Although crystallized intelligence (gc) is a prominent factor in contemporary theories of individual differences in intelligence, its structure and optimal measurement are elusive. Analogously to the personality trait hierarchy, we propose the following hierarchy of declarative fact knowledge as a key component of gc: a general fact knowledge factor at the apex, followed by broad knowledge areas (e.g., natural sciences, social sciences, humanities), knowledge domains (e.g., chemistry, law, art), and nuances. In most scientific contexts we are predominantly concerned with aggregate levels, but we argue that the sampling of knowledge items strongly affects distinctions at higher levels of the hierarchy. We illustrate the magnitude of item-level heterogeneity by predicting chronological age differences through knowledge differences at different levels of the hierarchy. Analyses were based on an online sample of 1629 participants between age 18 and 70 who completed 120 broadly sampled declarative knowledge items across twelve domains. The results of linear and elastic net regressions, respectively, demonstrated that the majority of the age variance was located at the item level, and the strength of the prediction decreased with increasing aggregation. Knowledge nuances seem to tap important variance that is not covered by aggregate scores (e.g., sum or factor scores) and that is useful in the prediction of age. In turn, these effects extend our understanding how knowledge is acquired and imparted. On a more general stance, to gain new insights into the nature of knowledge, its optimal measurement and psychometric representation, item and person sampling issues should be considered.

尽管结晶智能（gc）是现代个体智能差异理论中的一个突出因素，但其结构和最佳测量却难以捉摸。类似于人格特质层次结构，我们提出以下陈述性事实知识层次结构作为gc的关键组成部分：顶点处的一般事实知识因素，其次是广泛的知识领域（例如，自然科学，社会科学，人文科学），知识领域（例如化学，法律，艺术）和细微差别。在大多数科学环境中，我们主要关注汇总级别，但是我们认为知识项的采样强烈影响层次结构较高级别的区别。我们通过预测层次结构不同级别上的知识差异，通过预测年代差异来说明项目级异质性的程度。分析是基于在线样本，该样本来自18岁至70岁之间的1629名参与者，他们完成了十二个领域中的120个广泛采样的声明性知识项目。线性和弹性净回归的结果分别表明，年龄差异的大部分位于项目级别，并且预测强度随着聚集的增加而降低。知识的细微差别似乎可以利用重要的方差，而总方差（例如，总和或因子得分）则无法涵盖这些方差，这对预测年龄很有用。反过来，这些影响扩展了我们对知识的获取和传递方式的理解。在更一般的立场上，