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Journal articles on the topic 'Rasch model'

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Published: 4 June 2021
Last updated: 31 August 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Andrich, David. "Controversy and the Rasch Model." Medical Care 42, Supplement (January 2004): I—7. http://dx.doi.org/10.1097/01.mlr.0000103528.48582.7c.

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13

Holster, Trevor A., and J. Lake. "Guessing and the Rasch Model." Language Assessment Quarterly 13, no. 2 (April 2, 2016): 124–41. http://dx.doi.org/10.1080/15434303.2016.1160096.

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14

Kelderman, Henk. "Common Item Equating Using the Loglinear Rasch Model." Journal of Educational Statistics 13, no. 4 (December 1988): 319–36. http://dx.doi.org/10.3102/10769986013004319.

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A method is proposed to equate different sets of items administered to different groups of individuals using the Rasch model. A Rasch equating model is formulated that describes one common Rasch scale in different groups with different but overlapping sets of items. The item parameters can then be estimated simultaneously, avoiding different parameter estimates of common items in different groups. The model can be tested globally to test the hypothesis of one common Rasch scale, and the goodness of link can be tested. The method is based on the quasi-loglinear Rasch model.
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15

Robitzsch, Alexander. "Relating the One-Parameter Logistic Diagnostic Classification Model to the Rasch Model and One-Parameter Logistic Mixed, Partial, and Probabilistic Membership Diagnostic Classification Models." Foundations 3, no. 3 (September 21, 2023): 621–33. http://dx.doi.org/10.3390/foundations3030037.

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Diagnostic classification models (DCMs) are statistical models with discrete latent variables (so-called skills) to analyze multiple binary variables (i.e., items). The one-parameter logistic diagnostic classification model (1PLDCM) is a DCM with one skill and shares desirable measurement properties with the Rasch model. This article shows that the 1PLDCM is indeed a latent class Rasch model. Furthermore, the relationship of the 1PLDCM to extensions of the DCM to mixed, partial, and probabilistic memberships is treated. It is argued that the partial and probabilistic membership models are also equivalent to the Rasch model. The fit of the different models was empirically investigated using six datasets. It turned out for these datasets that the 1PLDCM always had a worse fit than the Rasch model and mixed and partial membership extensions of the DCM.
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16

Nitta, Hideo, and Takuya Aiba. "An Alternative Learning Gain Based on the Rasch Model." Physics Educator 01, no. 01 (March 2019): 1950005. http://dx.doi.org/10.1142/s2661339519500057.

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Using the Rasch model for the pretest–posttest analysis, a learning gain, the “Rasch gain”, is introduced as the simple difference of the estimated ability parameter for students. It is shown that, although the Rasch gain strongly correlates with the normalized learning gain introduced by Hake, the Rasch gain has advantages over the Hake gain as a scientific measure.
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17

Basran, Afiqah, and Denis Lajium. "APPLICATION OF RASCH MODEL IN TESTING FORCE CONCEPT INVENTORY." International Journal of Modern Education 2, no. 6 (September 10, 2020): 14–27. http://dx.doi.org/10.35631/ijmoe.26003.

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Inventori Konsep Daya is an instrument that is adapted from the Force Concept Inventory (FCI). It is an instrument consisting of 30 diagnostic items related to the concept of force and motion. This instrument is widely used in physics education. However, the validity of this instrument in Bahasa Malaysia is not well studied to ensure that the items in the instrument function properly. Based on previous research, one of the major issues that are often questioned in the FCI is the reliability of the instrument when administered to different groups. When studies conducted in this country, researchers often use the reliability analysis under the Classical Test Theory. Various weaknesses are identified when evaluating using the analysis under the theory. Therefore, the purpose of this study is to apply the Rasch model under Item Response Theory in analyzing the items in Inventori Konsep Daya. Several analyzes were selected to determine the validity of the items and instruments. This study will be conducted on three levels of students involved in the learning of force and motion concepts. 300 samples will be taken from school students, elementary or matriculation students as well as undergraduate students who have studied this topic. The data will be analyzed using Windstep software. The results showed that Inventori Konsep Daya was a good instrument with high reliability and separation index, positive polarity value for every item, and fit the Rasch model. However, the instrument was quite difficult for the respondents in this study. This study is important in providing information to other researchers who will use FCI as an instrument in their study. In addition, the findings of this study can also be used to compare with the previous studies to draw more accurate conclusions.
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18

de Jong, John H. A. L. "Le Modele De Rasch." Taaltoetsen 31 (January 1, 1988): 57–70. http://dx.doi.org/10.1075/ttwia.31.07jon.

