Skip to main content
Log in

What Does the Digital Administration Format of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) Measure?

  • Published:
Contemporary School Psychology Aims and scope Submit manuscript

Abstract

Despite the fact that the digital administration format of Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) was published in 2016, no research to date has examined its factor structure using all 10 of the primary subtests to measure intellectual ability. The purpose of this study, therefore, was to use exploratory and confirmatory factor analysis to examine the internal structure of the digital administration format of the WISC-V. Results of this study indicated that the theoretical and scoring structure of the WISC-V fit the data well. In addition, consistent with prior research on the standard administration format of the WISC-V, results revealed that the digital administration format is an excellent measure of general cognitive ability or psychometric g. The general factor accounted for approximately 77% of the common factor variance on the WISC-V. The first-order factors, which reflect the composite scores, however, had insufficient unique, reliable variance to warrant clinical interpretation. Results of our study suggest that the structure of the digital administration format of the WISC-V is similar to the standard format and that both are excellent measures of psychometric g, but little else. Implications of these results for practice are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. The technical report by Daniel et al. (2014) was later published by Daniel and Wahlstrom (2019).

References

  • Bartlett, M. S. (1954). A further note on the multiplying factors for various chi-square approximations in factor analysis. Journal of the Royal Statistical Society, Series B, 16, 296–298.

    Google Scholar 

  • Beauducel, A. (2011). Indeterminacy of factor score estimates in slightly misspecified confirmatory factor models. Journal of Modern Applied Statistical Methods, 10, 583–598. https://doi.org/10.22237/jmasm/1320120900

    Article  Google Scholar 

  • Beaujean, A. A. (2015). John Carroll’s views on intelligence: Bi-factor vs. higher-order models. Journal of Intelligence, 3, 121–136. https://doi.org/10.3390/jintelligence3040121

    Article  Google Scholar 

  • Beaujean, A. A., & Benson, N. F. (2019). Theoretically-consistent cognitive ability test development and score interpretation. Contemporary School Psychology, 23(2), 126–137. https://doi.org/10.1007/s40688-018-0182-1

    Article  Google Scholar 

  • Benson, N. F., Beaujean, A. A., McGill, R. J., & Dombrowski, S. C. (2018). Revisiting Carroll’s survey of factor-analytic studies: Implications for the clinical assessment of intelligence. Psychological Assessment, 30(8), 1028–1038. https://doi.org/10.1037/pas0000556

    Article  PubMed  Google Scholar 

  • Benson, N. F., Floyd, R. G., Kranzler, J. H., Eckert, T. L., Fefer, S. A., & Morgan, G. B. (2019). Test use and assessment practices of school psychologists in the United States: Findings from the 2017 National Survey. Journal of School Psychology, 72, 29–48.

    Article  PubMed  Google Scholar 

  • Bonifay, W. E., Reise, S. P., Scheines, R., & Meijer, R. R. (2015). When are multidimensional data unidimensional enough for structural equation modeling? An evaluation of the DETECT multidimensionality index. Structural Equation Modeling, 22, 504–516. https://doi.org/10.1080/10705511.2014.938596

    Article  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 Kaufman A. S., Raiford S. E., Coalson D. L. (Eds.), Intelligent testing with the WISC-V (pp. 683–702). Wiley.

  • Canivez, G. L., & Youngstrom, E. A. (2019). Challenges to the Cattell-Horn-Carroll theory: Empirical, clinical, and policy implications. Applied Measurement in Education, 32(3), 232–248. https://doi.org/10.1080/08957347.2019.1619562

    Article  Google Scholar 

  • Canivez, G. L., Watkins, M. W., & Dombrowski, S. C. (2016). 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.

    Article  PubMed  Google Scholar 

  • Canivez G. L., Watkins M. W., & Dombrowski S. C. (2017). Structural validity of the Wechsler Intelligence Scale for Children–Fifth Edition: Confirmatory factor analyses with the 16 primary and secondary subtests. Psychological Assessment, 29, 458-472.

