Building 2SLGBTQ+ Data Literacy in Social Work Education

One of the most important roles for social work educators is to help students learn to work with data in thoughtful and critically engaging ways. Rather than just asking students to find and read relevant social work-related statistics, students should be encrouaged to ask critical questions about what those numbers represent, who is counted, and who is missing. Bringing data literacy into the classroom can help future social workers see how evidence shapes practice and how they can use data to compliment lived realities.

Pooling , Oversampling and Snowballing

Because 2SLGBTQ+ populations are often smaller in number, researchers often struggle with sample size. Instead of treating these studies as less valuable, it helps show students how even small studies can convey important information—especially when combined with qualitative research. Using mixed methods or drawing on larger national surveys through aggregate data, public use microdata files or secure Research Data Centre microdata are ways to work around these limitations and give students a sense of what’s possible.

One strategy to address small sample sizes in 2SLGBTQ+ research is oversampling. Oversampling means intentionally recruiting more respondents from smaller populations so that the data are robust enough to support detailed analysis. For example, a national Statistics Canada survey may deliberately include a higher proportion of transgender or non-binary participants than would appear in the general population. While the overall percentages are later adjusted through weighting, oversampling ensures that these groups are not lost in the data.

Another useful approach is pooling data across survey years (or waves) or combining multiple compatible datasets. By merging several years of Statistics Canada surveys, for instance, researchers can increase the number of 2SLGBTQ+ respondents and generate more reliable estimates for marginalized subgroups from analysis with sufficient statistical power. For example, a study examining the impact of community belonging for 2SLGTQ+ pre and during COVID may pools 2018 and 2019 annual files with 2021 and 2022 files. This is especially important when examining intersections such as 2SLGBTQ+ people who are also immigrants, Indigenous, or living with disabilities. Without pooling across years the numbers in any single year may be too small for inferential statistical tests.

Furthermore. snowball sampling can also be used in qualitative research to complement large-scale survey data. By asking participants to refer others in their communities, researchers can reach 2SLGBTQ+ people who may not be visible in national surveys. While this does not constitute representative ampling, these approaches generate rich qualitative insights into lived experiences and fill in gaps in quantitative data.

For social work educators, these strategies demonstrate how methodological choices can either obscure or illuminate inequities. Teaching students about oversampling and pooling helps them understand both the possibilities and the limitations of using large-scale survey data to inform practice, advocacy, and policy.

Addressing Gaps and Inconsistencies in 2SLGBTQ+ Data

Different surveys often ask about gender and sexuality in different ways or introduce measure at different points in time. This makes it difficult to compare results across studies and across time. In teaching, it can be useful to work with students on how to compare across multiple data sources—which includes reading and understanding documentation (also referred to as user guides and codebooks) from data sources like Statistics Canada. The goal is not to ignore the gaps or give up on 2SLGBTQ+ research, but to show how to put together a more complete picture (e.g., supplementing with qualitative methods) while also advocating for more inclusive data collection through opportunities like public consultations through Statistics Canada.

Ethics and Reflexivity in 2SLGBTQ+ Research

Ethics are especially important when working with marginalized communities. Students should learn to think carefully about issues like confidentiality, cultural safety, and who benefits from the research. While this OER focuses on quantitative sources introducing community-based or participatory approaches can help shift the balance so that research directly supports and empowers the vulnerable groups it studies. Even if you are not collecting original research data, individuals are encouraged to introduce reflexivity as a way of being open about their own positionality and biases when working with secondary data sources.

For quantitative research as covered here, it is important that social work students understand the careful ways in which Statistics Canada protects privacy by, for example, suppressing and aggregating variables, or making sensitive disaggregated microdata available for restricted-use in a protected environment (i.e., the Statistics Canada secure Research Data Centres).

Case Example for Reflexivity with Secondary Data

Imagine a social work PhD student is analyzing Statistics Canada’s Canadian Community Health Survey (CCHS) to explore mental health among transgender youth. Because Statistics Canada suppresses small cell counts and aggregates variables (e.g., transgender men, transgender women may be aggregated to transgender | non-binary youth may be aggregated to gender diverse youth) and response options (e.g., age ranges) to protect confidentiality, the student cannot always break down the data into every subgroup they wish to study.

This safeguard is intentional as it ensures that individuals from small populations cannot be identified. In other words, Statistics Canada adheres to the federal Statistics Act and federal Privacy Act to protect confidentiality of participants.

In their write up, the student practices reflexivity by acknowledging both the strengths and limits of the dataset. They note that while the CCHS provides valuable national evidence for 2SLGBTQ+ mental health, it underrepresents the specific experiences of trans youth say in smaller provinces or rural areas. Recognizing these limits, and linking them to the ethical responsibility to protect participants privacy, strengthens the integrity of their research.

Practical Tools for Social Work Practice

Data literacy becomes most useful when students can put it into practice. Classroom activities can help. For example, you can compare surveys and ask students what is missing. You could also facilitate a discussion about how open 2SLGBTQ+ data can affect change and promote social justice work. These kinds of exercises encourage students to treat data as something relevant and empowering for their practice.

Advocacy and Policy Influence

Even when data is incomplete, it can still be used to advocate for change. Social workers are often in a position to combine statistical findings with qualitative narratives from lived experience. Teaching students to use data critically and strategically shows how evidence can be part of anti-oppressive practice and part of broader efforts toward equity and social justice.

In Canada, national initiatives such as the 2022 Federal 2SLGBTQI+ Action Plan demonstrate how government relies on both official statistics and consultation with communities, organizations and individuals to shape policy priorities for areas such as health, housing, safety, and inclusion. On a community level, projects like Trans PULSE Canada also exist and highlight how knowledge mobilization can ensure that the voices of transgender and non-binary people directly inform public debates, funding decisions, and service design and delivery. Reports, infographics, and clearly written briefs produced by Trans PULSE are often cited in advocacy and policy discussions, showing the impact that community-based evidence can have alongside official statistics.

By way of example, Trans PULSE Canada has created accessible knowledge mobilization resources, see Data in Action which currently highlights a powerful video essay series addressing health and well-being for trans and nonbinary youth.

See for example this video Trans Youth: When Our Doctors Hurt Us, which demonstrates how survey data can be transformed into an accessible format for education and advocacy:

Trans Youth: When Our Doctors Hurt Us – Trans PULSE Canada

By teaching students to critically assess and embrace both secondary data (such as national surveys, censuses, opinion poll data) and community-based knowledge (such as Trans PULSE research outputs), social work education prepares social work practitioners to mobilize evidence in ethical and impactful ways.

Reflective Questions after Watching Trans Youth: When Our Doctors Hurt Us:

1. How does this video translate statistical survey findings into a story that is accessible and emotionally engaging?

2. What are the strengths and limitations of presenting data in this way compared to reading a formal research report?

3. In what ways can visual and narrative knowledge mobilization tools support data literacy for social work students and practitioners?

4. How might you, as a future social worker, use both numbers (survey results) and stories (lived experience) together in your own advocacy or practice?