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Ferrando, Pere J.; Navarro-González, David – Educational and Psychological Measurement, 2021
Item response theory "dual" models (DMs) in which both items and individuals are viewed as sources of differential measurement error so far have been proposed only for unidimensional measures. This article proposes two multidimensional extensions of existing DMs: the M-DTCRM (dual Thurstonian continuous response model), intended for…
Descriptors: Item Response Theory, Error of Measurement, Models, Factor Analysis
Monroe, Scott – Journal of Educational and Behavioral Statistics, 2019
In item response theory (IRT) modeling, the Fisher information matrix is used for numerous inferential procedures such as estimating parameter standard errors, constructing test statistics, and facilitating test scoring. In principal, these procedures may be carried out using either the expected information or the observed information. However, in…
Descriptors: Item Response Theory, Error of Measurement, Scoring, Inferences
Falk, Carl F.; Cai, Li – Grantee Submission, 2015
In this paper, we present a flexible full-information approach to modeling multiple userdefined response styles across multiple constructs of interest. The model is based on a novel parameterization of the multidimensional nominal response model that separates estimation of overall item slopes from the scoring functions (indicating the order of…
Descriptors: Response Style (Tests), Item Response Theory, Outcome Measures, Models
Lee, Won-Chan – Applied Psychological Measurement, 2007
This article introduces a multinomial error model, which models an examinee's test scores obtained over repeated measurements of an assessment that consists of polytomously scored items. A compound multinomial error model is also introduced for situations in which items are stratified according to content categories and/or prespecified numbers of…
Descriptors: Simulation, Error of Measurement, Scoring, Test Items
Millman, Jason – 1972
Procedures for establishing standards and determining the number of items needed in criterion-referenced measures are reviewed. The discussion of setting a passing score is organized around five factors: performance of others, item content, educational consequences, psychological and financial costs, and measurement error. Classical test theory,…
Descriptors: Academic Achievement, Criterion Referenced Tests, Error of Measurement, Models
Wood, Phillip – 1994
The Reflective Judgment Model and associated interview (RJI) (Kitchener and King, 1981) measure the ability of individuals to reason about ill-structured problems. It has gained popularity as a measure of college outcomes associated with postsecondary education. This study examines general claims for the model made from existing data from 15 of 25…
Descriptors: Error of Measurement, Higher Education, Individual Differences, Interviews
Kolakowski, Donald – 1972
Empirical results are presented as regards the implementation of a latent-trait psychometric model by means of conditional maximum likelihood estimation. Items are scored polychotomously into varying numbers of nominal categories and the test and item characteristic curves and information functions are examined. It is concluded that scoring items…
Descriptors: Error of Measurement, Item Analysis, Item Sampling, Measurement Techniques
Suddick, David E.; And Others – 1985
The Test of Standard Written English (TSWE) is a 50-item multiple choice instrument designed to assess the ability of college students to use English. In this study, based upon a sample of 45 students, the TSWE was revalidated with writing samples. The coefficient of 0.54 was most impressive given that the TSWE scores were restricted to those…
Descriptors: Correlation, Error of Measurement, Essay Tests, Higher Education
Rizavi, Saba; Way, Walter D.; Davey, Tim; Herbert, Erin – Educational Testing Service, 2004
Item parameter estimates vary for a variety of reasons, including estimation error, characteristics of the examinee samples, and context effects (e.g., item location effects, section location effects, etc.). Although we expect variation based on theory, there is reason to believe that observed variation in item parameter estimates exceeds what…
Descriptors: Adaptive Testing, Test Items, Computation, Context Effect