    Article  PubMed  Google Scholar 

  • Canivez, G. L., Dombrowski, S. C., & Watkins, M. W. (2018). Factor structure of the WISC-V in four standardization age groups: Exploratory and hierarchical factor analyses with the 16 primary and secondary subtests. Psychology in the Schools, 55, 741– 769.

    Article  Google Scholar 

  • Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.

  • Daniel, M. H., Wahlstrom, D., & Zhang, O. (2014). Equivalence of Q-interactive and paper administrations of cognitive tasks: WISC-V Q-interactive technical report. Pearson. https://www.pearsonassessments.com/content/dam/school/global/clinical/us/assets/wisc-v/q-interactive-wisc-v.pdf

  • Daniel, M. H. (2013). User survey on Q-interactive examinee behavior. Pearson. https://www.pearsonassessments.com/content/dam/school/global/clinical/us/assets/q-interactive/003-User_Survey_on_Effect_of_Q-interactive_Examinee_Behavior_final.pdf

  • Dombrowski, S. C., Canivez, G. L., & Watkins, M. W. (2018). Factor structure of the 10 WISC-V primary subtests across four standardization age groups. Contemporary School Psychology, 22, 90–104. https://doi.org/10.1007/s40688-017-0125-2

    Article  Google Scholar 

  • Dombrowski, S. C., Beaujean A. A., McGill R. J., Benson, N F., & Schneider, WJ. (2019) Using exploratory bifactor analysis to understand the latent structure of multidimensional psychological measures: An example featuring the WISC-V. Structural Equation Modeling: A Multidisciplinary Journal, 26, 847–860. https://doi.org/10.1080/10705511.2019.1622421

  • Dombrowski, S. C., McGill, R. J., & Morgan, G. W. (2021). Monte Carlo modeling of contemporary intelligence test (IQ) factor structure: Implications for IQ assessment, interpretation and theory. Assessment, 28(3), 977–993. https://doi.org/10.1177/1073191119869828

    Article  PubMed  Google Scholar 

  • Gilbert, K., Kranzler, J.H., & Benson, N. (2021). An independent examination of the standard and digital administration formats of the Wechsler Intelligence Scale for Children-5th Edition. Journal of School Psychology, 85, 113–124. https://doi.org/10.1016/j.jsp.2021.01.002

  • Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future—A Festschrift in honor of Karl Jöreskog (pp. 195–216). Scientific Software International.

    Google Scholar 

  • Horn J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185.

  • Jensen, A. R. (1998). The g factor. Praeger.

    Google Scholar 

  • Kaiser, H. F. (1970). A second-generation little jiffy. Psychometrika, 35, 401–416. https://doi.org/10.1007/BF02291817

    Article  Google Scholar 

  • Kranzler, J. H., Maki, K. E., Benson, N. F., Floyd, R. G., & Fefer, S. A. (2020). How do school psychologists interpret intelligence tests for the identification of specific learning disabilities? Contemporary School Psychology, 24, 445–456.

    Article  Google Scholar 

  • Kranzler, J. H., & Floyd, R. G. (2020). Assessing intelligence in children and adolescents: A practical guide for evidence-based assessment (2nd ed.). Rowman & Littlefield.

  • McGill, R. J., Dombrowski, S. C., & Canivez, G. L. (2018). Cognitive profile analysis in school psychology: History, issues, and continued concerns. Journal of School Psychology, 78, 108–121. https://doi.org/10.1016/j.jsp.2018.10.007

    Article  Google Scholar 

  • Muthén, B. O., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431–462. https://doi.org/10.1007/BF02294365

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2019). Mplus user’s guide. Eighth Edition. Muthén & Muthén.

  • Pearson, Inc. (2021). Q-interactive system requirements. Pearson. https://www.pearsonassessments.com/content/dam/school/global/clinical/us/assets/q-interactive/q-interactive-system-requirements.pdf

  • Preacher, K. J., & Coffman, D. L. (2006). Computing power and minimum sample size for RMSEA. Quantpsy. http://quantpsy.org/.

