Abstract
Research involving Indigenous communities has been traditionally dominated by Western methodologies which may not fully capture Indigenous perspectives, often marginalising Indigenous knowledge systems and voices. Employing Indigenous data analysis methods supports decolonising interpretation of research data and supports a decolonised approach. This review aimed to identify what Indigenous research data analysis methods exist. Searches were conducted on Medline (via Ovid SP), PsycINFO (via Ovid SP), Web of Science (Clarivate Analytics), Scopus (Elsevier), Cumulated Index to Nursing and Allied Health Literature CINAHL (EBSCOhost), ProQuest Central, ProQuest Social Sciences Premium (Clarivate). ProQuest (Theses and Dissertations) were searched for unpublished material. This review identified 17 studies that integrated four Indigenous data Analysis methods: The Kaupapa Māori framework, Yarning method, Talking Circles and NAKPA method (Cree words meaning Medicine/Healing Stories, picked, sorted and stored). A further method “Thought Ritual” is discussed but remains untested in primary research hence was not included in this review. While decolonising research methodologies are growing in use, there is a significant gap in Indigenous data analysis methods.
Keywords
Introduction
In the evolving landscape of inclusive research, incorporating Indigenous research methodologies has become increasingly important (Geia et al., 2013). Indigenous research methodologies not only involve the lenses and perspectives of Indigenous researchers but also incorporates research that is framed and undertaken with an Indigenous worldview, epistemology, and ontology at the centre (Moreton-Robinson and Walter, 2009). These approaches to research are transformative, aiming to benefit, both the academic community, and the Indigenous communities at the heart of the research (Williams, 2023). These approaches seek to build bridges between Western and Indigenous ways of knowing, being and doing, and fosters a dialogue that enriches both worlds.
Traditionally, research involving Indigenous communities was always conducted from a Western perspective, where Western epistemologies and methodologies dominated (Hayward et al., 2021). This approach frequently led to the marginalisation of Indigenous knowledge systems, voices, and priorities, rendering them invisible or secondary within academic and policy-making spheres (Kennedy et al., 2022; Smith, 2021). While there has been a growing recognition of the importance of using Indigenous research methods, many studies still rely on Western data analyses frameworks, such as thematic analysis developed by non-Indigenous scholars. Western methods often fail to capture the full depth of Indigenous perspectives and risk imposing external structures onto Indigenous knowledge (Hayward et al., 2021). By only adding an Indigenous lens to Western analysis, researchers limit the potential for truly decolonised approaches to research. It is crucial to include Indigenous analysis methods in research because Indigenous methodologies offer unique ways of understanding, interpreting, and analysing data that are rooted in cultural knowledge, values, and lived experiences (Kennedy et al., 2022).
Indigenous analyses methods, which focus on relationality, storytelling, and holistic understandings, are better suited to reflect the cultural context and worldview of Indigenous communities (Yunkaporta and Moodie, 2021). If Indigenous research methods are prioritised in the planning and implementation phases, it is inconsistent to revert to Western frameworks for analyses. Using Indigenous analysis methods ensures that research is not only about Indigenous people but is also interpreted in ways that resonate with their ways of knowing (Geia et al., 2013). This shift towards Indigenous analyses methodologies is essential for advancing more culturally grounded and authentic research outcomes that reflect and honour Indigenous knowledge systems (Hayward et al., 2021). This is especially pertinent because development of Indigenous research methodologies emerged as a critical response to the historical and continued impacts of colonisation on Indigenous peoples around the world (Kennedy et al., 2022; Moreton-Robinson and Walter, 2009; Sivertsen et al., 2022; Smith, 2021). Indigenous peoples were often subjects of research rather than partners in research, their cultures examined through an outsider's lens that lacked depth, respect, or understanding of the complexities and richness of Indigenous ways of knowing (Smith, 2021).
Incorporating Indigenous data analyses methods in research is crucial because Indigenous research methods are grounded in the cultural contexts and lived experiences of Indigenous communities. This ensures that data interpretation aligns with the cultural values, knowledge systems, and worldviews of the communities involved, leading to more relevant and accurate findings (Hayward et al., 2021). Using Indigenous methods respects the sovereignty and autonomy of Indigenous peoples. It empowers communities by acknowledging their expertise and perspectives in interpreting their own data, fostering a sense of ownership and control over the research process (Kukutai and Taylor, 2016).
