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Carlos Cinelli; Andrew Forney; Judea Pearl – Sociological Methods & Research, 2024
Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is…
Descriptors: Regression (Statistics), Robustness (Statistics), Error of Measurement, Testing Problems
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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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Christine E. DeMars; Paulius Satkus – Educational and Psychological Measurement, 2024
Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal…
Descriptors: Item Response Theory, Statistical Distributions, Error of Measurement, Bayesian Statistics
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Yanxuan Qu; Sandip Sinharay – ETS Research Report Series, 2024
The goal of this paper is to find better ways to estimate the internal consistency reliability of scores on tests with a specific type of design that are often encountered in practice: tests with constructed-response items clustered into sections that are not parallel or tau-equivalent, and one of the sections has only one item. To estimate the…
Descriptors: Test Reliability, Essay Tests, Construct Validity, Error of Measurement
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Huang, Francis L.; Zhang, Bixi; Li, Xintong – Journal of Research on Educational Effectiveness, 2023
Binary outcomes are often analyzed in cluster randomized trials (CRTs) using logistic regression and cluster robust standard errors (CRSEs) are routinely used to account for the dependent nature of nested data in such models. However, CRSEs can be problematic when the number of clusters is low (e.g., < 50) and, with CRTs, a low number of…
Descriptors: Robustness (Statistics), Error of Measurement, Regression (Statistics), Multivariate Analysis
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Philipp Sterner; Kim De Roover; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2025
When comparing relations and means of latent variables, it is important to establish measurement invariance (MI). Most methods to assess MI are based on confirmatory factor analysis (CFA). Recently, new methods have been developed based on exploratory factor analysis (EFA); most notably, as extensions of multi-group EFA, researchers introduced…
Descriptors: Error of Measurement, Measurement Techniques, Factor Analysis, Structural Equation Models
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Hung-Yu Huang – Educational and Psychological Measurement, 2025
The use of discrete categorical formats to assess psychological traits has a long-standing tradition that is deeply embedded in item response theory models. The increasing prevalence and endorsement of computer- or web-based testing has led to greater focus on continuous response formats, which offer numerous advantages in both respondent…
Descriptors: Response Style (Tests), Psychological Characteristics, Item Response Theory, Test Reliability
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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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Xijuan Zhang; Hao Wu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A full structural equation model (SEM) typically consists of both a measurement model (describing relationships between latent variables and observed scale items) and a structural model (describing relationships among latent variables). However, often researchers are primarily interested in testing hypotheses related to the structural model while…
Descriptors: Structural Equation Models, Goodness of Fit, Robustness (Statistics), Factor Structure
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Duane Knudson – Measurement in Physical Education and Exercise Science, 2025
Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four…
Descriptors: Kinesiology, Exercise, Biomechanics, Movement Education
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Daniel McNeish; Melissa G. Wolf – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Despite the popularity of traditional fit index cutoffs like RMSEA [less than or equal to] 0.06 and CFI [greater than or equal to] 0.95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been proposed to avoid overgeneralization by deriving cutoffs specifically tailored to the characteristics…
Descriptors: Structural Equation Models, Cutting Scores, Generalizability Theory, Error of Measurement
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Hyunjung Lee; Heining Cham – Educational and Psychological Measurement, 2024
Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Goodness of Fit
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Bang Quan Zheng; Peter M. Bentler – Structural Equation Modeling: A Multidisciplinary Journal, 2025
This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can…
Descriptors: Monte Carlo Methods, Structural Equation Models, Goodness of Fit, Robustness (Statistics)
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Dandan Tang; Steven M. Boker; Xin Tong – Structural Equation Modeling: A Multidisciplinary Journal, 2025
The replication crisis in social and behavioral sciences has raised concerns about the reliability and validity of empirical studies. While research in the literature has explored contributing factors to this crisis, the issues related to analytical tools have received less attention. This study focuses on a widely used analytical tool -…
Descriptors: Test Validity, Factor Analysis, Replication (Evaluation), Social Science Research
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Breanne J. Byiers; Alyssa M. Merbler; Chantel C. Burkitt; Frank J. Symons – American Journal on Intellectual and Developmental Disabilities, 2025
Sleep problems are common in Rett syndrome and other neurogenetic syndromes. Actigraphy is a cost-effective, objective method for measuring sleep. Current guidelines require caregiver-reported bed and wake times to facilitate actigraphy data scoring. The current study examined missingness and consistency of caregiver-reported bed and wake times…
Descriptors: Sleep, Neurodevelopmental Disorders, Psychomotor Skills, Genetic Disorders
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