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Lane, Sean P.; Kelleher, Bridgette L. – Developmental Psychology, 2023
Recruiting participants for studies of early-life longitudinal development is challenging, often resulting in practical upper bounds in sample size and missing data due to attrition. These factors pose risks for the statistical power of such studies depending on the intended analytic model. One mitigation strategy is to increase measurement…
Descriptors: Longitudinal Studies, Child Development, Hierarchical Linear Modeling, Research Design
Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model
Akaeze, Hope O.; Lawrence, Frank R.; Wu, Jamie Heng-Chieh – Educational and Psychological Measurement, 2023
Multidimensionality and hierarchical data structure are common in assessment data. These design features, if not accounted for, can threaten the validity of the results and inferences generated from factor analysis, a method frequently employed to assess test dimensionality. In this article, we describe and demonstrate the application of the…
Descriptors: Measures (Individuals), Multidimensional Scaling, Tests, Hierarchical Linear Modeling