Developments in survey methods and analysis about LGBTI+ populations 2 |
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Coordinator 1 | Dr Angelo Moretti (Utrecht University) |
Empirical evidence is crucial in shaping and implementing policies focusing on lesbian, gay, bisexual, transgender, transexual, intersexual and any other individuals whose sexual and/or gender identity differs from the cis-heterosexual (LGBTI+). In recent years, much progress has been made regarding the legislation focusing on the protection and support of LGBTI+ communities. However, obstacles are still present in practice when it comes to data collection and analysis to provide reliable outputs to be used by policy makers. Particularly, there remains gaps in data collection initiatives aiming to investigate a wide range of issues affecting LGBTI+ populations. This lack of data affects policies, and this has an important impact on the lives and well-being of LGBTI+ people. These populations can be seen as ‘hard-to-reach populations’, meaning that they are difficult for survey researchers to access; hence, relevant sampling designs should be considered and formulated carefully. Furthermore, there are important methodological issues arising from when collected data are analysed. Specifically, confidentiality and privacy issues are crucial here. This can lead onto measurement error issues in the reporting stage, e.g., discriminations in various contexts, and victimisations. Thus, these problems must be taken into account when developing and applying statistical models. In addition, due to the nature of this phenomena object of study, distributions might suffer from a large number of zeros so that the data do not readily fit standard distributions, e.g., variables measuring victimisation of LGBTI+ people. We welcome substantive and methodological papers that address issues related to survey methods and analysis of LGBTI+ populations. Methodological papers can be related to sampling designs, data integration developments considering new forms of data too, statistical modelling approaches that seek to produce robust analysis.