Skip to main content
SpringerLink
Log in

Using Latent Change Score Analysis to Model Co-Development in Fluency Skills

  • Chapter
  • First Online:
The Fluency Construct

Abstract

Numerous methodologies exist to analyze longitudinal fluency data, including individual growth curve analysis in both observed and latent frameworks, cross-lagged regression to assess interrelations between variables, and multilevel frameworks that consider time as nested within individual. This chapter discusses latent change score (LCS) analysis of oral reading fluency (ORF) and reading comprehension data from a longitudinal sample of 16,000 students from first to fourth grade and compares to linear and nonlinear growth curve analysis. Results highlight the benefits of LCS models compared to traditional linear and nonlinear latent growth models, as well as implications for modeling change with correlated skills.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Al Otaiba, S., Petscher, Y., Pappamihiel, N. E., Williams, R. S., Drylund, A. K., & Connor, C. M. (2009). Modeling oral reading fluency development in Latino students: A longitudinal study across second and third grade. Journal of Educational Psychology, 101, 315–329.

    Article  PubMed Central  PubMed  Google Scholar 

  • Arbuckle, J. L. (2006). AMOS (Version 7.0). Chicago: SPSS.

    Google Scholar 

  • Ardoin, S. P., & Christ, T. J. (2009). Curriculum based measurement of oral reading: Estimates of standard error when monitoring progress using alternate passage sets. School Psychology Review, 38, 266–283.

    Google Scholar 

  • Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods and Research, 32, 336–383.

    Article  Google Scholar 

  • Bowles, R. P., & Montroy, J. J. (2013). Latent growth curve modeling using structural equation modeling. In Y. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis in education and the social sciences (pp. 265–303). New York: Routledge.

    Google Scholar 

  • Branum-Martin, L. (2013).Multilevel modeling: Practical examples to illustrate a special case of SEM. In Y. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis in the social sciences (pp. 95–124). New York: Routledge.

    Google Scholar 

  • Calhoon, M. B., & Petscher, Y. (2013). Individual sensitivity to instruction: Examining reading gains across three middle school reading projects. Reading and Writing, 26, 565–592.

    Article  PubMed Central  PubMed  Google Scholar 

  • Catts, H. W., Petscher, Y., Schatschneider, C., Bridges, M. S., & Mendoza, K. (2009). Floor effects associated with universal screening and their impact on early identification. Journal of Learning Disabilities, 42, 163–176.

    Article  PubMed Central  PubMed  Google Scholar 

  • Crowe, E. C., Connor, C. M., & Petscher, Y. (2009). Examining the core relations between poverty, reading curriculums, and first through third grade reading achievement. Journal of School Psychology, 47, 187–214.

    Article  PubMed  Google Scholar 

  • Cummings, K. D., Dewey, B., Latimer, R., & Good, R. H. (2011). Pathways to word reading and decoding: The roles of automaticity and accuracy.School Psychology Review, 40, 284–295.

    Google Scholar 

  • Cummings, K. D., Park, Y., & Schaper, H. A. B. (2013). Form effects on DIBELS next oral reading fluency progress-monitoring passages. Assessment for Effective Intervention, 38, 91–104.

    Article  Google Scholar 

  • Fletcher, J. M., Coulter, W. A., Reschly, D. J., & Vaughn, S. (2004). Alternative approaches to the definition and identification of learning disabilities: Some questions and answers. Annals of Dyslexia, 54, 304–331.

    Article  PubMed  Google Scholar 

  • Foorman, B. R., Petscher, Y., Lefsky, E. B., & Toste, J. R. (2010). Reading first in florida: Five years of improvement. Journal of Literacy Research, 42(1), 71–93. doi:http://dx.doi.org/10.1080/10862960903583202.

    Article  Google Scholar 

  • Francis, D. J., Santi, K. L., Barr, C., Fletcher, J. M., Varisco, A., & Foorman, B. R. (2008). Form effects on the estimation of students’ ORF using DIBELS. Journal of School Psychology, 46, 315–342.

