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Executive function: association with multiple reading skills

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Abstract

Executive function (EF) is related to reading. However, there is a lack of clarity around (a) the relative contribution of different components of EF to different reading components (word reading, fluency, comprehension), and (b) how EF operates in the context of known strong language predictors (e.g., components of the simple view of reading or SVR), and other skills theoretically related to reading (e.g., vocabulary, processing speed) and/or to EF (e.g., short-term memory, motor function). In a large sample of 3rd to 5th graders oversampled for struggling readers, this paper evaluates the impact of EF derived from a bifactor model (Cirino, Ahmed, Miciak, Taylor, Gerst, & Barnes, 2018) in the context of well-known covariates and demographics. Beyond common EF, five specific factors (two related to working memory, and factors of fluency, self-regulated learning, and behavioral inattention/metacognition) were addressed. EF consistently showed a unique contribution to already-strong predictive models for all reading outcomes; for reading comprehension, EF interacted with SVR indices (word reading and listening comprehension). The findings extend and refine our understanding of the contribution of EF to reading skill.

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References

  • Aboud, K. S., Bailey, S. K., Petrill, S. A., & Cutting, L. E. (2016). Comprehending text versus reading words in young readers with varying reading ability: Distinct patterns of functional connectivity from common processing hubs. Developmental Science, 19, 632–656.

    Google Scholar 

  • Ahmed, Y., Francis, D. J., York, M., Fletcher, J. M., Barnes, M., & Kulesz, P. (2016). Validation of the direct and inferential mediation (DIME) model of reading comprehension in grades 7 through 12. Contemporary Educational Psychology, 44, 68–82.

    Google Scholar 

  • Altemeier, L. E., Abbott, R. D., & Berninger, V. W. (2008). Executive functions for reading and writing in typical literacy development and dyslexia. Journal of Clinical and Experimental Neuropsychology, 30, 588–606. https://doi.org/10.1080/13803390701562818.

    Google Scholar 

  • Anderson, V. A., Anderson, P., Northam, E., Jacobs, R., & Catroppa, C. (2001). Development of executive functions through late childhood and adolescence in an Australian sample. Developmental Neuropsychology, 20, 385–406. https://doi.org/10.1207/S15326942DN2001_5.

    Google Scholar 

  • Arrington, C. N., Kulesz, P. A., Francis, D. J., Fletcher, J. M., & Barnes, M. A. (2014). The contribution of attentional control and working memory to reading comprehension and decoding. Scientific Studies of Reading, 18, 325–346. https://doi.org/10.1080/10888438.2014.902461.

    Google Scholar 

  • Barnes, M. A., Stuebing, K. K., Fletcher, J. M., Barth, A. E., & Francis, D. J. (2016). Cognitive difficulties in struggling comprehenders and their relation to reading comprehension: A comparison of group selection and regression-based models. Journal of Research on Educational Effectiveness, 9, 153–172.

    Google Scholar 

  • Bental, B., & Tirosh, E. (2007). The relationship between attention, executive functions and reading domain abilities in attention deficit hyperactivity disorder and reading disorder: A comparative study. Journal of Child Psychology and Psychiatry, 48, 455–463. https://doi.org/10.1111/j.1469-7610.2006.01710.x.

    Google Scholar 

  • Best, J. R., Miller, P. H., & Jones, L. L. (2009). Executive functions after age 5: Changes and correlates. Developmental Review, 29, 180–200. https://doi.org/10.1016/j.dr.2009.05.002.

    Google Scholar 

  • Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7, 161–186. https://doi.org/10.1016/S0959-4752(96)00015-1.

    Google Scholar 

  • Burgess, P. W. (1997). Theory and methodology in executive function research. In P. Rabbitt (Ed.), Methodology of frontal and executive function (pp. 81–116). East Sussex: Psychology Press.

    Google Scholar 

  • Butterfuss, R., & Kendeou, P. (2017). The role of executive functions in reading comprehension. Educational Psychology Review. https://doi.org/10.1007/s10648-017-9422-6.

    Google Scholar 

  • 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, 31–42. https://doi.org/10.1037/0022-0663.96.1.31.

