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Dynamic assessment as a screening tool for early identification of reading disabilities: a latent change score approach

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Abstract

We examined whether dynamic decoding assessment (DDA) predicts growth in word reading skill during first grade using latent change score models. In addition, we compared classification accuracy of the DDA to static measures for identifying students at risk for reading disabilities (RD) designated using the dual discrepancy criteria. At the beginning of first grade, students (N = 104) were assessed on the DDA and static measures of word reading, arithmetic, and domain-general and domain-specific skills. They were assessed again at the end of first grade on static measures of word reading and arithmetic. In DDA, students were taught six novel symbols associated with English sounds and how to read words in this new orthography. Instructional prompts were provided incrementally, from least to most explicit. The amount of instructional prompts required for a student to decode words in this new orthography was indexed as students’ learning potential for decoding. Results from a series of latent change score models indicate the DDA is positively associated with growth in word reading but not with arithmetic skill growth. The DDA made significant, positive contributions to word reading growth beyond domain-specific (phonological awareness, rapid automatized naming) and domain-general (behavioral attention, intelligence) predictors. Furthermore, the DDA improved classification accuracy by improving sensitivity when added to the static measures of word reading predictors, supporting the use of DDA as a supplementary screener for early prediction of RD.

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  1. When the measurement model of the LCS model was retained in the SEM with WR2 as outcome, the model fit was very poor, χ2 (65) = 283.28, p < .01; RMSEA = .18, CFI = .80, TLI = .76, SRMR = .23, AIC = 7486.30, SBIC = 7466.23. Thus, we started from testing the measurement model for the alternative SEM model. To improve the model fit of this measurement model, we estimated the co-variances between wid1 and wid2, swe1 and swe2, and cbmT1 and cbmT2, resulting in adequate fit to data, χ2 (59) = 95.62, p < .01, RMSEA = .08, CFI = .87, TLI = .96, SRMR = .06, AIC = 7311.32, SBIC = 7288.17. Then, we estimated the structural path predicting WR2 with WR1 and DA and predicting AR2 with AR1 and DA. This model show inadequate fit to data, χ2 (61) = 178.38, p < .01, RMSEA = .14, CFI = .89, TLI = .86, SRMR = .18, AIC = 7387.45, SBIC = 7365.33. Structural path coefficients cannot be compared between the two models due to differences in the measurement models.

  2. When the measurement model of the LCS model was retained in the SEM with WR2 as outcome, the model fit was very poor, χ2 (52) = 240.27, p < .01, RMSEA = .18, CFI = .78, TLI = .70, SRMR = .15, AIC = 5260.45, SBIC = 5238.85. Thus, we started from testing the measurement model for the alternative SEM model. To improve the model fit of this measurement model, we estimated the covariances between SWE1 and SWE2, SWE1 and RAN-L, and fixed residual variance of WID to 0 to address Heywood case. This CFA model demonstrated good fit to data, χ2 (49) = 63.56, p < .01, RMSEA = .05, CFI = .98, TLI = .98, SRMR = .05, AIC = 6635.84, SBIC = 6607.54. Then, we estimated the structural path predicting WR2 with six predictors. Several modifications were necessary to resolve Heywood case, including fixing residual variance of WID2 and MR to 0. No other modifications improved the fit. This model showed poor fit to data, χ2 (51) = 203.88, p < .01, RMSEA = .17, CFI = .84, TLI = .75, SRMR = .19, AIC = 6770.81, SBIC = 6743.53. Structural path coefficients cannot be compared between the two models due to differences in the measurement model.

  3. Item-level data was initially not entered for the standardized assessment to calculate sample-based reliability coefficients.

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Acknowledgements

This study was in part supported by award number RG100898 from Dunn Family Foundation to Michigan State University. Study data were entered and managed using REDCap electronic data capture tools hosted at Vanderbilt University, which was supported by Vanderbilt Institute for Clinical and Translational Research Grant UL1 TR000445 from NCATS/NIH. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

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  1. Department of Counseling, Educational Psychology, and Special Education, Michigan State University, 620 Farm Lane, East Lansing, MI, 48824, USA

    Eunsoo Cho & Cynde Katherine Josol

  2. Department of Psychology, Florida State University, 2010 Levy Ave, Tallahassee, FL, 32310, USA

    Donald L. Compton

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Cho, E., Compton, D.L. & Josol, C.K. Dynamic assessment as a screening tool for early identification of reading disabilities: a latent change score approach. Read Writ 33, 719–739 (2020). https://doi.org/10.1007/s11145-019-09984-1

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