    Article  PubMed Central  PubMed  Google Scholar 

  • Gollob, H. F., & Reichardt, C. S. (1987). Taking account of time lags in causal models. Child Development, 58, 80–92.

    Article  PubMed  Google Scholar 

  • Good, R. H., & Kaminski, R. A. (2002). Dynamic Indicators of Basic Early Literacy Skills (6th ed.). Eugene: Institute for the Development of Educational Achievement.

    Google Scholar 

  • Good, R. H., Kaminski, R. A., Smith, S., Laimon, D., & Dill, S. (2001). Dynamic indicators of basic early literacy skills (5th ed.). Eugene: University of Oregon.

    Google Scholar 

  • Good, R. H., Wallin, J., Simmons, D. C., Kameenui, E. J., & Kaminski, R. A. (2002). System-wide percentile ranks for DIBELS benchmark assessment (Technical Report, No. 9). Eugene, OR: University of Oregon

    Google Scholar 

  • Grimm, K. J. (2012). Intercept centering and time coding in latent difference score models. Structural Equation Modeling: A Multidisciplinary Journal, 19, 137–151.

    Article  Google Scholar 

  • Grimm, K. J., & Widaman, K. F. (2010). Residual structures in latent growth curve modeling. Structural Equation Modeling, 17, 424–442. (Mplus Scripts).

    Article  Google Scholar 

  • Hertzog, C., & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development, 58, 93–109.

    Article  PubMed  Google Scholar 

  • Hox, J. J. (2000).Multilevel analyses of grouped and longitudinal data. Mahwah: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Joreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239–251.

    Article  Google Scholar 

  • Joreskog, K. G., & Sorbom, D. (1979). Advances in factor analysis and structural equation models. New York: University Press of America.

    Google Scholar 

  • Joreskog, K. G., & Van Thillo, M. (1972). LISREL: A general computer program for estimating a linear structural equation system involving multiple indicators of unmeasured variables. Princeton: Educational Testing Service.

    Google Scholar 

  • Kim, Y. S., Petscher, Y., Schatschneider, C., Foorman, B. R. (2010). Does growth in oral reading fluency matter in reading comprehension achievement? Journal of Educational Psychology, 102, 652–667.

    Article  Google Scholar 

  • Malone, P. S., Lansford, J. E., Castellino, D. R., Berlin, L. J., Dodge, K. A., Bates, J. E., & Peettit, G. S. (2004). Divorce and child behavior problems: Applying latent change score models to life event data. Structural Equation Modeling, 11, 401–423.

    Article  PubMed Central  PubMed  Google Scholar 

  • McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605.

    Article  PubMed  Google Scholar 

  • McArdle, J. J., & Grimm, K. J. (2010). Five steps in latent curve and latent change modeling with longitudinal data. In K. van Montfort, J. Oud, & A. Satorra (Eds.), Longitudinal research with latent variables (pp. 245–274). New York: Springer.

    Chapter  Google Scholar 

  • McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analysis with incomplete longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change. Washington, D.C.: American Psychological Association.

    Google Scholar 

  • McArdle, J. J., & Nesselroade, J. R. (1994). Structuring data to study development and change. In S. H. Cohen & H. W. Reese (Eds.), Life-span developmental psychology: Methodological innovations (pp. 223–267). Mahwah: Erlbaum.

    Google Scholar 

  • McArdle, J. J., & Prindle, J. J. (2008). A latent change score analysis of a randomized clinical trial in reasoning training. Psychology and Aging, 23(4), 702–719. doi:http://dx.doi.org/10.1037/a0014349.

    Article  PubMed  Google Scholar 

  • Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equation models. Psychological Methods, 10, 259–284.

    Article  PubMed  Google Scholar 

  • Mehta, P. D., Neale, M. C., & Flay, B. R. (2004). Squeezing interval change from ordinal panel data: Latent growth curves with ordinal outcomes. Psychological Methods, 9, 301–333.

    Article  PubMed  Google Scholar 

  • Millsap, R. E. (2012). Statistical approaches to measurement invariance. New York: Taylor and Francis.

