The present study examined whether global ability influences on reading, writing, and math achievement are generalizable across gender in children and adolescents in Grades 1 to 4 (n = 1,276), 5 to 8 (n = 1,265), and 9 to 12 (n = 1,042) using multiple group structural equation modeling with the standardization samples for the Woodcock–Johnson IV. Results showed a small female advantage in writing achievement across grade levels. The General Intellectual Ability (GIA) composite showed some evidence of slope bias with math achievement, and the Fluid-Crystallized (Gf-Gc) composite showed some evidence of slope bias with math and reading achievement. The GIA and Gf-Gc composite scores showed evidence of intercept bias mostly in the area of writing achievement. Findings are generally consistent with previous research showing minimal gender bias in global intelligence predicting achievement.
ArbuckleJ. L. (2017). Amos (Version 25.0) [Computer Program]. IBM SPSS.
2.
BensonN. F.KranzlerJ. H.FloydR. 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.
3.
CaemmererJ. M.MaddocksD. L. S.KeithT. Z.ReynoldsM. R. (2018). Effects of cognitive abilities on child and youth academic achievement: Evidence from the WISC-V and WIAT-III. Intelligence, 68, 6–20. https://doi.org/10.1016/j.intell.2018.02.005
4.
CamarataS.WoodcockR. (2006). Sex differences in processing speed: Developmental effects in males and females. Intelligence, 34(3), 231–252. https://doi.org/10.1016/j.intell.2005.12.001
5.
CarrollJ. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.
6.
ColomR.LynnR. (2004). Testing the developmental theory of sex differences in intelligence on 12–18 year olds. Personality and Individual Differences, 36(1), 75–82. https://doi.org/10.1016/S0191-8869(03)00053-9
7.
CormierD. C.BulutO.McGrewK. S.FrisonJ. (2016). The role of the Cattell-Horn-Carroll (CHC) cognitive abilities in predicting writing achievement during the school-age years. Psychology in the Schools, 53(8), 787–803. https://doi.org/10.1002/pits.21945
8.
DöhlaD.HeimS. (2015). Developmental dyslexia and dysgraphia: What can we learn from the one about the other?Frontiers in Psychology, 6, 1–12. https://doi.org/10.3389/fpsyg.2015.02045
9.
GanleyC. M.LubienskiS. T. (2016). Mathematics confidence, interest, and performance: Examining gender patterns and reciprocal relations. Learning and Individual Differences, 47, 182–193. https://doi.org/10.1016/j.lindif.2016.01.002
10.
GerstenR.ClarkeB.MazzoccoM. M. M. (2007). Historical and contemporary perspectives on mathematical learning disabilities. In BerchD. B.MazzoccoM. M. M. (Eds.), Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities (pp. 7–28). Brookes.
11.
HajovskyD. B.NiilekselaC. R.VilleneuveE. F.ReynoldsM. R. (2019). Gender matters in neuropsychological assessment of child and adolescent writing skill. Journal of Pediatric Neuropsychology, 5, 103–118. doi.org/10.1007/s40817-019-00071-5
12.
HajovskyD. B.ReynoldsM. R.FloydR. G.TurekJ. J.KeithT. Z. (2014). A multigroup investigation of latent cognitive abilities and reading achievement relations. School Psychology Review, 43(4), 385–406. https://doi.org/10.17105/SPR-13-0060.1
13.
HajovskyD. B.VilleneuveE. F.ReynoldsM. R.NiilekselaC. R.MasonB. A.ShudakN. J. (2018). Cognitive ability influences on written expression: Evidence for developmental and sex-based differences in school-age children. Journal of School Psychology, 67, 104–118. doi.org/10.1016/j.jsp.2017.09.001
14.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
HydeJ. S.FennemaE.LamonS. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139–155. https://doi.org/10.1037/0033-2909.107.2.139
17.
KaufmanA. S.KaufmanJ. C.LiuX.JohnsonC. K. (2009). How do educational attainment and gender relate to fluid intelligence, crystallized intelligence, and academic skills at ages 22–90 years?Archives of Clinical Neuropsychology, 24(2), 153–163. https://doi.org/10.1093/arclin/acp015
18.
KeithT. Z. (2019). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (3rd ed.). Taylor & Francis.
19.
