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法国韦氏成人智力量表(WAIS-III)的正交高阶结构和验证性因素分析。

Orthogonal higher order structure and confirmatory factor analysis of the French Wechsler Adult Intelligence Scale (WAIS-III).

机构信息

Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland.

出版信息

Psychol Assess. 2011 Mar;23(1):143-52. doi: 10.1037/a0021230.

Abstract

According to the most widely accepted Cattell-Horn-Carroll (CHC) model of intelligence measurement, each subtest score of the Wechsler Intelligence Scale for Adults (3rd ed.; WAIS-III) should reflect both 1st- and 2nd-order factors (i.e., 4 or 5 broad abilities and 1 general factor). To disentangle the contribution of each factor, we applied a Schmid-Leiman orthogonalization transformation (SLT) to the standardization data published in the French technical manual for the WAIS-III. Results showed that the general factor accounted for 63% of the common variance and that the specific contributions of the 1st-order factors were weak (4.7%-15.9%). We also addressed this issue by using confirmatory factor analysis. Results indicated that the bifactor model (with 1st-order group and general factors) better fit the data than did the traditional higher order structure. Models based on the CHC framework were also tested. Results indicated that a higher order CHC model showed a better fit than did the classical 4-factor model; however, the WAIS bifactor structure was the most adequate. We recommend that users do not discount the Full Scale IQ when interpreting the index scores of the WAIS-III because the general factor accounts for the bulk of the common variance in the French WAIS-III. The 4 index scores cannot be considered to reflect only broad ability because they include a strong contribution of the general factor.

摘要

根据最广为接受的 Cattell-Horn-Carroll(CHC)智力测量模型,韦氏成人智力量表(第 3 版;WAIS-III)的每个分量测验分数都应反映一阶和二阶因素(即 4 或 5 种广泛的能力和 1 种一般因素)。为了厘清每个因素的贡献,我们对 WAIS-III 法语技术手册中公布的标准化数据应用了 Schmid-Leiman 正交化转换(SLT)。结果表明,一般因素占共同方差的 63%,一阶因素的特定贡献较弱(4.7%-15.9%)。我们还通过使用验证性因素分析来解决这个问题。结果表明,双因素模型(一阶组和一般因素)比传统的高阶结构更适合数据。还测试了基于 CHC 框架的模型。结果表明,高阶 CHC 模型比经典的 4 因素模型更适合,但是 WAIS 双因素结构是最合适的。我们建议用户在解释 WAIS-III 的指数分数时不要低估全量表智商,因为一般因素占法国 WAIS-III 共同方差的大部分。这 4 个指数分数不能被认为仅反映广泛的能力,因为它们包含一般因素的强烈贡献。

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