Relevant bibliographies by topics / Rasch model

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Academic literature on the topic 'Rasch model'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 August 2024

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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 (February 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 (September 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 (January 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 (March 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 (September 1, 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 (November 10, 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 (June 7, 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 Akaike information criterion (CAIC), and sample-size adjusted Bayesian information criterion (SABIC), were compared for use in model selection with a two-level mixture Rasch model in the context of a real data example and a simulation study. Level 1 consisted of students and Level 2 consisted of schools. The performances of the model selection criteria under different sample sizes were investigated in a simulation study. Total sample size (number of students) and Level 2 sample size (number of schools) were studied for calculation of information criterion indices to examine the performance of these fit indices. Simulation study results indicated that CAIC and BIC performed better than the other indices at detection of the true (i.e., generating) model. Furthermore, information indices based on total sample size yielded more accurate detections than indices at Level 2.
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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 (May 20, 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 (June 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 (October 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.
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 results in indirect dependence between registrations. Psychometric models, such as the Rasch model, may be successfully applied. We propose the use of the multidimensional Rasch model in the capture-recapture context. In particular, we assume that registrations may be divided into two or more subgroups, such that they can be view as indicators of the latent variables which account for correlations among registrations. To do so, the extension of the Dutch Identity for the multidimensional partial credit model can be utilized. It allows us to express the multidimensional Rasch model in a log-linear representation and to derive the parameters of the traditional log-linear model from those of the multidimensional Rasch model.
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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)
Series: Research Report Series / Department of Statistics and Mathematics
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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 the entire dimension is being measured. The overwhelming use of the most positive answer category, in traditional scales, illustrates that they fail to capture highly positive evaluations. Affective Response to Consumption (ARC) is conceptualised as an extension to satisfaction. The conceptualisation shifts the emphasis from a scale relying on one, rather weak, emotional feeling -- satisfaction -- to a multitude of emotional feelings, including highly positive terms. A scale measuring ARC is developed in an alternative measurement paradigm -- Rasch Modelling -- to the dominant paradigm for scale development in marketing -- Classical Test Theory. The characteristics of Rasch Modelling are particularly useful, when measuring a concept like ARC, that captures the entire dimension of unfavourable/favourable evaluations and includes terms of markedly different intensity.
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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 integrated a modern test theory called Item Response Theory (IRT) and evaluated the properties of subscales in the RELATionship Evaluation (RELATE) using the existing data from 4,784 participants. Using the Rasch rating scale or partial credit model which is one of the IRT models, the author demonstrated that some of the RELATE subscales had items and response categories that functioned less optimally or in an unexpected way. The results suggested that some items misfit the model or overlapped with other items, many scales did not cover the entire range of the measured construct, and response categories for many items malfunctioned. The author made recommendations on possible remedies that could be adopted to improve the function of individual scales and items.
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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. Fourth edition. | New York, NY : Routledge, 2020.: 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. Helsinki: 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. Newtown, PA: Law School Admission Council, 2005.

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Fischer, Gerhard H., and Ivo W. Molenaar, eds. Rasch Models. New York, NY: 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. 2455 Teller Road, Thousand Oaks California 91320 United States of America: 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. Hoboken, NJ USA: 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. Basel: Birkhäuser, 2011.

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Garner, Mary L. Advances in Rasch measurement. Maple Grove, Minn: Jam Press, 2010.

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von Davier, Matthias. Multivariate and Mixture Distribution Rasch Models. Edited by Claus H. Carstensen. New York, NY: 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. New York: 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., 6–7. Washington: 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, 69–95. New York, NY: 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, 215–37. New York, NY: 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, 15–38. New York, NY: 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, 131–55. New York, NY: 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, 181–201. New York, NY: 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, 239–55. New York, NY: 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, 53–68. New York, NY: 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, 3–14. New York, NY: 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, 259–81. Chichester, UK: 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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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). Paris, France: 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). Paris, France: 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 2024 AERA Annual Meeting. Washington DC: AERA, 2024. http://dx.doi.org/10.3102/2103364.

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

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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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Mahamood, Mohd Nor Mamat, and Siti Fatahiyah Mahamood. "Measurement of Rubrical Essay-based Test Using Rasch Model." In ICEEPSY 2016 International Conference on Education and Educational Conference. Cognitive-crcs, 2016. http://dx.doi.org/10.15405/epsbs.2016.11.80.

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Reports on the topic "Rasch model"

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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, April 2010. http://dx.doi.org/10.17125/fsu.1526312891.

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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.], June 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, traffic analysis and visualization of network topologies are maintained. The article describes the experience of teaching students of the specialty “Pedagogical education. ICT” in the course “CCNA Cyber Operations” with the use of virtual cloud laboratories. The authors have been conducted a survey of students who studied at the course. Its purpose was to determine how much they satisfied were with the course. Statistical processing of the results was performed on the basis of the Rasch model using the software MiniSteps.
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Zhou, Ziwei. Detecting Rater Effects Using Many-Facet Rasch Models and Bootstrap Techniques. Ames (Iowa): Iowa State University, January 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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