    Google Scholar 

  • Christopher, M. E., Miyake, A., Keenan, J. M., Pennington, B., DeFries, J. C., Wadsworth, S. J., et al. (2012). Predicting word reading and comprehension with executive function and speed measures across development: a latent variable analysis. Journal of Experimental Psychology: General, 141, 470–488. https://doi.org/10.1037/a0027375.

    Google Scholar 

  • Cirino, P. T. (2012). Student contextual learning scales. Houston, TX: Author.

    Google Scholar 

  • Cirino, P. T., Ahmed, Y., Miciak, J., Taylor, W. P., Gerst, E. H., & Barnes, M. A. (2018). A framework for executive function in the late elementary years. Neuropsychology, 32(2), 176–189.

    Google Scholar 

  • Cirino, P. T., Fletcher, J. M., Ewing-Cobbs, L., Barnes, M. A., & Fuchs, L. S. (2007). Cognitive arithmetic differences in learning difficulty groups and the role of behavioral inattention. Learning Disabilities Research & Practice, 22, 25–35. https://doi.org/10.1111/j.1540-5826.2007.00228.x.

    Google Scholar 

  • Cirino, P. T., Miciak, J., Gerst, E., Barnes, M. A., Child, A., & Huston-Warren, E. (2017). Executive function, self-regulated learning, and reading comprehension: Training studies. The Journal of Learning Disabilities, 50, 450–467. https://doi.org/10.1177/0022219415618497.

    Google Scholar 

  • Cirino, P. T., Romain, M. A., Barth, A. E., Tolar, T. D., Fletcher, J. M., & Vaughn, S. (2013). Reading skill components and impairments in middle school struggling readers. Reading and Writing: An Interdisciplinary Journal, 26, 1059–1086. https://doi.org/10.1007/s11145-012-9406-3.

    Google Scholar 

  • Claessens, A., & Dowsett, C. (2014). Growth and change in attention problems, disruptive behavior, and achievement from kindergarten to fifth grade. Psychological Science, 25, 2241–2251. https://doi.org/10.1177/0956797614554265.

    Google Scholar 

  • Compton, D. L., Fuchs, D., Fuchs, L. S., Elleman, A. M., & Gilbert, J. K. (2008). Tracking children who fly below the radar: Latent transition modeling of students with late-emerging reading disability. Learning and Individual Differences, 18, 329–337. https://doi.org/10.1016/j.lindif.2008.04.003.

    Google Scholar 

  • Connor, C. M., Day, S. L., Phillips, B., Sparapani, N., Ingebrand, S. W., McLean, L., et al. (2016). Reciprocal effects of self-regulation, semantic knowledge, and reading comprehension in early elementary school. Child Development, 87, 1813–1824. https://doi.org/10.1111/cdev.12558.

    Google Scholar 

  • Corsi, P. (1972). Memory and the medial temporal region of the brain. Unpublished doctoral dissertation), McGill University, Montreal, QB.

  • Cowan, R., & Powell, D. (2014). The contributions of domain-general and numerical factors to third-grade arithmetic skills and mathematical learning disability. Journal of Educational Psychology, 106, 214–229. https://doi.org/10.1037/a0034097.

    Google Scholar 

  • Cutting, L. E., Bailey, S. K., Barquero, L. A., & Aboud, K. (2015). Neurobiological bases of word recognition and reading comprehension: Distinctions, overlaps, and implications for instruction and intervention. In C. M. Connor & P. McCardle (Eds.), Advances in reading intervention: Research to practice to research (pp. 73–84). Baltimore, MD: Brookes Publishing.

    Google Scholar 

  • Cutting, L. E., Materek, A., Cole, C. A., Levine, T. M., & Mahone, E. M. (2009). Effects of fluency, oral language, and executive function on reading comprehension performance. Annals of Dyslexia, 59, 34–54. https://doi.org/10.1007/s11881-009-0022-0.

    Google Scholar 

  • 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, 277–299. https://doi.org/10.1207/s1532799xssr1003_5.

    Google Scholar 

  • Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466. https://doi.org/10.1016/S0022-5371(80)90312-6.

