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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Rasch model analysis"

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Halimah, Nur, Gatot Subroto, and Nana Raihana Askurny. "Students' Reading Anxiety: A Rasch Model Analysis." Journal of Language, Literature, and English Teaching (JULIET) 2, no. 2 (September 13, 2021): 32–39. http://dx.doi.org/10.31629/juliet.v2i2.3659.

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The aim of this study is to determine the instrument and the students’ responses of reading anxiety. The approach of this research is qualitative descriptive. The gathered data were analyzed qualitatively by using Rasch Model. A questionnaire was used as an instrument to get the data. 14 female students of SMP Islam De Green Camp were involved in this research. The FLRAS by Horwitz et al was used to create a questionnaire that contained 20 items. The study showed that the quality of the instrument and the students’ responses were not good enough to be used due to their small amount. Therefore, teachers must choose and creates a good instrument to know their students’ responses about reading anxiety. Then, students are recommended to read more English texts to minimize their reading anxiety.
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Lohgheswary, N., S. Salmaliza, and H. Othman. "Transferring Raw Data for Rasch Model Analysis." Journal of Engineering and Applied Sciences 14, no. 21 (October 31, 2019): 7846–52. http://dx.doi.org/10.36478/jeasci.2019.7846.7852.

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Herwin, Herwin, Andi Tenriawaru, and Abdoulaye Fane. "Math elementary school exam analysis based on the Rasch model." Jurnal Prima Edukasia 7, no. 2 (July 29, 2019): 106–13. http://dx.doi.org/10.21831/jpe.v7i2.24450.

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This study aims to analyze the quality of mathematics exam tests in elementary schools using the Rasch model. This research is a type of descriptive quantitative research. The subject of this study were all items of School Examination Mathematical Questions in SDN Region III of Donri Donri Subdistrict, Soppeng Regency. The Mathematics Problem is 40 items. Besides that, in this study, 125 answer sheets from the participants were collected from 125 participants. The technique of data collection is done through documentation. This data collection technique is used to get a set of questions, answers, and a list of names of examinees. The data obtained were analyzed using the Rasch Model. The results showed that based on the Rash Model of 40 items on the mathematics exam 33 items (82.5%) were in a good category, while the other seven items (17.5%) were in a bad category. Test results indicate that the test information value is 13.8 on the ability scale -1.5 with a measurement error of 0.26.
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Lidinillah, Dindin Abdul Muiz, Mila Aprilia, Dodi Suryana, and Aslina Binti Ahmad. "Development of Creativity Instrument through Rasch Model Analysis." Universal Journal of Educational Research 8, no. 4 (April 2020): 1620–27. http://dx.doi.org/10.13189/ujer.2020.080455.

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Sasmoko, B. S. Abbas, Y. Indrianti, and S. A. Widhoyoko. "Indonesian teacher engagement index: a rasch model analysis." IOP Conference Series: Materials Science and Engineering 296 (January 2018): 012027. http://dx.doi.org/10.1088/1757-899x/296/1/012027.

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Drouin, Michelle, Sherri L. Horner, and Toni A. Sondergeld. "Alphabet knowledge in preschool: A Rasch model analysis." Early Childhood Research Quarterly 27, no. 3 (July 2012): 543–54. http://dx.doi.org/10.1016/j.ecresq.2011.12.008.

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Baghaei, Purya, Hamdollah Ravand, and Mahsa Nadri. "Is the d2 Test of Attention Rasch Scalable? Analysis With the Rasch Poisson Counts Model." Perceptual and Motor Skills 126, no. 1 (November 30, 2018): 70–86. http://dx.doi.org/10.1177/0031512518812183.

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The d2 test is a cancellation test to measure attention, visual scanning, and processing speed. It is the most frequently used test of attention in Europe. Although it has been validated using factor analytic techniques and correlational analyses, its fit to item response theory models has not been examined. We evaluated the fit of the d2 test to the Rasch Poisson Counts Model (RPCM) by examining the fit of six different scoring techniques. Only two scoring techniques—concentration performance scores and total number of characters canceled—fit the RPCM. The individual items fit the RPCM, with negligible differential item functioning across sex. Graphical model check and likelihood ratio test confirmed the overall fit of the two scoring techniques to RPCM.
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Al Ali, Rommel M. A., and Rami T. Shehab. "Psychometric Properties of Social Perception of Mathematics:‎‎ Rasch Model Analysis." International Education Studies 13, no. 12 (November 28, 2020): 102. http://dx.doi.org/10.5539/ies.v13n12p102.

