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Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J. – Psychometrika, 2009
Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…
Descriptors: Test Items, Markov Processes, Item Response Theory, Measurement Techniques
Miyazaki, Kei; Hoshino, Takahiro; Mayekawa, Shin-ichi; Shigemasu, Kazuo – Psychometrika, 2009
This study proposes a new item parameter linking method for the common-item nonequivalent groups design in item response theory (IRT). Previous studies assumed that examinees are randomly assigned to either test form. However, examinees can frequently select their own test forms and tests often differ according to examinees' abilities. In such…
Descriptors: Test Format, Item Response Theory, Test Items, Test Bias
Yoo, Jin Eun – Educational and Psychological Measurement, 2009
This Monte Carlo study investigates the beneficiary effect of including auxiliary variables during estimation of confirmatory factor analysis models with multiple imputation. Specifically, it examines the influence of sample size, missing rates, missingness mechanism combinations, missingness types (linear or convex), and the absence or presence…
Descriptors: Monte Carlo Methods, Research Methodology, Test Validity, Factor Analysis
Meijer, Rob R.; And Others – 1994
Three methods for the estimation of the reliability of single dichotomous items are discussed. All methods are based on the assumptions of nondecreasing and nonintersecting item response functions and the Mokken model of double monotonicity. Based on analytical and Monte Carlo studies, it is concluded that one method is superior to the other two…
Descriptors: Estimation (Mathematics), Foreign Countries, Item Response Theory, Monte Carlo Methods
Carlson, James E.; Spray, Judith A. – 1986
This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…
Descriptors: Cutting Scores, Data Analysis, Difficulty Level, Error of Measurement