    Google Scholar 

  • De Franchis, V., Usai, M. C., Viterbori, P., & Traverso, L. (2017). Preschool executive functioning and literacy achievement in Grades 1 and 3 of primary school: A longitudinal study. Learning and Individual Differences, 54, 184–195. https://doi.org/10.1016/j.lindif.2017.01.026.

    Google Scholar 

  • Delis, D., Kaplan, E., & Kramer, J. (2001). Delis–Kaplan executive function scale. San Antonio, TX: The Psychological Corporation.

    Google Scholar 

  • Follmer, D. J. (2017). Executive function and reading comprehension: A meta-analytic review. Educational Psychologist, 53, 42–60. https://doi.org/10.1080/00461520.2017.1309295.

    Google Scholar 

  • Foorman, B. R., Herrera, S., Petscher, Y., Mitchell, A., & Truckenmiller, A. (2015). The structure of oral language and reading and their relation to comprehension in Kindergarten through Grade 2. Reading and Writing: An Interdisciplinary Journal, 28, 655–681. https://doi.org/10.1007/s11145-015-9544-5.

    Google Scholar 

  • Francis, D. J., Fletcher, J. M., Stuebing, K. K., Lyon, G. R., Shaywitz, B. A., & Shaywitz, S. E. (2005). Psychometric approaches to the identification of LD: IQ and achievement scores are not sufficient. Journal of Learning Disabilities, 38, 98–108. https://doi.org/10.1177/00222194050380020101.

    Google Scholar 

  • Fuchs, L., Geary, D., Compton, D., Fuchs, D., Hamlett, C., & Bryant, J. (2010). The contributions of numerosity and domain-general abilities to school readiness. Child Development, 81, 1520–1533. https://doi.org/10.1111/j.1467-8624.2010.01489.x.

    Google Scholar 

  • Fuchs, L. S., Fuchs, D., Compton, D., Hamlett, C., & Wang, A. (2015). Is word-problem solving a form of text comprehension? Scientific Studies of Reading, 19(3), 204–223. https://doi.org/10.1080/10888438.2015.1005745.

    Google Scholar 

  • Fuchs, L. S., Fuchs, D., Compton, D. L., Powell, S. R., Seethaler, P. M., Capizzi, A. M., et al. (2006). The cognitive correlates of third-grade skill in arithmetic, algorithmic computation, and arithmetic word problems. Journal of Educational Psychology, 98, 29–43. https://doi.org/10.1037/0022-0663.98.1.29.

    Google Scholar 

  • Fuchs, L. S., Fuchs, D., Powell, S. R., Seethaler, P. M., Cirino, P. T., & Fletcher, J. M. (2008). Intensive intervention for students with mathematics disabilities: Seven principles of effective practice. Learning Disabilities Quarterly, 31, 79–92. https://doi.org/10.2307/20528819.

    Google Scholar 

  • Fuchs, L. S., Schumacher, R. F., Long, J., Namkung, J., Hamlett, C. L., Cirino, P. T., et al. (2013). Improving at-risk learners’ understanding of fractions. Journal of Educational Psychology, 105, 683–700. https://doi.org/10.1037/a0032446.

    Google Scholar 

  • Georgiou, G. K., Das, J. P., & Hayward, D. (2009). Revisiting the “simple view of reading” in a group of children with poor reading comprehension. Journal of Learning Disabilities, 42(1), 76–84. https://doi.org/10.1177/0022219408326210.

    Google Scholar 

  • Gerst, E. H., Cirino, P. T., Fletcher, J. M., & Yoshida, H. (2015). Cognitive and behavioral rating measures of executive function as predictors of academic outcomes in children. Child Neuropsychology, 23, 381–407. https://doi.org/10.1080/09297049.2015.1120860.

    Google Scholar 

  • Gioia, G. A., Isquith, P. A., Guy, S. C., & Kenworthy, L. (2000). Behavior rating inventory of executive function. Odessa, FL: Psychological Assessment Resources.

    Google Scholar 

  • Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7, 6–10. https://doi.org/10.1037/0022-0663.99.2.311.

    Google Scholar 

  • Gray, S. A., Carter, A. S., Briggs-Gowan, M. J., Jones, S. M., & Wagmiller, R. L. (2014). Growth trajectories of early aggression, overactivity, and inattention: Relations to second-grade reading. Developmental Psychology, 50, 2255–2263. https://doi.org/10.1037/a0037367.

