Latent Mean and Covariance Differences With Measurement Equivalence in College Students With Developmental Difficulties Versus the Wechsler Adult Intelligence Scale–III/Wechsler Memory Scale–III Normative Sample
Restricted accessOtherFirst published online August, 2008
Latent Mean and Covariance Differences With Measurement Equivalence in College Students With Developmental Difficulties Versus the Wechsler Adult Intelligence Scale–III/Wechsler Memory Scale–III Normative Sample
Intelligence tests are usually part of the assessment battery for the diagnosis of adults with learning disabilities (LD) and attention deficit hyperactivity disorder (ADHD). Professionals must ensure that inferences drawn from such test scores are equivalent across populations with and without disabilities. Examination of measurement equivalence provides a direct test of the hypothesis that the same set of latent variables underlies a set of test scores in different groups and metric relationships between observed scores and the corresponding latent variables are the same. The hypothesis of measurement equivalence was examined in two samples of college students: one sample with LD and one sample with ADHD. Scores on the third editions of the Wechsler Adult Intelligence and Memory Scales were compared with an age-matched subset of the conorming sample. Results supported the assumption of measurement equivalence but revealed marked differences across samples in latent variable variances and covariances and latent variable means.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1999). Standards for educational and psychological testing . Washington, DC: Authors.
2.
Bates, E., Appelbaum, M., Salcedo, J., Saygin, A.P., & Pizzamiglio, L. (2003). Quantifying dissociations in neuropsychological research. Journal of Clinical and Experimental Neuropsychology, 25, 1128-1153.
3.
Bowden, S.C., Carstairs, J.R., & Shores, E.A. (1999). Confirmatory factor analysis of combined Wechsler Adult Intelligence Scale-Revised and Wechsler Memory Scale-Revised scores in a healthy community sample. Psychological Assessment, 11, 339-344.
4.
Bowden, S.C., Cook, M.J., Bardenhagen, F.J., Shores, E.A., & Carstairs, J.R. (2004). Measurement invariance of core cognitive abilities in heterogeneous neurological and community samples . Intelligence, 33, 363-389.
5.
Bowden, S.C., Ritter, A.J., Carstairs, J.R., Shores, E.A., Pead, J., Greeley, J.D., et al. (2001). Factorial invariance for combined WAIS-R and WMS-R scores in a sample of patients with alcohol dependency. Clinical Neuropsychologist, 15, 69-80.
6.
Bowden, S.C., Weiss, L.G., & Holdnack, J.A. (2007). Examination of factorial invariance across education categories in the WAIS-III/WMS-III standardization sample. Manuscript in preparation.
7.
Bowden, S.C., Weiss, L.G., Holdnack, J.A., Bardenhagen, F.J., & Cook, M.J. (in press). Equivalence of a measurement model of cognitive abilities in the U.S. standardization and Australian clinical samples. Journal of Clinical and Experimental Neuropsychology.
8.
Bowden, S.C., Weiss, L.G., Holdnack, J.A., & Lloyd, D. (2006). Age-related invariance of abilities measured with the Wechsler Adults Intelligence Scale-III. Psychological Assessment, 18, 334-339.
9.
Byrne, B.M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.
10.
Byrne, B.M., Shavelson, R.J., & Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456-466.
11.
Carroll, J.B. (1993). Human cognitive abilities. Cambridge, UK: Cambridge University Press.
Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). New York : Academic.
14.
Dudgeon, P.L. (2004). A note on extending Steiger's (1998) multiple sample RMSEA adjustment to other noncentrality parameter-based statistics . Structural Equation Modeling, 11, 305-319.
15.
Glutting, J.J., Watkins, M.W., Konold, T.R., & McDermott, P.A. (2006). Distinctions without a difference: The utility of observed versus latent factors from the WISC-IV in estimating reading and math achievement on the WIAT-II. Journal of Special Education, 40, 103-114.
16.
Gregg, N., Coleman, C., Davis, M., Lindstrom, W., & Hartwig, J. (2006). Critical issues for the diagnosis of learning disabilities in the adult population. Psychology in the Schools , 43, 889-900.
17.
