Age and Gender Invariance in the Taiwan Wechsler Intelligence Scale for Children,Fifth Edition: Higher Order Five-Factor Model

Abstract

This study investigated the factorial invariance of the Taiwan Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V) across age and gender. A higher order five-factor model was tested on a nationally representative sample of 1,034 children aged 6–16 years. The results demonstrated full factorial invariance for Taiwan children of different ages and gender. The WISC-V subtests demonstrated the same underlying theoretical latent constructs, strength of relations among factors and subtests, validity of each first-order factor, and communalities, regardless of age and gender, which supported the same interpretive approach of the WISC-V. These results accord with findings in the United States, indicating a full factorial invariance of the WISC-V five-factor structure across ages and gender.

Keywords

age factorial invariance gender Taiwan Wechsler Intelligence Scales

Get full access to this article

View all access options for this article.

References

American Educational Research Association , American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.

Archer

R. P.

Buffington-Vollum Stredny

J. K.

, & Handel

R. W.

(2006). A survey of test use patterns among forensic psychologists. Journal of Personality Assessment, 87, 64-94. doi:10.1207/s15327752jpa8701_07

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. doi:10.1016/j.jsp.2018.12.004

Bentler

P. M.

, & Bonett

D. G.

(1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606. doi:10.1037/0033-2909.88.3.588

Bickley

P. G.

Keith

T. Z.

, & Wolfle

L. M.

(1995). The three-stratum theory of cognitive abilities: Test of the structure of intelligence across the life span. Intelligence, 20, 309-328. doi:10.1016/0160-2896(95)90013-6

Bowden

S. C.

(2013). Theoretical convergence in assessment of cognition. Journal of Psychoeducational Assessment, 31(2), 148-156. doi:10.1177/0734282913478035

Byrne

B. M.

Baron

, & Campbell

T. L.

(1993). Measuring adolescent depression: Factorial validity and invariance of the Beck Depression Inventory across gender. Journal of Research on Adolescence, 3, 127-143.

Camara

W. J.

Nathan

J. S.

, & Puente

A. E.

(2000). Psychological test usage: Implications in professional psychology. Professional Psychology: Research and Practice, 31, 141-154. doi:10.1037/0735-7028.31.2.141

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.), Intelligence testing with the WISC-V (pp. 683-702). Hoboken: John Wiley.

10.

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(4), 458-472. doi:10.1037/pas0000358

11.

Chen

Keith

Chen

, & Chang

(2009). What does the WISC-IV measure? Validation of the scoring and CHC-based interpretative approaches. Journal of Research in Education Sciences, 54(3), 85-108.

12.

Chen

Zhang

Raiford

S. E.

Zhu

, & Weiss

(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

13.

Cheng

C. H. K.

, & Watkins

(2000). Age and gender invariance of self-concept factor structure: An investigation of a newly developed Chinese self-concept instrument. International Journal of Psychology, 35(5), 186-193. doi:10.1080/00207590050171120

14.

Cheung

G. W.

, & Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233-255. doi:10.1207/S15328007SEM0902_5

15.

Emerson

Guhn

Gademann

Emerson

S. D.

, & Gademann

A. M.

(2017). Measurement invariance of the Satisfaction with Life Scale: Reviewing three decades of research. Quality of Life Research, 26(9), 2251-2264. doi:10.1007/s11136-017-1552-2

16.

Flanagan

D. P.

, & Alfonso

A. S.

(2017). Essentials of WISC-V assessment. John Wiley.

17.

Georgas

Weiss

L. G.

van de Vijver

F. J. R.

, & Saklofske

D. H.

(Eds.). (2003). Culture and children’s intelligence: Cross-cultural analysis of the WISC-III. Academic Press.

18.

Groth-Marnat

, & Wright

A. J.

(2016). Handbook of psychological assessment (6th ed.). John Wiley.

19.

Gutafsson

J. E.

(1984). A unifying model for the structure of intellectual abilities. Intelligence, 8, 179-203. doi:10.1016/0160-2896(84)90008-4

20.

Hoyle

R. H.

, & Panter

A. T.

(1995). Writing about structural equation models. In Hoyle

R. H.

(Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158-176). SAGE.

21.

, & Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453. doi:10.1037/1082-989X.3.4.424

22.

, & Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. doi:10.1080/10705519909540118.

23.

Jöreskog

K. G.

, & Sörbom

(2006). LISREL 8.8 statistical program. Scientific Software.

24.

Kang

McNeish

D. M.

, & Hancock

G. R.

(2016). The role of measurement quality on practical guidelines for assessing measurement and structural invariance. Educational and Psychological Measurement, 76(4), 533-561. doi:10.1177/0013164415603764

25.

Kaplan

(2000). Structural equation modeling: Foundations and extensions. SAGE.

26.

Keith

T. Z.

(2015). Multiple regression and beyond: An introduction to multiple regression and structure equation modeling (2nd ed.). Routledge.

27.

Keith

T. Z.

(2019). Multiple regression and beyond: An introduction to multiple regression and structure equation modeling (3rd ed.). Routledge.

28.

Keith

T. Z.

Caemmerer

J. M.

, & Reynolds

M. R.

(2016). Comparison of methods for factor extraction for cognitive test-like data: Which overfactor, which underfactor? Intelligence, 54, 37-54. doi:10.1016/j.intell.2015.11.003

29.

