Invariance of Woodcock-Johnson III scores for students with learning disorders and students without learning disorders

School Psychology Quarterly

Volumn 28, Issue 3, 2013, Pages 256-272

  Benson, Nicholas ^a   Taub, Gordon E ^b  

^a UNIVERSITY OF SOUTH DAKOTA   (United States)

^b UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

Cattell horn carroll theory; Confirmatory factor analysis; Learning disabilities; Woodcock johnson III tests of academic achievement; Woodcock johnson III tests of cognitive ability

Indexed keywords

ADOLESCENT; ARTICLE; CASE CONTROL STUDY; CHILD; COGNITIVE DEFECT; EDUCATION; EDUCATIONAL STATUS; FACTORIAL ANALYSIS; FEMALE; HUMAN; INTELLIGENCE TEST; LEARNING DISORDER; MALE; PRESCHOOL CHILD; PSYCHOLOGICAL ASPECT; STANDARD; STUDENT;

ADOLESCENT; CASE-CONTROL STUDIES; CHILD; CHILD, PRESCHOOL; COGNITION DISORDERS; EDUCATIONAL MEASUREMENT; EDUCATIONAL STATUS; FACTOR ANALYSIS, STATISTICAL; FEMALE; HUMANS; INTELLIGENCE TESTS; LEARNING DISORDERS; MALE; STUDENTS;

EID: 84883783062     PISSN: 10453830     EISSN: None     Source Type: Journal    
DOI: 10.1037/spq0000028     Document Type: Article

References (56)