    Google Scholar 

  • Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16, 174–180. https://doi.org/10.1016/j.tics.2012.01.006.

    Google Scholar 

  • Ilkowska, M., & Engle, R. W. (2010). Trait and state differences in working memory capacity. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition. The Springer series on human exceptionality. New York, NY: Springer. https://doi.org/10.1007/978-1-4419-1210-7_18.

    Google Scholar 

  • Inquisit 3. (2003). Millisecond Software [Computer Software]. Seattle, WA.

  • Instruments, L. (1999). Purdue pegboard model #32020: Instructions and normative data. Lafayette, IN: Lafayette Instruments.

    Google Scholar 

  • Jacob, R., & Parkinson, J. (2015). The potential for school-based interventions that target executive function to improve academic achievement: A review. Review of Educational Research, 85, 512–552. https://doi.org/10.3102/0034654314561338.

    Google Scholar 

  • Jacobson, L. A., Koriakin, T., Lipkin, P., Boada, R., Frijters, J. C., Lovett, M. W., et al. (2017). Executive functions contribute uniquely to reading competence in minority youth. Journal of Learning Disabilities, 50(4), 422–433. https://doi.org/10.1177/0022219415618501.

    Google Scholar 

  • Joshi, R. M., & Aaron, P. G. (2000). The component model of reading: Simple view of reading made a little more complex. Reading Psychology, 21, 85–97.

    Google Scholar 

  • Kamil, M. L., Borman, G. D., Dole, J., Kral, C. C., Salinger, T., & Torgesen, J. (2008). Improving adolescent literacy: Effective classroom and intervention practices. IES Practice Guide. NCEE 2008-4027. National Center for Education Evaluation and Regional Assistance. Retrieved from http://ies.ed.gov/ncee/wwc.

  • Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman brief intelligence test. New York: Wiley.

    Google Scholar 

  • Keenan, J. M., Betjemann, R. S., & Olson, R. K. (2008). Reading comprehension tests vary in the skills they assess: Differential dependence on decoding and oral comprehension. Scientific Studies of Reading, 12, 281–300. https://doi.org/10.1080/10888430802132279.

    Google Scholar 

  • Kershaw, S., & Schatschneider, C. (2012). A latent variable approach to the simple view of reading. Reading and Writing: An Interdisciplinary Journal, 25, 433–464. https://doi.org/10.1007/s11145-010-9278-3.

    Google Scholar 

  • Kieffer, M. J., Vukovic, R. K., & Berry, D. (2013). Roles of attention shifting and inhibitory control in fourth-grade reading comprehension. Reading Research Quarterly, 48, 333–348. https://doi.org/10.1002/rrq.54.

    Google Scholar 

  • Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction–integration model. Psychological Review, 95, 163.

    Google Scholar 

  • Kirchner, W. K. (1958). Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, 55, 352. https://doi.org/10.1037/h0043688.

    Google Scholar 

  • Korkman, M., Kirk, U., & Kemp, S. (2007). A developmental neuropsychological assessment (NEPSY)-II. San Antonio, TX: The Psychological Corporation.

    Google Scholar 

  • Locascio, G., Mahone, E. M., Eason, S., & Cutting, L. (2010). Executive dysfunction among children with reading comprehension deficits. Journal of Learning Disabilities, 43, 441–454. https://doi.org/10.1177/0022219409355476.

    Google Scholar 

  • Logan, G. D., & Cowan, W. B. (1984). On the ability to inhibit thought and action: A theory of an act of control. Psychological Review, 91(3), 295. https://doi.org/10.1037/0033-295X.91.3.295.

    Google Scholar 

  • Luciana, M., & Nelson, C. A. (2002). Assessment of neuropsychological function through use of the Cambridge Neuropsychological Testing Automated Battery: Performance in 4- to 12-year-old children. Developmental Neuropsychology, 22, 595–624. https://doi.org/10.1207/S15326942DN2203_3.

    Google Scholar 

  • MacGinitie, W. H. (2000). Gates-MacGinitie reading tests. Itasca, IL: Riverside.

