The Growing Family of Rasch Models

2001 ◽  
pp. 25-42 ◽  
Author(s):  
Jürgen Rost
Keyword(s):  
Rasch Models

Review of Rasch Models for Measurement.

1989 ◽  
Vol 34 (3) ◽  
pp. 297-297
Author(s):  
No authorship indicated
Keyword(s):  
Rasch Models

Right-Wing Authoritarians Aren't Very Funny: RWA, Personality, and Creative Humor Production

2020 ◽  
Author(s):  
Paul Silvia ◽  
Alexander P. Christensen ◽  
Katherine N. Cotter
Keyword(s):  
Young Adults ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Openness To Experience ◽  
Rasch Models ◽  
Right Wing ◽  
Right Wing Authoritarianism ◽  
Humor Appreciation ◽  
Negative Effect

Right-wing authoritarianism (RWA) has well-known links with humor appreciation, such as enjoying jokes that target deviant groups, but less is known about RWA and creative humor production—coming up with funny ideas oneself. A sample of 186 young adults completed a measure of RWA, the HEXACO-100, and 3 humor production tasks that involved writing funny cartoon captions, creating humorous definitions for quirky concepts, and completing joke stems with punchlines. The humor responses were scored by 8 raters and analyzed with many-facet Rasch models. Latent variable models found that RWA had a large, significant effect on humor production (β = -.47 [-.65, -.30], p < .001): responses created by people high in RWA were rated as much less funny. RWA’s negative effect on humor was smaller but still significant (β = -.25 [-.49, -.01], p = .044) after controlling for Openness to Experience (β = .39 [.20, .59], p < .001) and Conscientiousness (β = -.21 [-.41, -.02], p = .029). Taken together, the findings suggest that people high in RWA just aren’t very funny.

scholarly journals Comparing item parameter estimates and fit statistics of the Rasch model from three different traditions

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Bahrul Hayat ◽  
Muhammad Dwirifqi Kharisma Putra ◽  
Bambang Suryadi
Keyword(s):  
Factor Analysis ◽  
Rasch Model ◽  
Measurement Model ◽  
Rasch Measurement ◽  
Item Parameter ◽  
Rasch Models ◽  
Rasch Measurement Model ◽  
Item Factor Analysis ◽  
The Rasch Model ◽  
Special Case

Rasch model is a method that has a long history in its application in the fields of social and behavioral sciences including educational measurement. Under certain circumstances, Rasch models are known as a special case of Item response theory (IRT), while IRT is equivalent to the Item Factor Analysis (IFA) models as a special case of Structural Equation Models (SEM), although there are other ‘tradition’ that consider Rasch measurement models not part of both. In this study, a simulation study was conducted to using simulated data to explain how the interrelationships between the Rasch model as a constraint version of 2-parameter logistic (2-PL) IRT, Rasch model as an item factor analysis were compared with the Rasch measurement model using Mplus, IRTPRO and WINSTEPS program, each of which came from its own 'tradition'. The results of this study indicate that Rasch models and IFA as a special case of SEM are mathematically equal, as well as the Rasch measurement model, but due to different philosophical perspectives people might vary in their understanding about this concept. Given the findings of this study, it is expected that confusion and misunderstanding between the three can be overcome.

A Comparison of Uniform DIF Effect Size Estimators Under the MIMIC and Rasch Models

2012 ◽  
Vol 73 (2) ◽  
pp. 339-358 ◽  
Author(s):  
Ying Jin ◽  
Nicholas D. Myers ◽  
Soyeon Ahn ◽  
Randall D. Penfield
Keyword(s):  
Effect Size ◽  
Rasch Models

Parameter recovery and model selection in mixed Rasch models

2011 ◽  
Vol 65 (2) ◽  
pp. 251-262 ◽  
Author(s):  
David Preinerstorfer ◽  
Anton K. Formann
Keyword(s):  
Model Selection ◽  
Rasch Models ◽  
Parameter Recovery

