This study demonstrates the utility of applying a causal indicator modeling framework to investigate important predictors of reading comprehension in third, seventh, and tenth grade students. The results indicated that a 4-factor multiple indicator multiple indicator cause (MIMIC) model of reading comprehension provided adequate fit at each grade level. This model included latent predictor constructs of decoding, verbal reasoning, nonverbal reasoning, and working memory and accounted for a large portion of the reading comprehension variance (73–87 %) across grade levels. Verbal reasoning contributed the most unique variance to reading comprehension at all grade levels. In addition, we fit a multiple group 4-factor MIMIC model to investigate the relative stability (or variability) of the predictor contributions to reading comprehension across development (i.e., grade levels). The results revealed that the contributions of verbal reasoning, nonverbal reasoning, and working memory to reading comprehension were stable across the three grade levels. Decoding was the only predictor that could not be constrained to be equal across grade levels. The contribution of decoding skills to reading comprehension was higher in third grade and then remained relatively stable between seventh and tenth grade. These findings illustrate the feasibility of using MIMIC models to explain individual differences in reading comprehension across the development of reading skills.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Adlof, S. M., Catts, H. W., & Lee, J. (2010). Kindergarten predictors of second versus eighth grade reading comprehension impairments. Journal of Learning Disabilities, 43(4), 332–345.
Adlof, S. M., Catts, H. W., & Little, T. D. (2006). Should the simple view of reading include a fluency component? Reading and Writing: An Interdisciplinary Journal, 19, 933–958.
Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106, 20–29.
Asbell, S., Donders, J., Van Tubbergen, M., & Warschausky, S. (2010). Predictors of reading comprehension in children with cerebral palsy and typically developing children. Child Neuropsychology, 16, 313–325.
Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), Psychology of learning and motivation (Vol. 8, pp. 47–87). New York, NY: Academic Press.
Beck, I., & McKeown, M. (1991). Conditions of vocabulary acquisition. In R. Barr, M. Kamil, P. Mosenthal, & P. D. Pearson (Eds.), Handbook of reading research (Vol. 2, pp. 789–814). Mahwah, NJ: Lawrence Erlbarum Associates.
Bollen, K. A., & Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265–284.
Bollen, K. A., & Davis, W. F. (2009). Causal indicator models: Identification, estimation, and testing. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 498–522.
Brace, Harcourt. (1997). Stanford achievement test (9th ed.). San Antonio, TX: Author.
Cain, K., & Oakhill, J. (1998). Comprehension skill and inference-making ability: Issues of causality. In C. Hulme & R. Joshi (Eds.), Reading and spelling: Development and disorders (pp. 329–342). Mahwah, NJ: Lawrence Erlbaum Associates.
Cain, K., & Oakhill, J. (Eds.). (2007). Children’s comprehension problems in oral and written language: A cognitive perspective. New York, NY: The Guilford Press.
Cain, K., Oakhill, J., & Bryant, P. (2004). Children’s reading comprehension ability: Concurrent prediction by working memory, verbal ability, and component skills. Journal of Educational Psychology, 96(1), 31–42.
Catts, H. W., Hogan, T. P., & Adlof, S. M. (2005). Developmental changes in reading and reading disabilities. In H. W. Catts & A. G. Kamhi (Eds.), Connections between language and reading disabilities (pp. 2025–2040). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
Chen, R., & Vellutino, F. R. (1997). Prediction of reading ability: A cross-validation study of the simple view of reading. Journal of Literacy Research, 29(1), 1–24.
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255.
Corporation, Psychological. (1999). Wechsler abbreviated scale of intelligence (WASI) manual. San Antonio, TX: Author.
Cutting, L. E., & Scarborough, H. S. (2006). Prediction of reading comprehension: Relative contributions of word recognition, language proficiency, and other cognitive skills can depend on how comprehension is measured. Scientific Studies of Reading, 10(3), 277–299.
Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466.
Daneman, M., & Merikle, P. M. (1996). Working memory and language comprehension: A meta-analysis. Psychonomic Bulletin & Review, 3(4), 422–433.
de Jong, P. F., & van der Leij, A. (2002). Effects of phonological abilities and linguistic comprehension on the development of reading. Scientific Studies of Reading, 6(1), 51–77.
