Factor Structure of the 10 WISC-V Primary Subtests Across Four Standardization Age Groups
- 139 Downloads
The Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V; Wechsler 2014a) Technical and Interpretation Manual (Wechsler 2014b) dedicated only a single page to discussing the 10-subtest WISC-V primary battery across the entire 6 to 16 age range. Users are left to extrapolate the structure of the 10-subtest battery from the 16-subtest structure. Essentially, the structure of the 10-subtest WISC-V primary battery remains largely uninvestigated particularly at various points across the developmental period. Using principal axis factoring and the Schmid–Leiman orthogonalization procedure, the 10-subtest WISC-V primary structure was examined across four standardization sample age groups (ages 6–8, 9–11, 12–14, 15–16). Forced extraction of the publisher’s promoted five factors resulted in a trivial fifth factor at all ages except 15–16. At ages 6 to 14, the results suggested that the WISC-V contains the same four first-order factors as the prior WISC-IV (Verbal Comprehension, Perceptual Reasoning, Working Memory, Processing Speed; Wechsler 2003). Results suggest interpretation of the Visual Spatial and Fluid Reasoning indexes at ages 6 to 14 may be inappropriate due to the fusion of the Visual Spatial and Fluid Reasoning subtests. At ages 15–16, the five-factor structure was supported. Results also indicated that the WISC-V provides strong measurement of general intelligence and clinical interpretation should reside primarily at that level. Regardless of whether a four- or five-factor index structure is emphasized, the group factors reflecting the WISC-V indices do not account for a sufficient proportion of variance to warrant primary interpretive emphasis.
KeywordsWISC-V Exploratory factor analysis Factor extraction criteria Schmid–Leiman higher-order analysis Structural validity
Compliance with Ethical Standards
Conflict of Interest
Stefan Dombrowski, Gary Canivez, and Marley Watkins declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Adams, K. M. (2000). Practical and ethical issues pertaining to test revisions. Psychological Assessment, 12, 281–286. doi: 10.1037/1040-3518.104.22.1681.
- Alexandre, J. S., Morin, A., Arens, A. K., Antoine, T., & Herve, C. (2015). Exploring sources of construct-relevant multidimensionality in psychiatric measurement: a tutorial and illustration using the composite scale of morningness. International Journal of Methods in Psychiatric Research . doi: 10.1002/mpr.1485.Advanced online publicationGoogle Scholar
- Beaujean, A. A. (2015b). Adopting a new test edition: psychometric and practical considerations. Research and Practice in the Schools, 3, 51–57.Google Scholar
- Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: Guilford.Google Scholar
- Canivez, G. L. (2010). Review of the Wechsler Adult Intelligence Test–Fourth Edition. In R. A. Spies, J. F. Carlson, & K. F. Geisinger (Eds.), The eighteenth mental measurements yearbook (pp. 684–688). Lincoln: Buros Institute of Mental Measurements.Google Scholar
- Canivez, G. L. (2014a). Review of the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition. In J. F. Carlson, K. F. Geisinger, & J. L. Jonson (Eds.), The nineteenth mental measurements yearbook (pp. 732–737). Lincoln: Buros Institute of Mental Measurements.Google Scholar
- Canivez, G. L. (2016). Bifactor modeling in construct validation of multifactored tests: implications for understanding multidimensional constructs and test interpretation. In K. Schweizer & C. DiStefano (Eds.), Principles and methods of test construction: standards and recent advancements (pp. 247–271). Gottingen: Hogrefe.Google Scholar
- Canivez, G. L., & Watkins, M. W. (2016). Review of the Wechsler Intelligence Scale for Children–Fifth Edition: Critique, commentary, and independent analyses. In A. S. Kaufman, S. E. Raiford, & D. L. Coalson (Authors), Intelligent testing with the WISC–V (pp. 683–702). Hoboken, NJ: Wiley.Google Scholar
- Canivez, G. L., Watkins, M. W., James, T., James, K., & Good, R. (2014). Incremental validity of WISC–IVUK factor index scores with a referred Irish sample: predicting performance on the WIAT–IIUK. British Journal of Educational Psychology, 84, 667–684. doi: 10.1111/bjep.12056.CrossRefPubMedGoogle Scholar
