Gender-based Analysis Plus (GBA+) is a method for examining the intersection of sex and gender with other identity factors. When applied to government work, GBA+ can aid us in understanding how Canadian women, men and gender-diverse people experience public policy. GBA+ is a method for collecting and reviewing data in an unbiased manner, one that leaves aside many of the assumptions that can mask the GBA+ impacts of a given initiativeFootnote i.
Gender ≠ women. Does your research look at the situation of women, men and gender-diverse people? GBA+ acknowledges that gender is a social construct that ascribes an individual with roles, responsibilities, norms, aptitudes, behaviours and expectations. In reality, gender roles are affected by a variety of other identity factors including age, class, race, ethnicity, religion and ideology.
All women are not the same; all men are not the same; all gender-diverse people are not the same. Does your research look at diverse groups of women, men and gender-diverse people?
At the heart of GBA+ is the understanding that people's identities are comprised of many different factors, including gender, ethnicity, age, religion, sexual orientation, etc. GBA+ is about looking at the differences between and among different groups of women, men and gender-diverse people, and helping to mitigate or eliminate differential negative impacts.
Avoid oversimplifying diversity. Does your research consider differences among diverse groups of women, men and gender-diverse people?
When conducting GBA+ it is important to understand the complexity of different aspects of identity or different socio-economic factors. For example, research that does not distinguish between the experiences of diverse Indigenous women (including Inuit, Métis and First Nations, on and off-reserve) could mask important differences.
Individual versus household-level analysis. Does your household-level research also consider individual members and account for intrahousehold inequalities?
Analysis and information gathered at the household level can hide intra-household gender differences and dynamicsFootnote ii. For example, the Low-Income Cut Off (LICO) measures household income and may not show intra-household disparities, i.e., one spouse/partner may have more resources than the other but not be sharing them equitably.
Avoid over-generalizing/over-specifying. Does your research ensure it does not take one gender or population group as the norm?
Over-generalization occurs when a single group is considered the norm for all; i.e., a study of only one sex presumes findings apply to both. Over-specificity occurs when research is reported in such a way as to make it impossible to determine whether it applies to one or both sexesFootnote iii.
Question data. Does your research consider the existence of bias in the data sources used or other resources consulted?
Most data (quantitative or qualitative) is “constructed”, i.e., shaped by the categories used to gather and interpret it, and – in the case of human subjects – by the way the research subject interprets and decides to react to what is being asked. In a large-scale survey questionnaire, this “social construction” (so called because of the influence of broad social norms on how we think about data) occurs largely during questionnaire designFootnote iv.
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