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This paper provides an elementary introduction to the one parameter psychometric model known as the Rasch model. It explains the basic principles underlying the model and the concepts of unidimensionality, local stochastic independence, and additivity in non-mathematical terms. The requirements of measurement procedures, the measurement of latent traits, the control on model fit, and the definition of a trait are discussed. It is argued that the Rasch model is particularly appropriate to understand the mutual dependence of test reliability and validity. Examples from foreign language listening comprehension tests are used to illustrate the application of the model to a test validation procedure.
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19

Tarigan, Elsa Febrina, Suriati Nilmarito, Khairani Islamiyah, Ayi Darmana, and Retno Dwi Suyanti. "Analisis Instrumen Tes Menggunakan Rasch Model dan Software SPSS 22.0." Jurnal Inovasi Pendidikan Kimia 16, no. 2 (July 1, 2022): 92–96. http://dx.doi.org/10.15294/jipk.v16i2.30530.

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Penelitian ini bertujuan untuk membandingkan perbedaan validitas dan reliabilitas instrumen tes menggunakan Rasch model dan software SPSS 22.0. Penelitian ini merupakan penelitian kualitatif dengan metode deskriptif, yang di ujicobakan kepada 40 mahasiswa Universitas Negeri Medan yang berjumlah 40 butir soal dengan 5 opsi (a, b, c, d, dan e). Selanjutnya data yang diperoleh dianalisis melalui pendekatan teori tes klasik menggunakan bantuan program model Rasch dengan softwareWinsteps dan SPSS versi 22.0. Hasil penelitian menggunakan SPSS 22.0 dan Rasch model memiliki keakuratan validitas yang baik yang menunjukkan taraf perbedaan hasil yang kecil pada hasil validitas butir soal. Berdasarkan analisis SPSS 22.0 diperoleh 20 item yang valid dan 20 item yang tidak valid. Sedangkan menggunakan Rasch model dengan bantuan software Winstep diperoleh 17 item yang valid dan 23 item tidak valid. Berdasarkan keakuratan data, analisis validasi menggunakan Rasch model dengan bantuan software Winstep lebih akurat dibandingkan dengan menggunakan program SPSS 22.0. Faktor yang membedakan hasil reliabilitas SPSS 22.0 dan Rasch model dengan bantuan dari hasil uji validasi. Sehingga, uji reliabilitas menggunakan SPSS 22.0 tingkat reliabilitasnya 0,828 (kategori tinggi) dan Rasch model yang diperoleh 0,69 (kategori cukup).
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20

Widhiarso, Wahyu. "APLIKASI MODEL RASCH CAMPURAN DALAM MENGEVALUASI PENGUKURAN HARGA DIRI." Jurnal Penelitian dan Evaluasi Pendidikan 17, no. 1 (June 6, 2013): 172–87. http://dx.doi.org/10.21831/pep.v17i1.1367.

