Skip to main content

Advertisement

SpringerLink
Log in

Developing Early Reading Profiles for Latinx Kindergarten Students Using Typical Universal Screeners

  • Original Research
  • Published:
Contemporary School Psychology Aims and scope Submit manuscript

Abstract

The purpose of the current study was to determine students’ early reading profiles at the beginning and end of kindergarten using existing school-based measures with a sample of Spanish speaking, Latinx students. Using latent profile analysis, a multivariate analytic technique that empirically identifies profiles based on student responses to multiple measures, students’ performance was compiled using typical universal screening measures of reading, a formal measure of English language development, and information regarding referral for special education evaluation and grade retention. At the beginning of the year, students demonstrating greater English proficiency also had increased ability on initial sound fluency and letter naming fluency tasks in English. By the end of the year, students demonstrating continued deficits in early literacy skills (phoneme segmentation fluency, letter naming, and nonsense word fluency) included students referred for special education or grade level retention than students who demonstrated greater proficiency in these skill areas. Recommendations for research and school-based practices are provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Adlof, S. M., Catts, H. W., & Little, T. (2006). Should the simple view of reading include a fluency component? Reading and Writing: An Interdisciplinary Journal, 19, 933–958.

    Article  Google Scholar 

  • Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21, 329–341. https://doi.org/10.1080/10705511.2014.915181.

    Article  Google Scholar 

  • August, D., & Shanahan, T. (2006). Developing literacy in second-language learners: report of the national literacy panel on language-minority children and youth. Mahwah: Center for Applied Linguistics, Lawrence Erlbaum Associates.

    Google Scholar 

  • Beach, K. D., & O’Connor, R. E. (2015). Early response-to-intervention measures and criteria as predictors of reading disability in the beginning of third grade. Journal of Learning Disabilities, 48, 196–223. https://doi.org/10.1177/0022219413495451.

    Article  PubMed  Google Scholar 

  • Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: one-step versus three-step estimators. Political Analysis, 12, 3–27. https://doi.org/10.1093/pan/mph001.

    Article  Google Scholar 

  • Bowman-Perrott, L. J., Herrera, S., & Murry, K. (2010). Reading difficulties and grade retention: what’s the connection for English language learners? Reading and Writing Quarterly, 26, 91–107.

    Article  Google Scholar 

  • Brown, E. J., & Sanford, A. (2011). RTI for English language learners: appropriately using screening and progress monitoring tools to improve instructional outcomes. Washington, DC: U.S. Department of Education, Office of Special Education Programs, National Center on Response to Intervention. Retrieved from http://www.rti4success.org/images/stories/pdfs/rtiforells.pdf.

  • The Annie E. Casey Foundation. (2010). Early warning! Why reading by the end of third grade matters. A kids counts special report. Baltimore, MD: Retrieved from http://www.aecf.org/resources/early-warning-why-reading-by-the-end-of-third-grade-matters/.

  • Cabell, S. Q., Justice, L. M., Logan, J. A., & Konold, T. R. (2013). Emergent literacy profiles among prekindergarten children from low-SES backgrounds: longitudinal considerations. Early Childhood Research Quarterly, 28, 608–620.

    Article  Google Scholar 

  • California Department of Education. (2006). California English Language Development Test. Retrieved August, 8, 2018, from http://www.cde.ca.gov/ta/tg/el/.

  • California Department of Education. (2005). California English Language Development Test: technical report for the California English Language Development Test (CELDT) 2004–2005. Sacramento: Department of Education.

    Google Scholar 

  • Center on Response to Intervention. (2019). Essential components of RTI: universal screening. Retrieved from http://www.rti4success.org/.

  • Dickinson, D. K., McCabe, A., Clark-Chiarelli, N., & Wolf, A. (2004). Cross-language transfer of phonological awareness in low-income Spanish and English bilingual preschool children. Applied Psycholinguistics, 25, 323-347. https://doi.org/10.1017/S0142716404001158.

  • Domínguez de Ramírez, R., & Shapiro, E. S. (2007). Cross-language relationship between Spanish and English oral reading fluency among Spanish-speaking English language learners in bilingual education classrooms. Psychology in the Schools, 44, 795–806.

    Article  Google Scholar 

  • Edynburn, K. L., Quirk, M., Felix, E., Swami, S., Goldstein, A. T., & Scheller, J. (2017). Literacy screening among Latino/a and dual language learner kindergarteners: predicting first grade reading achievement. Literacy Research and Instruction., 56, 250–267. https://doi.org/10.1080/19388071.2017.1305470.