Gregg, N., Hoy, C., Flaherty, D.A., Norris, P., Coleman, C., Davis, M., et al. (2005). Documenting decoding and spelling accommodations for postsecondary students demonstrating dyslexia-It's more than processing speed. Learning Disabilities: A Contemporary Journal, 3, 1-17.
18.
Gregg, N., & Scott, S. (2000). Definition and documentation: Theory, measurement and the court. Journal of Learning Disabilities, 33, 5-13.
19.
Gregg, N., Scott, S., McPeek, D., & Ferri, B.A. (1999). Definitions and eligibility criteria applied to the adolescent and adult populations with learning disabilities across agencies . Learning Disabilities Quarterly, 22, 213-223.
20.
Hancock, G.R. (2001). Effect size, power, and sample size determination for structured means modeling and MIMIC approaches to between-groups hypothesis testing of means on a single latent construct. Psychometrika , 66, 373-388.
21.
Hawkins, K.A., & Tulsky, D.S. (2003). WAIS-III WMS-III discrepancy analysis: Six-factor model index discrepancy based rates, implications, and a preliminary consideration of utility. In D. S. Tulsky, D. H. Saklofske, G. J. Chelune , R. K. Heaton, & R. J. Ivnik (Eds.), Clinical interpretation of the WAIS-III and WMS-III (pp. 211-272). San Diego, CA: Academic Press.
22.
Heaton, R.K., Taylor, M.J., & Manly, J. (2003). Demographic effects and use of demographically corrected norms with the WAIS-III and WMS-III. In D. S. Tulsky, D. H. Saklofske, G. J. Chelune, R. K. Heaton, & R. J. Ivnik (Eds.), Clinical interpretation of the WAIS-III and WMS-III. (pp. 181-210). San Diego, CA: Academic Press.
23.
Henson, R.K. (2006). Effect size measures and meta-analytic thinking in counseling psychology research. Counseling Psychologist , 34, 601-629.
24.
Holdnack, J.A., Moberg, P.J., Arnold, S.E., Gur, R.C., & Gur, R.E. (1995). Speed of processing and verbal learning deficits in adults diagnosed with ADD. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 8, 282-292.
25.
Horn, J.L., & McArdle, J.J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research , 18, 117-144.
Keith, T.Z. (1997). Using confirmatory factor analysis to aid in understanding the constructs measured by intelligence tests. In D. P. Flanagan, J. L. Genshaft, & P. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests and issues (pp. 373-402). New York: Guilford.
28.
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
Nigg, J.T., Stavro, G., Ettenhofer, M., Hambrick, D.Z., Miller, T., & Henderson, J.M. (2005). Executive functions and ADHD in adults: Evidence for selective effects on ADHD symptom domains. Journal of Abnormal Psychology, 114, 706-717.
31.
The Psychological Corporation. (2002). WAIS-III WMS-III technical manual (Rev. ed.). San Antonio, TX: Author.
32.
Reynolds, C.R., & French, C.L. (2003). The neuropsychological basis of intelligence: Some false starts and a clinical model. In A. N. Horton & L. C. Hartledge (Eds.), Handbook of forensic neuropsychology (pp. 35-92). New York: Springer.
33.
Rindskopf, D., & Rose, T. (1988). Some theory and applications for confirmatory second-order factor analysis. Multivariate Behavioural Research , 23, 51-67.
34.
Sattler, J.M. (2001). Assessment of children (4th ed.). San Diego, CA: Author.
35.
Tulsky, D.S., & Price, L.R. (2003). The joint WAIS-III and WMS-III factor structure: Development and cross-validation of a six-factor model of cognitive functioning . Psychological Assessment, 15, 149-162.
36.
Vandenberg, R.J., & Lance, C.E. (2000). A review and synthesis of the measurements invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-69.
37.
Widaman, K.F., & Reise, S.P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance abuse domain. In K. J. Bryant & M. Windle (Eds.), The science of prevention: Methodological advance from alcohol and substance abuse research (pp. 281-324). Washington, DC: American Psychological Association .
38.
Woodcock, R.W., McGrew, K., & Mather, N. (2001). Woodcock-Johnson III. Itasca, IL: Riverside Publishing.