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Penelitian ini bertujuan untuk mengeksplorasi keberadaan kelompok responden yang menyebabkan estimasi parameter butir melalui pemodelan Rasch tidak invarian pada keseluruhan responden. Teknik analisis yang dipakai adalah model Rasch campuran yang merupakan penggabungan antara Model Rasch dan analisis kelas laten. Dengan menggunakan data hasil pengukuran harga diri didapatkan hasil analisis bahwa keseluruhan responden penelitian sebanyak 2.987 dapat dikategorikan menjadi tiga kelas berdasarkan pola respons mereka pada skala. Hasil estimasi parameter butir pada responden pada masing-masing kelas dengan menggunakan model kredit parsial menunjukkan bahwa ketiga kelas memiliki parameter butir yang berbeda. Dua kelas relatif sesuai dengan model, sedangkan satu kelas tidak sesuai karena responden pada kelas tersebut merespons skala dengan cara yang unik. Keberadaan responden dengan respons unik ini relatif kecil (12,5%) sehingga tidak mengganggu estimasi parameter pada keseluruhan butir. Kata kunci: model Rasch campuran, parameter butir, kelas responden ______________________________________________________________ RASCH MIXED MODEL APPLICATION IN EVALUATING THE MEASUREMENT OF SELF-ESTEEMAbstract This study aimed to explore the existence of groups of items that cause Estimation of item parameters using Rasch modeling was not invariant for all respondents. Mixed Rasch model which is the combination between Rasch Models and Latent Class Analysis was employed. By using data from measuring self-esteem found for overall respondents (N=2987) can be categorized into three classes based on their item respons patterns on entire scale. Results based on estimation of item parameters to the respondents in each class using the Partial Credit Model found that each class has different item parameters. Two classes supported the model while the other class did not; due to respondents on this class give a response on the scale in a unique way. The proportion of the respondents with a unique response is relatively small (12,5%) therefore they do not much interfere the estimation of item parameters on the overall items.Keywords: mixed rasch model, Item Estimation Parameter, Class
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21

ZHAN, Peida, WANG Wen-Chung, Lijun WANG, and Xiaomin LI. "The Multidimensional Testlet-Effect Rasch Model." Acta Psychologica Sinica 46, no. 8 (2014): 1208. http://dx.doi.org/10.3724/sp.j.1041.2014.01208.

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22

Smith, Richard M. "Person Fit in the Rasch Model." Educational and Psychological Measurement 46, no. 2 (June 1986): 359–72. http://dx.doi.org/10.1177/001316448604600210.

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23

Müller, Hans. "A rasch model for continuous ratings." Psychometrika 52, no. 2 (June 1987): 165–81. http://dx.doi.org/10.1007/bf02294232.

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24

Andersen, Erling B. "Residualanalysis in the polytomous rasch model." Psychometrika 60, no. 3 (September 1995): 375–93. http://dx.doi.org/10.1007/bf02294382.

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25

Glas, C. A. W. "The Rasch Model and Multistage Testing." Journal of Educational Statistics 13, no. 1 (March 1988): 45–52. http://dx.doi.org/10.3102/10769986013001045.

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This paper concerns the problem of estimating the item parameters of latent trait models in a multistage testing design. It is shown that using the Rasch model and conditional maximum likelihood estimates does not lead to solvable estimation equations. It is also shown that marginal maximum likelihood estimation, which assumes a sample of subjects from a population with a specified distribution of ability, will lead to solvable estimation equations, both in the Rasch model and in the Birnbaum model.
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Glas, C. A. W. "The Rasch Model and Multistage Testing." Journal of Educational Statistics 13, no. 1 (1988): 45. http://dx.doi.org/10.2307/1164950.

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27

Avlund, Kirsten, Svend Kreiner, and Kirsten Schultz-Larsen. "Construct validation and the Rasch model." Scandinavian Journal of Social Medicine 21, no. 4 (December 1993): 233–44. http://dx.doi.org/10.1177/140349489302100403.

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28

Graßhoff, Ulrike, Heinz Holling, and Rainer Schwabe. "Optimal Designs for the Rasch Model." Psychometrika 77, no. 4 (July 21, 2012): 710–23. http://dx.doi.org/10.1007/s11336-012-9276-2.

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29

Komboz, Basil, Carolin Strobl, and Achim Zeileis. "Tree-Based Global Model Tests for Polytomous Rasch Models." Educational and Psychological Measurement 78, no. 1 (October 6, 2016): 128–66. http://dx.doi.org/10.1177/0013164416664394.