Deacon, S. H., & Kirby, J. R. (2004). Morphological awareness: Just “more phonological”? The roles of morphological and phonological awareness in reading development. Applied Psycholinguistics, 25, 223–238.
Diakidoy, I. N., Stylianou, P., Karefillidou, C., & Papageorgiou, P. (2005). The relationship between listening and reading comprehension of different types of text at increasing grade levels. Reading Psychology, 26, 55–80.
Dreyer, L. G., & Katz, L. (1992). An examination of “the simple view of reading”. National Reading Conference Yearbook, 41, 169–175.
Florida Department of Education. (2006). Florida Comprehensive Assessment Test: Technical Report 2006. Tallahassee, FL. Retrieved March 15, 2012, from http://fcat.fldoe.org/pdf/fc06tech.pdf
Fuchs, D., Compton, D. L., Fuchs, L. S., Bryant, V. J., Hamlett, C. L., & Lambert, W. (2012). First-grade cognitive abilities as long-term predictors of reading comprehension and disability status. Journal of Learning Disabilities, 45(3), 217–231.
Gaulin, C. A., & Campbell, T. F. (1994). Procedure for assessing verbal working memory in normal school-age children: Some preliminary data. Perceptual and Motor Skills, 79(1), 55–64.
Goff, D., Pratt, C., & Ong, B. (2005). The relations between children’s reading comprehension, working memory, language skills and components of reading decoding in a normal sample. Reading and Writing: An Interdisciplinary Journal, 2, 127–160.
Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. RASE: Remedial and Special Education, 7(1), 6–10.
Hoover, W. A., & Gough, P. B. (1990). The simple view of reading. Reading and Writing: An Interdisciplinary Journal, 2, 127–160.
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453.
Jensen, A. R. (1980). Bias in mental testing. New York, NY: Free Press.
Kershaw, S., & Schatschneider, C. (2012). A latent variable approach to the simple view of reading. Reading and Writing: An Interdisciplinary Journal, 25(2), 433–464.
Kim, Y.-S., Petscher, Y., Schatschneider, C., & Foorman, B. (2010). Does growth rate in oral reading fluency matter in predicting reading achievement? Journal of Educational Psychology, 102(3), 652–667.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press.
McVay, J., & Kane, M. J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of Experimental Psychology: General, 141(2), 302–320.
Meade, A. W., & Lautenschlager, G. J. (2004). A comparison of item response theory and confirmatory factor analytic tests of measurement invariance. Journal of Applied Psychology, 93, 568–592.
Molloy, P. J. (1997). The role of individual differences in working memory in reading and listening comprehension in intermediate grade students. Doctoral Dissertation. Available from Dissertation Abstracts International, 2072A. (UMI No. AAMNN19626).
Muthén, L. K., & Muthén, B. O. (1998-2010). Mplus user’s guide. (6th ed.). Los Angeles, CA: Muthén & Muthén.
Nagy, W. E., Berninger, V., Abbott, R., Vaughan, K., & Vermeulen, K. (2003). Relationship of morphology and other language skills to literacy skills in at-risk second-grade readers and at-risk fourth-grade writers. Journal of Educational Psychology, 95, 730–742.
Nation, K., Adams, J. W., Bowyer-Crane, C. A., & Snowling, M. J. (1999). Working memory deficits in poor comprehenders reflect underlying language impairments. Journal of Experimental Child Psychology, 73(2), 139–158.
Oakhill, J. V., & Cain, K. (2012). The precursors of reading ability in young readers: Evidence from a four-year longitudinal study. Scientific Studies of Reading, 16(2), 91–121.
Oakhill, J. V., Cain, K., & Bryant, P. E. (2003). The dissociation of single-word reading and text comprehension: Evidence from component skills. Language and Cognitive Processes, 18, 443–468.
SAS Institute Inc. (2012). Base SAS 9.2 utilities: Reference. Cary, NC: Author.