    Article  Google Scholar 

  • Esparza-Brown, J., & Sanford, A. (2011). RTI for English language learners: appropriately using screening and progress monitoring tools to improve instructional outcomes. Washington, DC: U.S. Department of Education, Office of Special Education Programs, National Center on Response to Intervention. Retrieved from rti4success.org/sites/default/files/rtiforells.pdf.

  • Fien, H., Baker, S. K., Smolkowski, K., Mercier Smith, J. L., Kame’enui, E. J., & Thomas Beck, C. (2008). Using nonsense word fluency to predict reading proficiency in kindergarten through second grade for English learners and native English speakers. School Psychology Review, 37, 391–408.

    Article  Google Scholar 

  • Fien, H., Kame’enui, E. J., & Good, R. (2009). Schools engaged in school-wide reading reform: an examination of the school and individual student predictors of kindergarten early reading outcomes. School Effectiveness and School Improvement, 20, 1–25. https://doi.org/10.1080/09243450802605571.

    Article  Google Scholar 

  • Gonzalez, J., Pollard-Durodola, S., Saenz, L., Soares, D., Davis, H., Resendez, N., & Zhu, L. (2016). Spanish and English early literacy profiles of preschool Latino English language learner children. Early Education and Development, 27, 513–531. doi: 10.1080/10409289.2015.1077038.

  • Good, R. H., Kaminski, R. A., Shinn, M., Bratten, J., Shinn, M., Laimon, D., Smith, S., & Flindt, N. (2004). Technical Adequacy of DIBELS: results of the Early Childhood Research Institute on measuring growth and development (Technical Report, No. 7). Eugene: University of Oregon.

    Google Scholar 

  • Grimm, R. P. Solari, E. J., & Gerber, M. M. (2018). A longitudinal investigation of reading development from kindergarten to grade eight in a Spanish-speaking bilingual population. Reading and Writing, 31(3), 559–581

  • Haager, D. (2007). Promises and cautions regarding using response to intervention with English language learners. Learning Disabilities Quarterly, 30, 213–218.

    Article  Google Scholar 

  • Hughes, C. A., & Dexter, D. D. (2011). Response to intervention: a research-based summary. Theory Into Practice, 50, 4–11.

    Article  Google Scholar 

  • Kiminski, R. A., & Good III, R. H. (1996). Toward a technology for assessing basic early literacy skills. School Psychology Review, 25, 215–227.

  • Kena, G., Musu-Gillette, L., Robinson, J., Wang, X., Rathbun, A., Zhang, J., Wilkinson-Flicker, S., Barmer, A., and Dunlop Velez, E. (2015). The condition of education 2015 (NCES 2015-144). U.S. Department of Education, National Center for education statistics. Washington, DC. Retrieved from http://nces.ed.gov/pubsearch.

  • Lonigan, C. J., Goodrich, J. M., & Farver, J. M. (2018). Identifying differences in early literacy skills across subgroups of language-minority children: a latent profile analysis. Developmental Psychology, 54, 631–647. https://doi.org/10.1037/dev0000477.

    Article  PubMed  Google Scholar 

  • Masyn, K. (2013). Latent class analysis and finite mixture modeling. In T. Little (Ed.), The Oxford handbook of quantitative methods in psychology (Vol. 2, pp. 375–393). Oxford: Oxford University Press.

    Google Scholar 

  • McFarland, J., Hussar, B., Wang, X., Zhang, J., Wang, K., Rathbun, A., Barmer, A., Forrest Cataldi, E., and Bullock Mann, F. (2018). The condition of education 2018 (NCES 2018-144). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2018144.

  • McLachlan, G., & Peel, D. (2004). Finite mixture models. John Wiley & Sons.

  • Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003). Psychological Methods, 8, 369–377. https://doi.org/10.1037/1082-989X.8.3.369.

    Article  PubMed  Google Scholar 

  • Muyskens, P., Betts, J., Lau, M., & Marston, D. (2009). Predictive validity of curriculum-based measures in the reading assessment of students who are English language learners. The California School Psychologist, 14, 11–21. https://doi.org/10.1007/BF03340947.

    Article  Google Scholar 

  • National Early Literacy Panel. (2008). Developing early literacy: report of the National Early Literacy Panel. Washington, DC: National Institute for Literacy.

