Jari Metsämuuronen’s (2022) article introduces a significant advancement in how reliability is estimated within psychological assessments. The study critiques traditional methods for their tendency to yield deflated results and proposes new attenuation-corrected estimators to address these limitations. This review examines the article’s contributions and its implications for improving measurement precision. …
Lenhard and Lenhard (2021) investigate how regression-based continuous norming can enhance the quality of norm scores in psychometric testing. Their study compares semiparametric continuous norming (SPCN) with conventional methods, evaluating performance across a wide range of simulated test conditions and sample sizes. Background Norm scores are crucial in psychological and …
The study by Xiao and Bulut (2020) evaluates how different methods for handling missing data perform when estimating ability parameters from sparse datasets. Using two Monte Carlo simulations, the research highlights the strengths and limitations of four approaches, providing valuable insights for researchers and practitioners in educational and psychological measurement. …
Short-form (SF) IQ estimations are often used in clinical settings to provide efficient assessments of intelligence without administering the full test. Lace et al. (2022) examined the effectiveness of various five- and four-subtest combinations for estimating full-scale IQ (FSIQ) on the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V). Their findings …
Babcock and Hodge (2020) address a significant challenge in educational measurement: accurately equating exam scores when sample sizes are limited. Their study evaluates the performance of Rasch and classical equating methods, particularly for credentialing exams with small cohorts, and introduces data pooling as a potential solution. Background Equating ensures fairness …
The study by Shi and Maydeu-Olivares (2020) analyzes how different estimation methods influence key fit indices in Structural Equation Modeling (SEM). By focusing on methods such as Maximum Likelihood (ML), Unweighted Least Squares (ULS), and Diagonally Weighted Least Squares (DWLS), the authors explore the nuances of model fit across various …
