Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

• Published in: Journal of safety research 2020-06, Vol.73 (73), p.133-142
• Main Authors: Campos, Cintia Isabel de, Pitombo, Cira Souza, Delhomme, Patricia, Quintanilha, José Alberto
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Automobile Driving - statistics & numerical data; Brazil; Categorical Principal Component Analysis; Comparative analysis; Cross-loading; Data reduction; Driver behavior; Explained variance; Exploratory behavior; Factor analysis; Factor Analysis, Statistical; Female; Humanities and Social Sciences; Humans; Likert Scale; Male; Males; Middle Aged; Portugal; Principal Component Analysis; Principal components analysis; Psychology; Questionnaires; Self Report - statistics & numerical data; Surveys and Questionnaires - statistics & numerical data; Young Adult
• ISSN: 0022-4375; 1879-1247
• DOI: 10.1016/j.jsr.2020.02.004

•Techniques usually used in Likert Scale questionnaire validation are indicated to metric measures.•CATPCA enables researchers to deal with different levels of measures.•Techniques comparison allows evaluating appropriated technique to deal with ordinal data.•CATPCA demonstrated conformity with the other techniques used and literature results.•Some differences indicated that the CATPCA might be more advantageous. Introduction: 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. Results: 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.