Indigenous data analyses methods also promote ethical research practices by emphasising informed consent, mutual respect, and reciprocal benefits. These approaches help to build trust between researchers and Indigenous communities, which is essential for long-term collaboration and the ethical conduct of research (Kukutai and Taylor, 2016). Incorporating Indigenous methods can also enhance the validity, reliability and authenticity of the research findings (Drawson et al., 2017). By using culturally appropriate analysis techniques, researchers can avoid misinterpretations and ensure that the results genuinely reflect the community's perspectives and experiences (Lipscombe et al., 2021). Appropriate methods align with research objectives, providing accurate and relevant data while minimising bias and errors. This enhances the credibility of the research, allowing for meaningful conclusions and informed decision-making. Applying appropriate methods also ensures that participants’ rights and cultural contexts are respected, while trust and collaboration are fostered between researchers and communities (Creswell and Creswell, 2018). Ultimately, using the optimal research methods is essential for advancing knowledge, addressing complex issues, and contributing positively to scientific and societal progress.
In a project exploring Aboriginal and Torres Strait Islander community voices to improve maternity services in mainstream health care in Australia, the culturally diverse research team, consisting of both Indigenous and non-Indigenous researchers, sought to identify Indigenous data analysis methods to enable an appropriate lens for interpretation of findings. Maternal health encompasses the health of women during pregnancy, childbirth, and the postnatal period (World Health Organization, 2024). Negative birthing experiences can lead to psychological consequences such as postpartum depression, anxiety, and post-traumatic stress disorder, affecting a mother's ability to bond with her child and potentially influencing the child's short-, medium- and long-term development and wellbeing. Understanding the satisfaction and dissatisfaction of Indigenous women with their birthing experiences is crucial because it impacts not only the immediate wellbeing of the woman but also the medium- and long-term health of both the woman and child. Women from marginalised communities, including racial and ethnic minorities, often report higher rates of dissatisfaction due to (but not limited to) systemic biases and discrimination in healthcare settings, inequities of access, and experiencing vulnerability. Acknowledging and addressing these disparities is essential for promoting equitable health outcomes (Sivertsen et al., 2022).
This review will focus on, but not be limited to, studies in Australia, Sápmi (Sámi homelands across Norway, Sweden, Finland), Canada, US, and Aotearoa (New Zealand). The Sámi are the Indigenous people inhabiting Sápmi, which is not a political entity, but a cultural and geographic area significant to the Sámi people. These countries all comprise Indigenous populations, produce research anchored in Indigenous research methodologies, whilst also having similar histories inclusive of assimilation, and similar approaches to health systems, services and research (Montgomery-Andersen et al., 2010; Paberzyte, 2020).
The studies included will draw from Indigenous scholars’ development of Indigenous research methods for data analysis. Knowing about and applying Indigenous data analyses methods in health research ensures cultural relevance and accuracy, reflecting the unique health perspectives and needs of Indigenous communities. It promotes ethical research practices by respecting Indigenous knowledge systems and empowering communities to actively participate in the research process. Additionally, these approaches help to address historical injustices in health research, fostering trust and improving health outcomes for Indigenous populations (Kennedy et al., 2022). Incorporating Indigenous data analysis methods is not only a matter of ethical responsibility but enhances the quality and impact of research by ensuring it is culturally sensitive, relevant, and inclusive.
This is the first scoping review exploring Indigenous data analysis methods in research. Identifying key factors in Indigenous data analysis will enhance the interpretation of data by Indigenous communities and improve the outcomes of Indigenous-focused research.
