Skip to main content
SpringerLink
Log in

Factor Structure of the 10 WISC-V Primary Subtests Across Four Standardization Age Groups

  • Published:
Contemporary School Psychology Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Adams, K. M. (2000). Practical and ethical issues pertaining to test revisions. Psychological Assessment, 12, 281–286. doi:10.1037/1040-3590.12.3.281.

  • 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 publication

    Google Scholar 

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

    Article  Google 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 

  • Beaujean, A. A. (2016). Reproducing the Wechsler Intelligence Scale for Children-Fifth Edition: factor model results. Journal of Psychoeducational Assessment, 34, 404–408. doi:10.1177/0734282916642679.

    Article  Google Scholar 

  • Bodin, D., Pardini, D. A., Burns, T. G., & Stevens, A. B. (2009). Higher order factor structure of the WISC–IV in a clinical neuropsychological sample. Child Neuropsychology, 15, 417–424.

    Article  PubMed  PubMed Central  Google Scholar 

  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: Guilford.

    Google Scholar 

  • Brunner, M., Nagy, G., & Wilhelm, O. (2012). A tutorial on hierarchically structured constructs. Journal of Personality, 80, 796–846. doi:10.1111/j.1467–6494.2011.00749.x.

    Article  PubMed  Google Scholar 

  • Canivez, G. L. (2008). Orthogonal higher-order factor structure of the Stanford–Binet Intelligence Scales for children and adolescents. School Psychology Quarterly, 23, 533–541. doi:10.1037/a0012884.

    Article  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. (2014b). Construct validity of the WISC–IV with a referred sample: direct versus indirect hierarchical structures. School Psychology Quarterly, 29, 38–51. doi:10.1037/spq0000032.

    Article  PubMed  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., & Kush, J. C. (2013). WISC–IV and WAIS–IV structural validity: alternate methods, alternate results. Commentary on Weiss et al. (2013a) and Weiss et al. (2013b). Journal of Psychoeducational Assessment, 31, 157–169. doi:10.1177/0734282913478036.

    Article  Google Scholar 

  • Canivez, G. L., & McGill, R. J. (2016). Factor structure of the Differential Ability Scales–Second Edition: exploratory and hierarchical factor analyses with the core subtests. Psychological Assessment, 28, 1475–1488. doi:10.1037/pas0000279.

    Article  PubMed  Google Scholar 

  • Canivez, G. L., & Watkins, M. W. (2010a). Investigation of the factor structure of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV): exploratory and higher-order factor analyses. Psychological Assessment, 22, 827–836. doi:10.1037/a0020429.

    Article  PubMed  Google Scholar 

  • Canivez, G. L., & Watkins, M. W. (2010b). Exploratory and higher-order factor analyses of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV) adolescent subsample. School Psychology Quarterly, 25, 223–235. doi:10.1037/a0022046.

    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 A. S. Kaufman, S. E. Raiford, & D. L. Coalson (Authors), Intelligent testing with the WISC–V (pp. 683–702). Hoboken, NJ: Wiley.

  • Canivez, G. L., Konold, T. R., Collins, J. M., & Wilson, G. (2009). Construct validity of the Wechsler Abbreviated Scale of Intelligence and Wide Range Intelligence Test: convergent and structural validity. School Psychology Quarterly, 24, 252–265. doi:10.1037/a0018030.

    Article  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.

    Article  PubMed  Google 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.

    Article  PubMed  Google 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 publication

    Google Scholar 

  • Carretta, T. R., & Ree, J. J. (2001). Pitfalls of ability research. International Journal of Selection and Assessment, 9, 325–335.

    Article  Google Scholar 

  • Carroll, J. B. (1993). Human cognitive abilities. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Carroll, J. B. (1995). On methodology in the study of cognitive abilities. Multivariate Behavioral Research, 30, 429–452. doi:10.1207/s15327906mbr3003_6.

    Article  PubMed  Google 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 

  • Carroll, J. B. (2003). The higher-stratum structure of cognitive abilities: current evidence supports g and about ten broad factors. In H. Nyborg (Ed.), The scientific study of general intelligence: tribute to Arthur R. Jensen (pp. 5–21). New York: Pergamon.

