.org/10.1097/prs.0b013e318278d5ac.

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van der Linden, Wim J. "Applying the Rasch Model." International Journal of Testing 1, no. 3-4 (2001): 319–26. http://dx.doi.org/10.1080/15305058.2001.9669478.

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SNYDER, SCOTT, and ROBERT SHEEHAN. "The Rasch Measurement Model." Journal of Early Intervention 16, no. 1 (1992): 87–95. http://dx.doi.org/10.1177/105381519201600108.

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Wang, Wen-Chung, and Mark Wilson. "The Rasch Testlet Model." Applied Psychological Measurement 29, no. 2 (2005): 126–49. http://dx.doi.org/10.1177/0146621604271053.

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van der Linden, Wim. "Applying the Rasch Model." International Journal of Testing 1, no. 3 (2001): 319–26. http://dx.doi.org/10.1207/s15327574ijt013&4_10.

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Robitzsch, Alexander. "Regularized Mixture Rasch Model." Information 13, no. 11 (2022): 534. http://dx.doi.org/10.3390/info13110534.

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The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders the interpretability of model parameters. This article proposes regularized estimation of the mixture Rasch model that imposes some sparsity structure on class-specific item difficulties. We illustrate the feasibility of the proposed modeling approach by means of one simulation study and two simulated case studies.
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Sen, Sedat, Allan S. Cohen, and Seock-Ho Kim. "Model Selection for Multilevel Mixture Rasch Models." Applied Psychological Measurement 43, no. 4 (2018): 272–89. http://dx.doi.org/10.1177/0146621618779990.

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Mixture item response theory (MixIRT) models can sometimes be used to model the heterogeneity among the individuals from different subpopulations, but these models do not account for the multilevel structure that is common in educational and psychological data. Multilevel extensions of the MixIRT models have been proposed to address this shortcoming. Successful applications of multilevel MixIRT models depend in part on detection of the best fitting model. In this study, performance of information indices, Akaike information criterion (AIC), Bayesian information criterion (BIC), consistent Akai
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DeMars, Christine E. "Multilevel Rasch Modeling: Does Misfit to the Rasch Model Impact the Regression Model?" Journal of Experimental Education 88, no. 4 (2019): 605–19. http://dx.doi.org/10.1080/00220973.2019.1610859.

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Roskam, Edward E., and Paul G. W. Jansen. "Conditions for rasch-dichotomizability of the unidimensional polytomous rasch model." Psychometrika 54, no. 2 (1989): 317–32. http://dx.doi.org/10.1007/bf02294523.

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Weitzman, R. A. "The Rasch Model Plus Guessing." Educational and Psychological Measurement 56, no. 5 (1996): 779–90. http://dx.doi.org/10.1177/0013164496056005005.

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Dissertations / Theses on the topic "Rasch model"

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Antal, Judit. "Fit indices for the Rasch model." Columbus, OH : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1054222470.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xiii, 102 p.: ill (some col.). Includes abstract and vita. Advisor: Ayres G.D'Costa, College of Education. Includes bibliographical references (p. 97-102).
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Verhelst, Norman D., Reinhold Hatzinger, and Patrick Mair. "The Rasch Sampler." Foundation for Open Access Statistics, 2007. http://dx.doi.org/10.18637/jss.v020.i04.

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The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the stationary distribution is uniform. The user has control on the serial dependency. (authors' abstract)
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Leung, Man-tak, and 梁文德. "Applicability of Rasch model in psychological measurement." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31976591.

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Melnyk, K. V., and N. V. Borysova. "Rasch model usage for testing results assessment." Thesis, Національний технічний університет "Харківський політехнічний інститут", 2019. http://repository.kpi.kharkov.ua/handle/KhPI-Press/44632.

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Leung, Man-tak. "Applicability of Rasch model in psychological measurement." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13064812.

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PELLE, ELVIRA. "Log-linear multidimensional Rasch model for capture-recapture." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52008.

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One of the most important task for epidemiologists, biologists, ecologists and sociologists is to analyse and forecast possible changes and dynamics in a population. Capture-recapture experiments may be used to obtain meaningful information from population under study. The rational behind this method is to account for unobserved individuals by using observed individual trapping histories. A central assumption in traditional capture-recapture approach is the homogeneity of the capture probability. However, differences of character or behaviour between individuals may occur and this fact result
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Strasser, Helmut. "Numerical studies for the Rasch model with many items." WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3618/4/Report119.pdf.

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This paper is concerned with numerical studies on the theoretical results obtained in Strasser [1] and [2]. These papers provide asymptotic expansions for conditional expectations of non i.i.d. Bernoulli trials and their application to the covariance structure of conditional maximum likelihood estimates for the Rasch model. In the present paper systematic numerical studies of the accuracy of the approximations given in Strasser [1] and [2] are presented. It is shown that the order of approximation claimed by the theoretical results can be established numerically. (author's abstract)<br>Series:
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Ganglmair-Wooliscroft, Alexandra, and n/a. "Measuring affective response to consumption using Rasch modelling." University of Otago. Department of Marketing, 2005. http://adt.otago.ac.nz./public/adt-NZDU20060809.091317.