Methods
A scoping review was chosen for its exploratory nature, as it systematically maps existing literature, helps understand current knowledge, and identifies key concepts and gaps (Arksey and O’Malley, 2005). The review title was registered with Open Science Framework OSF ID NO: osf.io/vnzd9 https://doi.org/10.17605/OSF.IO/VNZD9 prior to the study in June 2024 (Sivertsen et al., 2024). A scoping review, unlike a systematic review, explores all available evidence on a broad topic. It is used when literature is diverse, complex, and requires broad exploration to identify gaps (Peters et al., 2015). For this reason, a scoping review was selected as the most suitable form of review for this research. This scoping review was conducted in accordance with the Arksey and O’Malley (2005) framework for scoping reviews, which proposes the following stages: Defining the research question, identifying relevant studies, selecting studies, collating, summarising and reporting findings, and including expert consultation. The reporting of this review adhered to the guidelines provided by the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) (Tricco et al., 2018).
Defining the research question
The research question for this review was: What is known about implementing Indigenous data analysis methods in primary research?
Identifying relevant studies
A three-step search strategy was used in this review. First an initial limited search of OVID Medline was undertaken to identify articles on the topic (NS, SB). The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were used to develop a full search strategy. Keywords and relevant index terms were adapted for other bibliographic databases by a research librarian (SB), including PsycINFO (via Ovid SP), Cumulated Index to Nursing and Allied Health Literature CINAHL (EBSCOhost), Web of Science (Clarivate Analytics), Scopus (Elsevier), ProQuest Central, and ProQuest Social Science Premium Collection (Clarivate). ProQuest Dissertations & Theses was searched for unpublished material. Additionally, a supplemental search was conducted using Google Scholar and hand searching of key journals to identify studies meeting inclusion criteria. Keywords included in the search strategy were related to variations of ‘First Nations’, ‘Indigenous’, ‘Data Analysis’, and ‘Indigenous, decolonised, cultural data analysis’. A variety of spellings and syntax were used. No other limits were applied. A research protocol was designed to ensure a rigourous evidence synthesis, outlining the inclusion and exclusion criteria as well as the methods for data extraction and presentation. The search encompassed articles from the inception of each database without imposing any limitations on the publication dates of articles that met the keyword inclusion criteria. Just prior to publication, a search update was conducted on the 3 April 2025 to identify new literature published since the original search date. The full search strategy is available in the Supplemental Material.
To broaden the scope of the search, reference lists from all included sources were reviewed for additional studies. The citations were then entered into Research Rabbit, an artificial intelligence (AI) tool used for data mining academic publications, to perform a final forward and backward search and analyse timeline patterns (Chandra et al., 2021). Research Rabbit is specifically designed to facilitate unstructured searches by mining publicly accessible scholarly papers and information relevant to uploaded seed papers (Cole and Boutet, 2023). This tool served two key purposes: (1) Conducted a final forward/backward search for any studies that may have been overlooked during the systematic and manual search process and (2) identified timeline trends across the included studies and related literature. No additional relevant studies were identified.
Selecting studies
Following the search, 6716 citations were identified, collated and uploaded into Covidence (NS) (Veritas Health Innovation Melbourne Australia, 2024). Subsequently, 2060 duplicates were removed. The updated search yielded 10,888 citations, with 8042 duplicates removed.
Prior to screening titles and abstracts, inclusion and exclusion criteria were discussed by the research team, which comprised five Indigenous and five non-Indigenous members. To ensure rigourous evidence synthesis, a research protocol was developed highlighting the inclusion and exclusion criteria and identifying how data would be extracted and presented. Titles and abstracts of studies were screened for relevance, and full texts were obtained when abstracts did not provide enough information to determine eligibility. Full-text articles were evaluated based on the following criteria: (a) Primary research studies employing Indigenous data analysis methods. Only studies published in English from the inception of the databases were considered. Exclusion criteria included: (a) Opinion pieces; (b) studies that were not primary research; (c) unavailable full texts; (d) duplicates of included articles; (e) studies that utilised Indigenous research methods but applied a Western analytical framework (e.g., Thematic Analysis); (f) studies unrelated to the research question; and (g) publications in languages other than English.
