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Showing 91 to 105 of 3,822 results Save | Export
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Huang, Hung-Yu – Educational and Psychological Measurement, 2020
In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior--such as performance decline, dropping out beyond a certain point, and skipping certain items…
Descriptors: Item Response Theory, Response Style (Tests), Test Items, Statistical Analysis
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Ranger, Jochen; Kuhn, Jörg Tobias; Ortner, Tuulia M. – Educational and Psychological Measurement, 2020
The hierarchical model of van der Linden is the most popular model for responses and response times in tests. It is composed of two separate submodels--one for the responses and one for the response times--that are joined at a higher level. The submodel for the response times is based on the lognormal distribution. The lognormal distribution is a…
Descriptors: Reaction Time, Tests, Statistical Distributions, Models
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Liang, Xinya; Kamata, Akihito; Li, Ji – Educational and Psychological Measurement, 2020
One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance…
Descriptors: Bayesian Statistics, Randomized Controlled Trials, Effect Size, Sampling
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Zhou, Sherry; Huggins-Manley, Anne Corinne – Educational and Psychological Measurement, 2020
The semi-generalized partial credit model (Semi-GPCM) has been proposed as a unidimensional modeling method for handling not applicable scale responses and neutral scale responses, and it has been suggested that the model may be of use in handling missing data in scale items. The purpose of this study is to evaluate the ability of the…
Descriptors: Models, Statistical Analysis, Response Style (Tests), Test Items
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Liu, Yuan; Hau, Kit-Tai – Educational and Psychological Measurement, 2020
In large-scale low-stake assessment such as the Programme for International Student Assessment (PISA), students may skip items (missingness) which are within their ability to complete. The detection and taking care of these noneffortful responses, as a measure of test-taking motivation, is an important issue in modern psychometric models.…
Descriptors: Response Style (Tests), Motivation, Test Items, Statistical Analysis
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Lee, HyeSun; Smith, Weldon Z. – Educational and Psychological Measurement, 2020
Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response…
Descriptors: Bayesian Statistics, Item Response Theory, Monte Carlo Methods, Test Format
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Murrah, William M. – Educational and Psychological Measurement, 2020
Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by…
Descriptors: Error of Measurement, Statistical Bias, Multiple Regression Analysis, Predictor Variables
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Ulitzsch, Esther; von Davier, Matthias; Pohl, Steffi – Educational and Psychological Measurement, 2020
So far, modeling approaches for not-reached items have considered one single underlying process. However, missing values at the end of a test can occur for a variety of reasons. On the one hand, examinees may not reach the end of a test due to time limits and lack of working speed. On the other hand, examinees may not attempt all items and quit…
Descriptors: Item Response Theory, Test Items, Response Style (Tests), Computer Assisted Testing
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Babcock, Ben; Hodge, Kari J. – Educational and Psychological Measurement, 2020
Equating and scaling in the context of small sample exams, such as credentialing exams for highly specialized professions, has received increased attention in recent research. Investigators have proposed a variety of both classical and Rasch-based approaches to the problem. This study attempts to extend past research by (1) directly comparing…
Descriptors: Item Response Theory, Equated Scores, Scaling, Sample Size
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Chen, Michelle Y.; Liu, Yan; Zumbo, Bruno D. – Educational and Psychological Measurement, 2020
This study introduces a novel differential item functioning (DIF) method based on propensity score matching that tackles two challenges in analyzing performance assessment data, that is, continuous task scores and lack of a reliable internal variable as a proxy for ability or aptitude. The proposed DIF method consists of two main stages. First,…
Descriptors: Probability, Scores, Evaluation Methods, Test Items
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Ippel, Lianne; Magis, David – Educational and Psychological Measurement, 2020
In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general…
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Standards
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Cui, Zhongmin – Educational and Psychological Measurement, 2020
In test security analyses, answer copying, collusion, and the use of a shared brain dump site can be detected by checking similarity between item response strings. The similarity, however, can possibly be contaminated by aberrant data resulted from careless responding or rapid guessing. For example, some test-takers may answer by repeating a…
Descriptors: Repetition, Cheating, Response Style (Tests), Pattern Recognition
Shi, Dexin; Maydeu-Olivares, Alberto – Educational and Psychological Measurement, 2020
We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual…
Descriptors: Structural Equation Models, Computation, Maximum Likelihood Statistics, Least Squares Statistics
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Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2020
This note raises caution that a finding of a marked pseudo-guessing parameter for an item within a three-parameter item response model could be spurious in a population with substantial unobserved heterogeneity. A numerical example is presented wherein each of two classes the two-parameter logistic model is used to generate the data on a…
Descriptors: Guessing (Tests), Item Response Theory, Test Items, Models
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McGrath, Kathleen V.; Leighton, Elizabeth A.; Ene, Mihaela; DiStefano, Christine; Monrad, Diane M. – Educational and Psychological Measurement, 2020
Survey research frequently involves the collection of data from multiple informants. Results, however, are usually analyzed by informant group, potentially ignoring important relationships across groups. When the same construct(s) are measured, integrative data analysis (IDA) allows pooling of data from multiple sources into one data set to…
Descriptors: Educational Environment, Meta Analysis, Student Attitudes, Teacher Attitudes
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