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Satisfaction is a central concept in marketing. However in recent years, satisfaction has come under increasing criticism. Its ability to predict post-purchase behaviour has not been established and the importance of the word satisfaction to consumers has been questioned. Current satisfaction measures are inadequate, as they fail to discriminate between respondents, with the majority of respondents regularly endorsing the most positive answer category available. The limited discrimination of existing scales suggests that only a small part of the unfavourable/favourable evaluation, rather than
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Yoshida, Keitaro. "Evaluation of RELATE Using Rasch Analysis." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2343.

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The importance of valid and reliable couple assessment has been increasing with growth in research on couple and family relationships as well as in therapeutic and educational interventions for couples and families. However, self-report instruments–the most popular type of couple assessment–have been criticized at least partly due to limitations in Classical Test Theory (CTT) which has been used solely in developing and evaluating couple assessments for decades. In an effort to address the limitations in the sole use of CTT in developing self-report couple assessments, the present study integr
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Yang, Seungho. "A comparison of unidimensional and multidimensional rasch models using parameter estimates and fit indices when assumption of unidimensionality is violated." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1195695378.

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Books on the topic "Rasch model"

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Bond, Trevor G., Zi Yan, and Moritz Heene. Applying the Rasch Model. Routledge, 2020. http://dx.doi.org/10.4324/9780429030499.

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Yli-Luoma, Pertti V. J. Predictors of critical thinking abilities: A Rasch-model approach. Department of Education, University of Helsinki, 1992.

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Glas, Cees A. W. Adaptive mastery testing using the Rasch model and Bayesian sequential decision theory. Law School Admission Council, 2005.

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Fischer, Gerhard H., and Ivo W. Molenaar, eds. Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7.

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Andrich, David. Rasch Models for Measurement. SAGE Publications, Inc., 1988. http://dx.doi.org/10.4135/9781412985598.

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Christensen, Karl Bang, Svend Kreiner, and Mounir Mesbah, eds. Rasch Models in Health. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118574454.

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Martin, Rauch, ed. Haus Rauch: Ein Modell moderner Lehmarchitektur = The Rauch house : a model of advanced clay architecture. Birkhäuser, 2011.

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Garner, Mary L. Advances in Rasch measurement. Jam Press, 2010.

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von Davier, Matthias. Multivariate and Mixture Distribution Rasch Models. Edited by Claus H. Carstensen. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3.

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H, Fischer Gerhard, and Molenaar Ivo W, eds. Rasch models: Foundations, recent developments, and applications. Springer-Verlag, 1995.

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Book chapters on the topic "Rasch model"

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Embretson, Susan E., and Kim A. Diehl. "Rasch model." In Encyclopedia of psychology, Vol. 7. American Psychological Association, 2000. http://dx.doi.org/10.1037/10522-002.

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Glas, Cees A. W., and Norman D. Verhelst. "Testing the Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_5.

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Verhelst, Norman D., and Cees A. W. Glas. "The One Parameter Logistic Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_12.

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Fischer, Gerhard H. "Derivations of the Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_2.

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Fischer, Gerhard H. "The Linear Logistic Test Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_8.

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Verhelst, Norman D., and Cees A. W. Glas. "Dynamic Generalizations of the Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_10.

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Formann, Anton K. "Linear Logistic Latent Class Analysis and the Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_13.

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Hoijtink, Herbert, and Anne Boomsma. "On Person Parameter Estimation in the Dichotomous Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_4.

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Molenaar, Ivo W. "Some Background for Item Response Theory and the Rasch Model." In Rasch Models. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4230-7_1.

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De Battisti, Francesca, Giovanna Nicolini, and Silvia Salini. "The Rasch Model." In Modern Analysis of Customer Surveys. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119961154.ch14.

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Conference papers on the topic "Rasch model"

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Tagupa, Eubert R., and Neil Cris B. Pasok. "Improving General Mathematics Instructional Competency: Identifying & Addressing Conceptual Difficulties with the Rasch Model." In 2025 16th International Conference on E-Education, E-Business, E-Management and E-Learning (IC4e). IEEE, 2025. https://doi.org/10.1109/ic4e65071.2025.11075341.

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Darmana, Ayi, Ahmad Nasir Pulungan, Haqqi Annazili Nasution, Mutiara Agustina Nst, and Putri Faradilla. "Test Instrument Validation with Rasch Model." In Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia. EAI, 2024. http://dx.doi.org/10.4108/eai.24-10-2023.2342115.

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Siow, Hoo Leong, Rasidah Mahdi, and Eng Ling Siew. "Mixture Rasch model for guessing group identification." In PROCEEDINGS OF THE 20TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation. AIP, 2013. http://dx.doi.org/10.1063/1.4801273.