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Psychometric measurement models are only valid if measurement invariance holds between test takers of different groups. Global model tests, such as the well-established likelihood ratio (LR) test, are sensitive to violations of measurement invariance, such as differential item functioning and differential step functioning. However, these traditional approaches are only applicable when comparing previously specified reference and focal groups, such as males and females. Here, we propose a new framework for global model tests for polytomous Rasch models based on a model-based recursive partitioning algorithm. With this approach, a priori specification of reference and focal groups is no longer necessary, because they are automatically detected in a data-driven way. The statistical background of the new framework is introduced along with an instructive example. A series of simulation studies illustrates and compares its statistical properties to the well-established LR test. While both the LR test and the new framework are sensitive to differential item functioning and differential step functioning and respect a given significance level regardless of true differences in the ability distributions, the new data-driven approach is more powerful when the group structure is not known a priori—as will usually be the case in practical applications. The usage and interpretation of the new method are illustrated in an empirical application example. A software implementation is freely available in the R system for statistical computing.
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30

Preinerstorfer, David, and Anton K. Formann. "Parameter recovery and model selection in mixed Rasch models." British Journal of Mathematical and Statistical Psychology 65, no. 2 (June 15, 2011): 251–62. http://dx.doi.org/10.1111/j.2044-8317.2011.02020.x.

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31

Triana, Apri. "Analisis Model Rasch pada Tes Diagnostik Kesulitan Belajar Matematika Siswa SMA." Jurnal Tarbiyah dan Ilmu Keguruan Borneo 5, no. 1 (January 20, 2024): 119–32. http://dx.doi.org/10.21093/jtikborneo.v5i3.7135.

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Penelitian ini bertujuan untuk mengetahui karakteristik tes diagnostik kesulitan belajar Matematika menggunakan analisis model Rasch. Data yang dianalisis dengan model Rasch adalah sebanyak 323 respon jawaban siswa kelas XII IPA pada bidang studi Matematika. Hasil penelitian menunjukkan bahwa setiap butir tes diagnostik cocok dengan analisis model Rasch. Dari 323 siswa yang mengikuti tes, hanya sebanyak 298 respon jawaban siswa yang cocok dan bisa dianalisis dengan model Rasch. Analisis dengan model Rasch memiliki karakteristik satu parameter, yaitu tingkat kesulitan butir. Tingkat kesulitan butir yang cocok dengan model Rasch sesuai dengan karakteristik tes diagnostik adalah butir tes dengan tingkat sedang yaitu 0,30 sampai 0,80. Adapun karakteristik butir tes yang sesuai dengan karakteristik tes diagnostik dan parameter model analisis sebanyak 26 butir dan ada empat butir yang harus disisihkan pada perangkat tes diagnostik. Koefisien reliabilitas perangkat tes diagnostik sebesar 0,84 yang artinya tes diagnostik sangat reliabel untuk mengukur kemampuan matematika kelas XII IPA. Perangkat tes diagnostik tidak cocok digunakan pada peserta tes memiliki kemampuan dibawah -3 atau diatas 3.
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Baghaei, Purya, and Philipp Doebler. "Introduction to the Rasch Poisson Counts Model: An R Tutorial." Psychological Reports 122, no. 5 (September 5, 2018): 1967–94. http://dx.doi.org/10.1177/0033294118797577.

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The Rasch Poisson Counts Model is the oldest Rasch model developed by the Danish mathematician Georg Rasch in 1952. Nevertheless, the model has had limited applications in psychoeducational assessment. With the rise of neurocognitive and psychomotor testing, there is more room for new applications of the model where other item response theory models cannot be applied. In this paper, we give a general introduction to the Rasch Poisson Counts Model and then using data of an attention test walk the reader through how to use the “lme4” package in R to estimate the model and interpret the outputs.
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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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Lee, Seungbak, Hyo-Jun Yun, Minsoo Jeon, and Minsoo Kang. "Validating Athletes’ Subjective Performance Scale: A Rasch Model Analysis." IJASS(International Journal of Applied Sports Sciences) 35, no. 2 (December 31, 2023): 238–50. http://dx.doi.org/10.24985/ijass.2023.35.2.238.