Data analysis is the process of systematically applying logical techniques to describe and illustrate, condense and recap, and evaluate data (Creswell and Creswell, 2018). This review considered primary research studies that reported utilising Indigenous data analysis methods in research studies including qualitative, quantitative and mixed methods studies. Research, researchers and projects that had applied Indigenous data analysis methods from several Indigenous cultural groups were included, such as (but not limited to) Aboriginal and/or Torres Strait Islander research in Australia, Sámi research in Norway, Sweden, Finland, Aboriginal, First Nations, Inuit and Mētis in Canada, any First Nations group from the US such as Algonquin, Iroquois, Huron, Wampanoag, Mohican, Mohegan, Ojibwa, Ho-chunk (Winnebago), Sauk, Fox, and Illinois, and Māori research from Aotearoa.
Indigenous data refers to information, in any format or medium, collected, analysed, stored, and interpreted within the context of Indigenous individuals, collectives, populations, entities, lifeways, cultures, knowledge systems, lands, biodiversity, water and other resources (Australian Research Data Commons, 2016; Kukutai and Taylor, 2016). Studies not containing Indigenous data analyses methods were excluded. Studies reporting other Indigenous research methods or methodologies, or general Western data analyses methods were also excluded. Other forms of evidence such as reviews, policies or opinion papers were excluded.
Title and abstract screening were conducted on 4788 articles in original search, and 2847 articles in updated search by five reviewers (SS, NS, TJ, AB, and TS). The relevant studies were retrieved in full, and their citation details imported into Covidence (NS). The full text of the original 73 citations and 32 citations from the updated search were assessed in detail against the inclusion criteria by three independent reviewers (SS, NS, MC). The reasons for excluding papers at the full-text stage, which did not meet the inclusion criteria, were documented as shown in Figure 1 (Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) chart). Any disagreements between reviewers during the selection process were resolved through discussion or, if necessary, with the involvement of an additional reviewer. A total of seven conflicts arose in Covidence, primarily due to ambiguity regarding the definition of mainstream health services. These conflicts were resolved collaboratively through thorough discussions to maintain consistency and rigour in the inclusion decisions. This team-based approach ensured consensus on all disputed studies, enhancing the reliability of the selection process. At the end of the selection process, 17 full-text articles were identified. The results of the search and the study inclusion process are presented in a PRISMA flow diagram (Page et al., 2021) (Figure 1).
Preferred reporting items for systematic reviews and meta-analyses (PRISMA).
Organising and charting the data
Eligible studies were critically reviewed and charted in table format by two independent reviewers (NS, SS). Each article that met the inclusion criteria was independently reviewed using a data extraction tool developed by the authors, adapted from the Joanna Briggs Institute (JBI) (2017) and pilot-tested before being utilised in Covidence for data extraction. Authors of papers were contacted to request missing or additional data for clarification, where required. The reviewers (NS, SS) extracted details such as author names, publication date, title, study setting, study design, data collection methods, and sample characteristics. Also extracted were outcome of studies and cultural context. Key themes and patterns were collaboratively identified to provide a descriptive overview of the main content and findings of each article. These themes and patterns were then discussed, further developed, and refined in consultation with the entire authorship team. The results are presented in narrative form in Table 1 Summary of included studies.
Summary of studies.
Summarising, synthesising, and reporting the findings
The included articles were analysed using basic descriptive summaries based on a template developed and pilot-tested by two authors (NS and SS). An abridged thematic synthesis approach (Thomas and Harden, 2008) was employed to assess and extract data from the studies, summarising content and identifying recurring themes related to Indigenous data analysis methods. The research team met every week during the scoping review process and applied a Yarning approach to discuss and understand the findings in a cultural context (Kennedy et al., 2022). This adapted synthesis method systematically organised and interpreted the findings, offering a comprehensive perspective on the use of Indigenous data analysis in primary research. The analysis was specifically guided by the scoping review's research question.
The Research Rabbit AI tool generated a timeline visualisation for the included studies (indicated in green) and identified 1466 related works, of which the 50 most relevant (indicated in blue) are shown in Figure 2. This visualisation revealed a growing interest in Indigenous research methods from 2003 to 2019. However, the related works primarily centred around Indigenous research methods in general. The earlier blue dots, including a seminal study on decolonisation from as far back as 1999, suggest that while related topics were explored earlier, the specific focus on Indigenous methodologies is a more recent development, especially evident in the blue dots from 2011 to 2019.
