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The Impact of Measurement Noninvariance across Time and Group in Longitudinal Item Response Modeling
In-Hee Choi – Asia Pacific Education Review, 2024
Longitudinal item response data often exhibit two types of measurement noninvariance: the noninvariance of item parameters between subject groups and that of item parameters across multiple time points. This study proposes a comprehensive approach to the simultaneous modeling of both types of measurement noninvariance in terms of longitudinal item…
Descriptors: Longitudinal Studies, Item Response Theory, Growth Models, Error of Measurement
Grimm, Kevin J.; Fine, Kimberly; Stegmann, Gabriela – International Journal of Behavioral Development, 2021
Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores…
Descriptors: Error of Measurement, Weighted Scores, Accuracy, Item Response Theory
Sanford R. Student; Derek C. Briggs; Laurie Davis – Educational Measurement: Issues and Practice, 2025
Vertical scales are frequently developed using common item nonequivalent group linking. In this design, one can use upper-grade, lower-grade, or mixed-grade common items to estimate the linking constants that underlie the absolute measurement of growth. Using the Rasch model and a dataset from Curriculum Associates' i-Ready Diagnostic in math in…
Descriptors: Elementary School Mathematics, Elementary School Students, Middle School Mathematics, Middle School Students
Shi, Dexin; DiStefano, Christine; Zheng, Xiaying; Liu, Ren; Jiang, Zhehan – International Journal of Behavioral Development, 2021
This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML…
Descriptors: Growth Models, Maximum Likelihood Statistics, Factor Analysis, Sample Size
Nazari, Sanaz; Leite, Walter L.; Huggins-Manley, A. Corinne – Journal of Experimental Education, 2023
The piecewise latent growth models (PWLGMs) can be used to study changes in the growth trajectory of an outcome due to an event or condition, such as exposure to an intervention. When there are multiple outcomes of interest, a researcher may choose to fit a series of PWLGMs or a single parallel-process PWLGM. A comparison of these models is…
Descriptors: Growth Models, Statistical Analysis, Intervention, Comparative Analysis
Fan Pan – ProQuest LLC, 2021
This dissertation informed researchers about the performance of different level-specific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using…
Descriptors: Goodness of Fit, Item Response Theory, Growth Models, Monte Carlo Methods