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Download full textSkibbe, Lori E.; Bowles, Ryan P.; Goodwin, Sarah; Troia, Gary A.; Konishi, Haruka – Language, Speech, and Hearing Services in Schools, 2020
Purpose: The Access to Literacy Assessment System--Phonological Awareness (ATLAS-PA) was developed for use with children with speech and/or language impairment. The subtests (Rhyming, Blending, and Segmenting) are appropriate for children who are 3-7 years of age. ATLAS-PA is composed entirely of receptive items, incorporates individualized levels…
Descriptors: Phonological Awareness, Speech Impairments, Language Impairments, Young Children
Direct linkSkibbe, Lori E.; Bowles, Ryan P.; Goodwin, Sarah; Troia, Gary A.; Konishi, Haruka – Grantee Submission, 2020
Purpose: The Access to Literacy Assessment System--Phonological Awareness (ATLAS-PA) was developed for use with children with speech and/or language impairment. The subtests (rhyming, blending, segmenting) are appropriate for children who are 3 to 7 years of age. ATLAS-PA is comprised entirely of receptive items, incorporates individualized levels…
Descriptors: Phonological Awareness, Speech Impairments, Language Impairments, Young Children
Lori E. Skibbe; Nicholas E. Waters; Ryan P. Bowles – Journal of Research in Reading, 2025
Background: The current study examined whether the home literacy environment (HLE) has the same meaning and predictive value for children with and without speech and/or language impairment. Methods: Parents reported on their HLE for children with typical development (n = 811) and those with speech and/or language impairment (S/LI, n = 235). All…
Descriptors: Family Literacy, Family Environment, Preschool Children, Speech Impairments
Clark, D. Angus; Bowles, Ryan P. – Grantee Submission, 2018
In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present…
Descriptors: Factor Analysis, Goodness of Fit, Factor Structure, Monte Carlo Methods
Clark, D. Angus; Nuttall, Amy K.; Bowles, Ryan P. – Grantee Submission, 2018
Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study…
Descriptors: Robustness (Statistics), Statistical Analysis, Longitudinal Studies, Statistical Bias
Clark, D. Angus; Nuttall, Amy K.; Bowles, Ryan P. – International Journal of Behavioral Development, 2021
Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change…
Descriptors: Structural Equation Models, Longitudinal Studies, Risk, Accuracy