  • R Development Core Team. (2021). R: A language and environment for statistical computing. . R Foundation for Statistical Computing.

  • Raiford, S. E., Zhang, O., Drozdick, L. W., Getz, K., Wahlstrom, D., Gabel, A.,...Daniel, M. (2016). WISC-V coding and symbol search in digital format: reliability, validity, special group studies, and interpretation. Pearson. https://www.pearsonassessments.com/content/dam/school/global/clinical/us/assets/q-interactive/002-Qi-Processing-Speed-Tech-Report_FNL2.pdf

  • Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696. https://doi.org/10.1080/00273171.2012.715555

    Article  PubMed  PubMed Central  Google Scholar 

  • Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92, 544–559. https://doi.org/10.1080/00223891.2010.496477

    Article  PubMed  PubMed Central  Google Scholar 

  • Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling a bifactor perspective. Educational and Psychological Measurement, 73, 5–26. https://doi.org/10.1177/0013164412449831

    Article  Google Scholar 

  • Revelle, W. (2012). Psych: Procedures for psychological, psychometric, and personality research (version 1.2.4). Northwestern University.

  • Reynolds, M. R., & Keith, T. Z. (2017). Multi-group and hierarchical confirmatory factor analysis of the Wechsler Intelligence Scale for Children-Fifth edition: What does it measure? Intelligence, 62, 31–47.

    Article  Google Scholar 

  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98, 223–237. https://doi.org/10.1080/00223891.2015.1089249

    Article  PubMed  Google Scholar 

  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21, 137–150. https://doi.org/10.1037/met0000045

    Article  PubMed  Google Scholar 

  • Schneider W. J., & McGrew K. S. (2012). The Cattell-Horn-Carroll model of intelligence. In Flanagan D. P., Harrison P. L. (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (3rd ed., pp. 99–144). Guilford Press.

  • Spearman, C. (1927). The abilities of man: Their nature and measurement. Macmillan.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education.

  • Ten Berge, J. M. F., & Sŏcan, G. (2004). The greatest lower bound to the reliability of a test and the hypothesis of unidimensionality. Psychometrika, 69, 613–625.

    Article  Google Scholar 

  • Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.

    Book  Google Scholar 

  • Thorndike, R. L. (1985). The central role of general ability in prediction. Multivariate Behavioral Research, 20, 241–254.

    Article  PubMed  Google Scholar 

  • Thorndike, R. L. (1986). The role of general ability in prediction. Journal of Vocational Behavior, 29, 332–339.

    Article  Google Scholar 

  • Van de Vijver, F. J. R., & Poortinga, Y. H. (2005). Conceptual and methodological issues in adapting tests. In R. K. Hambleton, P. F. Merenda, & C. D. Spielberger (Eds.), Adapting educational and psychological tests for cross-cultural assessment (pp. 39–63). Erlbaum.

    Google Scholar 

  • Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327. https://doi.org/10.1007/BF02293557

    Article  Google Scholar 

  • Velicer, W. F., & Fava, J. L. (1998). Effects of variable and subject sampling on factor pattern recovery. Psychological Methods, 3, 231–251. https://doi.org/10.1037/1082-989X.3.2.231

    Article  Google Scholar 

  • Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: a review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy (pp. 41–71). Kluwer Academic.

  • Wechsler, D. (2014). Wechsler Intelligence Scale for Children, 5th Edition: administration and scoring manual. Pearson.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kacey Gilbert.

Ethics declarations

Ethics Approval

IRB approval was provided prior to data collection.

Consent to Participate

Parental consent to participate was obtained as described in the “Procedures” section.

Consent for Publication

A statement regarding publication of de-identified, aggregated data was included in the consent document.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gilbert, K., Benson, N.F. & Kranzler, J.H. What Does the Digital Administration Format of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) Measure?. Contemp School Psychol 27, 623–633 (2023). https://doi.org/10.1007/s40688-022-00447-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40688-022-00447-z

Keywords

Navigation