- Canivez, G. L., Watkins, M. W., & Dombrowski, S. C. (2016a). Factor structure of the Wechsler Intelligence Scale for Children–Fifth Edition: exploratory factor analyses with the 16 primary and secondary subtests. Psychological Assessment, 28, 975–986. doi: 10.1037/pas0000238.CrossRefPubMedGoogle Scholar
- Canivez, G. L., Watkins, M. W., & Dombrowski, S. C. (2016b). Structural validity of the Wechsler Intelligence Scale for Children–Fifth Edition: confirmatory factor analyses with the 16 primary and secondary subtests. Psychological Assessment . doi: 10.1037/pas0000358.Advance online publicationGoogle Scholar
- Carroll, J. B. (1998). Human cognitive abilities: a critique. In J. J. McArdle & R. W. Woodcock (Eds.), Human cognitive abilities in theory and practice (pp. 5–23). Mahwah: Erlbaum.Google Scholar
- Cattell, R. B. (1987). Intelligence: its structure, growth, and action. New York: Elsevier.Google Scholar
- Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J.–. P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: a comparison of the bifactor model to other approaches. Journal of Personality, 80, 219–251. doi: 10.1111/j.1467-6494.2011.00739.x.CrossRefPubMedGoogle Scholar
- Child, D. (2006). The essentials of factor analysis (3rd ed.). New York: Continuum.Google Scholar
- Dombrowski, S. C., McGill, R. J., & Canivez, G. L. (2016). Exploratory and hierarchical factor analysis of the WJ IV cognitive at school age. Psychological Assessment. Advance online publication. doi: 10.1037/pas0000350.
- Gignac, G. (2008). Higher-order models versus direct hierarchical models: g as superordinate or breadth factor? Psychology Science Quarterly, 50, 21–43.Google Scholar
- 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 WIAI–II. Journal of Special Education, 40, 103–114. doi: 10.1177/00224669060400020101.CrossRefGoogle Scholar
- Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale: Erlbaum.Google Scholar
- Gustafsson, J. E., & Snow, R. E. (1997). Ability profiles. In R. F. Dillon (Ed.), Handbook on testing (pp. 107–135). Westport: Greenwood Press.Google Scholar
- Horn, J. L. (1991). Measurement of intellectual capabilities: A review of theory. In K. S. McGrew, J. K. Werder & R. W. Woodcock (Eds.), Woodcock-Johnson technical manual (Rev. ed., pp. 197–232). Itasca, IL: Riverside.Google Scholar
- Horn, J. L., & Blankson, A. N. (2012). Foundations for better understanding of cognitive abilities. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: theories, tests, and issues (3rd ed., pp. 73–98). New York: Guilford.Google Scholar
- Jensen, A. R. (1998). The g factor: the science of mental ability. Westport: Praeger.Google Scholar
- Kaufman, A. S. (1994). Intelligent testing with the WISC–III. New York: Wiley.Google Scholar
- Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford.Google Scholar
- Kline, R. B. (2016). Principles and practices of structural equation modeling, Fourth edition. New York: Guilford Press.Google Scholar
- Ree, M. J., Carretta, T. R., & Green, M. T. (2003). The ubiquitous role of g in training. In H. Nyborg (Ed.), The scientific study of general intelligence: tribute to Arthur R. Jensen (pp. 262–274). New York: Pergamon Press.Google Scholar
- Spearman, C. (1927). The abilities of man. New York: Cambridge University Press.Google Scholar
- Strauss, E., Spreen, O., & Hunter, M. (2000). Implications of test revisions for research. Psychological Assessment, 12, 237–244. doi: 10.1037/1040-3522.214.171.124.
- Watkins, M. W. (2013). Omega. [Computer software]. Phoenix, AZ: Ed & Psych Associates.Google Scholar
- Wechsler, D. (2003). Wechsler Intelligence Scales for Children--Fouth Edition. San Antonio, TX: The Psychological Corporation.Google Scholar
- Wechsler, D. (2014a). Wechsler Intelligence Scale for Children–Fifth Edition. San Antonio: NCS Pearson.Google Scholar
- Wechsler, D. (2014b). Wechsler Intelligence Scale for Children–Fifth Edition technical and interpretive manual. San Antonio: NCS Pearson.Google Scholar
- Wechsler, D. (2014c). Technical and interpretive manual supplement: special group validity studies with other measures and additional tables. San Antonio: NCS Pearson.Google Scholar
- Yuan, K. H., & Chan, W. (2005). On nonequivalence of several procedures of structural equation modeling. Psychometrika, 70, 791–798. doi: 10.1007/s11336–001–0930–910.1007/s11336–001–0930–9.CrossRefGoogle Scholar