Robustness of Marginal Maximum Likelihoo Estimation in the Rasch odel

Zwinderman, Aeilko H.; Wollenberg, Arnold L. van den

doi:10.1177/014662169001400107

Cited by 41 publications

(33 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the ability parameter is not estimated jointly with the item parameters, the item parameters are first estimated from the item responses with the influence of the ability parameter taken away; the ability parameter is either eliminated through conditioning or integrated out through marginalization. Techniques used in parameter estimation include the maximum likelihood procedure (Baker, 1992); logistic regression (Reynolds, Perkins and Brutten, 1994); minimum chi-quadrant (Zwinderman and van der Wollenberg, 1990), and…”

Section: Ability Estimationmentioning

confidence: 99%

Ability Estimation Under Different Item Parameterization and Scoring Models

Si¹,

Schumacker²

2004

International Journal of Testing

View full text Add to dashboard Cite

A Monte Carlo simulation study investigated the effect of scoring format, item parameterization, threshold configuration, and prior ability distribution on the accuracy of ability estimation given various IRT models. Item response data on 30 items from 1,000 examinees was simulated using known item parameters and ability estimates. The item response data sets were submitted to seven dichotomous or polytomous IRT models with different item parameterization to estimate examinee ability. The accuracy of the ability estimation for a given IRT model was assessed by the recovery rate and the root mean square errors. The results indicated that polytomous models produced more accurate ability estimates than the dichotomous models, under all combinations of research conditions, as indicated by higher recovery rates and lower root mean square errors. For the item parameterization models, the one-parameter model outperformed the two-parameter and three-parameter models under all research conditions. Among the polytomous models, the partial credit model had more accurate ability estimation than the other three polytomous models. The nominal categories model performed better than the general partial credit model and the multiple-choice model with the multiple-choice model the least accurate. The results further indicated that certain prior ability distributions had an effect on the accuracy of ability estimation; however, no clear order of accuracy among the four prior distribution groups was identified due to an interaction between prior ability distribution and threshold configuration. The recovery rate was lower when the test items had categories with unequal threshold distances, were close at one end of the ability/difficulty continuum, and were administered to a sample of examinees whose population ability distribution was skewed to the same end of the ability continuum.ii ACKNOWLEDGMENTS

show abstract

Section: Ability Estimationmentioning

confidence: 99%

Ability Estimation Under Different Item Parameterization and Scoring Models

Si¹,

Schumacker²

2004

International Journal of Testing

View full text Add to dashboard Cite

show abstract

“…Contrary to the beliefs held by many substantive researchers, simulation research indicates that results from IRT models can be nontrivially biased when the true population distribution is nonnormal (Abdel-fattah, 1994;Boulet, 1996;de Ayala & SavaBolesta, 1999;DeMars, 2003;Kirisci, Hsu, & Yu, 2001;Seong, 1990 ;Stone, 1992; van den Oord, 2005;Zwinderman & van den Wollenberg, 1990). Specifically, MML estimates of item parameters increase in bias as the distribution deviates further from normality (Boulet, 1996;Stone, 1992;Woods, 2006Woods, , 2007aWoods, , 2007bWoods, , 2008Woods & Lin, 2009;Woods & Thissen, 2006).…”

Section: Effects Of Nonnormalitymentioning

confidence: 98%

Estimating the Nominal Response Model Under Nonnormal Conditions

Preston

Reise

2013

Educational and Psychological Measurement

View full text Add to dashboard Cite

The nominal response model (NRM), a much understudied polytomous item response theory (IRT) model, provides researchers the unique opportunity to evaluate within-item category distinctions. Polytomous IRT models, such as the NRM, are frequently applied to psychological assessments representing constructs that are unlikely to be normally distributed in the population. Unfortunately, models estimated using estimation software with the MML/EM algorithm frequently employs a set of normal quadrature points, effectively ignoring the true shape of the latent trait distribution. To address this problem, the current research implements an alternative estimation approach, Ramsay Curve Item Response Theory (RC-IRT), to provide more accurate item parameter estimates modeled under the NRM under normal, skewed, and bimodal latent trait distributions for ordered polytomous items. Based on the results of improved item parameter recovery under RC-IRT, it is recommended that RC-IRT estimation be implemented whenever a researcher considers the construct being measured has the potential of being nonnormally distributed.

show abstract

“…In the special case of the Rasch model, conditional maximum likelihood (CML) estimation (Andersen, 1973) can be used for item parameter estimation, but in general marginal maximum likelihood (MML) estimation (Bock & Aitkin, 1981;Thissen, 1982;Zwinderman & van den Wollenberg, 1990) is used. This estimation method is based on a distributional assumption about the latent variable, typically that θ 1 , .…”

Section: Item Parameter Estimationmentioning

confidence: 99%

SAS macros for longitudinal IRT models

Olsbjerg

2018

Preprint

View full text Add to dashboard Cite

IRT models are often applied when observed items are used to measure a unidimensional latent variable. Originally used in educational research, IRT models are now widely used when focus is on physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to, simulating from, and visualizing longitudinal Abstract IRT models are often applied when observed items are used to measure a unidimensional latent variable. Originally used in educational research, IRT models are now widely used when focus is on physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to, simulating from, and visualizing longitudinal IRT models. The macros encompass dichotomous as well as polytomous item response formats and are sufficiently flexible to accommodate changes in item parameters across time points and local dependence between responses at different time points.

show abstract

Robustness of Marginal Maximum Likelihoo Estimation in the Rasch odel

Cited by 41 publications

References 9 publications

Ability Estimation Under Different Item Parameterization and Scoring Models

Ability Estimation Under Different Item Parameterization and Scoring Models

Estimating the Nominal Response Model Under Nonnormal Conditions

SAS macros for longitudinal IRT models

Contact Info

Product

Resources

About