    Chapter  Google Scholar 

  • Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245–276. doi:10.1207/s15327906mbr0102_10.

    Article  PubMed  Google Scholar 

  • Cattell, R. B. (1987). Intelligence: its structure, growth, and action. New York: Elsevier.

    Google Scholar 

  • Cattell, R. B., & Horn, J. L. (1978). A check on the theory of fluid and crystallized intelligence with description of new subtest designs. Journal of Educational Measurement, 15, 139–164. doi:10.1111/j.1745–3984.1978.tb00065.x.

    Article  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.

    Article  PubMed  Google Scholar 

  • Chen, H., Zhang, O., Raiford, S. E., Zhu, J., & Weiss, L. G. (2015). Factor invariance between genders on the Wechsler Intelligence Scale for Children–Fifth Edition. Personality and Individual Differences, 86, 1–5. doi:10.1016/j.paid.2015.05.020.

    Article  Google Scholar 

  • Child, D. (2006). The essentials of factor analysis (3rd ed.). New York: Continuum.

    Google Scholar 

  • Deary, I. J. (2013). Intelligence. Current Biology, 23, 673–676. doi:10.1016/j.cub.2013.07.021.

    Article  Google Scholar 

  • DiStefano, C., & Dombrowski, S. C. (2006). Investigating the theoretical structure of the Stanford–Binet–Fifth Edition. Journal of Psychoeducational Assessment, 24, 123–136. doi:10.1177/0734282905285244.

    Article  Google Scholar 

  • Dombrowski, S. C. (2013). Investigating the structure of the WJ–III cognitive at school age. School Psychology Quarterly, 28, 154–169. doi:10.1037/spq0000010.

    Article  PubMed  Google Scholar 

  • Dombrowski, S. C. (2014a). Exploratory bifactor analysis of the WJ–III cognitive in adulthood via the Schmid–Leiman procedure. Journal of Psychoeducational Assessment, 32, 330–341. doi:10.1177/0734282913508243.

    Article  Google Scholar 

  • Dombrowski, S. C. (2014b). Investigating the structure of the WJ–III cognitive in early school age through two exploratory bifactor analysis procedures. Journal of Psychoeducational Assessment, 32, 483–494. doi:10.1177/0734282914530838.

    Article  Google Scholar 

  • Dombrowski, S. C. (2015). Psychoeducational assessment and report writing. New York: Springer Science.

    Book  Google Scholar 

  • Dombrowski, S. C., & Watkins, M. W. (2013). Exploratory and higher order factor analysis of the WJ–III full test battery: a school aged analysis. Psychological Assessment, 25, 442–455. doi:10.1037/a0031335.

    Article  PubMed  Google Scholar 

  • Dombrowski, S. C., Ambrose, D. A., & Clinton, A. (2007). Dogmatic insularity in learning disabilities diagnosis and the critical need for a philosophical analysis. International Journal of Special Education, 22(1), 3–10.

    Article  Google Scholar 

  • Dombrowski, S. C., Watkins, M. W., & Brogan, M. J. (2009). An exploratory investigation of the factor structure of the Reynolds Intellectual Assessment Scales (RIAS). Journal of Psychoeducational Assessment, 27, 494–507. doi:10.1177/0734282909333179.

    Article  Google Scholar 

  • Dombrowski, S. C., Canivez, G. L., Watkins, M. W., & Beaujean, A. (2015). Exploratory bifactor analysis of the Wechsler Intelligence Scale for Children–Fifth Edition with the 16 primary and secondary subtests. Intelligence, 53, 194–201. doi:10.1016/j.intell.2015.10.009.

    Article  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.

  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299. doi:10.1037/1082-989X.4.3.272.

    Article  Google Scholar 

  • Frazier, T. W., & Youngstrom, E. A. (2007). Historical increase in the number of factors measured by commercial tests of cognitive ability: are we overfactoring? Intelligence, 35, 169–182. doi:10.1016/j.intell.2006.07.002.

    Article  Google Scholar 

  • Gignac, G. E. (2005). Revisiting the factor structure of the WAIS–R: insights through nested factor modeling. Assessment, 12, 320–329. doi:10.1177/1073191105278118.

