On Person Parameter Estimation in the Dichotomous Rasch Model

Rasch Models ◽  
1995 ◽  
pp. 53-68 ◽  
Author(s):  
Herbert Hoijtink ◽  
Anne Boomsma
Keyword(s):  
Parameter Estimation ◽  
Rasch Model ◽  
Person Parameter

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

Logit based parameter estimation in the Rasch model

1988 ◽  
Vol 42 (4) ◽  
pp. 273-295 ◽  
Author(s):  
N. Verhelst ◽  
I.W. Molenaar
Keyword(s):  
Parameter Estimation ◽  
Rasch Model ◽  
The Rasch Model

Consistency of Rasch Model Parameter Estimation: A Simulation Study

1988 ◽  
Vol 12 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Arnold L. van den Wollenberg ◽  
Folkert W. Wierda ◽  
Paul G. W. Jansen
Keyword(s):  
Parameter Estimation ◽  
Simulation Study ◽  
Rasch Model ◽  
Model Parameter ◽  
Model Parameter Estimation

scholarly journals GGUM-RANK Statement and Person Parameter Estimation With Multidimensional Forced Choice Triplets

2018 ◽  
Vol 43 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Philseok Lee ◽  
Seang-Hwane Joo ◽  
Stephen Stark ◽  
Oleksandr S. Chernyshenko
Keyword(s):  
Parameter Estimation ◽  
Forced Choice ◽  
Future Research ◽  
Simulation Studies ◽  
Scoring Methods ◽  
Mcmc Algorithm ◽  
High Stake ◽  
Parameter Recovery ◽  
Irt Model ◽  
Person Parameter

Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.

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