Research rabbit timeline.
This timeline highlights a shift toward incorporating Indigenous perspectives in research, while still revealing a larger body of work that may rely on traditional Western analysis frameworks. The clustering of green dots in recent years, 2019 to 2023, indicates an important trend in research methodology, especially towards Indigenous-driven data analysis approaches.
Expert consultation, cultural considerations, and researcher reflexivity
Expert consultation is essential in cross-cultural research, especially with Indigenous communities (Geia et al., 2013). Integrating Indigenous researchers, consumer representatives, and nurses or midwives in maternal health research makes the process more culturally informed and ethically sound (Sivertsen et al., 2022). Engaging Indigenous stakeholders enhances relevance, promotes cross-cultural dialogue, and ensures outcomes serve both Indigenous communities and broader healthcare systems, leading to culturally appropriate results that impact Indigenous health and well-being (Geia et al., 2013). This scoping review was led by and comprised of five Indigenous researchers holding roles and skills as clinician nurses (NS), midwives (TJ, TS, SD), and consumer representatives (LT). Additionally, five non-Indigenous women research team members contributed varied experienced working in Indigenous setting and clinical midwifery skills (AB, JD, MC, SS) and research systematic review skills (SB). These experts provided insights into culturally safe, clinical, and research practices, ensured respect for Indigenous knowledge, and aligned the research with community priorities. Their involvement bridged understanding gaps, built trust, and grounded the research in Indigenous perspectives. The expert consultation in this scoping review included the planning, execution, searching, analysis, manuscript preparation and authorship.
Findings
Study selection
The original search yielded 6716 papers in which 2060 duplicates were identified and removed. Additionally, Google Scholar resulted in 132 initial records, with 3 duplicates removed before 129 were excluded due to not meeting inclusion criteria. Title and abstract screening of the remaining 4788 papers identified 73 papers for full-text screening. 66 papers were excluded and 7 papers were identified for inclusion and underwent data extraction.
An additional search was run on April 3, 2025, with broader search terms. The search update identified 10,888 additional papers, with 8042 duplicates removed, one study added from hand-searching reference lists, and 2847 studies selected for title and abstract screening, resulting in 32 full-text studies retrieved. However, further investigation of the extracted data showed 22 papers needed to be excluded. The main reasons for exclusion were the data were not an Indigenous study, there were no evident discussion of analysis method, or Indigenous research methodology were used for data collection, but Western analysis method applied. Therefore, 10 papers (reporting data from 10 studies) were included in the scoping review analysis. A total of 17 studies from original and updated search were included in this review.
Study characteristics
From our systematic scoping review, we identified that although many studies integrate Indigenous research methodologies for data collection, however very few studies utilised an Indigenous research data analysis method. This review identified four forms of Indigenous analyses that have been effectively employed in research. These include Kaupapa Māori, a form of analysis employed in Aotearoa, the Yarning method, an Indigenous research practice which is used widely in Australia and has been adapted for use as an analysis method, the Talking Circle method, which is grounded in cultural and ceremonial protocols, as an Indigenous research and analysis methodology allows Indigeneity into every facet of the research process, and the NAKPA collaborative method, where a panel of experts, community members, participants, Elders, Knowledge Keepers, and the researchers are gather together to do the collective data analysis. A further Indigenous analysis method Thought Ritual remains untested in primary research and therefore is not included in this review. However, it was included in the discussion to highlight its potential significance and to encourage future exploration and application in Indigenous data analysis methodologies.
The reviewed articles focused on countries with Indigenous populations, but most studies located were from Aotearoa, Australia and Canada. The findings from the studies identified similar trends across the studies. These included the strong preference by researchers to understand, incorporate and interpret data from studies including Indigenous participants by implementing culturally appropriate Indigenous data analysis methods in their research.