    Google Scholar 

  • MacGinitie, W. H., MacGinitie, R. K., Maria, K., Dreyer, L., & Hughes, K. E. (2007). GMRT manual for scoring and interpretation. Rolling Meadows, IL: Riverside Publishing.

    Google Scholar 

  • Martinussen, R., Grimbos, T., & Ferrari, J. L. (2014). Word-level reading achievement and behavioral inattention: Exploring their overlap and relations with naming speed and phonemic awareness in a community sample of children. Archives of Clinical Neuropsychology, 29(7), 680–690. https://doi.org/10.1093/arclin/acu040.

    Google Scholar 

  • McGrew, K. S., & Woodcock, R. W. (2001). Woodcock-Johnson III: Normative update technical manual. Rolling Meadows, IL: Riverside Publications.

    Google Scholar 

  • Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49, 270. https://doi.org/10.1037/a002822.

    Google Scholar 

  • Miciak, J., Taylor, W. P., Denton, C. A., & Fletcher, J. M. (2015). The effect of achievement test selection on identification of learning disabilities within a patterns of strengths and weaknesses framework. School Psychology Quarterly, 30, 321. https://doi.org/10.1037/spq0000091.

    Google Scholar 

  • Miciak, J., Williams, J. L., Taylor, W. P., Cirino, P. T., Fletcher, J. M., & Vaughn, S. (2016). Do processing patterns of strengths and weaknesses predict differential treatment response? Journal of Educational Psychology, 108(6), 898–909. https://doi.org/10.1037/edu0000096.

    Google Scholar 

  • Miller, A. C., Fuchs, D., Fuchs, L. S., Compton, D., Kearns, D., Zhang, W., et al. (2014). Behavioral attention: A longitudinal study of whether and how it influences the development of word reading and reading comprehension among at-risk readers. Journal of Research on Educational Effectiveness, 7, 232–249. https://doi.org/10.1080/19345747.2014.906691.

    Google Scholar 

  • Minguela, M., Solé, I., & Pieschl, S. (2015). Flexible self-regulated reading as a cue for deep comprehension: evidence from online and offline measures. Reading and Writing: An Interdisciplinary Journal, 28, 721–744. https://doi.org/10.1007/s11145-015-9547-2.

    Google Scholar 

  • Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. https://doi.org/10.1006/cogp.1999.0734.

    Google Scholar 

  • Oakhill, J. V., Cain, K., & Bryant, P. E. (2003). The dissociation of word reading and text comprehension: Evidence from component skills. Language and Cognitive Processes, 18, 443–468.

    Google Scholar 

  • Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434–447. https://doi.org/10.1037/1082-989X.8.4.434.

    Google Scholar 

  • Peng, P., Barnes, M., Wang, C., Wang, W., Li, S., Swanson, H. L., et al. (2018). A meta-analysis on the relation between reading and working memory. Psychological Bulletin, 144, 48–76.

    Google Scholar 

  • Peng, P., & Fuchs, D. (2017). A Randomized control trial of working memory training with and without strategy instruction: Effects on young children’s working memory and comprehension. Journal of Learning Disabilities, 50, 62–80. https://doi.org/10.1177/0022219415594609.

    Google Scholar 

  • Perfetti, C. A. (1999). Comprehending written language: A blueprint of the reader. In C. Brown & P. Hagoort (Eds.), The neurocognition of language (pp. 167–208). New York, NY: Oxford University Press.

    Google Scholar 

  • Perfetti, C., & Stafura, J. (2014). Word knowledge in a theory of reading comprehension. Scientific Studies of Reading, 18, 22–37. https://doi.org/10.1080/10888438.2013.827687.

    Google Scholar 

  • Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48, 85–112. https://doi.org/10.1016/j.jsp.2009.09.002.

    Google Scholar 

  • Pham, A. V. (2013). Differentiating behavioral ratings of inattention, impulsivity, and hyperactivity in children: Effects on reading achievement. Journal of Attention Disorders, 20, 674–683.

    Google Scholar 

  • Pickering, S., & Gathercole, S. E. (2001). Working memory test battery for children (WMTB-C). London: Psychological Corporation Europe.

