Abstract

In this study, we developed an age-invariant 18-item short form of the HEXACO Personality Inventory for use in developmental personality research. We combined the item selection procedure ant colony optimization (ACO) and the model estimation approach local structural equation modeling (LSEM). ACO is a metaheuristic algorithm that evaluates items based on the quality of the resulting short scale, thus directly optimizing criteria that can only be estimated with combinations of items, such as model fit and measurement invariance. LSEM allows for model estimation and measurement invariance testing across a continuous age variable by weighting participants, rather than splitting the sample into artificial age groups. Using a HEXACO-100 dataset of N = 6,419 participants ranging from 16 to 90 years of age, we selected a short form optimized for model fit, measurement invariance, facet coverage, and balance of item keying. To achieve scalar measurement invariance and brevity, but maintain construct coverage, we selected 18 items to represent three out of four facets from each HEXACO trait domain. The resulting HEX-ACO-18 short scale showed adequate model fit and scalar measurement invariance across age. Furthermore, the usefulness and versatility of the item and person sampling procedures ACO and LSEM is demonstrated.

摘要

在这项研究中，我们开发了一种年龄不变的 18 项简短形式的 HEXACO 人格量表，用于发展性人格研究。我们结合了项目选择程序蚁群优化 (ACO) 和模型估计方法局部结构方程建模 (LSEM)。ACO 是一种元启发式算法，它根据生成的短尺度的质量来评估项目，从而直接优化只能通过项目组合来估计的标准，例如模型拟合和测量不变性。LSEM 允许通过对参与者进行加权来跨连续年龄变量进行模型估计和测量不变性测试，而不是将样本分成人为的年龄组。使用N的 HEXACO-100 数据集 = 6,419 名 16 至 90 岁的参与者，我们选择了一种针对模型拟合、测量不变性、方面覆盖和项目键控平衡进行了优化的简短表格。为了实现标量测量的不变性和简洁性，但保持构造覆盖率，我们选择了 18 个项目来代表每个 HEXACO 特征域的四个方面中的三个。由此产生的HEX-ACO-18短尺度显示出足够的模型拟合和跨年龄的标量测量不变性。此外，项目和人员抽样程序 ACO 和 LSEM 的有用性和多功能性得到了证明。