17 manuscripts, each reporting studies published within the last six years (2008–2024) were included in this synthesis. A summary of key characteristics of all articles is presented in Table 1. Studies reported Indigenous data analysis methods used in primary research in three countries (Australia n = 4, Canada n = 2, and Aotearoa n = 11). The professional disciplines of the primary research studies included management and business (n = 1), medicine, science and public health and nursing (n = 11), architecture/urban design/public health (n = 1), tourism and leisure (n = 2), Architecture (n = 1), Education (n = 1), and Climate (n = 1). All primary studies were conducted with, by and for an Indigenous context, and six (n = 6) research teams comprised both Indigenous and non-Indigenous researchers (Ingham et al., 2022; Raerino et al., 2021; Wolfgramm et al., 2022; King et al., 2008; Wills et al., 2024; Wilson et al., 2022), with three teams being unclear about cultural connections (Ingham et al., 2023; Walters and Ruwhiu, 2021; McIlduff et al., 2022). Nine of the studies (n = 9) were led by Indigenous researchers (Anderson et al., 2024; Barrett et al., 2024; Bourque Bearskin et al., 2025; Davis and Came, 2022; Fox, 2024; Hiha, 2015; Marriott et al., 2019; Puriri and McIntosh, 2019). All studies employed Indigenous research methods and specific Indigenous data analysis methods (Table 2).
Studies located according to analysis method.
Additionally, the experiences of Indigenous data analysis for research studies represented at least two specific cultural groups including Aboriginal and/or Torres Strait Islander participants in the Australian studies, Māori participants from the Aotearoa studies, and Cree, Pellt’iq’t, St’uxwstews, Syilx, Tk’emlúps te Secwépemc Nations, Métis in the Canadian studies. The findings from these studies identified two different Indigenous data analyses methods for research utilised in primary research studies: the Kaupapa Māori framework, and the Yarning analytical method.
While this scoping review did not employ a formal quality assessment tool, as its aim was to provide an overview of existing evidence regardless of quality (Peters et al., 2015), the reviewers systematically evaluated each study for credibility, dependability, confirmability, transferability, and authenticity. This involved assessing the rigour of the methodology, the representativeness of the sample size, and the transparency of reported limitations. Studies were reviewed for their design robustness, relevance to Indigenous research and analysis methods, and reliability of findings. Using this approach, studies were broadly categorised by quality. While some studies featured smaller sample sizes or lacked detailed methodologies, they still provided valuable insights. This method balanced inclusivity, given the limited number of available studies, with a critical analytical framework to interpret and contextualise the findings effectively (see Table 3 – Critical Findings).
Critical findings.
Kaupapa Māori
Kaupapa Māori is an emancipatory Indigenous research paradigm or philosophical framework which was first described by Linda Tuhiwai Smith (Pōmare and Te Rōpū Rangahau Hauora a Eru Pōmare, 1996) and has been used by others in recent times (Ingham et al., 2022, 2023; Puriri and McIntosh, 2019; Raerino et al., 2021; Walters and Ruwhiu, 2021; Wolfgramm et al., 2023). Kaupapa Māori has been described as a Māori ontology and epistemology as well as a philosophy and set of principles which privileges the Māori perspectives and voices. Kaupapa Māori provides an Indigenous window on research which enables, empowers and unlocks access to research by Māori for Māori (Puriri and McIntosh, 2019). It ensures that matauranga Māori (Indigenous Knowledge) is not only acknowledged but is prioritised. Kaupapa Māori takes for granted the validity and legitimacy of Māori and the importance of Māori language and culture, with a focus on autonomy over Māori health and wellbeing. It is concerned with both the methodological developments and the forms of research methods utilised. According to Ingham et al. (2022, 2023) it is both a theory and a form of analysis of research which involves Māori. Hence this process is by Māori for Māori (Ingham et al., 2022, 2023b; Puriri and McIntosh, 2019; Raerino et al., 2021).