    Article  PubMed  Google Scholar 

  • Gignac, G. E. (2006). The WAIS–III as a nested factors model: a useful alternative to the more conventional oblique and higher-order models. Journal of Individual Differences, 27, 73–86. doi:10.1027/1614–0001.27.2.73.

    Article  Google Scholar 

  • Gignac, G. (2008). Higher-order models versus direct hierarchical models: g as superordinate or breadth factor? Psychology Science Quarterly, 50, 21–43.

    Google Scholar 

  • Gignac, G. E., & Watkins, M. W. (2013). Bifactor modeling and the estimation of model-based reliability in the WAIS–IV. Multivariate Behavioral Research, 48, 639–662. doi:10.1080/00273171.2013.804398.

    Article  PubMed  Google Scholar 

  • Glutting, J. J., Youngstrom, E. A., Ward, T., Ward, S., & Hale, R. (1997). Incremental efficacy of WISC–III factor scores in predicting achievement: what do they tell us? Psychological Assessment, 9, 295–301.

    Article  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.

    Article  Google 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. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.

    Article  PubMed  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.

  • 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 

  • Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligence. Journal of Educational Psychology, 57, 253–270.

    Article  PubMed  Google Scholar 

  • Jensen, A. R. (1998). The g factor: the science of mental ability. Westport: Praeger.

    Google Scholar 

  • Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. doi:10.1177/001316446002000116.

    Article  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.

  • Le, H., Schmidt, F. L., Harter, J. K., & Lauver, K. J. (2010). The problem of empirical redundancy of constructs in organizational research: an empirical investigation. Organizational Behavior and Human Decision Processes, 112, 112–125. doi:10.1016/j.obhdp.2010.02.003.

    Article  Google Scholar 

  • Lubinski, D. (2000). Scientific and social significance of assessing individual differences: “sinking shafts at a few critical points.”. Annual Review of Psychology, 51, 405–444. doi:10.1146/annurev.psych.51.1.405.

    Article  PubMed  Google Scholar 

  • Luciana, M., Conklin, H. M., Hooper, C. J., & Yarger, R. S. (2005). The development of nonverbal working memory and executive control processes in adolescents. Child Development, 76, 697–712.

    Article  PubMed  Google Scholar 

  • McClain, A. L. (1996). Hierarchical analytic methods that yield different perspectives on dynamics: aids to interpretation. Advances in Social Science Methodology, 4, 229–240. doi:10.1177/0734282915624293.

    Google Scholar 

  • Nelson, J. M., & Canivez, G. L. (2012). Examination of the structural, convergent, and incremental validity of the Reynolds Intellectual Assessment Scales (RIAS) with a clinical sample. Psychological Assessment, 24, 129–140. doi:10.1037/a0024878.

    Article  PubMed  Google Scholar 

  • Nelson, J. M., Canivez, G. L., Lindstrom, W., & Hatt, C. (2007). Higher-order exploratory factor analysis of the Reynolds Intellectual Assessment Scales with a referred sample. Journal of School Psychology, 45, 439–456. doi:10.1016/j.jsp.2007.03.003.

    Article  Google Scholar 

  • Nelson, J. M., Canivez, G. L., & Watkins, M. W. (2013). Structural and incremental validity of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV) with a clinical sample. Psychological Assessment, 25, 618–630. doi:10.1037/a0032086.

    Article  PubMed  Google Scholar 

  • Oakland, T., Douglas, S., & Kane, H. (2016). Top ten standardized tests used internationally with children and youth by school psychologists in 64 countries: a 24-year follow-up study. Journal of Psychoeducational Assessment, 34, 166–176.

    Article  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 

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

    Article  PubMed  PubMed Central  Google Scholar 

  • Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95, 129–140. doi:10.1080/00223891.2012.725437.

    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. doi:10.1037/met0000045.

    Article  PubMed  Google Scholar 

  • Schmid, J., & Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61. doi:10.1007/BF02289209.

    Article  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:10.1037/1040-3590.12.3.237.

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

    Book  Google Scholar 

  • Velicer, W. F. (1976). Determining the number of components form the matrix of partial correlations. Psychometrika, 31, 321–327. doi:10.1007/BF02293557.

