Higher-order exploratory factor analysis of the Reynolds Intellectual Assessment Scales with a referred sample

Nelson, Jason M.; Canivez, Gary L.; Lindstrom, Will; Hatt, Clifford V.

doi:10.1016/j.jsp.2007.03.003

Cited by 53 publications

(87 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has been observed in the WISC-IV (Bodin et al, 2009;Watkins, 2006;Watkins et al, in press;Watkins et al, 2006), French WISC-IV (Golay et al. 2012), SB-5 (Canivez, 2008), WASI and WRIT (Canivez et al, 2009), RIAS (Dombrowski et al, 2009;Nelson & Canivez, 2012;Nelson et al,, 2007), CAS (Canivez, 2011), French WAIS-III (Golay & Lecerf, 2011), WAIS-IV (Canivez & Watkins, 2010a, 2010bNiileksela et al, 2012), and the WJ-III (Dombrowski & Watkins, 2013), These consistent findings imply that the overall omnibus FSIQ should retain primary interpretive weight rather than the specific factor-based index scores. The particularly low variance estimates of PR on Picture Concepts and Matrix Reasoning and of WM on Digit Span and Letter-Number Sequencing (see Ta-ble 3) were also quite low in the WISC-IV standardization sample (Watkins, 2006) and another referred sample .…”

Section: Resultssupporting

confidence: 51%

Construct validity of the WISC-IV with a referred sample: Direct versus indirect hierarchical structures.

Canivez¹

2014

School Psychology Quarterly

Self Cite

View full text Add to dashboard Cite

The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is one of the most frequently used intelligence tests in clinical assessments of children with learning difficulties. Construct validity studies of the WISC-IV have generally supported the higher order structure with four correlated first-order factors and one higher-order general intelligence factor, but recent studies have supported an alternate model in which general intelligence is conceptualized as a breadth factor rather than a superordinate factor (M. W. Watkins, 2010, Structure of the Wechsler Intelligence Scale for Children-Fourth Edition among a national sample of referred students, Psychological Assessment, Vol. 22, pp. 782-787; M. W. Watkins, G. L. Canivez, T. James, K. & R. Good, in press, Construct validity of the WISC-IVUK with a large referred Irish sample, International Journal of School and Educational Psychology). WISC-IV core subtest data obtained from evaluations to assess learning difficulties in 345 children (224 boys, 121 girls) were examined. One through four, first order factor models and indirect versus direct hierarchical models were compared using confirmatory factor analyses. The correlated four-factor Wechsler model provided good fit to these data, but the direct hierarchical model showed statistically significant improvement over the indirect hierarchical model and correlated four-factor model. The direct hierarchical model was judged the best explanation of the WISC-IV factor structure, with the general factor accounting for 71.6% of the common variance while the first order factors accounted for 2.4-10.3% of the common variance. Thus, the results with the present sample of referred children were similar to those from other investigations (G. E. Gignac, 2005, Revisiting the factor structure of the WAIS-R: Insights through nested factor modeling, Assessment, Vol. 12, pp. 320-329; G. E. Gignac, 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, Vol. 27, pp. 73-86; P. Golay, I. Reverte, J. Rossier, N. Favez, & T. Lecerf, 2012, Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling. Psychological Assessment, advance online publication; M. W. Watkins, 2010, Structure of the Wechsler Intelligence Scale for Children-Fourth Edition among a national sample of referred students, Psychological Assessment, Vol. 22, pp. 782-787; M. W. Watkins, G. L. Canivez, T. James, K. & R. Good, in press, Construct validity of the WISC-IV(UK) with a large referred Irish sample, International Journal of School and Educational Psychology) supporting primary interpretation of the Full Scale IQ rather than the factor index scores.

show abstract

Section: Resultssupporting

confidence: 51%

Construct validity of the WISC-IV with a referred sample: Direct versus indirect hierarchical structures.

Canivez¹

2014

School Psychology Quarterly

Self Cite

View full text Add to dashboard Cite

show abstract

“…This redundancy is common among intellectual tests (Canivez, 2008;Carroll, 1993, Figure 2. One-factor model with confirmatory factor analysis (CFA) estimated standardized coefficients (squared multiple correlations shown above the observed variable boxes (Roid, 2003a(Roid, , 2003b(Roid, , 2003c, and Reynolds Intellectual Assessment Scales (Reynolds & Kamphaus, 2003), most variability measured by the WASI and WRIT corresponds to the second-order general intelli- gence factor (g) or Stratum III of the CHC model, and smaller portions of variance are attributed to the first-order factors or CHC Stratum II (Canivez, 2008;Carroll, 1993Carroll, , 2003Nelson, Canivez, Lindstrom, & Hatt, 2007;Watkins, 2006;Watkins et al, 2006). As such, interpretation of the WASI and WRIT should primarily reside at the FSIQ-GIQ levels.…”

Section: Discussionmentioning

confidence: 99%

Construct validity of the Wechsler Abbreviated Scale of Intelligence and Wide Range Intelligence Test: Convergent and structural validity.

