The purpose of this pilot study was to determine if a single math-based chronometric task could accurately discriminate between college students with and without a diagnosed math disorder. Analyzing data from 31 students (6 in the case group, 25 in the clinical comparison group), it was found that the single chronometric task could accurately predict students who did not have a diagnosed math disorder, but not students who had the diagnosis. Moreover, no other non-math psychometric task could add to the predictive power of the chronometric task, indicating that the role of chronometry warrants further study.
Get full access to this article
View all access options for this article.
References
1.
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: Author.
2.
CeciS.J. (1990). On the relation between microlevel processing efficiency and macrolevel measures of intelligence: Some arguments against current reductionism. Intelligence, 14, 141–150.
3.
CiseroC.A.RoyerJ.M.MarchantH.G.JacksonS.J. (1997). Can the Computer-based Academic Assessment System (CAAS) be used to diagnose reading disability in college students?Journal of Educational Psychology, 89, 599–620.
4.
DearyI.J. (2000). Simple information processing and intelligence. In SternbergR.J. (Ed.), Handbook of intelligence (pp. 267–284). New York: Cambridge University.)
5.
DondersF.C. (1969). Over de snelheid van psychische processen. Onderzoekingen gedann in het Psysiologish Laboratorium der Utrechtsche Hoogeschool. In KosterW.G. (Ed. & Trans.), Attention and performance II (Vol. 30, pp. 412–431). (Original work published 1868, 1869)
6.
FleischnerJ.E.ManheimerM.A. (1997). Math interventions for students with learning disabilities: Myths and realities. School Psychology Review, 26, 397–413.
7.
GaltonF. (1883). Inquiries into human faculty and its developmentLondon: Macmillan.
8.
GearyD.C. (1994). Children's mathematical development: Research and practical applications. Washington, DC: American Psychological Association.
9.
GonzalezJ.E.J.EspinelA.I.G. (1999). Is IQ-achievement discrepancy relevant in the definition of arithmetic learning disabilities?Learning Disability Quarterly, 22, 291–301.
10.
HayesF.B.HyndG.W.WisenbakerJ. (1986). Learning disabled and normal college students' performance on reaction time and speed classification tasks. Journal of Educational Psychology, 78, 39–43.
11.
JensenA.R. (1985). Methodological and statistical techniques for the chronometric study of mental abilities. In ReynoldsC.R.WilsonV.L. (Eds.), Methodological and statistical advances in the study of individual differences (pp. 51–116). New York: Plenum.)
12.
JensenA.R. (1987). Mental chronometry in the study of learning disabilities. The Mental Retardation & Learning Disability Bulletin, 15, 67–88.
13.
JensenA.R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.
14.
KazdinA.E. (2002). Research design in clinical psychology (4th ed.). Boston: Allyn & Bacon.
15.
KnoopA.BeaujeanA.A.HollidayG. (2005, March). Descriptive differences in mathematics performance in the early lifespan: Kindergarten through college. Presentation at the annual Lifespan Development Initiative, Columbia, Missouri.
16.
KranzlerJ.H. (1994). Application of the techniques of mental chronometry to the study of learning disabilities. Personality and Individual Differences, 16, 853–859.
17.
KranzlerJ.H.JensenA.R. (1989). Inspection time and intelligence: A meta-analysis. Intelligence, 13, 329–347.
18.
KulakA.G. (1993). Parallels between math and reading disability: Common issues and approaches. Journal of Learning Disabilities, 26, 666–673.
19.
LucianoM.WrightM.J.GeffenG.M.GeffenL.B.SmithG.A.MartinN.G. (2004). A genetic investigation of the covariation among inspection time, choice reaction time, and IQ subtest scores. Behavior Genetics, 34, 41–50.
20.
MeyerD.E.OsmanA.M.IrwinD.E.YantisS. (1988). Modern mental chronometry. Biological Psychology, 26, 3–67.
21.
NeterJ.KutnerM.H.NachtsheimC.J.WassermanW.(1996). Applied linear statistical models (4th ed.). Boston: McGraw-Hill.
22.
PosnerM.I. (1978). Chronometric explorations of mind. Hillsdale, NJ: Lawrence Erlbaum.
23.
RoyerJ. (1999). The Computer-based Academic Assessment System (Windows version). Belcherton, MA: Educational Help.
24.
SpearmanC. (1904). “General intelligence”: Objectively defined and measured. American Journal of Psychology, 15, 201–292.
25.
SwansonH.L. (1987). Information processing theory and learning disabilities: A commentary and future perspective. Journal of Learning Disabilities, 20, 155–166.
26.
VernonP.A. (Ed.). (1987). Speed of information-processing and intelligence. Norwood, NJ: Ablex.)
27.
WechslerD. (1997a). Wechsler Adult Intelligence Scale-Third Edition. San Antonio, TX: The Psychological Corporation.
28.
WechslerD. (1997b). Wechsler Memory Scale-Third Edition. San Antonio, TX: The Psychological Corporation.
29.
WhyteJ.CurryC.HaleD. (1985). Inspection time and intelligence in dyslexic children. Journal of Child Psychology and Psychiatry, 26, 423–428.
30.
WoodcockR.McGrewK.MatherN. (2001). Woodcock-Johnson III Tests of Achievement. Itasca, IL: Riverside Publishing Company.
