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The Bifactor Model Fits Better Than the Higher-Order Model in More Than 90 of Comparisons for Mental Abilities Test Batteries

机译:在心理能力测试电池比较的90%以上中Bifactor模型比高级模型更适合

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摘要

The factor structure of mental abilities has most often been depicted using a higher-order model. Under this model, general mental ability (g) is placed at the top of a pyramid, with “loading” arrows going from it to the other factors of intelligence, which in turn go to subtest scores. In contrast, under the bifactor model (also known as the nested factors/direct hierarchical model), each subtest score has its own direct loading on g; the non-g factors (e.g., the broad abilities) do not mediate the relationships of the subtest scores with g. Here we summarized past research that compared the fit of higher-order and bifactor models using confirmatory factor analysis (CFA). We also analyzed additional archival datasets to compare the fit of the two models. Using a total database consisting of 31 test batteries, 58 datasets, and 1,712,509 test takers, we found stronger support for a bifactor model of g than for the traditional higher-order model. Across 166 comparisons, the bifactor model had median increases of 0.076 for the Comparative Fit Index (CFI), 0.083 for the Tucker-Lewis Index (TLI), and 0.078 for the Normed Fit Index (NFI) and decreases of 0.028 for the root mean square error of approximation (RMSEA) and 1343 for the Akaike Information Criterion (AIC). Consequently, researchers should consider using bifactor models when conducting CFAs. The bifactor model also makes the unique contributions of g and the broad abilities to subtest scores more salient to test users.
机译:心理能力的因素结构最经常使用高阶模型来描述。在这种模型下,一般的心理能力(g)被放置在金字塔的顶部,“加载”箭头从金字塔指向其他智力因素,这些智力因素反过来又对分数进行了测验。相反,在双因子模型(也称为嵌套因子/直接层次模型)下,每个子测验分数对g都有自己的直接负荷;非g因素(例如,广泛能力)不介导子测验分数与g的关系。在这里,我们总结了过去的研究,这些研究使用验证性因子分析(CFA)比较了高阶和双因子模型的拟合度。我们还分析了其他档案数据集,以比较两个模型的拟合度。使用包含31个测试电池,58个数据集和1,712,509个应试者的总数据库,我们发现g的双因子模型比传统的高阶模型更强大的支持。在166次比较中,双因素模型的比较拟合指数(CFI)的中位数增加了0.076,Tucker-Lewis指数(TLI)的中位数增加了0.083,标准拟合指数(NFI)的中位数增加了0.078,均方根减少了0.028 Akaike信息准则(AIC)的近似平方误差(RMSEA)和1343。因此,研究人员在进行CFA时应考虑使用双因素模型。双因子模型还使g的独特贡献以及对子测验分数的广泛能力对测试用户更加重要。

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