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

Person Parameter Estimation and Measurement in Rasch Models

2013 ◽  
pp. 63-78 ◽  
Author(s):  
Svend Kreiner ◽  
Karl Bang Christensen
Keyword(s):  
Parameter Estimation ◽  
Rasch Models ◽  
Person Parameter

Modern Test Theory Techniques for Adaptive Testing in Short Scales Comprising Polytomous Items: A Monte Carlo Simulation Study Comparing Rasch Measurement Theory to Unidimensional and Multidimensional Graded Response Models. (Preprint)

2021 ◽  
Author(s):  
Conrad J. Harrison ◽  
Bao Sheng Loe ◽  
Inge Apon ◽  
Chris J. Sidey-Gibbons ◽  
Marc C. Swan ◽  
...  
Keyword(s):  
Measurement Theory ◽  
Simulated Data ◽  
Rasch Models ◽  
Partial Credit ◽  
Limits Of Agreement ◽  
Real Patient ◽  
Response Models ◽  
Assessment Scores ◽  
Graded Response ◽  
The Face

BACKGROUND There are two philosophical approaches to contemporary psychometrics: Rasch measurement theory (RMT) and item response theory (IRT). Either measurement strategy can be applied to computerized adaptive testing (CAT). There are potential benefits of IRT over RMT with regards to measurement precision, but also potential risks to measurement generalizability. RMT CAT assessments have demonstrated good performance with the CLEFT-Q, a patient-reported outcome measure for use in orofacial clefting. OBJECTIVE To test whether the post-hoc application of IRT (graded response models, GRMs, and multidimensional GRMs) to RMT-validated CLEFT-Q appearance scales could improve CAT accuracy at given assessment lengths. METHODS Partial credit Rasch models, unidimensional GRMs and a multidimensional GRM were calibrated for each of the 7 CLEFT-Q appearance scales (which measure the appearance of the: face, jaw, teeth, nose, nostrils, cleft lip scar and lips) using data from the CLEFT-Q field test. A second, simulated dataset was generated with 1000 plausible response sets to each scale. Rasch and GRM scores were calculated for each simulated response set, scaled to 0-100 scores, and compared by Pearson’s correlation coefficient, root mean square error (RMSE), mean absolute error (MAE) and 95% limits of agreement. For the face, teeth and jaw scales, we repeated this in a an independent, real patient dataset. We then used the simulated data to compare the performance of a range of fixed-length CAT assessments that were generated with partial credit Rasch models, unidimensional GRMs and the multidimensional GRM. Median standard error of measurement (SEM) was recorded for each assessment. CAT scores were scaled to 0-100 and compared to linear assessment Rasch scores with RMSE, MAE and 95% limits of agreement. This was repeated in the independent, real patient dataset with the RMT and unidimensional GRM CAT assessments for the face, teeth and jaw scales to test the generalizability of our simulated data analysis. RESULTS Linear assessment scores generated by Rasch models and unidimensional GRMs showed close agreement, with RMSE ranging from 2.2 to 6.1, and MAE ranging from 1.5 to 4.9 in the simulated dataset. These findings were closely reproduced in the real patient dataset. Unidimensional GRM CAT algorithms achieved lower median SEM than Rasch counterparts, but reproduced linear assessment scores with very similar accuracy (RMSE, MAE and 95% limits of agreement). The multidimensional GRM had poorer accuracy than the unidimensional models at comparable assessment lengths. CONCLUSIONS Partial credit Rasch models and GRMs produce very similar CAT scores. GRM CAT assessments achieve a lower SEM, but this does not translate into better accuracy. Commonly used SEM heuristics for target measurement reliability should not be generalized across CAT assessments built with different psychometric models. In this study, a relatively parsimonious multidimensional GRM CAT algorithm performed more poorly than unidimensional GRM comparators.

Sign in / Sign up

Export Citation Format

Share Document