Schatschneider, C., Harrell, E., & Buck, J. (2007). An individual differences approach to the study of reading comprehension. In R. K. Wagner, A. E. Muse, & K. R. Tannenbaum (Eds.), Vocabulary acquisition: Implications for reading comprehension (pp. 249–275). New York, NY: The Guilford Press.
Seigneuric, A., & Ehrlich, M. F. (2005). Contribution of working memory capacity to children’s reading comprehension: A longitudinal investigation. Reading and Writing: An Interdisciplinary Journal, 18(7–9), 617–656.
Seigneuric, A., Ehrlich, M. F., Oakhill, J., & Yuill, N. (2000). Working memory resources and children’s reading comprehension. Reading and Writing: An Interdisciplinary Journal, 18, 617–656.
Sternberg, R. J., & Powell, J. S. (1983). Comprehending verbal comprehension. American Psychologist, 38, 878–893.
Sticht, T. G., & James, J. H. (1984). Listening and reading. In P. D. Pearson, R. Barr, M. L. Kamil, & P. Mosenthal (Eds.), Handbook of reading research (pp. 255–292). New York, NY: Longman.
Swanson, H. L., & Berninger, V. (1995). The role of working memory in skilled and less skilled readers’ comprehension. Intelligence, 21(1), 83–108.
Swanson, H. L., & Howell, M. (2001). Working memory, short-term memory, and speech rate as predictors of children’s reading performance at different ages. Journal or Educational Psychology, 93, 720–734.
Swanson, H. L., & O’Connor, R. (2009). The role of working memory and fluency practice on the reading comprehension of students who are dysfluent readers. Journal of Learning Disabilities, 42(6), 548–575.
Tighe, E. L. (2012). An investigation of the dimensionality of morphological and vocabulary knowledge in Adult Basic Education students. Unpublished masters thesis. Florida State University.
Tighe, E. L., & Schatschneider, C. (2014). A dominance analysis approach to determining predictor importance in third, seventh, and tenth grade reading comprehension skills. Reading and Writing: An Interdisciplinary Journal, 27(1), 101–127.
Tilstra, J., McMaster, K., Van den Broek, P., Kendeou, P., & Rapp, D. (2009). Simple but complex: Components of the simple view of reading across grade levels. Journal of Research in Reading, 32(4), 383–401.
Tiu, R. D, Jr, Thompson, L. A., & Lewis, B. A. (2003). The role of IQ in a component model of reading. Journal of Learning Disabilities, 36(5), 424–436.
Torgesen, J., Nettles, S., Howard, P., & Winterbottom, R. (2005). Brief report of a study to investigate the relationship between several brief measure of reading fluency and performance on the Florida Comprehensive Assessment Test-Reading in 4th, 6th, 8th, and 10th grades (Technical Report #6). Tallahassee, FL: Florida Center for Reading Research. Retrieved from: http://www.fcrr.org/TechnicalReports/Progress_monitoring_report.pdf
Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. (1999). Test of word reading efficiency. Austin, TX: Pro-Ed.
Treiblmaier, H., Bentler, P. M., & Mair, P. (2011). Formative constructs implemented via common factors. Structural Equation Modeling, 18, 1–17.
Vellutino, F. R., Tunmer, W. E., Jaccard, J. J., & Chen, R. (2007). Components of reading ability: Multivariate evidence for a convergent skills model of reading development. Scientific Studies of Reading, 11(1), 3–32.
Verhoeven, L., & van Leeuwe, J. (2008). Prediction of the development of reading comprehension: A longitudinal study. Applied Cognitive Psychology, 22, 407–423.
Wagner, R. K. (2013, January). A causal indicator model of reading comprehension. Paper presented at the Frontier Lecture Series at Texas A&M University, College Station, TX.
Support for this manuscript was provided by Grant Number P50 HD052120 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development awarded to the second and third authors. Support was also provided by Predoctoral Interdisciplinary Research Training Fellowships R305B04074 and R305B090021 from the Institute of Education Sciences awarded to the first author.