Canivez¹,

Konold²,

Collins³

et al. 2009

School Psychology Quarterly

Self Cite

105

View full text Add to dashboard Cite

) and the Wide Range Intelligence Test (WRIT; Glutting, Adams, & Sheslow, 2000) are two well-normed brief measures of general intelligence with subtests purportedly assessing verbal-crystallized abilities and nonverbal-fluid-visual abilities. With a sample of 152 children, adolescents, and adults, the present study reports meaningful convergent validity coefficients and a latent factor structure consistent with the theoretical intellectual models both tests were constructed to reflect. Consideration of the hierarchical model of intelligence tests and issues regarding test interpretation are presented.Keywords: construct validity, intelligence, WRIT, WASI, hierarchical structure Brief assessment of general intelligence may serve a variety of clinical (e.g., screening and reevaluation) and research purposes. Although some have argued that clinical assessments require that a "comprehensive" battery of intelligence tests be administered to fully understand the nature of performance deficits in the context of individual profiles, research on various subtest analyses (i.e., subtest strengths and weaknesses or unique profiles) reveals them to lack sufficient reliability and validity (Canivez & Watkins, 1998, 1999Glutting, McDermott, Konold, Snelbaker, & Watkins, 1998;Glutting, McDermott, Watkins, Kush, & Konold, 1997;Macmann & Barnett, 1997;McDermott, Fantuzzo, Glutting, Watkins, & Baggaley, 1992;Watkins & Canivez, 2004). The incremental validity (Haynes & Lench, 2003;Hunsley, 2003;Hunsley & Meyer, 2003) of factor-based scores of more "comprehensive" measures of intelligence has been questioned and found lacking (Glutting, Youngstrom, Ward, Ward, & Hale, 1997;Kahana, Youngstrom, & Glutting, 2002;Konold, 1999;Ree & Earles, 1991;Ree, Earles, & Treachout, 1994;Watkins & Glutting, 2000;Watkins, Glutting, & Lei, 2007;Youngstrom, Kogos, & Glutting, 1999). When estimating general intellectual functioning without regard to examining subtest performance, patterns, or profiles, intelligence tests with fewer subtests may provide more time-and costeffective yet valid assessment. Development of brief multidimensional (i.e., verbal and nonverbal estimates) measures of intelligence evolved out of the inadequacies of both single index intelligence screeners and short forms developed from comprehensive intelligence tests noted by A. S. Kaufman and Kaufman (1990) and Silverstein (1990). Among the problems identified were spuriously high correlations between short forms and the full- Data from Jason M. Collins' and Greg Wilson's specialist in school psychology theses were combined for analyses in the present study.We thank Peter W. Hartmann for helpful comments on a version of this article, and also thank Daniel Bialick for providing an electronic copy of his dissertation for review.

show abstract

“…Collins & Smith, 2006;Detert, Schroeder, & Cudeck, 2003;Ebesutani, Okamura, Higa-McMillan, & Chorpita, 2011;Garb, Wood, & Fiedler, 2011;Glisson & James, 2002;González-Romá, Peiró, & Tordera, 2002;Han, Chou, Chao, & Wright, 2006;Hatami, Motamed, & Ashrafzadeh, 2010;Keller, 2001;Law, Shek, & Ma, 2011;Merrell, Felver-Gant, & Tom, 2011;Nelson, Canivez, Lindstrom, & Hatt, 2007;Oliver, Jose, & Brough, 2006;Patterson et al, 2005;Philips et al, 2006;Raspa et al, 2010;Riordan, Vandenberg, & Richardson, 2005;Robert & Wasti, 2002;Salanova, Agut, & Peiró, 2005;Schaubroeck, Lam, & Cha, 2007;Takeuchi, Lepak, Wang, & Takeuchi, 2007;C. M. Tucker et al, 2011;van der Vegt & Bunderson, 2005;Zohar & Tenne-Gazit, 2008) and only a few studies that properly account for clustering (Breevaart, Bakker, Demerouti, & Hetland, 2012;Jackson, Levine, & Furnham, 2003;Mathisen, Torsheim, & Einarsen, 2006;Subramony, Krause, Norton, & Burns, 2008;Wang, Willett, & Eccles, 2011).…”

Section: Please Scroll Down For Articlementioning

confidence: 99%

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

Pornprasertmanit

Lee

Preacher

2014

Multivariate Behavioral Research

View full text Add to dashboard Cite

In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.

show abstract

Higher-order exploratory factor analysis of the Reynolds Intellectual Assessment Scales with a referred sample

Cited by 53 publications

References 26 publications

Construct validity of the WISC-IV with a referred sample: Direct versus indirect hierarchical structures.

Construct validity of the WISC-IV with a referred sample: Direct versus indirect hierarchical structures.

Construct validity of the Wechsler Abbreviated Scale of Intelligence and Wide Range Intelligence Test: Convergent and structural validity.

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

Contact Info

Product

Resources

About