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This study aimed to validate the athletes’ subjective performance scale (ASPS) and examine its optimal categorization measuring Korean university athletes using the Rasch model. A six-item ASPS with ten response categories was administered to 201 Korean university athletes participating in team sport events. The Rash measurement program, Winsteps (version 4.6.2.1), was used to perform Rasch analysis. The results showed that the model was a good fit for the data. The Wright-Andrich map indicated ceiling and floor effects, as ASPS items were unable to measure individuals with logits beyond 3 or below -2.5. Furthermore, the reliability of item separation and person separation demonstrated acceptable confidence. Lastly, the findings indicated that the ASPS, which utilized a 10-category rating scale, was problematic due to disordered thresholds. The exploratory analysis revealed that both six and seven-category rating scales appeared to comply with the effective classification criteria, but further research is needed for confirmatory analysis. Previous research has explored the relationships between psychometric factors and subjective performance; however, this study offers valuable insights into optimal categorization and introduces an innovative approach to measuring athletes’ subjective performance. To assess subjective sport performance satisfaction, the authors propose employing a six-category rating scale, which this study found to be reliable and valid in relation to construct.
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Agust, Satria. "How Does Rasch Model Reveal Dishonesty between Coastal Students and Easy Grammar Test?" Jurnal Iqra' : Kajian Ilmu Pendidikan 4, no. 2 (December 26, 2019): 214–30. http://dx.doi.org/10.25217/ji.v4i2.531.

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Academic dishonesty can occur with the supports of the technology devices and it also can be prevented with the help of the technology with its applications like Rasch Model. It can give detail information of the analyzed data and can trace the academic dishonesty like cheating. The aims of this research are to (1) analyze the grammar test items whether they are difficult items or easy ones using Rasch Model, (2) know the percentage of those who are assumed to do cheating based upon their origins and sex, and (3) expose their patterns in working on the grammar test in the form of multiple choice through the Rasch Model analysis. The researcher hypothesized that the academic dishonesty i.e. cheating was undergone by students who are from rural and urban areas of Riau Archipelago Province. The results of this research were: (1) through the students’ responses analyzed by Rasch Model, the grammar test was for medium ability, (2) The Rasch Model revealed that the percentage came to the number 5.71% or 4 of 70 students who were identified to cheat while working on the grammar test. They were two female students from rural area and the other two male students from urban area, and (3) The Rasch Model revealed that their responses did not represent their ability. The Rasch Model has helped the researcher to exposes the cheating deeds on exams. The practitioners just need methods, approaches, strategies, techniques, and media to prevent them in the future Keywords: Rasch Model, Wright Maps, Grammar Test.
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36

Iseppi, Luca, Marcella Rizzo, Enrico Gori, Federico Nassivera, Ivana Bassi, and Alessandro Scuderi. "Rasch Model for Assessing Propensity to Entomophagy." Sustainability 13, no. 8 (April 14, 2021): 4346. http://dx.doi.org/10.3390/su13084346.

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The Food and Agriculture Organization of the United Nations supports the production of edible insects as a promising and sustainable source of nutrients to meet the increasing demand for animal-derived products by the growing world population. Even if insects are part of the diet of more than two billion people worldwide, the practice of eating insects (entomophagy) raises challenging questions for Western countries where this is not a habit. The research applied the Rasch models and showed that, in the case of hunger or need, 70.8% of the sample declared that they would be willing to eat insects. The willingness to habitually consume and pay for insect food is very low, but the percentages are higher than people who had actually had insect tasting experiences. This demonstrates that a communication process is necessary that aims to overcome psychological/cultural barriers. Only in this way will it be possible to increase the propensity to consume insects.
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37

Anandita, Rina, and Lukman Cahyadi. "Aplikasi Model Rasch dalam Mengukur Komitmen Dosen." Jurnal Manajemen dan Supervisi Pendidikan 4, no. 3 (August 10, 2020): 220–31. http://dx.doi.org/10.17977/um025v4i32020p220.

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Wigati, Indah. "PEMBELAJARAN DARING PERSPEKTIF GENDER MALALUI MODEL RASCH." Muaddib : Studi Kependidikan dan Keislaman 11, no. 1 (November 25, 2021): 54–66. http://dx.doi.org/10.24269/muaddib.v1i1.4070.

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Wahyudi, Amien. "ANALISIS MODEL RASCH PADA PENGEMBANGAN SKALA RESILIENSI." Jurnal Fokus Konseling 6, no. 2 (August 31, 2020): 68–74. http://dx.doi.org/10.52657/jfk.v6i2.1157.