    Google Scholar 

  • Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385–407. https://doi.org/10.1007/s10648-004-0006-x.

    Google Scholar 

  • Potocki, A., Sanchez, M., Ecalle, J., & Magnan, A. (2017). Linguistic and cognitive profiles of 8- to 15-year-old children with specific reading comprehension difficulties: The role of executive functions. Journal of Learning Disabilities, 50(2), 128–142.

    Google Scholar 

  • Pressley, M., & Ghatala, E. S. (1990). Self-regulated learning: Monitoring learning from text. Educational Psychologist, 25(1), 19–33. https://doi.org/10.1207/s15326985ep2501_3.

    Google Scholar 

  • Redick, T. S., Calvo, A., Gay, C. E., & Engle, R. W. (2011). Working memory capacity and go/no-go task performance: Selective effects of updating, maintenance, and inhibition. Journal of Experimental Psychology. Learning, Memory, and Cognition, 37, 308. https://doi.org/10.1037/a0022216.

    Google Scholar 

  • Roberts, G., Rane, S., Fall, A., Denton, C. A., Fletcher, J. M., & Vaughn, S. (2015). The impact of intensive reading intervention on level of attention in middle school students. Journal of Clinical Child & Adolescent Psychology, 44(6), 942–953. https://doi.org/10.1080/15374416.2014.913251.

    Google Scholar 

  • Robison, M. K., & Unsworth, N. (2017). Working memory capacity, strategic allocation of study time, and value-directed remembering. Journal of Memory and Language, 93, 231–244. https://doi.org/10.1016/j.jml.2016.10.007.

    Google Scholar 

  • Sala, G., & Gobet, F. (2017). Does far transfer exist? Negative evidence from chess, music, and working memory training. Current Directions in Psychological Science, 26, 515–520. https://doi.org/10.1177/0963721417712760.

    Google Scholar 

  • Savage, R. (2006). Reading comprehension is not always the product of nonsense word decoding and linguistic comprehension: Evidence from teenagers who are extremely poor readers. Scientific Studies of Reading, 10, 143–164. https://doi.org/10.1207/s1532799xssr1002_2.

    Google Scholar 

  • Savage, R., Lavers, N., & Pillay, V. (2007). Working memory and reading difficulties: What we know and what we don’t know about the relationship. Educational Psychology Review, 19, 185–221. https://doi.org/10.1007/s10648-006-9024-1.

    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.

    Google Scholar 

  • Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D., & Mermelstein, R. J. (2012). A practical guide to calculating Cohen’s f 2, a measure of local effect size, from PROC MIXED. Frontiers in Psychology, 3, 1–6.

    Google Scholar 

  • Sesma, H. W., Mahone, E. M., Levine, T., Eason, S. H., & Cutting, L. E. (2009). The contribution of executive skills to reading comprehension. Child Neuropsychology, 15, 232–246. https://doi.org/10.1080/09297040802220029.

    Google Scholar 

  • Shallice, T. (1982). Specific impairments of planning. Philosophical Transactions of the Royal Society of London, B, 298, 199–209.

    Google Scholar 

  • Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychological Bulletin, 138, 628. https://doi.org/10.1037/a0027473.

    Google Scholar 

  • Sims, D. M., & Lonigan, C. J. (2013). Inattention, hyperactivity, and emergent literacy: Different facets of inattention relate uniquely to preschoolers’ reading-related skills. Journal of Clinical Child & Adolescent Psychology, 42, 208–219. https://doi.org/10.1080/15374416.2012.738453.

    Google Scholar 

  • Smith, L. E., Borkowski, J. G., & Whitman, T. L. (2008). From reading readiness to reading competence: The role of self-regulation in at-risk children. Scientific Studies of Reading, 12, 131–152. https://doi.org/10.1080/10888430801917167.

    Google Scholar 

  • Speece, D. L., Ritchey, K. D., Silverman, R., Schatschneider, C., Walker, C. Y., & Andrusik, K. N. (2010). Identifying children in middle childhood who are at risk for reading problems. School Psychology Review, 39, 258–276.

