A stochastic growth model applied to repeated tests of academic knowledge

Albers, Willem; Does, Ronald J. M. M.; Imbos, Tj.; Janssen, M. P. E.

doi:10.1007/bf02294628

Cited by 25 publications

(39 citation statements)

References 7 publications

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“…Such models are appropriate if items are sampled from an item bank. One application in educational testing is sampling a different set of items from the same pool of items at different time points to prevent cheating by item exposure (Albers et al, 1989). Even for items that are not randomly sampled, we may want to generalize to the ''universe of items'' as in generalizability theory (see also Briggs and Wilson, 2007).…”

Section: Item Response Models With Random Item Parametersmentioning

confidence: 99%

Alternating imputation posterior estimation of models with crossed random effects

Cho

Rabe‐Hesketh

2011

Computational Statistics & Data Analysis

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a b s t r a c tGeneralized linear mixed models or latent variable models for categorical data are difficult to estimate if the random effects or latent variables vary at non-nested levels, such as persons and test items. Clayton and Rasbash (1999) suggested an Alternating Imputation Posterior (AIP) algorithm for approximate maximum likelihood estimation. For item response models with random item effects, the algorithm iterates between an item wing in which the item mean and variance are estimated for given person effects and a person wing in which the person mean and variance are estimated for given item effects. The person effects used for the item wing are sampled from the conditional posterior distribution estimated in the person wing and vice versa. Clayton and Rasbash (1999) used marginal quasi-likelihood (MQL) and penalized quasi-likelihood (PQL) estimation within the AIP algorithm, but this method has been shown to produce biased estimates in many situations, so we use maximum likelihood estimation with adaptive quadrature. We apply the proposed algorithm to the famous salamander mating data, comparing the estimates with many other methods, and to an educational testing dataset. We also present a simulation study to assess performance of the AIP algorithm and the Laplace approximation with different numbers of items and persons and a range of item and person variances.

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Section: Item Response Models With Random Item Parametersmentioning

confidence: 99%

Alternating imputation posterior estimation of models with crossed random effects

Cho

Rabe‐Hesketh

2011

Computational Statistics & Data Analysis

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“…Since then, there seems to be an increasing interest in these methods among educational researchers (Albers, Does, Imbos, & Janssen, 1989;Willoughby & Hutcheson, 1978). For example, at the University of Missouri Medical School, Kansas City, a within-school longitudinal method for achievement testing was developed (Willoughby & Hutcheson, 1978).…”

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confidence: 99%

“…This problem has been analyzed using an IRTmodel, motivated by Rasch (1960), for the longitudinal measurement of change with stochastic parameters that has been developed by Albers et al (1989). In the method section, we summarize the statistical model and estimation procedure.…”

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confidence: 99%

A Distribution‐Free Approach for Comparing Growth of Knowledge

Tan

Imbos

Does

1994

J Educational Measurement

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A distribution free approach for comparing growth of knowledgeTan, E.S.; Imbos, TJ.; Does, R.J.M.M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Download date: 12 May 2018Journal of Educational Measurement Spring 1994, Vol. 31, No. 1, pp. 51--65 Albers, Does, lmbos, and Janssen (1989). Furthermore, growth of knowledge has been described by means of a general regression model. Statistical inferences are possible using a randomization design extended to the situation where the observations are time-dependent proportions of correct answers.

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“…Albers et al (1989) defined a Rasch model with random item difficulty parameters for an application where items are obtained from an item bank. De Boeck (2008) also considered the Rasch model with random item difficulty parameters.…”

Section: Random Item Effects Modelingmentioning

confidence: 99%

“…Recently, random item parameters to model groupings of items or item characteristics with an error term have been used in several studies (Albers et al, 1989;De Boeck, 2008;Janssen et al, 2000;Glas et al, 2003;Glas, Van der Linden & Geerlings, 2010). Here, the conditional modeling of random item characteristics is extended with covariates explaining item parameter differences between items and between countries simultaneously using some combination of fixed and random explanatory item effects.…”

Section: Conditional Modeling: Explaining Variancementioning

confidence: 99%

Bayesian item response theory models for measurement variance

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Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without written permission of the author. Alle rechten voorbehouden. Niets uit deze uitgave mag worden verveelvuldigd, in enige vorm of op enige wijze, zonder voorafgaande schriftelijke toestemming van de auteur.

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A stochastic growth model applied to repeated tests of academic knowledge

Abstract: Rasch model, growth curves, regression, itembanking,

Cited by 25 publications

References 7 publications

Alternating imputation posterior estimation of models with crossed random effects

Alternating imputation posterior estimation of models with crossed random effects

A Distribution‐Free Approach for Comparing Growth of Knowledge

Bayesian item response theory models for measurement variance

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