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Social perception is an evaluation process, which uses any information available ‎in order to form impressions, ‎understanding, and judgments about others. It is also ‎considered as an essential element of social skills. This study ‎aims to examine the psychometric analysis of students’ social perceptions of mathematics using Rasch model ‎analysis.‎ This study uses a quantitative survey approach. The sample comprised 40 first year students at King Faisal University‎. The Rasch model is used because it is considered an effective tool for assessing constructs’ validity and reliability of the instrument. It also generalizes results and inferential studies. The developed questionnaire consists of six dimensions. Every dimension consists of six items. They are verifying the validity based on the Rasch model using item polarity, item fit, and dimensionality. In addition, the reliability was verified using person and item reliability, and item and person separation. The results of the Rasch model analysis show that the items of social perception of mathematics SPoM fit the model appropriately.
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Maier, Kimberly S. "A Rasch Hierarchical Measurement Model." Journal of Educational and Behavioral Statistics 26, no. 3 (September 2001): 307–30. http://dx.doi.org/10.3102/10769986026003307.

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In this article, a hierarchical measurement model is developed that enables researchers to measure a latent trait variable and model the error variance corresponding to multiple levels. The Rasch hierarchical measurement model (HMM) results when a Rasch IRT model and a one-way ANOVA with random effects are combined ( Bryk & Raudenbush, 1992 ; Goldstein, 1987 ; Rasch, 1960 ). This model is appropriate for modeling dichotomous response strings nested within a contextual level. Examples of this type of structure include responses from students nested within schools and multiple response strings nested within people. Model parameter estimates of the Rasch HMM were obtained using the Bayesian data analysis methods of Gibbs sampling and the Metropolis-Hastings algorithm ( Gelfand, Hills, Racine-Poon, & Smith, 1990 ; Hastings, 1970 ; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953 ). The model is illustrated with two simulated data sets and data from the Sloan Study of Youth and Social Development. The results are discussed and parameter estimates for the simulated data sets are compared to parameter estimates obtained using a two-step estimation approach.
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Apple, Matthew. "Point-to-Point: A Reply to “A Critique to ‘Using Rasch Analysis to Create and Evaluate a Measurement Instrument for Foreign Language Classroom Speaking Anxiety’ ”." JALT Journal 35, no. 2 (November 1, 2013): 210. http://dx.doi.org/10.37546/jaltjj35.2-5.

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In his response to my paper concerning the use of the Rasch model for creating and evaluating foreign language classroom speaking anxiety (Apple, 2013), Dr. Panayides makes some interesting observations; however, there also appear to be several points of misinterpretation of the study results. The initial issue is his opening assertion that my paper was designed to show advantages of the Rasch model over classical test theory (CTT) models as well as item response theory (IRT) models. In fact, the paper was designed only to demonstrate the advantages of the Rasch model for Japan-based classroom teachers of English. I made no mention whatsoever of other IRT models. I also did not set the Rasch model against all CTT methods; I merely demonstrated that simple descriptive statistics were not as informative or useful as Rasch analysis when creating and evaluating questionnaires.
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Дисертації з теми "Rasch model analysis"

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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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Harrell, Leigh Michelle. "Accuracy of Global Fit Indices as Indictors of Multidimensionality in Multidimensional Rasch Analysis." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29656.

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Most research on confirmatory factor analysis using global fit indices (AIC, BIC, AICc, and CAIC) has been in the structural equation modeling framework. Little research has been done concerning application of these indices to item response models, especially within the framework of multidimensional Rasch analysis. The results of two simulations studies that investigated how sample size, between-dimension correlation, and test length affect the accuracy of these indices in model recovery using a multidimensional Rasch analysis are described in this dissertation. The first study analyzed dichotomous data, with model-to-data misfit as an additional independent variable. The second study analyzed polytomous data, with rating scale structure as an additional independent variable. The interaction effect between global fit index and between-dimension correlation had very large effect sizes in both studies. At higher values of between-dimension correlation, AIC indicated the correct two-dimension generating structure slightly more often than does the BIC or CAIC. The correlation by test length interaction had an odds ratio indicating practical importance in the polytomous study but not the dichotomous study. The combination of shorter tests and higher correlations resulted in a difficult-to-detect distinction being modeled with less statistical information. The correlation by index interaction in the dichotomous study had an odds ratio indicating practical importance. As expected, the results demonstrated that violations of the Rasch model assumptions are magnified at higher between-dimension correlations. Recommendations for practitioners working with highly correlated multidimensional data include creating moderate length (roughly 40 items) instruments, minimizing data-to-model misfit in the choice of model used for confirmatory factor analysis (MRCMLM or other MIRT models), and making decisions based on multiple global indices instead of depending on one index in particular.
Ph. D.
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Bourke, Mary P. "Measuring nursing educators' beliefs about diversity in personal and professional contexts : Rasch model diagnostics and scale analysis /." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3303325.

