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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

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

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

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

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

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

Sideridis, Georgios D.; Tsaousis, Ioannis; Alamri, Abeer A. – Educational and Psychological Measurement, 2020

The main thesis of the present study is to use the Bayesian structural equation modeling (BSEM) methodology of establishing approximate measurement invariance (A-MI) using data from a national examination in Saudi Arabia as an alternative to not meeting strong invariance criteria. Instead, we illustrate how to account for the absence of…

Descriptors: Bayesian Statistics, Structural Equation Models, Foreign Countries, Error of Measurement

Fujimoto, Ken A.; Neugebauer, Sabina R. – Educational and Psychological Measurement, 2020

Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are…

Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Factor Structure

Feuerstahler, Leah M.; Waller, Niels; MacDonald, Angus, III – Educational and Psychological Measurement, 2020

Although item response models have grown in popularity in many areas of educational and psychological assessment, there are relatively few applications of these models in experimental psychopathology. In this article, we explore the use of item response models in the context of a computerized cognitive task designed to assess visual working memory…

Descriptors: Item Response Theory, Psychopathology, Intelligence Tests, Psychological Evaluation

Kim, Jinho; Wilson, Mark – Educational and Psychological Measurement, 2020

This study investigates polytomous item explanatory item response theory models under the multivariate generalized linear mixed modeling framework, using the linear logistic test model approach. Building on the original ideas of the many-facet Rasch model and the linear partial credit model, a polytomous Rasch model is extended to the item…

Descriptors: Item Response Theory, Test Items, Models, Responses

Goretzko, David; Heumann, Christian; Bühner, Markus – Educational and Psychological Measurement, 2020

Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis--and especially the process of factor…

Descriptors: Factor Analysis, Data Analysis, Research Methodology, Psychological Studies

Liu, Yue; Cheng, Ying; Liu, Hongyun – Educational and Psychological Measurement, 2020

The responses of non-effortful test-takers may have serious consequences as non-effortful responses can impair model calibration and latent trait inferences. This article introduces a mixture model, using both response accuracy and response time information, to help differentiating non-effortful and effortful individuals, and to improve item…

Descriptors: Item Response Theory, Test Wiseness, Response Style (Tests), Reaction Time

Using Differential Item Functioning to Test for Interrater Reliability in Constructed Response Items

Walker, Cindy M.; Göçer Sahin, Sakine – Educational and Psychological Measurement, 2020

The purpose of this study was to investigate a new way of evaluating interrater reliability that can allow one to determine if two raters differ with respect to their rating on a polytomous rating scale or constructed response item. Specifically, differential item functioning (DIF) analyses were used to assess interrater reliability and compared…

Descriptors: Test Bias, Interrater Reliability, Responses, Correlation

Liang, Xinya – Educational and Psychological Measurement, 2020

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both…

Descriptors: Factor Structure, Bayesian Statistics, Structural Equation Models, Goodness of Fit