My main research stream focuses on interrelated issues dealing with statistical data analysis of possible fake data or fraudulent observations in self-report measures. This aspect is particularly relevant for researchers working on sensitive topics such as, for example, risky sexual behaviors, drug addictions, political preferences, and personnel selection. Unfortunately, standard statistical models do not deal with possible fake observations. Therefore, it is important to construct new methods and models to efficiently analyze and elaborate fake data. This will allow us to answer questions like: how sensitive are the empirical results to possible fake observations? Are the conclusions still valid under one or more scenarios of faking (e.g., slight, moderate, and extreme faking)?
I also like to study and propose new formal models of higher level cognition, such as decision strategies, induction, similarity evaluation, and classification. My current interest is on developing cognitive models and statistical procedures to explore motor components associated with some cognitive dynamics involved in experimental tasks like categorization, decision-making, and language comprehension. Moreover, I am also interested on problems that are related to how people categorize and integrate semantic information as well as probabilistic cues in concept name retrieval tasks.
- Fake data analysis and modeling
- Probabilistic models of higher level cognition
- Mathematical psychology and psychometrics
- Calcagni' A., Lombardi L., & Sulpizio S. (2017). Analysing spatial data from mouse tracker methodology: An entropic approach. Behavior Research Methods, 1-19. Online first article.
- Lombardi L. & Pastore M. (2016). Robust evaluation of fit-indices to fake-good perturbation of ordinal data. Quality & Quantity, 50, 2651-2675.
- Calcagni' A. & Lombardi L. (2014). Dynamic Fuzzy Rating Tracker (DYFRAT): A novel methodology for modeling real-time dynamic cognitive processes in rating scales. Applied Soft Computing, 24, 948-961.
- Lombardi L. & Pastore M. (2014). sgr: A package for simulating conditional fake ordinal data. The R Journal, 6(1), 164-177.
- Lombardi L. & Pastore M. (2012). Sensitivity of fit indices to fake perturbation of ordinal data: A sample by replacement approach. Multivariate Behavioral Research, 47, 519-546.
- Kemp C., Chang K-M. & Lombardi L. (2010). Category and feature identification. Acta Psychologica, 133, 216-233.
- Crupi V., Tentori K. & Lombardi L. (2009). Pseudodiagnosticity revisited. Psychological Review, 116, 971-983.
- Lombardi L. & Sartori G. (2007). Models of relevant cue integration in name retrieval. Journal of Memory and Language, 57, 101-125.
- Van Mechelen I., Lombardi L., & Ceulemans E., (2007). Hierarchical classes modeling of rating data. Psychometrika, 72, 475-488.
- Sartori G., & Lombardi L. (2004). Semantic relevance and semantic disorders. Journal of Cognitive Neuroscience, 16 439-452.
Further information can be found at the following webpage: Luigi Lombardi