Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, Brazil.
Department of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, Brazil.
J Safety Res. 2020 Jun;73:133-142. doi: 10.1016/j.jsr.2020.02.004. Epub 2020 Mar 20.
Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data.
The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.
探索性数据分析技术，如因子分析（FA）和主成分分析（PCA），广泛应用于有序数据（如 Likert 量表数据）的问卷验证中，尽管这两种技术都适用于度量测量。在这种情况下，本研究提出了一项电子调查，旨在根据驾驶员行为问卷（DBQ）中的 20 个项目，基于五点 Likert 量表，获取巴西驾驶员（N=1354，男性占 55.2%）和葡萄牙驾驶员（N=348，男性占 46.6%）的自我报告行为。本研究旨在使用 FA 和 PCA 以及更适用于 Likert 量表数据的分类主成分分析（CATPCA）来检验 DBQ 的验证。
所有技术的结果均证实了 DBQ 最具重复性的因子结构，将行为区分为错误、普通违规和攻击性违规。然而，在 Varimax 旋转后，CATPCA 比 FA 解释了 11%更多的方差，比 PCA 多解释了 2%。我们在技术的成分之间发现了交叉负荷。一个项目在 CATPCA 结果中改变了其维度，但并没有改变结构的可解释性。CATPCA 维度 1 的个体得分与 FA 和 PCA 有显著差异。CATPCA 因子 1 的个体得分与 FA 和 PCA 有显著差异。实际应用：CATPCA 似乎更有利，以便代表原始数据并考虑数据约束。除了找到可解释的因子结构外，代表原始数据也被认为是相关的，因为因子分数可用于未来分析中的碰撞预测。
