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Hans-Peter Piepho; Johannes Forkman; Waqas Ahmed Malik – Research Synthesis Methods, 2024
Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for…
Descriptors: Maximum Likelihood Statistics, Evidence, Networks, Meta Analysis
Mostafa Hosseinzadeh; Ki Lynn Matlock Cole – Educational and Psychological Measurement, 2024
In real-world situations, multidimensional data may appear on large-scale tests or psychological surveys. The purpose of this study was to investigate the effects of the quantity and magnitude of cross-loadings and model specification on item parameter recovery in multidimensional Item Response Theory (MIRT) models, especially when the model was…
Descriptors: Item Response Theory, Models, Maximum Likelihood Statistics, Algorithms
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Grantee Submission, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Journal of Educational Measurement, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Journal of Educational Measurement, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy
Kim, Su-Young; Huh, David; Zhou, Zhengyang; Mun, Eun-Young – International Journal of Behavioral Development, 2020
Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in…
Descriptors: Bayesian Statistics, Maximum Likelihood Statistics, Longitudinal Studies, Models
Ranger, Jochen; Kuhn, Jörg-Tobias; Wolgast, Anett – Journal of Educational Measurement, 2021
Van der Linden's hierarchical model for responses and response times can be used in order to infer the ability and mental speed of test takers from their responses and response times in an educational test. A standard approach for this is maximum likelihood estimation. In real-world applications, the data of some test takers might be partly…
Descriptors: Models, Reaction Time, Item Response Theory, Tests
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Grantee Submission, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy
Cho, April E.; Wang, Chun; Zhang, Xue; Xu, Gongjun – Grantee Submission, 2020
Multidimensional Item Response Theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that…
Descriptors: Item Response Theory, Mathematics, Statistical Inference, Maximum Likelihood Statistics
Hosseinzadeh, Mostafa – ProQuest LLC, 2021
In real-world situations, multidimensional data may appear on large-scale tests or attitudinal surveys. A simple structure, multidimensional model may be used to evaluate the items, ignoring the cross-loading of some items on the secondary dimension. The purpose of this study was to investigate the influence of structure complexity magnitude of…
Descriptors: Item Response Theory, Models, Simulation, Evaluation Methods
Chengyu Cui; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…
Descriptors: Item Response Theory, Accuracy, Simulation, Psychometrics
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
Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?
Schweizer, Karl; Troche, Stefan – Educational and Psychological Measurement, 2018
In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…
Descriptors: Investigations, Difficulty Level, Factor Analysis, Models
Jackson, Dan; Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C.; Baker, Rose – Research Synthesis Methods, 2017
Network meta-analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta-analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between-study heterogeneity. Models for network meta-analysis with random…
Descriptors: Meta Analysis, Network Analysis, Comparative Analysis, Outcomes of Treatment
Liu, Chen-Wei; Wang, Wen-Chung – Journal of Educational Measurement, 2017
The examinee-selected-item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set of items (e.g., choose one item to respond from a pair of items), always yields incomplete data (i.e., only the selected items are answered and the others have missing data) that are likely nonignorable. Therefore, using…
Descriptors: Item Response Theory, Models, Maximum Likelihood Statistics, Data Analysis

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