A novel CFA + EFA model to detect aberrant respondents

Aberrant respondents are common but yet extremely detrimental to the quality of social surveys or questionnaires. Recently, factor mixture models (FMMs) have been employed to identify individuals providing deceptive or careless responses. We propose a comprehensive FMM for continuous outcomes that combines confirmatory and exploratory factor models to classify both the nonaberrant and aberrant respondents. The flexibility of the proposed classification model allows for the identification of two of the most common aberrant response styles, namely faking and careless responding. We validated our approach by means of two simulations and two case studies. The results indicate the effectiveness of the proposed model in dealing with aberrant responses in social and behavioural surveys.