Antonio Calcagnì

DPSS - University of Padova
Psicostat research group

    I am a researcher in Psychometrics at the University of Padova, Department of Developmental and Social Psychology (DPSS).

    My research area mainly focuses on the development and application of statistical methods to the analysis and interpretation of data arising from behavioral studies. Most of it concentrates on dynamic modeling of computerized tracking data, statistical methods for handling uncertainty in behavioral data (e.g., fuzzy statistics), information-theoretic methods in statistical estimation (e.g., maximum entropy). I am also interested in applications of Bayesian statistics to behavioral sciences and measurement problems.

    I did doctoral research at the University of Trento, where I earned my PhD, and KU Leuven. As part of my post-doctorate research, I worked at the MTA Wigner Research Centre for Physics (NAP-B PATTERN Group). My Erdős number is 4.


    • Ongoing research projects:
      1. State-space modeling of computerized tracking data [arXiv] [R package]
      2. Computing confidence intervals for sign-flipping score tests
      3. Latent models with ordinal data


    Speaking (selected contributions)

    • 2018. Calcagnı̀ A., Altoè G., Pastore M. Handling with categorical data in factor analysis: A copula-based approach. Annual Meeting of the European Mathematical Psychology Group (EMPG), Genova, July 30 - August 2. [pdf]
    • 2018. Calcagnı̀ A., Lombardi L., D'Alessandro M. Probabilistic modeling of mouse-tracking data: A state-space approach. Annual Meeting of the European Mathematical Psychology Group (EMPG), Genova, July 30 - August 2. [pdf]
    • 2018. Calcagnı̀ A., Lombardi L., D'Alessandro M., Sulpizio S. subjected-oriented state space approach to model mouse-tracking data. 60th Conference of Experimental Psychologists - TEAP, Marburg, March 11-14. [pdf]
    • 2017. Calcagnı̀ A., Altoè G. Il valore aggiunto dell'inferenza bayesiana nell'analisi dei dati in psicologia. XXIII Congresso Nazionale AIP - sezione sperimentale, Bari, 20-22 Settembre. [pdf]
    • 2015. Calcagnı̀ A., Lombardi L. An information theoretic approach for modeling spatial information from mouse tracker methodology. Annual Meeting of the European Mathematical Psychology Group (EMPG), Padova, September 1-3. [pdf]
    • 2013. Calcagnı̀ A., Ciavolino E. A Generalized Maximum Entropy (GME) approach to crisp-input/fuzzy-output regression model. SIS International Statistical Conference, Brescia, June 19-21. [pdf]

    Research (selected publications)

      The full list of publications is available at my RG profile

    • 2019. Calcagnì, A., Finos, L., Altoè, G., Pastore, M. A Maximum Entropy Procedure to Solve Likelihood Equations. Entropy, 21, 596. [web] [arXiv] [R code]
    • 2019. Pastore, M., Calcagnì, A.. Measuring distribution similarities between samples: a distribution-free overlapping index. Frontiers in Psychology: Quantitative Psychology and Measurements, 10, 1089. [web] [pdf] [R package]
    • 2019. Toffalini, E., Buono, S., Zagaria, T., Calcagnì, A., Cornoldi, C. Using Z and age-equivalent scores to address WISC-IV floor effects for children with intellectual disability. Journal of Intellectual Disability Research, 63, 528-538. [web] [pdf]
    • 2019. Invitto, S., Calcagnì, A., Piraino, G., Ciccarese, V., Balconi, M., De Tommaso, M., Toraldo, D.M. Obstructive sleep apnea syndrome and olfactory perception: An OERP study. Respiratory Physiology and Neurobiology, 259, pp. 37-44. [web] [pdf]
    • 2017. Calcagnì, A., Lombardi, L., Pascali, E. Multiple mediation analysis for interval-valued data. Statistical Papers. Article in Press. pp. 1-27.
      [web] [pdf] [Matlab package]
    • 2017. Calcagnì, A., Lombardi, L., Sulpizio, S. Analyzing spatial data from mouse tracker methodology: An entropic approach. Behavior Research Methods, 49 (6), pp. 2012-2030. , 259, pp. 37-44. [web] [pdf] [Matlab package]
    • 2017. Doove, L.L., Wilderjans, T.F., Calcagnì, A., Van Mechelen, I. Deriving optimal data-analytic regimes from benchmarking studies. Computational Statistics and Data Analysis, 107, pp. 81-91. [web] [pdf]
    • 2016. Calcagnì, A., Lombardi, L., Pascali, E. A dimension reduction technique for two-mode non-convex fuzzy data. Soft Computing, 20 (2), pp. 749-762. [web] [pdf] [Matlab package]
    • 2015. Ciavolino, E., Calcagnì, A. Generalized cross entropy method for analysing the SERVQUAL model. Journal of Applied Statistics, 38, pp. 51-63. [web] [pdf]
    • 2014. Calcagnì, A., Lombardi, L. Dynamic Fuzzy Rating Tracker (DYFRAT): A novel methodology for modeling real-time dynamic cognitive processes in rating scales. Applied Soft Computing Journal, 24, pp. 948-961. [web] [pdf]
    • 2014. Ciavolino, E., Salvatore, S., Calcagnì, A. A fuzzy set theory based computational model to represent the quality of inter-rater agreement. Quality and Quantity, 48 (4), pp. 2225-2240. [web] [pdf]

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