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Thesis (Ph.D.)--Indiana University, Dept. of Education, 2007.
Title from PDF t.p. (viewed Nov. 3, 2008). Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 0928. Adviser: Jesse Goodman.
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Liu, Mingyang Liu. "Differential Item Functioning in Large-scale Mathematics Assessments: Comparing the Capabilities of the Rasch Trees Model to Traditional Approaches." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513266587329066.

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Knutson, Nichole Marie. "APPLYING THE RASCH MODEL TO MEASURE AND COMPARE FIRST- GENERATION AND CONTINUING-GENERATION COLLEGE STUDENTS’ ACADEMIC SELF-EFFICACY." UKnowledge, 2011. http://uknowledge.uky.edu/epe_etds/1.

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Students who are the first in their families to attend college are less likely to earn a college degree as compared to their continuing-generation peers. In efforts to increase college graduation rates for first-generation college students, support programs designed to assist first-generation college students are increasing in numbers. These first- generation programs are relying on existing research to build effective curriculums. Even though an extensive body of literature exists in the fields of self-efficacy and first- generation college students, research investigating the self-efficacy of first-generation college students are extremely limited. The research is further limited when examining academic self-efficacy and generational status. The purpose of this study is to investigate if parental levels of education affect college students’ self-reported levels of academic self-efficacy. The following research questions guided this study: 1) Do survey response hierarchies differ between first-generation college students and their continuing- generation counterparts on a scale that measures academic self-efficacy?, 2) Do levels of item endorsability vary based upon parental levels of education? and 3) Do the results produced from the college student survey support the existing literature on first- generation college students and academic-self-efficacy? Quality control indicators were utilized to assess the soundness of the instrument and to ensure that the rating scale functioned appropriately. Variable maps were used to compare and contrast student responses and item hierarchies. Pairwise differential item functioning (DIF) was used to examine item endorsability based upon levels of parental education. Results encourage practitioners to be mindful of the importance of data-informed decision making.
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Stammen, Andria. "The Development and Validation of the Middle School-Life Science Concept Inventory (MS-LSCI) Using Rasch Analysis." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523631816773049.

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Maier, Marco J., and Ingrid Koller. "Supplement to Koller, Maier, & Hatzinger: "An Empirical Power Analysis of Quasi-Exact Tests for the Rasch Model: Measurement Invariance in Small Samples"." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4340/1/Report127.pdf.

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This document is a supplementary text to "An Empirical Power Analysis of Quasi-Exact Tests for the Rasch Model: Measurement Invariance in Small Samples" by Koller, Maier, & Hatzinger (to be published in Methodology, ISSN-L 1614-1881), which covers all technical details regarding the simulation and its results. First, the simulation scenarios and the introduction of differential item functioning (DIF) are described. Next, the different populations' distributions that were investigated are discussed, and finally, actual type-I-error rates and empirical power are displayed for all simulated scenarios. (authors' abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Wang, Wenjia. "Item Response Theory in the Neurodegenerative Disease Data Analysis." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0624/document.