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Penelitian ini berfokus pada pengembangan skala resiliensi dengan menggunakan pendekatan rasch model.Rasc model merupakan suatu pendekatan yang lebih komfrehensif dalam mengembangkan skala dibandingkan pendekatan klasik.Partisipan penelitian ini sebanyak 56 mahasiswa bimbingan dan konseling yang diambil berdasarkan teori Slovin.Berdasarkan analisa menggunakan program winstep dari 25 aitem pernyataan yang dikembangkan, 4 aitem tidak memenuhi standar dan 21 aitem memenuhi standar yang telah ditentukan. Nilai reliabelitas skala sebesar 0,87 atau dalam kategori bagus. Secara keseluruhan skala resiliensi yang dikembangkan dapat digunakan untuk mengetahui resiliensi indvidu.
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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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Howard, Elizabeth Ptaszynski. "Applying the Rasch Model to Test Administration." Journal of Nursing Education 24, no. 8 (October 1985): 340–43. http://dx.doi.org/10.3928/0148-4834-19851001-08.

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Verguts, Tom, Paul De Boeck, and Gert Storms. "Analyzing experimental data using the Rasch model." Behavior Research Methods, Instruments, & Computers 30, no. 3 (September 1998): 501–5. http://dx.doi.org/10.3758/bf03200683.

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Wang, Huqing, Feng Xiang, Wenbing Zhao, and Zhixin Sun. "ONS resolution prediction based on Rasch model." Mathematical Biosciences and Engineering 16, no. 6 (2019): 6683–95. http://dx.doi.org/10.3934/mbe.2019333.

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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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Ludlow, Larry H., and Stephen M. Haley. "Rasch Model Logits: Interpretation, Use, and Transformation." Educational and Psychological Measurement 55, no. 6 (December 1995): 967–75. http://dx.doi.org/10.1177/0013164495055006005.

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Zaporozhets, Olga, Christine M. Fox, Svetlana A. Beltyukova, John M. Laux, Nick J. Piazza, and Kathleen Salyers. "Refining Change Measure With the Rasch Model." Measurement and Evaluation in Counseling and Development 48, no. 1 (January 2015): 59–74. http://dx.doi.org/10.1177/0748175614544686.

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Kyngdon, Andrew. "Conjoint Measurement, Error and the Rasch Model." Theory & Psychology 18, no. 1 (February 2008): 125–31. http://dx.doi.org/10.1177/0959354307086927.

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Eggen, Theo J. H. M. "Computerized classification testing with the Rasch model." Educational Research and Evaluation 17, no. 5 (October 2011): 361–71. http://dx.doi.org/10.1080/13803611.2011.630528.

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López Pina, José Antonio, and M. Dolores Hidalgo Montesinos. "Fitting Rasch Model using Appropriateness Measure Statistics." Spanish Journal of Psychology 8, no. 1 (May 2005): 100–110. http://dx.doi.org/10.1017/s113874160000500x.

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In this paper, the distributional properties and power rates of the Lz, Eci2z, and Eci4z statistics when they are used as item fit statistics were explored. The results were compared to t-transformation of Outfit and Infit mean square. Four sample sizes were selected: 100, 250, 500, and 1000 examinees. The abilities were uniform and normal with mean 0 and standard deviation 1, and uniform and normal with mean –1 and standard deviation 1. The pseudo-guessing parameter was fixed at .25. Two ranges of difficulty parameters were selected: ±1 logits and ±2 logits. Two test lengths were selected: 15 and 30 items. The results showed important differences between the T-infit, T-outfit, Lz, Eci2z, and Eci4z statistics. The T-oufit, T-infit, and Lz statistics showed poor standardization with estimated parameters because their distributional properties were not close to the expected values. However, the Eci2z and Eci4z statistics showed satisfactory standardization on all conditions. Further, the power rates of Eci2z and Eci4z were 5% to 10% higher than the power rates of Lz, T-outfit, and T-infit to detect items that do not fit Rasch model.
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Zwinderman, Aeilko H. "A generalized rasch model for manifest predictors." Psychometrika 56, no. 4 (December 1991): 589–600. http://dx.doi.org/10.1007/bf02294492.

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