    Google Scholar 

  • Spira, E. G., & Fischel, J. E. (2005). The impact of preschool inattention, hyperactivity, and impulsivity on social and academic development: A review. Journal of Child Psychology and Psychiatry, 46, 755–773. https://doi.org/10.1111/j.1469-7610.2005.01466.x.

    Google Scholar 

  • Swanson, H. L., Orosco, M. J., & Kudo, M. (2017). Does growth in the executive system of working memory underlie growth in literacy for bilingual children with and without reading disabilities? Journal of Learning Disabilities, 50, 386–407. https://doi.org/10.1177/0022219415618499.

    Google Scholar 

  • Swanson, J. M., Schuck, S., Porter, M. M., Carlson, C., Hartman, C. A., Sergeant, J. A., et al. (2012). Categorical and dimensional definitions and evaluations of symptoms of ADHD: History of the SNAP and the SWAN rating scales. The International Journal of Educational and Psychological Assessment, 10, 51.

    Google Scholar 

  • 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, 27, 101–127. https://doi.org/10.1007/s11145-013-9435-6.

    Google Scholar 

  • Tippey, K. G., & Longnecker, M. T. (2016). An ad hoc method for computing pseudo-effect size for mixed models. In Proceedings of south central SAS users group forum.

  • Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Practitioner review: Do performance-based measures and ratings of executive function asses the same construct? The Journal of Child Psychology and Psychiatry, 54, 131–143. https://doi.org/10.1111/jcpp.12001.

    Google Scholar 

  • Torgesen, J., Wagner, R., & Rashotte, C. A. (1999). TOWRE: Test of word reading efficiency—Examiner’s manual. Austin, TX: PRO-ED.

    Google Scholar 

  • Unsworth, N., & Spillers, G. J. (2010). Variation in working memory capacity and episodic recall: The contributions of strategic encoding and contextual retrieval. Psychonomic Bulletin & Review, 17, 200–205. https://doi.org/10.3758/PBR.17.2.200.

    Google Scholar 

  • van den Broek, P., & Espin, C. A. (2012). Connecting cognitive theory and assessment: Measuring individual differences in reading comprehension. School Psychology Review, 41, 315–326.

    Google Scholar 

  • van den Broek, P., Young, M., Tzeng, Y., & Linderholm, T. (1999). The landscape model of reading: Inferences and the online construction of a memory representation. In H. van Oosterdorp & S. R. Goldman (Eds.), The Construction of mental representations during reading. Mahwah, NJ: Taylor & Francis.

    Google Scholar 

  • van der Sluis, S., de Jong, P. F., & van der Leij, A. (2004). Inhibition and shifting in children with learning deficits in arithmetic and reading. Journal of Experimental Child Psychology, 87(3), 239–266. https://doi.org/10.1016/j.jecp.2003.12.002.

    Google Scholar 

  • Vaughn, S., Solis, M., Miciak, J., Taylor, W. P., & Fletcher, J. M. (2016). Effects from a randomized control trial comparing research and school implemented treatments with fourth graders with significant reading difficulties. Journal of Research on Educational Effectiveness. https://doi.org/10.1080/19345747.2015.1126386. (Advance online publication).

    Google Scholar 

  • Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1999). Examiner’s Manual: The comprehensive test of phonological processing. Austin, TX: PRO-ED Inc.

    Google Scholar 

  • Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock–Johnson tests of achievement. Itasca, IL: Riverside Publishing.

    Google Scholar 

  • Ylvisaker, M., & Feeney, T. (2002). Executive functions, self-regulation, and learned optimism in paediatric rehabilitation: A review and implications for intervention. Pediatric Rehabilitation, 5, 51–70. https://doi.org/10.1080/1363849021000041891.

    Google Scholar 

  • Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82–91. https://doi.org/10.1006/ceps.1999.1016.

    Google Scholar 

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Acknowledgements

This research was supported by Award Number P50 HD052117, Texas Center for Learning Disabilities, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development to the University of Houston. The content is the sole responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development or the National Institutes of Health.

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Correspondence to Paul T. Cirino.

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Cirino, P.T., Miciak, J., Ahmed, Y. et al. Executive function: association with multiple reading skills. Read Writ 32, 1819–1846 (2019). https://doi.org/10.1007/s11145-018-9923-9

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