Appendix: Establishing measurement invariance in multiple group causal indicator models
Appendix: Establishing measurement invariance in multiple group causal indicator models
Measurement invariance (MI) refers to scores on a latent construct having the same operational definition across different groups, time points, or methods of measurement administration (Meade & Lautenschlager, 2004). To establish MI across the structural components of our three independent groups (third, seventh, and tenth grade), we followed a six-step process and inspected and compared model fit indices at each step (Cheung & Rensvold, 2002; Kline, 2011). It is important to note that the steps proceed in hierarchical fashion such that subsequent levels impose additional equivalence constraints and therefore, indicate stronger MI.
Step one is to ensure that the same general model fits reasonably well at all grade levels. As demonstrated in our results section, a 4-factor MIMIC model provided adequate fit at each grade level. Inspecting the model fit indices and factor loadings of the observed measures onto the latent constructs also showed similar loadings across the three grades. Thus, we can explore building a multiple group MIMIC model.
Step two is to obtain a configural or baseline model (labeled M0 in Table 6) by fitting the 4-factor MIMIC model across the three grades without any invariances (or cross-group equality constraints). By default for a multiple group model, Mplus imposes cross-group equality constraints on factor loadings and intercepts. We are unable to relax the cross-group equality constraints on intercepts because this would lead to an unidentified model. However, we were able to run a baseline model that freed up the default cross-group constraints on factor loadings. This model is referred to as a baseline model (M0) because this model was compared with subsequent models in our later steps of testing for MI. Comparing Chi square values from the three models (one per grade) and M0 demonstrated that the baseline model is merely the sum of Chi square values and degrees of freedom from each of the individual models.
Step three is to test for construct-level metric invariance or equal factor loadings. To achieve this, we constrained all unstandardized factor loadings to be equal across the three groups (Table 6). A Chi square difference test between this model (M1) and the baseline model (M0) revealed a non-significant difference [M1–M0 χ2(12) = 15.39, p = .221]. Inspection of the model fit indices revealed an increase in RMSEA and TLI values in the constrained M1 model, indicating better model fit. Therefore, we established invariance of factor loadings, which indicates that the latent constructs (decoding, verbal reasoning, nonverbal reasoning, and working memory) are equivalent across grades.
Step four is to test for the equivalence of construct variances and covariances. To do this, we imposed constraints on factor loadings and factor variances and covariances (Table 6). A Chi square difference test between this model (M2) and the Step three model (M1) revealed no significant differences [M2–M1 χ2(8) = 1.37, p = .994]. The model fit indices show increased model fit for the RMSEA, TLI, and CFI values. Thus, cross-group equivalence of factor variances and covariances is supported.
Step five is to test for invariance of the correlations among our latent constructs (decoding, verbal reasoning, nonverbal reasoning, and working memory). To do this, cross-group equality constraints were imposed on factor loadings, factor covariances, and correlations (Table 6). A Chi square difference test between this model (M3) and the model in Step four (M2) revealed no significant differences [M3–M2 χ2(12) = 18.05, p = .114]. Inspection of the model fit indices revealed increased model fit for the RMSEA and TLI values. Thus, cross-group equivalence of predictor correlations is supported.
Step six is to test for invariance of the residual variances. To do this, cross-group equality constraints were enforced on factor loadings, factor variances and covariances, construct correlations, and residual variances (Table 6). A Chi square difference test between this model (M4) and the model in Step five (M3) revealed a significant difference [M4–M3(22) = 48.76, p < .001]. Inspection of the model fit indices demonstrated decreased model fit for the RMSEA, CFI, and TLI values. Although model fit was still in an overall acceptable range (Hu & Bentler, 1998), we could not declare cross-group equivalence of residual variances. Thus, we utilized model M3 as our baseline model to test for the stability of the estimates of the four causal indicator pathways.
About this article
Cite this article
Tighe, E.L., Wagner, R.K. & Schatschneider, C. Applying a multiple group causal indicator modeling framework to the reading comprehension skills of third, seventh, and tenth grade students. Read Writ 28, 439–466 (2015). https://doi.org/10.1007/s11145-014-9532-1