Advertisement

Gender Matters in Neuropsychological Assessment of Child and Adolescent Writing Skill

  • Daniel B. HajovskyEmail author
  • Christopher R. Niileksela
  • Ethan F. Villeneuve
  • Matthew R. Reynolds
Article
  • 15 Downloads

Abstract

Gender differences in Cattell-Horn-Carroll cognitive explanatory variables of basic writing skills and written expression in children and adolescents in grades 1–12 were explored using multiple-group structural equation modeling with the standardization samples for the Woodcock Johnson IV (N = 3569). Results showed small female advantages in cognitive processing speed and written expression across grade levels. Crystallized ability, fluid reasoning, short-term working memory, processing speed, and auditory processing were significant predictors of basic writing skills with learning efficiency showing stronger effects on basic writing skills for males compared to females in grades 9–12. Additionally, fluid reasoning, short-term working memory, processing speed, learning efficiency, and visual processing were significant predictors of written expression. Processing speed had stronger effects on written expression for males compared to females in grades 9–12, whereas auditory processing had stronger effects on written expression for females compared to males in grades 9–12. Theoretical and practical implications of findings are discussed.

Keywords

Writing achievement Gender differences Multiple group structural equation models CHC abilities Woodcock Johnson tests 

Notes

Acknowledgments

This study was supported by Texas Woman’s University Woodcock Institute Research Grant.

