The primary aim was to perform a systematic literature review and extract data necessary for a meta-analytic factor analysis of the RBANS. Secondary aims were to examine the potential validity and utility of the resulting factor structure. Literature was identified through a review of PsycINFO, PubMed, MEDLINE, Academic Search Complete, Psychology & Behavioral Sciences Collection, CINAHL Complete, Health Source: Nursing/Academic Edition, and SocINDEX. A two-stage meta-analytic structural equation modeling method was implemented to pool correlation matrices from primary studies and perform confirmatory factor analyses. Following model selection, factor scores were computed for two datasets and subjected to correlation and diagnostic accuracy analyses. A pooled correlation matrix was computed from 24 sample correlation matrices (N = 5299). Confirmatory factor analysis revealed that the theoretical five-factor model produced the best fit but only when error terms between Story Memory and Story Recall as well as between Figure Copy and Figure Recall were included. Regression-based factor scores showed mixed relationships with the manual-defined indices, and the overall diagnostic accuracy of the factor scores was adequate in both samples examined (AUC = 0.71 and 0.87). The five-factor model was an unexpected result given the failure of multiple previous studies to find support for that model. The five-factor model demonstrates several areas of potential improvement, including better representation of the factors by the indicators. The factor scores implied by this model also require further validation.

中文翻译：

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重新考虑RBANS因子结构：系统文献综述和荟萃分析因子分析。

主要目的是进行系统的文献综述并提取RBANS的荟萃分析因子所必需的数据。次要目标是检查所得因子结构的潜在有效性和实用性。文献通过对PsycINFO，PubMed，MEDLINE，学术搜索完成，心理学与行为科学收藏，CINAHL完成，健康资料来源：护理/学术版和SocINDEX的评论进行鉴定。实施了两阶段的元分析结构方程建模方法，以合并来自基础研究的相关矩阵并进行验证性因子分析。在模型选择之后，针对两个数据集计算因子得分，并进行相关性和诊断准确性分析。从24个样本相关矩阵中计算出一个汇总的相关矩阵（N  = 5299）。验证性因素分析表明，理论上的五因素模型产生了最佳拟合，但前提是包括了故事记忆和故事调用之间以及图复制和图调用之间的误差项。基于回归的因子得分显示出与手动定义的指数的混合关系，并且因子得分的总体诊断准确性在所检查的两个样本中都足够（AUC = 0.71和0.87）。考虑到先前的多项研究未能找到对该模型的支持，五因素模型是出乎意料的结果。五因素模型显示了几个潜在的改进领域，包括通过指标更好地代表因素。该模型隐含的因子得分也需要进一步验证。