    Article  Google Scholar 

  • Watkins, M. W. (2006). Orthogonal higher-order structure of the Wechsler Intelligence Scale for Children–Fourth Edition. Psychological Assessment, 18, 123–125. doi:10.1037/1040-3590.18.1.123.

    Article  PubMed  Google Scholar 

  • Watkins, M. W. (2010). Structure of the Wechsler Intelligence Scale for Children–Fourth Edition among a national sample of referred students. Psychological Assessment, 22, 782–787. doi:10.1037/a0020043.

    Article  PubMed  Google Scholar 

  • Watkins, M. W. (2013). Omega. [Computer software]. Phoenix, AZ: Ed & Psych Associates.

  • Watkins, M. W., & Beaujean, A. A. (2014). Bifactor structure of the Wechsler Preschool and Primary Scale of Intelligence–Fourth edition. School Psychology Quarterly, 29, 52–63. doi:10.1037/spq0000038.

    Article  PubMed  Google Scholar 

  • Watkins, M. W., Wilson, S. M., Kotz, K. M., Carbone, M. C., & Babula, T. (2006). Factor structure of the Wechsler Intelligence Scale for Children–Fourth Edition among referred students. Educational and Psychological Measurement, 66, 975–983. doi:10.1177/0013164406288168.

    Article  Google Scholar 

  • Watkins, M. W., Canivez, G. L., James, T., James, K., & Good, R. (2013). Construct validity of the WISC–IVUK with a large referred Irish sample. International Journal of School & Educational Psychology, 1, 102–111. doi:10.1080/21683603.2013.794439.

    Article  Google Scholar 

  • Wechsler, D. (2003). Wechsler Intelligence Scales for Children--Fouth Edition. San Antonio, TX: The Psychological Corporation.

  • 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 

  • Weiner, I. B. (1989). On competence and ethicality in psychodiagnostic assessment. Journal of Personality Assessment, 53, 827–831. doi:10.1207/s15327752jpa5304_18.

    Article  PubMed  Google Scholar 

  • Weiss, L. G., Keith, T. Z., Zhu, J., & Chen, H. (2013b). WISC-IV and clinical validation of the four- and five-factor interpretative approaches. Journal of Psycheducational Assessment, 31, 114–131.

    Article  Google Scholar 

  • Wolff, H. G., & Preising, K. (2005). Exploring item and higher order factor structure with the Schmid-Leiman solution: syntax codes for SPSS and SAS. Behavior Research Methods, 37, 48–58.

    Article  PubMed  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.

    Article  Google Scholar 

  • Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s alpha, Revelle’s beta, and McDonald’s omega h: their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133. doi:10.1007/s11336–003–0974–7.

    Article  Google Scholar 

  • Zinbarg, R. E., Yovel, I., Revelle, W., & McDonald, R. P. (2006). Estimating generalizability to a latent variable common to all of a scale’s indicators: a comparison of estimators for ωh. Applied Psychological Measurement, 30, 121–144. doi:10.1177/0146621605278814.

    Article  Google Scholar 

  • Zoski, K. W., & Jurs, S. (1996). An objective counterpart to the visual scree test for factor analysis: the standard error scree. Educational and Psychological Measurement, 56, 443–451. doi:10.1177/0013164496056003006.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School Psychology Program, Department of Graduate Education, Leadership & Counseling, Rider University, 2083 Lawrenceville Road, Lawrenceville, NJ, 08648, USA

    Stefan C. Dombrowski

  2. Eastern Illinois University, Charleston, IL, USA

    Gary L. Canivez

  3. Baylor University, Waco, TX, USA

    Marley W. Watkins

Authors
  1. Stefan C. Dombrowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gary L. Canivez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marley W. Watkins
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan C. Dombrowski.

Ethics declarations

Conflict of Interest

Stefan Dombrowski, Gary Canivez, and Marley Watkins declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Electronic supplementary material

ESM 1

(DOCX 86 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dombrowski, S.C., Canivez, G.L. & Watkins, M.W. Factor Structure of the 10 WISC-V Primary Subtests Across Four Standardization Age Groups. Contemp School Psychol 22, 90–104 (2018). https://doi.org/10.1007/s40688-017-0125-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40688-017-0125-2

Keywords

Search

Navigation