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Les maladies neurodégénératives, telles que la maladie d'Alzheimer (AD) et Charcot Marie Tooth (CMT), sont des maladies complexes. Leurs mécanismes pathologiques ne sont toujours pas bien compris et les progrès dans la recherche et le développement de nouvelles thérapies potentielles modifiant la maladie sont lents. Les données catégorielles, comme les échelles de notation et les données sur les études d'association génomique (GWAS), sont largement utilisées dans les maladies neurodégénératives dans le diagnostic, la prédiction et le suivi de la progression. Il est important de comprendre et d'interpréter ces données correctement si nous voulons améliorer la recherche sur les maladies neurodégénératives. Le but de cette thèse est d'utiliser la théorie psychométrique moderne: théorie de la réponse d’item pour analyser ces données catégoriques afin de mieux comprendre les maladies neurodégénératives et de faciliter la recherche de médicaments correspondante. Tout d'abord, nous avons appliqué l'analyse de Rasch afin d'évaluer la validité du score de neuropathie Charcot-Marie-Tooth (CMTNS), un critère important d'évaluation principal pour les essais cliniques de la maladie de CMT. Nous avons ensuite adapté le modèle Rasch à l'analyse des associations génétiques pour identifier les gènes associés à la maladie d'Alzheimer. Cette méthode résume les génotypes catégoriques de plusieurs marqueurs génétiques tels que les polymorphisme nucléotidique (SNPs) en un seul score génétique. Enfin, nous avons calculé l'information mutuelle basée sur la théorie de réponse d’item pour sélectionner les items sensibles dans ADAS-cog, une mesure de fonctionnement cognitif la plus utilisées dans les études de la maladie d'Alzheimer, afin de mieux évaluer le progrès de la maladie
Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Charcot Marie Tooth (CMT), are complex diseases. Their pathological mechanisms are still not well understood, and the progress in the research and development of new potential disease-modifying therapies is slow. Categorical data like rating scales and Genome-Wide Association Studies (GWAS) data are widely utilized in the neurodegenerative diseases in the diagnosis, prediction and progression monitor. It is important to understand and interpret these data correctly if we want to improve the disease research. The purpose of this thesis is to use the modern psychometric Item Response Theory to analyze these categorical data for better understanding the neurodegenerative diseases and facilitating the corresponding drug research. First, we applied the Rasch analysis in order to assess the validity of the Charcot-Marie-Tooth Neuropathy Score (CMTNS), a main endpoint for the CMT disease clinical trials. We then adapted the Rasch model to the analysis of genetic associations and used to identify genes associated with Alzheimer’s disease by summarizing the categorical genotypes of several genetic markers such as Single Nucleotide Polymorphisms (SNPs) into one genetic score. Finally, to select sensitive items in the most used psychometrical tests for Alzheimer’s disease, we calculated the mutual information based on the item response model to evaluate the sensitivity of each item on the ADAS-cog scale
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Brown, Kathleen Annette. "An Iterative Needs Assessment/Evaluation Model for a Japanese University English-language Program." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/66807.

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Анотація:
CITE/Language Arts
Ed.D.
The focus of this study is the development and implementation of the Iterative Needs Assessment/Evaluation Model for use as part of an English curriculum reform project at a four-year university in Japan. Three questions were addressed in this study: (a) what model components were necessary for use in a Japanese university setting; (b) what survey instruments would work with such a model; and (c) what needs would the stakeholders in the project report? The site for the study was a mid-sized private, four-year university in Japan. Set as an instrumental case study (Stake, 1998), multiple methods and sources were employed. Stakeholders in the project included university students (n = 1533), teaching staff (n = 33), university administrators and staff (n = 5), and domain experts (n = 7). Data collection included the use of questionnaires, unstructured and semi-structured interviews, and systems and materials analyses. Questionnaires were developed and analyzed using Rasch analysis. The Needs Assessment/Evaluation Model was assessed using a modified version of the Checklist for Judging the Adequacy of an Evaluation Design (Sanders & Nafziger, 1985). Implementation of a full iteration of the Model indicated that use of the Iterative Needs Assessment/Evaluation Model could guide the development and evaluation of the English language program. As part of the study, valid survey instruments that can continue to aid the assessment of needs for and evaluation of the courses were developed. Data from multiple sources indicated a difference in the perception of needs between stakeholders. The processes followed through the development and application of the Iterative Needs Assessment/Evaluation Model served to incorporate these different perceptions into a cohesive language program curriculum.
Temple University--Theses
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Paprzycki, Peter Pawel. "Developing a Methodological Framework for the Analysis of Perceptions: A Case Study of the National Public Opinion Survey “The EU in the Eyes of Asia-Pacific”." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1430493813.

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Книги з теми "Rasch model analysis"

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Faktorenanalyse oder Rasch-Modell?: Eine Kreuzvalidierung am Beispiel des Leistungs-Motivations-Tests. Frankfurt am Main: Peter Lang, 2001.