KeithT. Z.ReynoldsM. R.RobertsL. G.WinterA. L.AustinC. A. (2011). Sex differences in latent cognitive abilities ages 5 to 17: Evidence from the Differential Ability Scales–Second Edition. Intelligence, 39(5), 389–404. https://doi.org/10.1016/j.intell.2011.06.008
20.
KlineR. B. (2013). Assessing statistical aspects of test fairness with structural equation modeling. Educational Research and Evaluation, 19(2–3), 204–222. https://doi.org/10.1080/13803611.2013.767624
21.
KovacsK.ConwayA. R. A. (2019). A unified cognitive/differential approach to human intelligence: Implications for IQ testing. Journal of Applied Research in Memory and Cognition, 8, 255–272. https://doi.org/10.1016/j.jarmac.2019.05.003
22.
KranzlerJ. H.MillerM. D.JordanL. (1999). An examination of racial/ethnic and gender bias on curriculum-based measurement of reading. School Psychology Quarterly, 14(3), 327–342. https://doi.org/10.1037/h0089012
23.
LynnR.KanazawaS. (2011). A longitudinal study of sex differences in intelligence at ages 7, 11, and 16 years. Personality and Individual Differences, 51(3), 321–324. https://doi.org/10.1016/j.paid.2011.02.028
24.
MakiK. E.FloydR. G.RobersonT. (2015). State learning disability criteria: A comprehensive review. School Psychology Quarterly, 30(4), 457–469. https://doi.org/10.1037/spq0000109
25.
MatherN.WendlingB. J. (2014). Essentials of WJ IV Tests of Achievement. John Wiley.
26.
McGrewK. S.LaForteE. M.SchrankF. A. (2014). Technical manual: Woodcock-Johnson IV. Riverside.
27.
McGrewK. S.WendlingB. J. (2010). Cattell–Horn–Carroll cognitive-achievement relations: What we have learned from the past 20 years of research. Psychology in the Schools, 47(7), 651–675. https://doi.org/10.1002/pits
28.
MessickS. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749. https://doi.org/10.1037/0003-066X.50.9.741
29.
MeyersT. A. (2011). Goodbye, listwise deletion: Presenting hot deck imputation as an easy and effective tool for handling missing data. Communication Methods and Measures, 5(4), 297–310. https://doi.org/10.1080/19312458.2011.624490
30.
NaglieriJ. A.RojahnJ.MattoH. C. (2007). Hispanic and non-Hispanic children’s performance on PASS cognitive processes and achievement. Intelligence, 35(6), 568–579. https://doi.org/10.1016.j.intell.2006.11.001
31.
NaglieriJ. A.RojahnJ.MattoH. C.AquilinoS. A. (2005). Black-White differences in intelligence: A study of the PASS theory and Cognitive Assessment System. Journal of Psychoeducational Assessment, 23(2), 146–160. https://doi.org/10.1177/073428290502300204
32.
NiilekselaC. R.ReynoldsM. R.KeithT. Z.McGrewK. S. (2016). A special validity study of the WJ IV: Acting on evidence for specific abilities. In FlanaganD. P.AlfonsoV. C. (Eds.), WJ IV clinical use and interpretation: Scientist-practitioner perspectives (pp. 65–106). Elsevier.
33.
PajaresF.JohnsonM. J.UsherE. L. (2007). Sources of writing self-efficacy beliefs of elementary, middle, and high school students. Research in the Teaching of English, 42(1), 104–120.
34.
PajaresF.MillerM. D.JohnsonM. J. (1999). Gender differences in writing self-beliefs of elementary school students. Journal of Educational Psychology, 91(1), 50–61. https://doi.org/10.1037/0022-0663.91.1.50
35.
PargulskiJ. R.ReynoldsM. R. (2017). Sex differences in achievement: Distributions matter. Personality and Individual Differences, 104, 272–278. https://doi.org/10.1016/j.paid.2016.08.016
36.
PetersenJ. L.HydeJ. S. (2017). Trajectories of self-perceived math ability, utility value and interest across middle school as predictors of high school math performance. Educational Psychology, 37(4), 438–456. https://doi.org/10.1080/01443410.2015.1076765
37.