However, whilst Kaupapa Māori was the overarching framework for the six studies from Aotearoa identified, the way in which it was applied to analysis methods varied. The study by Raerino et al. (2021) employed a Te Pau Mahutonga (An Indigenous theoretical framework) in the analysis phase. Initial thematic analysis was conducted by Kaupapa Māori researchers and reviewed and refined by all authors. Final themes were agreed upon by all researchers. Similarly, the study by Walters and Ruwhiu (2021) employed both Māori and non-Māori researchers who coded independently whilst employing the four overarching principles of Te Ao Māori (the Māori world), Tino rangatiratanga (self-determination, governance and autonomy); whanaungatanga (relationships and connection) and Te reo (using Māori language). The researchers then met to discuss and achieve consensus on the findings. Wolfgramm et al. (2022) used a Whakapapa (Māori genealogy) approach as a framework for understanding cultural identity and the layering of relationships. The analysis involved identifying factors influencing Māori collective memory and explored how leadership identity was enacted through the examination of archival data. This approach also used a Kaupapa Māori researcher who developed the initial codes and themes that were later reviewed and discussed among all authors.
The study by Puriri and McIntosh (2019) also employed a Kaupapa Māori methodology but the data analysis was supported by Whakapapa (relationships). Protocols for this research were agreed upon at a series of Hui (meetings) with Whanau (family members). This study adopted the theoretical model of Whakaaro Pitau Whakarei, an evolving reflective method which retains what is valuable to the Whanau (Māori family) and discards what is not useful. However, this study does not identify in detail how the analysis was conducted or who by but relies upon the belief that there is no single correct way to analyse data when applying a kaupapa Māori methodology (Cohen et al., 2018).
The studies by Ingham et al. (2022); Ingham et al. (2023b) both adopted a Kaupapa Māori approach however, this was applied differently between the qualitative and quantitative studies. The qualitative study used thematic analysis where data were coded into themes and sub-themes agreed upon by two Māori researchers. The analysis was discussed via wanaga (the process of knowledge creation) with senior Māori researchers. However, the quantitative study applied the principles of Kaupapa Māori as a method of underpinning the research. In this way the research was deemed to be mana enhancing (respectful) and following tikanga (Māori values). This was achieved through the establishment of an advisory group to oversee the project.
Yarning
Yarning has been described as an Indigenist research process involving different types of yarns. Marriott et al. (2019) describe an Indigenous research methodology which utilises yarning as an analysis method. Traditionally, yarning takes many forms including: The social yarn, the research yarn, the collaborative yarn, the therapeutic yarn, the clinical yarn and the diagnostic yarn. All types of yarns include the use of Dadirri (deep listening)which is employed in this research process (Marriott et al., 2019). Yarning usually involves sharing details of kinship and stories of country. When using yarning as an analysis method, Marriott et al. (2019) described a process where Aboriginal and non-Aboriginal groups code in isolation, then reunite to further develop the codes and themes. Subsequent further analysis of the data ensues until themes are agreed upon by both groups and consensus achieved. Through listening to the rationale behind the coding of the Aboriginal investigators, non-Aboriginal researchers were able to view the data through a more nuanced lens. With each layer of analysis further insight was revealed and enabled the researchers to maintain a decolonising approach while respecting and relying on the cultural expertise of the Aboriginal researchers to increase cultural understanding.
Talking circles
Talking circles have long been employed as a method of communication and social discourse amongst First Nations people. Historically, this communication method was employed by first nations leaders to ensure that all present had a chance to speak in a tribal council. This use of Talking circles as an analysis method was first conceived by Bartlett et al. (2007). More recently this ancient method of shared communication has been applied as both a research methodology and an analysis method (Bourque Bearskin et al., 2025). This study used Talking circles in every facet of the research process including data collection and analysis, thereby avoiding an extraction process and honouring the stories shared by weaving ceremony through the data collection, analysis and mobilisation phases.
Nanâtawihowin Âcimowina Kika-Môsahkinikêhk Papiskîci-Itascikêwin Astâcikowin (NAKPA)
NAKPA is an indigenous analysis method which was first employed by McIlduff et al. (2022). NAKPA is Cree for medicine/healing stories, picked sorted and stores. This method uses a panel of experts including community members, participants, Elders, Knowledge keepers and researchers to gather together to conduct the data analysis. Thereby, directly involving the community to ensure the results directly reflect the views of the stake holders.