References

  1. Abbott, R. D., & Berninger, V. W. (1993). Structural equation modeling of relationships among developmental skills and writing skills in primary-and intermediate-grade writers. Journal of Educational Psychology, 85, 478–508.Google Scholar
  2. 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.Google Scholar
  3. Arbuckle, J. L. (2017). Amos (version 25.0) [computer program]. Chicago: IBM SPSS.Google Scholar
  4. Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5–37.Google Scholar
  5. Benson, N. F., Kranzler, J. H., & Floyd, R. G. (2016). Examining the integrity of measurement of cognitive abilities in the prediction of achievement: comparisons and contrasts across variables from higher order and bifactor models. Journal of School Psychology, 58, 1–19.Google Scholar
  6. Berninger, V. W. (1999). Coordinating transcription and text generation in working memory during composing: automatic and constructive processes. Learning Disability Quarterly, 22, 99–112.Google Scholar
  7. Berninger, V., & Winn, W. (2006). Implications of advancements in brain research and technology for writing development, writing instruction, and educational evolution. In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 96–114). New York: Guilford.Google Scholar
  8. Berninger, V. W., Nielsen, K. H., Abbott, R. D., Wijsman, E., & Raskind, W. (2008). Gender differences in severity of writing and reading disabilities. Journal of School Psychology, 46, 151–172.Google Scholar
  9. Berninger, V., Abbott, R., Cook, C. R., & Nagy, W. (2017). Relationships of attention and executive functions to oral language, reading, and writing skills and systems in middle childhood and early adolescence. Journal of Learning Disabilities, 50, 434–449.Google Scholar
  10. Burman, D. D., Bitan, T., & Booth, J. R. (2008). Sex differences in neural processing of language among children. Neuropsychologia, 46, 1349–1362.Google Scholar
  11. Caemmerer, J. M., Maddocks, D. L. S., Keith, T. Z., & Reynolds, M. R. (2018). Effects of cognitive abilities on child and youth academic achievement: evidence from the WISC-V and WIAT-III. Intelligence, 68, 6–20.Google Scholar
  12. Camarata, S., & Woodcock, R. (2006). Sex differences in processing speed: developmental effects in males and females. Intelligence, 34(3), 231–252.Google Scholar
  13. Carroll, J. B. (1993). Human cognitive abilities: a survey of factor-analytic studies. New York: Cambridge University Press.Google Scholar
  14. Chittooran, M. M., & Tait, R. C. (2005). Understanding and implementing neuropsychologically based written language interventions. In R. C. D’Amato, E. Fletcher-Janzen, & C. R. Reynolds (Eds.), Handbook of school neuropsychology (pp. 777–803). New Jersey: Hoboken.Google Scholar
  15. Clements, A., Rimrodt, S., Abel, J., Blanker, J., Mostofsky, S., Pekar, J., et al. (2006). Sex differences in cerebral laterality of language and visuospatial processing. Brain and Language, 98, 150–158.Google Scholar
  16. Cormier, D. C., Bulut, O., McGrew, K. S., & Frison, J. (2016). The role of Cattell-Horn-Carroll (CHC) cognitive abilities in predicting writing achievement during the school-age years. Psychology in the Schools, 53, 787–803.Google Scholar
  17. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29.Google Scholar
  18. Deane, P. (2018). The challenges of writing in school: conceptualizing writing development within a sociocognitive framework. Educational Psychologist. Advance online publication.  https://doi.org/10.1080/00461520.2018.1513844.
  19. Decker, S. L., Roberts, A. M., Roberts, K. L., Stafford, A. L., & Eckert, M. A. (2016). Cognitive components of developmental writing skill. Psychology in the Schools, 53, 617–625.Google Scholar
  20. Dombrowski, S. C., McGill, R. J., & Canivez, G. L. (2018). An alternative conceptualization of the theoretical structure of the Woodcock-Johnson IV tests of cognitive abilities at school age: a confirmatory factor analytic approach. Archives of Scientific Psychology, 6, 1–13.Google Scholar
  21. Elliott, C. D. (2007). Differential ability scales–second edition. San Antonio: Pearson.Google Scholar
  22. Ellis, A. W. (1982). Spelling and writing (and reading and speaking). In A. W. Ellis (Ed.), Normality and pathology in cognitive functions. London: Academic Press.Google Scholar
  23. Ellis, A. W. (1988). Normal writing processes and peripheral acquired dysgraphias. Language and Cognitive Processes, 3, 99–127.Google Scholar
  24. Evans, J. J., Floyd, R. G., McGrew, K. S., & Leforgee, M. H. (2002). The relations between measures of Cattell-Horn-Carroll (CHC) cognitive abilities and reading achievement during childhood and adolescence. School Psychology Review, 31(2), 246–262.Google Scholar
  25. Flanagan, D. P., Ortiz, S. O., & Alfonso, V. C. (2013). Essentials of cross-battery assessment (3rd ed.). New York: Wiley.Google Scholar
  26. Floyd, R. G., McGrew, K. S., & Evans, J. J. (2008). The relative contributions of the Cattell-Horn-Carroll cognitive abilities in explaining writing achievement during childhood and adolescence. Psychology in the Schools, 45, 132–144.Google Scholar
