scholarly journals A Comprehensive Simulation Study of Estimation Methods for the Rasch Model

Stats ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 814-836
Author(s):  
Alexander Robitzsch
Keyword(s):  
Maximum Likelihood ◽  
Simulation Study ◽  
Rasch Model ◽  
Item Parameter ◽  
Estimation Methods ◽  
Limited Information ◽  
Marginal Maximum Likelihood ◽  
Chi Square ◽  
Parameter Estimation Methods ◽  
The Rasch Model

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are systematically compared through a comprehensive simulation study: Different alternatives of joint maximum likelihood (JML) estimation, different alternatives of marginal maximum likelihood (MML) estimation, conditional maximum likelihood (CML) estimation, and several limited information methods (LIM). The type of ability distribution (i.e., nonnormality), the number of items, sample size, and the distribution of item difficulties were systematically varied. Across different simulation conditions, MML methods with flexible distributional specifications can be at least as efficient as CML. Moreover, in many situations (i.e., for long tests), penalized JML and JML with ε adjustment resulted in very efficient estimates and might be considered alternatives to JML implementations currently used in statistical software. Moreover, minimum chi-square (MINCHI) estimation was the best-performing LIM method. These findings demonstrate that JML estimation and LIM can still prove helpful in applied research.

scholarly journals A Comparison of Estimation Methods for the Rasch Model

2021 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Rasch Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Item Parameter ◽  
Estimation Methods ◽  
Limited Information ◽  
The Impact ◽  
The Rasch Model

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

The Rasch Model and Multistage Testing

1988 ◽  
Vol 13 (1) ◽  
pp. 45-52 ◽  
Author(s):  
C. A. W. Glas
Keyword(s):  
Maximum Likelihood ◽  
Rasch Model ◽  
Latent Trait ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Marginal Maximum Likelihood ◽  
Marginal Maximum Likelihood Estimation ◽  
Multistage Testing ◽  
Estimation Equations ◽  
The Rasch Model

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.

Rasch analysis of the firefighters’ critical incident inventory questionnaire

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Goris Nazari ◽  
Steve Lu ◽  
Tara Packham ◽  
Joy C. MacDermid
Keyword(s):  
Rasch Model ◽  
Critical Incident ◽  
Secondary Data ◽  
Chi Square ◽  
Service Workers ◽  
Response Options ◽  
Medical Trauma ◽  
Chi Square Test ◽  
Emergency Service Workers ◽  
The Rasch Model

BACKGROUND: The Critical Incident Inventory (CII) was developed to assess stressful exposures in firefighters and emergency service workers. The CII includes six subscales: trauma to self, victims known to fire-emergency worker, multiple casualties, incidents involving children, unusual or problematic tactical operations, and exposure to severe medical trauma. OBJECTIVES: To examine the construct validity of all subscales of the Critical Incident Inventory (CII) by assessing the unidimensionality of the scales, and the interval properties of CII subscales by examining fit to the Rasch model and ordering of item thresholds. METHODS: This was a secondary data analysis based on survey data collected from a sample of 390 firefighters. RESULTS: Item 4 and Item 20 were removed with the confirmation of unacceptable fit residual. This revised version of the CII showed satisfactory fit to the Rasch model by non-significant Chi-square test and acceptable level of item fit. We rescored the CII original version and considered all items as only dichotomous response options where 0 represented the original no experience, and 1 presents the combination of experiencing 1, 2, 3 cases. CONCLUSION: The re-appraisal of the revised version CII indicated a satisfactory level of Rasch model fit.

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.

scholarly journals Conditional or Pseudo Exact Tests with an Application in the Context of Modeling Response Times

Psych ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 198-208
Author(s):  
Clemens Draxler ◽  
Stephan Dahm
Keyword(s):  
Power Function ◽  
Rasch Model ◽  
Power Analysis ◽  
Response Times ◽  
Real Data ◽  
Small Sample ◽  
Chi Square ◽  
Research Context ◽  
Large Sample Theory ◽  
The Rasch Model

This paper treats a so called pseudo exact or conditional approach of testing assumptions of a psychometric model known as the Rasch model. Draxler and Zessin derived the power function of such tests. They provide an alternative to asymptotic or large sample theory, i.e., chi square tests, since they are also valid in small sample scenarios. This paper suggests an extension and applies it in a research context of investigating the effects of response times. In particular, the interest lies in the examination of the influence of response times on the unidimensionality assumption of the model. A real data example is provided which illustrates its application, including a power analysis of the test, and points to possible drawbacks.

Sign in / Sign up

Export Citation Format

Share Document