1. Alfonso, V. C., Flanagan, D. P., & Radwan, S. (2005). The impact of Cattell-Horn-Carroll Theory on test development and interpretation of cognitive and academic abilities. In D. P. Flanagan & P. L. Harrison, (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd ed., pp. 185-202). New York, NY: Guilford Press.
2. American Educational Research Association, American Psychological Association, & National Council of Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Psychological Association.
3. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.
4. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423.
5. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243-277). Mahwah, NJ: Erlbaum.
6. Arbuckle, J. L. (2007). Amos 7.0 [Computer software]. Chicago, IL: Smallwaters.
7. Baraldi, A. N., & Enders, C. K. (2009). An introduction to modern missing data analysis. Journal of School Psychology, 48, 5-37.
8. Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78-117.
9. 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.
10. Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York, NY: Cambridge University Press.
11. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255.
12. Cizek, G. J. (2003). [Review of the Woodcock-Johnson III]. In B. S. Plake, J. C. Impara, & R. A. Spies (Eds.), The fifteenth mental measurements yearbook (pp. 1020-1024). Lincoln, NE: Buros Institute of Mental Measurements.
13. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
14. Drasgow, F. (1987). Study of the measurement bias of two standardized psychological tests. Journal of Applied Psychology, 72, 19-29.
15. Fan, X., & Sivo, S. (2007). Sensitivity of fit indices to model misspecification and model types. Multivariate Behavioral Research, 42, 509-529.
16. Flanagan, D. P., Fiorello, C. A., & Ortiz, S. O. (2010). Enhancing practice through application of Cattell-Horn-Carroll theory and research: A "third method" approach to specific learning disability identification. Psychology in the Schools, 47, 739-760.
17. Fleishman, J., & Benson, J. (1987). Using LISREL to evaluate measurement models and scale reliability. Educational and Psychological Measurement, 47, 925-939.
18. Floyd, R. G., Shaver, R. B., & McGrew, K. S. (2003). Interpretation of the Woodcock-Johnson Tests of Cognitive Abilities: Acting on evidence. In F. A. Schrank & D. P. Flanagan (Eds.), WJ III clinical use and interpretation (pp. 1-46). New York, NY: Academic Press.
19. Hayduk, L. A. (1989). Structural equation modeling- Essentials and advances. Baltimore, MD: The John Hopkins University Press.
20. Horn, J. L., & Noll, J. (1997). Human cognitive capabilities: Gf-Gc theory. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison, (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 53-91). New York, NY: Guilford Press.
21. Individuals with Disabilities Education Improvement Act of 2004, Pub. L. No. 108-446, 118 Stat. 2647; 2004 Enacted H. R. 1350; 108 Enacted H. R. 1350. Final regulations implementing IDEA 2004 were published in the Federal Register, Monday, August 14, 2006, pp. 46540-46845.
22. Johnson, E. S., Humphrey, M., Mellard, D. F., Woods, K., & Swanson, H. (2010). Cognitive processing deficits and students with specific learning disabilities: A selective meta-analysis of the literature. Learning Disability Quarterly, 33, 3-18.
23. Keith, T. Z. (2005). Using confirmatory factor analysis to aid in understanding the constructs measured by intelligence tests. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd ed., pp. 581-614). New York, NY: Guilford Press.
24. Keith, T. Z., & Reynolds, M. R. (2010). Cattell-Horn-Carroll abilities and cognitive tests: What we've learned from 20 years of research. Psychology in the Schools, 47, 635-650.
25. Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press.
26. Mandrekar, J. N. (2010). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5, 1315-1316.
27. Mather, N., & Gregg, N. (2006). Specific learning disabilities: Clarifying, not eliminating, a construct. Professional Psychology: Research and Practice, 37, 99-106.
28. McFall, R. M., & Treat, T. A. (1999). Quantifying the information value of clinical assessments with signal detection theory. Annual Review of Psychology, 50, 215-241.
29. McGrew, K. S. (2005). The Cattell-Horn-Carroll theory of cognitive abilities: Past, present, and future. In D. P. Flanagan & P. L. Harrison, (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (2nd ed., pp. 136-181). New York, NY: Guilford Press.
30. McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37, 1-10.
31. McGrew, K. S., Schrank, F. A., & Woodcock, R. W. (2007). Technical manual. Woodcock-Johnson III Normative Update. Rolling Meadows, IL: Riverside Publishing.
32. McGrew, K., & Wendling, B. (2010). CHC cognitive- achievement relations: What we have learned from the past 20 years of research. Psychology in the Schools, 47, 651-675.
33. McGrew, K. S., & Woodcock, R. W. (2001). Technical manual. Woodcock-Johnson III. Rolling Meadows, IL: Riverside Publishing.
34. Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568-592.
35. Mellenburgh, G. J. (1989). Item bias and item response theory. International Journal of Educational Research, 13, 127-143.
36. Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525-543.
37. Meredith, W., & Millsap, R. E. (1992). On the misuse of manifest variables in the detection of measurement invariance. Psychometrika, 57, 289-311.
38. Miles, J., & Shevlin, M. (2007). A time and place for incremental fit indices. Personality and Individual Differences, 42, 869- 874.
39. Millsap, R. E. (1998). Group differences in regression intercept: Implication for factorial invariance. Multivariate Behavioral Research, 33, 403-424.
40. Millsap, R. E., & Meredith, W. (2007). Factorial invariance: Historical perspectives. In R. Cudeck & R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 131-152). Mahwah, NJ: Erlbaum.
41. Niileksela, C. R. (2012). Moderation of cognitiveachievement relations for children with specific learning disabilities: A multi-group latent variable analysis using CHC theory. Unpublished doctoral dissertation. Retrieved from http://kuscholarworks.ku.edu/dspace/bitstream/1808/10014/1/Niileksela_ku_0099D_11998_DATA_1.pdf
42. Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114, 552-566.
43. Sandoval, J. (2003). [Review of the Woodcock-Johnson III]. In B. S. Plake, J. C. Impara, & R. A. Spies (Eds.), The fifteenth mental measurements yearbook (pp. 1024-1028). Lincoln, NE: Buros Institute of Mental Measurements.
44. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London, UK: Chapman & Hall. doi: 10.1201/9781439821862
45. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
46. Steinmetz, H. (2013). Analyzing observed composite differences across groups: Is partial measurement invariance enough? Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9, 1-12.
47. Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 1, 1-26.
48. Taub, G. E., & McGrew, K. S. (2004). A confirmatory factor analysis of the Cattell-Horn-Carroll theory and the cross-age invariance of the Woodcock-Johnson Tests of Cognitive Abilities III. School Psychology Quarterly, 19, 72-87.
49. U.S. Department of Education. (2004). The Individuals with Disabilities Education Improvement Act of 2004 {IDEIA 2004}. Washington, DC: Author.
50. Wicherts, J. M., & Dolan, C. V. (2010). Measurement invariance in confirmatory factor analysis; An illustration using IQ test performance of minorities. Educational Measurement: Issues and Practice, 29, 3, 39-47.
51. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001a). Woodcock-Johnson III. Itasca, IL: Riverside Publishing.
52. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001a). Woodcock-Johnson III Tests of Cognitive Abilities. Itasca, IL: Riverside Publishing.
53. Woodcock, R. W., McGrew, K. S., & Mather, N. (2001c). Woodcock-Johnson III Tests of Achievement. Itasca, IL: Riverside Publishing.
54. World Health Organization. (2001). ICF: International Classification of Functioning, Disability and Health. Geneva, Switzerland: Author.
55. Wu, A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research and Evaluation, 12, 1-26.
56. Yuan, K. H. (2005). Fit indices versus test statistics. Multivariate Behavioral Research, 40, 115-148.