Keith

T. Z.

Fine

J. G.

Taub

G. E.

Reynolds

M. R.

, & Kranzler

J. H.

(2006). Higher order, multisample, confirmatory factor analysis of the Wechsler Intelligence Scale for Children-Fourth Edition: What does it measure? School Psychology Review, 35, 108-127.

30.

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. doi:10.1002/pits.20496

31.

Keith

T. Z.

Reynolds

M. R.

Patel

P. G.

, & Ridley

K. P.

(2008). Sex differences in latent cognitive abilities ages 6 to 59: Evidence from the Woodcock–Johnson III tests of cognitive abilities. Intelligence, 36(6), 502-525. doi:10.1016/j.intell.2007.11.001

32.

Kline

R. B.

(2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

33.

Kvist

A. V.

, & Gustafsson

J. E.

(2008). The relation between fluid intelligence and the general factor as a function of cultural background. A test of Cattell’s investment theory. Intelligence, 36, 422-436. doi:10.1016/j.intell.2007.08.004

34.

Loehlin

J. C.

(2004). Latent variable models: An introduction to factor, path, and structural equation analysis (4th Rev.ed.). Lawrence Erlbaum.

35.

Marsh

H. W.

Balla

J. R.

, & McDonald

R. P.

(1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410. doi:10.1037/0033-2909.103.3.391

36.

McDonald

R. P.

, & Ho

M. R.

(2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82. doi:10.1037/1082-989X.7.1.64

37.

McDonald

R. P.

, & Marsh

H. W.

(1990). Choosing a multivariate model: Noncentrality and goodness of fit. Psychological Bulletin, 107, 247-255. doi:10.1037/0033-2909.107.2.247

38.

Meade

A. W.

Johnson

E. C.

, & Braddy

P. W.

(2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568-592. doi:10.1037/0021-9010.93.3.568

39.

Meredith

(1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525-543. doi:10.1007/BF02294825

40.

Meyer

E. M.

, & Reynolds

M. W.

(2017). Scores in space: Multidimensional scaling of the WISC-V. Journal of Psychoeducational Assessment, 36(6), 562-575. doi:10.1177/0734282917696935

41.

Niileksela

C. R.

, & Reynolds

M. R.

(2019). Enduring the tests of age and time: Wechsler constructs across versions and revisions. Intelligence, 77. doi:10.1016/j.intell.2019.101403

42.

Pauls

Daseking

, & Petermann

(2019). Measurement Invariance across gender on the second-order five-factor model of the German Wechsler Intelligence Scale for Children-Fifth Edition. Assessment. Advance online publication. doi:10.1177/1073191119847762

43.

Rabin

L. A.

Barr

W. B.

, & Burton

L. A.

(2005). Assessment practices of clinical neuropsychologists in the United States and Canada: A survey of INS, NAN, and APA division 40 members. Archives of Clinical Neuropsychology, 20, 33-65. doi:10.1016/j.acn.2004.02.005

44.

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. doi:10.1016/j.intell.2017.02.005

45.

Sattler

J. M.

Dumont

, & Coalson

D. L.

(2016). Assessment of children: WISC-V and WPPSI-IV.

46.

Shogren

K. A.

Shaw

L. A.

Raley

S. K.

, & Wehmeyer

M. L.

(2018). The impact of personal characteristics on scores on the Self Determination Inventory: Student report in adolescents with and without disabilities. Psychology in the Schools, 55(9), 1013-1026. doi:10.1002/pits.22174

47.

Vandenberg

R. J.

(2002). Toward a further understanding of and improvement in measurement invariance methods and procedures. Organizational Research Methods, 5(2), 139-158. doi:10.1177/1094428102005002001

48.

Wechsler

(2014a). Wechsler Intelligence Scale for Children—Fifth Edition. Administration and Scoring Manual. Pearson.

49.

Wechsler

(2014b). Wechsler Intelligence Scale for Children—Fifth Edition. Technical and Interpretive Manual. Pearson.

50.

Wechsler

(2018a). Wechsler Intelligence Scale for Children—Fifth Edition. Administration and Scoring Manual (Taiwan version). Pearson.

51.

Wechsler

(2018b). Wechsler Intelligence Scale for Children—Fifth Edition. Technical and Interpretive Manual (Taiwan version). Pearson.

52.

Weiss

L. G.

Keith

Zhu

, & Chen

(2013a). WAIS-IV and clinical validation of the four- and five-factor interpretative approaches. Journal of Psychoeducational Assessment, 31(2), 94-113. doi:10.1177/0734282913478030

53.

Weiss

L. G.

Keith

Zhu

, & Chen

(2013b). WISC-IV and clinical validation of the four- and five-factor interpretative approaches. Journal of Psychoeducational Assessment, 31(2), 114-131. doi:10.1177/0734282913478032

54.

Weiss

L. G.

Saklofske

D. H.

Holdnack

J. A.

, & Prifitera

(Eds.). (2016). WISC-V assessment and interpretation. Academic Press.

55.

Weiss

L. G.

Saklofske

D. H.

Holdnack

J. A.

, & Prifitera

(Eds.). (2019). WISC-V clinical use and interpretation. Academic Press.

56.

West

S. G.

Finch

J. P.

, & Curran

P. J.

(1995). Structural equation models with nonnormal variables. In Hoyle

R. H.

(Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). SAGE.

57.

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. doi:10.1111/j.1745-3992.2010.00182.x