Queer Data by Kevin Guyan

Author:Kevin Guyan [Guyan, Kevin]
Language: eng
Format: epub
Tags: Social Science, Gender Studies, LGBTQ+ Studies, Transgender Studies, History, Europe, Great Britain, 21st Century, Literary Criticism, Lgbtq, Education, Inclusive Education, Sociology, General, Computers, Database Administration & Management
ISBN: 9781350230743
Google: uVVLEAAAQBAJ
Publisher: Bloomsbury Publishing
Published: 2022-01-13T02:37:09+00:00

Small numbers

Decisions made during analysis are particularly impactful when data about LGBTQ groups involve working with small numbers. The Gender Identity in US Surveillance Group, convened by the Williams Institute (a research centre based at the University of California Los Angeles), has reported hesitancy among US national data collection agencies to gather data on groups that would likely comprise less than 0.5 per cent of the total population.21 With an estimate that around 0.3 per cent of the adult population in the United States identify as trans, this arbitrary benchmark would therefore rule out inclusion in national counts.22 However, even when counted, the issue of small numbers can create several problems for LGBTQ people in how this data is analysed and subsequently used. Ridolfo et al. describe the asymmetrical size of minority and majority sexual and gender groups in nationally representative surveys, which means that ‘the slightest degree of error can dramatically impact estimates’.23 As described in the previous chapter’s account of same-sex couple data in the 2000 and 2010 US censuses, errors can render all data collected about LGB people suspect whether the count seems too high or too low. When those responsible for data collection fail to meaningfully engage and instil confidence among communities covered by the count, design questions that are exclusionary and/or fail to fully acknowledge the diversity and complexity of gender, sex and sexual identities, small numbers can become even smaller than originally anticipated. Browne has warned that if, for whatever reason, counts fail to match assumptions about the size of LGB populations this might weaken arguments for LGB equality and justify further inaction.24

A partial solution to the challenge of small numbers is the quantification of qualitative data collected via open-text boxes in surveys. In their article ‘What Sexual and Gender Minority People Want Researchers to Know About Sexual Orientation and Gender Identity Questions: A Qualitative Study’, Leslie W. Suen et al. highlight a strong desire for write-in answer choices for questions on sexual orientation and gender identity among the seventy-four people who participated in their focus groups and cognitive interviews, particularly among participants of colour.25 As data collected from open-text boxes do not usually map to items on an existing coding framework (for example, where female equals one and male equals two), analysis is potentially difficult and time-consuming, particularly in large-scale exercises such as a national census. The view that open-text data is hard to analyse has likely dissuaded researchers from including this type of question in surveys and diversity monitoring forms.26 In defence of open-text questions, Gloria Fraser et al. have noted how this approach ‘is rarely used as the sole measure of gender’ and ‘may represent a missed opportunity for quantitative researchers’.27 Fraser highlights how an open-text question ‘encourages participants to self-identify using as many terms as they wish’ and points to studies that demonstrate efficient and accurate methods to analyse the data collected.28 Approaches include the manual coding of a small sample of open-text data, for example 5 per cent of

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