Focuses on Indigenous and decolonizing methodologies and measurement, with a focus on narrative inquiry.

Historically, non-Indigenous researchers have often used methods rooted in colonization, which can misrepresent or ignore Indigenous knowledge and history, causing harm. In response, Indigenous communities and scholars have developed Indigenous and decolonizing methodologies to prevent harmful practices and support ethical research.

Decolonizing methodologies reshape Western scientific approaches within a pro-Indigenous ethic, advocating for Indigenous self-determination. In contrast, Indigenous methodologies draw directly from Indigenous knowledge systems, independent of Western frameworks.

The Wheel of AI/AN Specificity framework further ensures that research is conducted within Indigenous contexts, emphasizing collaborative relationships with tribal communities. This framework centers ongoing community engagement to align research with Indigenous practices, processes, and knowledge. 

Leadership: Dr. Jillian Fish, Dr. Miigis Gonzalez

Specializes in building Indigenous community partnerships and developing culturally responsive engagement strategies to support participant recruitment and retention.

CIRCLE’s work depends on strong, lasting partnerships with Indigenous communities, agencies, and organizations. Subcore 1b focuses on addressing power imbalances between researchers and the “researched”. This approach centers Indigenous communities as thriving interconnected systems that require authentic engagement and relationship building. This approach acknowledges and addresses past wrongdoings by building trust and relationships to support community action and long-term change. In addition to standard ICBPR practices, CIRCLE’s approach includes the following key considerations: 

1. Acknowledging historical experiences of research in Indigenous communities,
2. Upholding tribal sovereignty and Tribes’ inherent rights to govern research,
3. Valuing diversity within and across Tribes and Indigenous Peoples,
4. Honoring cultural knowledge keepers and incorporating cultural strengths into research,
5. Broadening metrics of success to include community action, dissemination, and team relational well-being,
6. Interpreting data in a way that respects diverse cultural contexts

Leadership: Dr. Joshuaa Allison-Burbank

Offers guidance in substance use measurement, ensuring standardized measures across projects while tailoring them to the needs of different Indigenous communities.

There is a critical need for more substance use epidemiological research in Indigenous communities that accurately reflects Indigenous communities. Existing data often suffer from major limitations, such as racial misclassification of American Indians, which leads to the underestimation of substance use-associated health issues and deaths. Epidemiological data collection at the state and national level often overlooks or marginalize Indigenous populations, with American Indians frequently classified as “Other Race(s)” or recorded only in footnotes as “insufficient numbers.” 

This subcore aims to support data that better support health initiatives and policies for Indigenous communities. 

Leadership: Dr. Sean Allen

Works with research teams to apply rigorous qualitative and mixed-methods approaches, enhancing the understanding of substance use and its impact.

Qualitative research centers on meaning and interpretation from the perspectives of participants, while mixed-methods research combines qualitative and quantitative approaches for a more comprehensive view. Mixed methods allows researchers to harness the strengths of each method, fostering “ways of knowing” that reveal deeper insights into complex issues.

When properly integrated, these methods help uncover the diversity within Indigenous communities, enabling public health initiatives to be more precisely tailored. Poorly integrated qualitative and quantitative data can reduce the impact of public health research—a common critique in NIH mixed methods grant applications. Qualitative methodologies, such as narrative inquiry, combined with quantitative analysis allow us to better explore heterogeneity and refine intervention strategies for substance use within Indigenous populations.

Provides analytic support for multilevel and mixture modeling to capture cross-sectional and longitudinal heterogeneity in substance use outcomes.

Multilevel modeling (MLM) is a statistical approach that accounts for nested data, such as individuals within communities or tribes, or in longitudinal studies where data is collected on the same individuals at multiple time points. Mixture models are a useful method for identifying clusters of individuals that share common elements (e.g., substance use attitudes, growth trajectories). Both methods allow researchers to examine different patterns in outcomes of interest (e.g., variance, response patterns across a set of observed variables). These approaches support culturally responsive research that can inform community-centered health interventions.

Key Techniques

• Multilevel Modeling (MLM): Enables analysis across different levels such as within-groups and between-groups. By examining variance at multiple levels, MLM allows us to understand how individual factors may interact with community influences to shape health outcomes. 
• Mixture Models: These models identify latent heterogeneous subgroups across a set of observed indicators, such as substance use patterns (e.g., polysubstance use), attitudes, or beliefs. 
  • Latent Class/Profile Analysis (LCA): A primary method used to identify latent heterogeneous groups based on observed behaviors or beliefs, such as substance use patterns or intervention attitudes.
  • Growth Mixture Modeling: A method for identifying heterogeneous groups of individuals who share common growth patterns on a single observed indicator (e.g., substance use frequency across adolescence).

Leadership: Dr. Dane Hautala, Dr. Kelley Sittner

Supports the selection and application of implementation research methods, theories, and frameworks to assess barriers, design strategies, and evaluate implementation effectiveness. Spotlighting and strengthening Indigenous-led implementation science.

Implementation science focuses on using strategies to bring evidence-based health interventions (EBIs) into real-world settings, such as clinics and communities, to improve health outcomes. Despite growing interest in implementation science to bridge the gap between research and practice, training and mentorship opportunities, especially in Indigenous health and community-engaged research, remain limited.  

Leadership: Dr. Christopher Kemp

Ensures standardized data collection and management processes that are responsive to tribal communities, promoting Indigenous data sovereignty and stewardship.

Over the past 50 years, there has been significant progress in Indigenous self-determination and the reclamation of cultural identity and traditional knowledge. This has led to the concept of Indigenous Data Sovereignty, which refers to Indigenous Peoples’ authority over information derived from its territories, citizens, communities, and collective interests. This includes data generated by Indigenous Peoples, as well as by governments and other institutions.

Currently, most Indigenous data are secured by non-Indigenous governments, institutions, and agencies, highlighting the need for increased participation in data governance processes. This includes both the stewardship and the processes necessary to implement Indigenous control over Indigenous data (collection, storage, analysis, use, reuse).

Leadership: Ms. Crystal Greensky