  27. Floyd, R. G., Bergeron, R., Hamilton, G., & Parra, G. R. (2010). How do executive functions fit with the Cattell-Horn-Carroll model? Some evidence from a joint factor analysis of the Delis-Kaplan executive function system and the Woodcock-Johnson III tests of cognitive abilities. Psychology in the Schools, 47(7), 721–738.Google Scholar
  28. Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132.Google Scholar
  29. Graham, S. (2018). A revised writer(s)-within-community model of writing. Educational Psychologist. Advance online publication.  https://doi.org/10.1080/00461520.2018.1481406.
  30. Graham, S., Berninger, V. W., Abbott, R. D., Abbott, S. P., & Whitaker, D. (1997). Role of mechanics in composing of elementary school students: a new methodological approach. Journal of Educational Psychology, 89, 170–182.Google Scholar
  31. Graham, S., Harris, K. R., Kiuhara, S. A., & Fishman, E. J. (2017). The relationship among strategic writing behavior, writing motivation, and writing performance with young developing writers. The Elementary School Journal, 118(1), 82–104.Google Scholar
  32. Hajovsky, D. B., Reynolds, M. R., Floyd, R. G., Turek, J. J., & Keith, T. Z. (2014). A multigroup investigation of latent cognitive abilities and reading achievement relations. School Psychology Review, 43(4), 385–406.Google Scholar
  33. Hajovsky, D. B., Mason, B. A., & McCune, L. A. (2017). Teacher-student relationship quality and academic achievement in elementary school: a longitudinal examination of gender differences. Journal of School Psychology, 63, 119–133.Google Scholar
  34. Hajovsky, D. B., Villeneuve, E. F., Mason, B. A., & De Jong, D. A. (2018a). A quantile regression analysis of cognitive ability and spelling predictors of written expression: evidence of gender, age, and skill level moderation. School Psychology Review, 47(3), 291–315.Google Scholar
  35. Hajovsky, D. B., Villeneuve, E. F., Reynolds, M. R., Niileksela, C. R., Mason, B. A., & Shudak, N. J. (2018b). Cognitive ability influences on written expression: evidence for developmental and sex-based differences in school-age children. Journal of School Psychology, 67, 104–118.Google Scholar
  36. Hayes, J. R. (2006). New directions in writing theory. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 28–40). New York: Guilford.Google Scholar
  37. Hayes, J. R., & Berninger, V. (2014). Cognitive processes in writing: a framework. In B. Arfe, J. Dockrell, & V. Berninger (Eds.), Writing development and instruction in children with hearing, speech, and language disorders (pp. 3–15). New York: Oxford University Press.Google Scholar
  38. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.Google Scholar
  39. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581–592.Google Scholar
  40. Jensen, A. R. (1998). The g factor. Westport: Prager.Google Scholar
  41. Jewell, J., & Malecki, C. K. (2005). The utility of CBM written language indices: an investigation of production-dependent, production-independent, and accurate-production scores. School Psychology Review, 34, 27–44.Google Scholar
  42. Jewsbury, P. A., & Bowden, S. C. (2017). Construct validity of fluency and implications for the factorial structure of memory. Journal of Psychoeducational Assessment, 35(5), 460–481.Google Scholar
  43. Kan, K.-J., Kievit, R. A., Dolan, C., & van der Maas, H. (2011). On the interpretation of the CHC factor Gc. Intelligence, 39(5), 292–302.Google Scholar
  44. Kaufman, A. S., & Kaufman, N. L. (2004a). Kaufman test of educational achievement–second edition: technical manual. Circle Pines: American Guidance Service.Google Scholar
  45. Kaufman, A. S., & Kaufman, N. L. (2004b). Kaufman test of educational achievement–second edition: technical manual. Circle Pines: American Guidance Service.Google Scholar
  46. Kaufman, S. B., Reynolds, M. R., Liu, X., Kaufman, A. S., & McGrew, K. S. (2012). Are cognitive g and academic achievement g one and the same g? An exploration on the Woodcock-Johnson and Kaufman tests. Intelligence, 40(2), 123–138.Google Scholar
  47. Keith, T. Z. (1999). Effects of general and specific abilities on student achievement: similarities and differences across ethnic groups. School Psychology Quarterly, 14, 239–262.Google Scholar
  48. Keith, T. Z. (2019). Multiple regression and beyond: an introduction to multiple regression and structural equation modeling (3rd ed). New York: Taylor & Francis.Google Scholar
  49. Keith, T. Z., Reynolds, M. R., Roberts, L. G., Winter, A. L., & Austin, C. A. (2011). Sex differences in latent cognitive abilities ages 5 to 17: evidence from the Differential Ability Scales–second edition. Intelligence, 39, 389–404.Google Scholar
  50. Kim, Y. S. G., & Schatschneider, C. (2017). Expanding the developmental models of writing: a direct and indirect effects model of developmental writing (DIEW). Journal of Educational Psychology, 109, 35–50.Google Scholar
  51. Mather, N., & Wendling, B. J. (2018). Linking cognitive abilities to academic interventions for students with specific learning disabilities. In D. P. Flanagan & E. M. McDonough (Eds.), Contemporary intellectual assessment: theories, tests, and issues 4th ed. (pp. 777–809). New York: Guilford Press.Google Scholar