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Chan, Arianis, Tetty Herawaty, Dadan Suryadipura, and Asep Miftahuddin. "Brand Personality Higher Education Using Rasch Model Approach." In 4th International Conference on Arts Language and Culture (ICALC 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200323.036.

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Rostiana, R., Zamralita Zamralita, and Erik Wijaya. "Rasch Model Analysis of Indonesian Spiritual Well-Being." In 3rd Tarumanagara International Conference on the Applications of Social Sciences and Humanities (TICASH 2021). Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.220404.001.

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Li, Jing. "Using Latent Regression Rasch Model to Evaluate Surveys." In AERA 2024. AERA, 2024. http://dx.doi.org/10.3102/ip.24.2103364.

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Li, Jing. "Using Latent Regression Rasch Model to Evaluate Surveys." In 2024 AERA Annual Meeting. AERA, 2024. http://dx.doi.org/10.3102/2103364.

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Shuli Yuan, Shouying Zhao, and Hui Bai. "The principle of Rasch model and compare with the other models." In 2011 International Conference on Computer Science and Service System (CSSS). IEEE, 2011. http://dx.doi.org/10.1109/csss.2011.5974856.

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Mamat, Mohd Nor, and Fattawi Mokhtar. "Reliable assessment model for affective environmental education: Rasch model made simple." In 2012 International Conference on e-Learning and e-Technologies in Education (ICEEE). IEEE, 2012. http://dx.doi.org/10.1109/icelete.2012.6333387.

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Ismail, Azianti, Liyana Roslan, and Ahmad Naufal Adnan. "Assessment on course outcome performance using Rasch measurement model." In 2017 IEEE 9th International Conference on Engineering Education (ICEED). IEEE, 2017. http://dx.doi.org/10.1109/iceed.2017.8251175.

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Reports on the topic "Rasch model"

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วงศ์โสธร, อัจฉรา, та ศิริพร พงษ์สุรพิพัฒน์. การสอบสมิทธิภาพทั่วไปทางภาษาอังกฤษ ครั้งที่ 2 ปี พ.ศ. 2535 ในโครงการ การสร้างและพัฒนาคุณภาพแบบทดสอบสมิทธิภาพทั่วไปทางภาษาอังกฤษและระบบการทดสอบที่เกี่ยวข้อง : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 1992. https://doi.org/10.58837/chula.res.1992.22.

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งานวิจัยเรื่อง "การสอบสมิทธิภาพทั่วไปทางภาษาอังกฤษ ครั้งที่ 2 ปี 2534" เป็นส่วนหนึ่งของโครงการ "การสร้างและพัฒนาคุณภาพแบบทดสอบสมิทธิภาพทั่วไปทางภาษาอังกฤษและระบบการทดสอบที่เกี่ยวข้อง" ซึ่งเป็นโครงการที่ฝ่ายวิชาการและสถาบันภาษาของจุฬาลงกรณ์มหาวิทยาลัย จัดขึ้นร่วมกัน วัดตถุประสงค์ของการดำเนินงานระยะที่ 2 พ.ศ. 2534 มีดังนี้ 1. เพื่อพัฒนาข้อทดสอบสมิทธิภาพทั่วไปทางภาษาอังกฤษด้านการฟัง การอ่าน และการเขียนเพิ่มมากขึ้นจากข้อทดสอบที่ได้จากการสร้างขึ้นในการวิจัยระยะที่ 1 และให้ได้แบบทดสอบ 4 ฉบับ ที่คู่ขนานกันในด้านเนื้อหาและสถิติโดยใช้ตารางวิเคราะห์เช่นเดียวกัน 2. เพื่อวิเคราะห์และปรับปรุงแบบทดสอบทั้งฉบ
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Balyk, Nadiia, Yaroslav Vasylenko, Vasyl Oleksiuk, and Galina Shmyger. Designing of Virtual Cloud Labs for the Learning Cisco CyberSecurity Operations Course. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3177.

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The article is devoted to the study of the problem of the cybersecurity basics teaching. The training of the ICT-specialties students using the course “CCNA Cyber Operations” of the network academy Cisco is considered. At present, many universities have similar academies, while others can open them. On the basis of free software platforms Apache CloudStack and EVE-NG Community authors designed and implemented a virtual cloud laboratory. It operates according to the “IaaS” model. Thanks to the technology of embedded virtualization, the work of many virtual machines, storing of their status, tra
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Mueller, Christian, Emily Bullock, and Stephen Leierer. Examining Psychometric and Measurement Properties of the Career Thoughts Inventory: Demonstration and Use of the Rasch Measurement Model in Career Assessment Research Technical Report No. 51. Florida State University Libraries, 2010. http://dx.doi.org/10.17125/fsu.1526312891.

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Zhou, Ziwei. Detecting Rater Effects Using Many-Facet Rasch Models and Bootstrap Techniques. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-516.

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D'Iorio, M. A. Sensitivity of the RASC model to its critical probit value. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128092.

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Using Rasch Modeling to Validate AI Scoring Models. The Learning Partnership, 2025. https://doi.org/10.51420/conf.2025.4.

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