PezzutiL.OrsiniA. (2016). Are there sex differences in the Wechsler Intelligence Scale for Children–Fourth edition. Learning and Individual Differences, 45, 307–312. https://doi.org/10.1016/j.lindif.2015.12.024
38.
ReillyD.NeumannD. L.AndrewsG. (2019). Gender differences in reading and writing achievement: Evidence from the National Assessment of Educational Progress (NAEP). American Psychologist, 74(4), 445–458. https://doi.org/10.1037/amp0000356
39.
ReynoldsC. R.LoweP. A. (2009). The problem of bias in psychological assessment. In GutkinT. B.ReynoldsC. R. (Eds.), The handbook of school psychology (4th ed., pp. 332–374). John Wiley.
40.
ReynoldsM. R.KeithT. Z.RidleyK. P.PatelP. G. (2008). Sex differences in latent general and broad cognitive abilities for children and youth: Evidence from higher-order MG-MACS and MIMIC models. Intelligence, 36, 236–260. https://doi.org/10.1016/j.intell.2007.06.003
41.
RosénM.HansenK. Y.WolffU. (2017). Cognitive abilities and educational outcomes. Springer.
42.
ScheiberC.KaufmanA. S. (2015). Which of the three KABC-II global scores is the least biased?Journal of Pediatric Neuropsychology, 1, 21–35. https://doi.org/10.1007/s40817-015-0004-6
43.
ScheiberC.ReynoldsM. R.HajovskyD. B.KaufmanA. S. (2015). Gender differences in achievement in a large, nationally representative sample of children and adolescents. Psychology in the Schools, 52(4), 335–348.
44.
Schermelleh-EngelK.MoosbruggerH.MaüllerH. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8, 23–74.
45.
SchneiderW. J.McGrewK. S. (2018). The Cattell–Horn–Carroll theory of cognitive abilities. In FlanaganD. P.McDonoughE. M. (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (4th ed., pp. 73–163). Guilford Press.
46.
SchrankF. A.McGrewK. S.MatherN. (2014a). Woodcock-Johnson IV Tests of Achievement. Riverside.
47.
SchrankF. A.McGrewK. S.MatherN. (2014b). Woodcock-Johnson IV Tests of Cognitive Abilities. Riverside.
48.
SchrankF. A.McGrewK. S.MatherN. (2015). The WJ Gf-Gc Composite and its use in the identification of specific learning disabilities (Woodcock-Johnson IV Assessment Service Bulletin No. 3). Riverside.
49.
VilleneuveE. F.HajvoskyD. B.MasonB. A.LewnoB. M. (2019). Cognitive ability and math computation developmental relations with math problem solving: An integrated, multi-group approach. School Psychology, 34(1), 96–108. https://doi.org/10.1037/spq0000267
50.
WarneR. T.PriceC. J.YoonM. (2014). Exploring the various interpretations of “test bias.”Cultural Diversity & Ethnic Minority Psychology, 20(4), 570–582. https://doi.org/10.1037/a0036503
51.
WatkinsM. W.GluttingJ. J.LeiP.-W. (2007). Validity of the Full-Scale IQ when there is significant variability among WISC-III and WISC-IV factor scores. Applied Neuropsychology, 14(1), 13–20. https://doi.org/10.1080/09084280701280353
52.
WeissL. G.PrifiteraA. (1995). An evaluation of differential prediction of WIAT achievement scores from WISC–III full-scale IQ across ethnic and gender groups. Journal of School Psychology, 33(4), 297–304.
53.
WeissL. G.PrifiteraA.RoidG. (1993). The WISC–III and the fairness of predicting achievement across ethnic and gender groups. Journal of Psychological Assessment, Monograph Series, Advances in Psychoeducational Assessment: Wechsler Intelligence Scale for Children-Third Edition (pp. 35–42).
54.
ZaboskiB. A.KranzlerJ. H.GageN. A. (2018). Meta-analysis of the relationship between academic achievement and broad abilities of the Cattell–Horn–Carroll theory. Journal of School Psychology, 71, 42–56. https://doi.org/10.1016/j.jsp.2018.10.001
55.
ZellE.KrizanZ.TeeterS. R. (2015). Evaluating gender similarities and differences using metasynthesis. American Psychologist, 70(1), 10–20. https://doi.org/10.1037/a0038208
