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Johan Lyrvall; Zsuzsa Bakk; Jennifer Oser; Roberto Di Mari – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Simulation
Kazuhiro Yamaguchi – Journal of Educational and Behavioral Statistics, 2025
This study proposes a Bayesian method for diagnostic classification models (DCMs) for a partially known Q-matrix setting between exploratory and confirmatory DCMs. This Q-matrix setting is practical and useful because test experts have pre-knowledge of the Q-matrix but cannot readily specify it completely. The proposed method employs priors for…
Descriptors: Models, Classification, Bayesian Statistics, Evaluation Methods
Yuan Tian; Xi Yang; Suhail A. Doi; Luis Furuya-Kanamori; Lifeng Lin; Joey S. W. Kwong; Chang Xu – Research Synthesis Methods, 2024
RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two…
Descriptors: Risk, Randomized Controlled Trials, Classification, Robotics
Jang, Yoona; Hong, Sehee – Educational and Psychological Measurement, 2023
The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations,…
Descriptors: Classification, Models, Prediction, Sample Size
Hyemin Yoon; HyunJin Kim; Sangjin Kim – Measurement: Interdisciplinary Research and Perspectives, 2024
We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial…
Descriptors: Classification, Artificial Intelligence, Prediction, Decision Making
Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
Klingbeil, David A.; Van Norman, Ethan R.; Osman, David J.; Berry-Corie, Kimberly; Carberry, Caroline K.; Kim, Jessica S. – Journal of Psychoeducational Assessment, 2023
Early identification of students needing additional support is a foundational component of Multi-Tiered Systems of Support (MTSS). Due to the resource-intensive nature of implementing MTSS, it is critical that universal screening procedures are maximally accurate and efficient. The purpose of this study was to compare the classification accuracy…
Descriptors: Comparative Analysis, Benchmarking, Evaluation Methods, Screening Tests
Ji, Yu; Qiu, Qingying; Feng, Peien; Wu, Jianwei – International Journal of Technology and Design Education, 2019
The objective of this paper is to analyze the impact of the factors that stimulate inspiration in the design process. An empirical study is proposed in this paper. Three factors were summarized, including knowledge, knowledge relations and innovative strategies. Representations of these three factors that were extracted from the international…
Descriptors: Intellectual Property, Classification, Design, Undergraduate Students
McCloskey, George – Journal of Psychoeducational Assessment, 2017
This commentary will take an historical perspective on the Kaufman Test of Educational Achievement (KTEA) error analysis, discussing where it started, where it is today, and where it may be headed in the future. In addition, the commentary will compare and contrast the KTEA error analysis procedures that are rooted in psychometric methodology and…
Descriptors: Achievement Tests, Error Patterns, Comparative Analysis, Psychometrics
Siebrase, Benjamin – ProQuest LLC, 2018
Multilayer perceptron neural networks, Gaussian naive Bayes, and logistic regression classifiers were compared when used to make early predictions regarding one-year college student persistence. Two iterations of each model were built, utilizing a grid search process within 10-fold cross-validation in order to tune model parameters for optimal…
Descriptors: Classification, College Students, Academic Persistence, Bayesian Statistics
Møller, Jørgen; Skaaning, Svend-Erik – Sociological Methods & Research, 2017
Explanatory typologies have recently experienced a renaissance as a research strategy for constructing and assessing causal explanations. However, both the new methodological works on explanatory typologies and the way such typologies have been used in practice have been affected by two shortcomings. First, no elaborate procedures for assessing…
Descriptors: Classification, Case Studies, Evaluation Methods, Comparative Analysis
Malec, Wojciech; Krzeminska-Adamek, Malgorzata – Practical Assessment, Research & Evaluation, 2020
The main objective of the article is to compare several methods of evaluating multiple-choice options through classical item analysis. The methods subjected to examination include the tabulation of choice distribution, the interpretation of trace lines, the point-biserial correlation, the categorical analysis of trace lines, and the investigation…
Descriptors: Comparative Analysis, Evaluation Methods, Multiple Choice Tests, Item Analysis
Lee, Jenny J.; Vance, Hillary; Stensaker, Bjørn; Ghosh, Sowmya – Comparative Education, 2020
This study examined how hierarchical positions within the global field of higher education influence the selection of strategic priorities by universities in different parts of the world. The study particularly focused on universities' commitment to third missions as reflected in their strategic plans and compared to their global rankings. The…
Descriptors: Reputation, Institutional Evaluation, Universities, Strategic Planning
Rajagopal, Prabha; Ravana, Sri Devi – Information Research: An International Electronic Journal, 2017
Introduction: The use of averaged topic-level scores can result in the loss of valuable data and can cause misinterpretation of the effectiveness of system performance. This study aims to use the scores of each document to evaluate document retrieval systems in a pairwise system evaluation. Method: The chosen evaluation metrics are document-level…
Descriptors: Information Retrieval, Documentation, Scores, Information Systems
Lamprianou, Iasonas – Educational and Psychological Measurement, 2018
It is common practice for assessment programs to organize qualifying sessions during which the raters (often known as "markers" or "judges") demonstrate their consistency before operational rating commences. Because of the high-stakes nature of many rating activities, the research community tends to continuously explore new…
Descriptors: Social Networks, Network Analysis, Comparative Analysis, Innovation