  52. Matthews, J. S., Ponitz, C. C., & Morrison, F. J. (2009). Early gender differences in self-regulation and academic achievement. Journal of Educational Psychology, 101, 689–704.Google Scholar
  53. McGrew, K. S., & Knopik, S. N. (1993). The relationship between the WJ-R Gf-Gc cognitive clusters and writing achievement across the life-span. School Psychology Review, 22, 687–695.Google Scholar
  54. McGrew, K. S., & Wendling, B. J. (2010). Cattell-Horn-Carroll cognitive-achievement relations: what we have learned from the past 20 years of research. Psychology in the Schools, 47, 651–675.Google Scholar
  55. McGrew, K. S., LaForte, E. M., & Schrank, F. A. (2014). Technical manual. Woodcock-Johnson IV. Rolling Meadows: Riverside.Google Scholar
  56. Niileksela, C. R., Reynolds, M. R., Keith, T. Z., & McGrew, K. S. (2016). A special validity study of the Woodcock–Johnson IV: acting on evidence for specific abilities. In D. P. Flanagan & V. C. Alfonso (Eds.), WJ IV clinical use and interpretation: scientist-practitioner perspectives (pp. 65–106). Boston: Elsevier.Google Scholar
  57. Proctor, C. M., Mather, N., Stephens-Pisecco, T. L., & Jaffe, L. E. (2017). Assessment of dyslexia. Communiqué, 46(1), 20–23.Google Scholar
  58. Rapcsak, S. Z. (1997). Disorders of writing. In L. J. G. Rothi & K. M. Heilman (Eds.), Apraxia: the neuropsychology of action (pp. 149–172). Hove: Psychology Press.Google Scholar
  59. Reilly, D., Neumann, D. L., & Andrews, G. (2019). Gender differences in reading and writing achievement: evidence from the National Assessment of Educational Progress (NAEP). American Psychologist, 74(4), 445–458.Google Scholar
  60. Reynolds, M. R., Scheiber, C., Hajovsky, D. B., Schwartz, B., & Kaufman, A. S. (2015). Gender differences in academic achievement: is writing an exception to the gender similarities hypothesis? Journal of Genetic Psychology: Theory and Research of Human Development, 176(4), 211–234.Google Scholar
  61. Roivainen, E. (2011). Gender differences in processing speed: a review of recent research. Learning and Individual Differences, 21(2), 145–149.Google Scholar
  62. Scheiber, C., Reynolds, M. R., Hajovsky, D. B., & Kaufman, A. S. (2015). Gender differences in achievement in a large, nationally representative sample of children and adolescents. Psychology in the Schools, 52(4), 335–348.Google Scholar
  63. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8, 23–74.Google Scholar
  64. Schneider, W. J., & Kaufman, A. S. (2017). Let’s not do away with comprehensive cognitive assessments just yet. Archives of Clinical Neuropsychology, 32(1), 8–20.Google Scholar
  65. Schneider, W. J., & McGrew, K. S. (2018). The Cattell-Horn-Carroll theory of cognitive abilities. In D. P. Flanagan & E. M. McDonough (Eds.), Contemporary intellectual assessment: theories, tests, and issues 4th ed. (pp. 73–163). New York: Guilford.Google Scholar
  66. Schrank, F. A., McGrew, K. S., & Mather, N. (2014a). Woodcock-Johnson IV tests of cognitive abilities. Rolling Meadows: Riverside.Google Scholar
  67. Schrank, F. A., Mather, N., & McGrew, K. S. (2014b). Woodcock-Johnson IV tests of achievement. Rolling Meadows: Riverside.Google Scholar
  68. Swanson, H. L., & Berninger, V. W. (1996). Individual differences in children’s writing: a function of working memory or reading or both processes? Reading and Writing, 8, 357–383.Google Scholar
  69. Troia, G. A., Graham, S., & Harris, K. R. (2017). Writing and students with language and learning disabilities. In J. M. Kauffman, D. P. Hallahan, & P. C. Pullen (Eds.), Handbook of special education 2nd ed. (pp. 537–557). London: Routledge.Google Scholar
  70. Villeneuve, E. F., Hajovsky, D. B., Mason, B. A., & Lewno, B. M. (2019). Cognitive ability and math computation developmental relations with math problem solving: an integrated, multigroup approach. School Psychology, 34(1), 96–108.Google Scholar
  71. Weiss, E. M., Ragland, J. D., Brensinger, C. M., Bilker, W. B., Deisenhammer, E. A., & Delazer, M. (2006). Sex differences in clustering and switching in verbal fluency tasks. Journal of the International Neuropsychological Society, 12, 502–509.Google Scholar
  72. Widaman, K. F., Early, D. R., & Conger, R. D. (2013). Special populations. In T. D. Little (Ed.), The Oxford handbook of quantitative methods, volume 1: foundations (pp. 55–81). New York: Oxford University Press.Google Scholar
  73. Woodcock, R. W., & Johnson, M. B. (1977). Woodcock-Johnson psycho-educational battery. Itasca: Riverside Publishing.Google Scholar
  74. Woodcock, R. W., & Johnson, M. B. (1989). Woodcock-Johnson psycho-educational battery-revised. Itasca: Riverside Publishing.Google Scholar
  75. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock–Johnson III. Itasca: Riverside Publishing.Google Scholar

Copyright information

© American Academy of Pediatric Neuropsychology 2019

Authors and Affiliations

  1. 1.Division of Counseling and Psychology in Education, School of Education Research CenterUniversity of South DakotaVermillionUSA
  2. 2.University of KansasLawrenceUSA
  3. 3.Fairfax County Public SchoolsFalls ChurchUSA

Personalised recommendations