    Google Scholar 

  • Norwalk, K. E., DiPerna, J. C., Lei, P., & Wu, Q. (2012). Examining early literacy skill differences among children in head start via latent profile analysis. School Psychology Quarterly, 27, 170–183.

    Article  Google Scholar 

  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535–569. https://doi.org/10.1080/10705510701575396.

    Article  Google Scholar 

  • O’Connor, R. E., Bocian, K., Beebe-Frankenberger, M., & Linklater, D. L. (2010). Responsiveness of students with language difficulties to early intervention in reading. The Journal of Special Education, 43, 220–235. https://doi.org/10.1177/0022466908317789.

    Article  Google Scholar 

  • Oh, D., Haager, D., & Windmeuller, M. (2007). A longitudinal study predicting reading success for English-language learners from kindergarten to grade 1. Multiple Voices for Ethnically Diverse Exceptional Learners, 10, 107–124.

    Google Scholar 

  • Schatschneider, C., Carlson, C. D., Francis, D. J., Foorman, B. R., & Fletcher, J. M. (2002). Relationship of rapid automatized naming and phonological awareness in early reading development: implications for the double-deficit hypothesis. Journal of Learning Disabilities, 35, 245–256. https://doi.org/10.1177/002221940203500306.

    Article  PubMed  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  • Solari, E. J., Petscher, Y., & Folsom, J. (2014). Differentiating literacy growth of ELL students with LD from other high-risk subgroups and general education peers: evidence from grades 3-10. Journal of Learning Disabilities, 47, 329–348.

    Article  Google Scholar 

  • Sullivan, A. L. (2011). Disproportionality in special education identification and placement of English language learners. Exceptional Children, 77, 317–334.

    Article  Google Scholar 

  • Vanderwood, M. L., Linklater, D., & Healy, K. (2008). Predictive accuracy of nonsense word fluency for English language learners. School Psychology Review, 37, 5–17.

    Article  Google Scholar 

  • Wasserman, L. (2000). Bayesian model selection and model averaging. Journal of Mathematical Psychology, 44, 92–107. https://doi.org/10.1006/jmps.1999.1278.

    Article  PubMed  Google Scholar 

  • Wolff, U. (2010). Subgrouping of readers based on performance measures: a latent profile analysis. Reading and Writing, 23, 209–238. https://doi.org/10.1007/s11145-008-9160-8.

    Article  Google Scholar 

  • Yesil-Dagli, U. (2011). Predicting ELL students’ beginning first grade English oral reading fluency from initial kindergarten vocabulary, letter naming, and phonological awareness skills. Early Childhood Research Quarterly, 26, 15–29.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge that the current project was an extension of Dr. Diane Haager and Dr. Michelle Windmueller’s PLUS research while at California State University, Los Angeles. We would also like to thank Dr. Haager and Dr. Emily Solari for their initial feedback to earlier drafts of this manuscript and Dr. Higareda for his earlier contributions to the methodology for this project’s proposal.

Author information

Authors and Affiliations

  1. Loyola Marymount University, 1 LMU Drive, University Hall, Suite 1500, Los Angeles, CA, 90045, USA

    Terese C. Aceves

  2. National University, San Diego, CA, USA

    Nanette S. Fritschmann

  3. SRI International, Menlo Park, CA, USA

    Ryan P. Grimm

  4. Palos Verdes Peninsula Unified School District, Palos Verdes, CA, USA

    Arturo Garcia

Authors
  1. Terese C. Aceves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nanette S. Fritschmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryan P. Grimm
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arturo Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Database material preparation and analyses were led by Dr. Ryan Grimm, with initial work by Arturo Garcia. The manuscript was written by Dr. Aceves, Dr. Fritschmann, and Dr. Grimm. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Terese C. Aceves.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethics Approval

Approval was obtained from the ethics committee of Loyola Marymount University. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent to Participate

Data from the school-site’s existing early screening procedures was obtained directly from the district in electronic format with masked student identification numbers to protect the personal identification of individual student information. Given the use of masked data, obtaining individual informed consent was not required.

Consent for Publication

Additional consent for publication was not required.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aceves, T.C., Fritschmann, N.S., Grimm, R.P. et al. Developing Early Reading Profiles for Latinx Kindergarten Students Using Typical Universal Screeners. Contemp School Psychol 25, 595–607 (2021). https://doi.org/10.1007/s40688-020-00288-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40688-020-00288-8

Keywords

Search

Navigation