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Частини книг з теми "Rasch model analysis"

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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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Wind, Stefanie A., and Cheng Hua. "Dichotomous Rasch Model." In Rasch Measurement Theory Analysis in R, 5–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003174660-2.

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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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Wind, Stefanie A., and Cheng Hua. "Many Facet Rasch Model." In Rasch Measurement Theory Analysis in R, 195–278. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003174660-6.

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Wind, Stefanie A., and Cheng Hua. "Rating Scale Model." In Rasch Measurement Theory Analysis in R, 83–132. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003174660-4.

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Wind, Stefanie A., and Cheng Hua. "Partial Credit Model." In Rasch Measurement Theory Analysis in R, 133–94. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003174660-5.

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Asril, Asrijanty, and Ida Marais. "Applying a Rasch Model Distractor Analysis." In Applications of Rasch Measurement in Learning Environments Research, 77–100. Rotterdam: SensePublishers, 2011. http://dx.doi.org/10.1007/978-94-6091-493-5_4.

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McArthur, David L. "The Rasch Model for Item Analysis." In Alternative Approaches to the Assessment of Achievement, 99–127. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3257-9_4.

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Storey, Ian. "Introductory Analysis of the Rasch Model." In Manage Your Own Learning Analytics, 41–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86316-6_3.

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Boone, William J., John R. Staver, and Melissa S. Yale. "The Rasch Model and Item Response Theory Models: Identical, Similar, or Unique?" In Rasch Analysis in the Human Sciences, 449–58. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6857-4_21.

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Тези доповідей конференцій з теми "Rasch model analysis"

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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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Parmaningsih, Triwik Jatu, and Dewi Retno Sari Saputro. "Rasch analysis on item response theory: Review of model suitability." In THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040305.

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Kowiyah, Kowiyah, Arita Marini, and Sihadi Wihardjo. "Rasch Model Analysis of Critical Thinking Instruments for Elementary School." In Proceedings of the 5th International Conference on Education in Muslim Society, ICEMS 2019,30 September - 01 October 2019, Jakarta, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.30-9-2019.2291176.

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Saibani, Na, Nb Arsad, A. Ariffin, D. Wahab, and A. A. Azrilah. "Course outcomes measurement analysis using Rasch model for an engineering course." In 2011 3rd International Congress on Engineering Education (ICEED 2011). IEEE, 2011. http://dx.doi.org/10.1109/iceed.2011.6235387.

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Idulfilastri, Rita Markus, Meylisa Permata Sari, and Cynthia Sutanto. "Validation of Cognitive Dimension of Managerial Aptitude Test: Rasch Model Analysis." In International Conference on Economics, Business, Social, and Humanities (ICEBSH 2021). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210805.007.

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Subroto, Gatot, Satria Agust, Amartya Angela, Anek Dezar, Della Zahra, Dina Mirarizka, Farizi Rianto, Vitri Rayani, and Muhammad Candra. "Coastal Students’ Perspectives on Digital Reading Comprehension: A Rasch Model Analysis." In Proceedings of the 1st International Conference on Maritime Education, ICOME 2021, 3-5 November 2021, Tanjungpinang, Riau Islands, Indonesia. EAI, 2022. http://dx.doi.org/10.4108/eai.3-11-2021.2314832.

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Hartanti, Jahju, and Lucky Nindi R. Marfu’i. "The Analysis of Self-Concept Scale in Engineering Faculty: A Rasch Model Analysis." In 2nd  International Seminar on Guidance and Counseling 2019 (ISGC 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200814.049.

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Kadaryanto, Budi, Suwarsih Madya, and Ashadi. "A Rasch Model Analysis on Designing Needs-Based Teacher Professional Development Programs." In International Conference on Educational Research and Innovation (ICERI 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200204.019.

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Friyatmi and Tri Kurniawati. "Attributes of Students’ Critical Thinking Skills on Economics : A Rasch Model Analysis." In The Fifth Padang International Conference On Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PICEEBA-5 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.201126.044.

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Rejeki, Asri, Siti Sulasmi, and Tatik Suryani. "Item Analysis in Affect Measurement on Decision to Car Purchase using Rasch Model." In 3rd ASEAN Conference on Psychology, Counselling, and Humanities (ACPCH 2017). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/acpch-17.2018.41.

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Звіти організацій з теми "Rasch model analysis"

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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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Анотація:
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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