    Google Scholar 

  • Muthén, B. (2004). Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), The sage handbook of quantitative methodology for the social sciences (pp. 345–368). Thousand Oaks: Sage Publications, Inc.

    Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus User’s Guide. (7th ed.). Los Angeles: Muthén & Muthén.

    Google Scholar 

  • O’Connell, A. A., & McCoach, D. B. (2008). Multilevel modeling of educational data. Charlotte: Information Age Publishing, Inc.

    Google Scholar 

  • O’Connell, A. A., Logan, J. A. R., Pentimonti, J. M., & McCoach, D. B. (2013). Linear and quadratic growth models for continuous and dichotomous outcomes. In Y. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis in education and the social sciences (pp. 125–168). New York: Routledge.

    Google Scholar 

  • Petscher, Y. (2012). Estimating multivariate and multilevel latent change scores. Paper presented at the Society for Research on Child Development, Tampa, Florida.

    Google Scholar 

  • Petscher, Y., & Kim, Y. S. (2011). The utility and accuracy of oral reading fluency score types in predicting reading comprehension. Journal of School Psychology, 49, 107–129.

    Article  PubMed Central  PubMed  Google Scholar 

  • Petscher, Y., Cummings, K. D., Biancarosa, G., & Fien, H. (2013). Advanced (measurement) applications of curriculum-based measurement in reading. Assessment for Effective Intervention, 38, 71–75.

    Article  PubMed Central  PubMed  Google Scholar 

  • Raftery, A. E. (1995). Bayesian model selection in social research (with discussion). Sociological Methodology, 25, 111–196.

    Article  Google Scholar 

  • Reynolds, M. R., & Turek, J. (2012). A dynamic developmental link between verbal comprehension knowledge (Gc) and reading comprehension: Verbal comprehension-knowledge drives positive change in reading comprehension. Journal of School Psychology, 50, 841–863.

    Article  PubMed  Google Scholar 

  • Roehrig, A. D., Petscher, Y., Nettles, S. M., Hudson, R. F., & Torgesen, J. K. (2008). Not just speed reading: Accuracy of the DIBELS oral reading fluency measure for predicting high-stakes third grade reading comprehension outcomes. Journal of School Psychology, 46, 343–366.

    Article  PubMed  Google Scholar 

  • Sivo, S. A., Fan, X., & Witta, E. L. (2005). The biasing effects of unmodeled ARMA time series processes on latent growth curve model estimates. Structural Equation Modeling, 12, 215–232.

    Article  Google Scholar 

  • Speece, D. L., & Case, L. P. (2001). Classification in context: An alternative approach to identifying early reading disability. Journal of Educational Psychology, 93, 735–749.

    Article  Google Scholar 

  • Stoel, R. D., Van den Wittenboer, G., & Hox, J. J. (2004). Methodological issues in the application of the latent growth curve model. In K. van Montfort, H. Oud, & A. Satorra (Eds.), Recent developments on structural equation modeling: Theory and applications (pp. 241–262). Amsterdam: Kluwer Academic Press.

    Chapter  Google Scholar 

  • Zhiyong, Z., McArdle, J. J., Hamagami, A., & Grimm, K. (2013). RAMpath: Structural equation modeling using RAM notation (R package version 0.3.6).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA

    Yaacov Petscher, Sharon Koon & Sarah Herrera

Authors
  1. Yaacov Petscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sharon Koon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sarah Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaacov Petscher .

Editor information

Editors and Affiliations

  1. University of Maryland, College Park, Maryland, USA

    Kelli D. Cummings

  2. Florida State University, Florida State University Florida Center for Reading Research, Tallahassee, Florida, USA

    Yaacov Petscher

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Petscher, Y., Koon, S., Herrera, S. (2016). Using Latent Change Score Analysis to Model Co-Development in Fluency Skills. In: Cummings, K., Petscher, Y. (eds) The Fluency Construct. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2803-3_12

Download citation

Publish with us

Policies and ethics

Search

Navigation