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Abdul Haq; Muhammad Usman; Manzoor Khan – Measurement: Interdisciplinary Research and Perspectives, 2024
Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined…
Descriptors: Sampling, Error of Measurement, Evaluation Methods, Statistical Bias
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Stefanie A. Wind; Benjamin Lugu; Yurou Wang – International Journal of Testing, 2025
Mokken Scale Analysis (MSA) is a nonparametric approach that offers exploratory tools for understanding the nature of item responses while emphasizing invariance requirements. MSA is often discussed as it relates to Rasch measurement theory, which also emphasizes invariance, but uses parametric models. Researchers who have compared and combined…
Descriptors: Item Response Theory, Scaling, Surveys, Evaluation Methods
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Susan K. Johnsen – Gifted Child Today, 2025
The author provides information about reliability and areas that educators should examine in determining if an assessment is consistent and trustworthy for use, and how it should be interpreted in making decisions about students. Reliability areas that are discussed in the column include internal consistency, test-retest or stability, inter-scorer…
Descriptors: Test Reliability, Academically Gifted, Student Evaluation, Error of Measurement
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Timothy R. Konold; Elizabeth A. Sanders; Kelvin Afolabi – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Measurement invariance (MI) is an essential part of validity evidence concerned with ensuring that tests function similarly across groups, contexts, and time. Most evaluations of MI involve multigroup confirmatory factor analyses (MGCFA) that assume simple structure. However, recent research has shown that constraining non-target indicators to…
Descriptors: Evaluation Methods, Error of Measurement, Validity, Monte Carlo Methods
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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
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Suppanut Sriutaisuk; Yu Liu; Seungwon Chung; Hanjoe Kim; Fei Gu – Educational and Psychological Measurement, 2025
The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two…
Descriptors: Structural Equation Models, Error of Measurement, Programming Languages, Goodness of Fit
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Jiaying Xiao; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to…
Descriptors: Error of Measurement, Models, Evaluation Methods, Item Analysis
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Tenko Raykov – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This note demonstrates that measurement invariance does not guarantee meaningful and valid group comparisons in multiple-population settings. The article follows on a recent critical discussion by Robitzsch and Lüdtke, who argued that measurement invariance was not a pre-requisite for such comparisons. Within the framework of common factor…
Descriptors: Error of Measurement, Prerequisites, Factor Analysis, Evaluation Methods
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So, Julia Wai-Yin – Assessment Update, 2023
In this article, Julia So discusses the purpose of program assessment, four common missteps of program assessment and reporting, and how to prevent them. The four common missteps of program assessment and reporting she has observed are: (1) unclear or ambiguous program goals; (2) measurement error of program goals and outcomes; (3) incorrect unit…
Descriptors: Program Evaluation, Community Colleges, Evaluation Methods, Objectives
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van Aert, Robbie C. M. – Research Synthesis Methods, 2023
The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta-analysis, but two of the assumptions of the common equal-effect and random-effects meta-analysis model are by definition violated.…
Descriptors: Correlation, Meta Analysis, Sampling, Simulation
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David Goretzko; Karik Siemund; Philipp Sterner – Educational and Psychological Measurement, 2024
Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs.…
Descriptors: Factor Analysis, Goodness of Fit, Psychological Studies, Measurement
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Rosanna Cole – Sociological Methods & Research, 2024
The use of inter-rater reliability (IRR) methods may provide an opportunity to improve the transparency and consistency of qualitative case study data analysis in terms of the rigor of how codes and constructs have been developed from the raw data. Few articles on qualitative research methods in the literature conduct IRR assessments or neglect to…
Descriptors: Interrater Reliability, Error of Measurement, Evaluation Methods, Research Methodology
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Carpentras, Dino; Quayle, Michael – International Journal of Social Research Methodology, 2023
Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…
Descriptors: Psychometrics, Error of Measurement, Models, Prediction
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Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
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Tong Wu; Stella Y. Kim; Carl Westine; Michelle Boyer – Journal of Educational Measurement, 2025
While significant attention has been given to test equating to ensure score comparability, limited research has explored equating methods for rater-mediated assessments, where human raters inherently introduce error. If not properly addressed, these errors can undermine score interchangeability and test validity. This study proposes an equating…
Descriptors: Item Response Theory, Evaluators, Error of Measurement, Test Validity
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