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Gender fluidity across the world: a Multilevel Item Response Theory approach

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  • Lara Fontanella
  • Mara Maretti
  • Annalina Sarra

Abstract

The concept of gender identity refers to the intrinsic self-identification of personal femaleness and maleness. Starting from 1970s, in the framework of gender studies, a theoretical and conceptual demarcation between sex and gender has been proposed. The term “gender” starts being referred to social and cultural constructions of masculinities and femininities, not to the state of being male or female. Recent studies on gender identity suggest that the binary notion of gender identity is changing, recognizing that there are different views on how gender may be performed or experienced. The purpose of our research is to provide accounts of gender code transformation around the world and identify differences in feelings, attitudes, beliefs and behaviors associated with gender across different identity profiles. Using a web-based survey, 1,600 respondents were recruited by a snowball sampling procedure. Based on the intersections of the responses given by participants on three basic dimensions (biological sex, gender self-identification and sexual orientation) the individuals have been categorized into nine groups. To comply with the aims of this study, a two-parameter Multilevel Item Response Theory model has been employed as an appropriate statistical tool for considering both person and item effects on the response data and assessing the effect of group membership on the overall variability. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Lara Fontanella & Mara Maretti & Annalina Sarra, 2014. "Gender fluidity across the world: a Multilevel Item Response Theory approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2553-2568, September.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:5:p:2553-2568
    DOI: 10.1007/s11135-013-9907-4
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    References listed on IDEAS

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    1. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
    2. Martine Selm & Nicholas Jankowski, 2006. "Conducting Online Surveys," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(3), pages 435-456, June.
    3. Fox, Jean-Paul, 2007. "Multilevel IRT Modeling in Practice with the Package mlirt," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i05).
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    Cited by:

    1. Sarra, Annalina & Di Zio, Simone & Cappucci, Marianna, 2015. "A quantitative valuation of tourist experience in Lisbon," Annals of Tourism Research, Elsevier, vol. 53(C), pages 1-16.

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