Kaupapa Māori, NAKPA, Yarning and Talking circles as data analysis methods all emphasised conducting research with and for Indigenous peoples, grounded in their culturally specific contexts. Central to this was prioritising Indigenous needs and perspectives throughout the research process. Key aspects of both methods included storytelling, relationality, and deep listening in data collection and analysis, ensuring that Indigenous voices were interpreted through a culturally informed lens with the depth and nuance they bring. These methods also stressed the importance of honouring interconnectedness, both within communities and research teams, and analysing data by interpreting knowledge through Indigenous cultural patterns.
Discussion
The use of Western research methodologies and methods in health research involving Indigenous populations has frequently proven ineffective (Starblanket et al., 2019). This ineffectiveness arises primarily because conventional Western approaches fail to adequately prioritise the critical elements of relationship-building, mutual respect, and reciprocity, all of which are foundational when engaging with Indigenous communities. Effective engagement empowers Indigenous communities, ensuring they actively shape the research processes and enhancing the relevance and applicability of research outcomes to their contexts. This engagement extends crucially into qualitative data analysis phases – including interviews, focus groups, and sharing circles – where, without sustained and meaningful participation, the analytical process risks defaulting to Western paradigms, marginalising Indigenous perspectives and voices (Starblanket et al., 2019).
Decolonising research frameworks, methodologies and paradigms are becoming more popular with authors recognising the importance of integrating a decolonising approach into research (Kovach, 2010; Smith, 2021)). However, shared decision-making is often lost when Indigenous research is conducted using Indigenous methodologies and data collection methods followed by traditionally Western analyses methods. This results in the perpetuation of a colonising approach (Kennedy et al., 2022). This scoping review has identified only two Indigenous analyses methods, validated by use in primary research studies, which suggests a significant gap in the research literature, which can suggest that the existing literature lacks sufficient development, documentation, or application of Indigenous-specific analytical frameworks. This also highlights a significant gap in knowledge and demonstrates a need for further research to develop, validate, and implement additional Indigenous analysis methods that can better align with and reflect Indigenous epistemologies and methodologies. Additionally, whilst studies exist which utilise Indigenous methodologies such as Photovoice, Storytelling and Conversation methodologies, few clearly delineate an Indigenous analysis method which can be reproduced (Castleden et al., 2008; Elder, 2013; Kovach, 2010; Wright et al., 2012).
A common theme among the identified Indigenous analysis methods is the use of reflective practice, shared decision making, respectful interaction, deep listening and recognition of the relevance of Indigenous knowledge, whether it be Māori, or Aboriginal and/or Torres Strait Islander peoples. The Kaupapa Māori analysis method has relevance to research in Aotearoa (Ingham et al., 2023). The yarning method has been devised with an Aboriginal and/or Torres Strait Islander community in mind (Marriott et al., 2019).
A further research analysis method has been postulated by Yunkaporta and Moodie (2021). The Thought Ritual is a hybridisation of Indigenous oral culture practise. This analysis method is grounded in Aboriginal protocols of communal knowledge production and is aligned with the principles of complexity theory. Complexity theory provides a way of understanding systems such as human beings, forest ecosystems or organisations (McDermott et al., 2024). Whilst this analysis method was used in Doctoral research in 2021, it remains unpublished. This method includes a four-step process involving: connection; diversity; interaction and adaption (Yunkaporta and Moodie, 2021).
The integration of Indigenous data analysis methods, such as Kaupapa Māori and Yarning, presents a much-needed approach to fostering more inclusive and culturally appropriate research methodologies (Ingham et al., 2022; Ingham et al., 2023; Puriri and McIntosh, 2019; Raerino et al., 2021; Walters and Ruwhiu, 2021; Wolfgramm et al., 2022). Kaupapa Māori, as both an ontology and epistemology, prioritises Māori voices and perspectives, ensuring that research processes are aligned with Indigenous values and worldviews. Similarly, Yarning facilitates the building of meaningful connections through conversational methods rooted in storytelling, promoting relationality and cultural safety (Marriott et al., 2019). These methods enable an holistic approach to data analyses that moves beyond conventional Western frameworks, offering a more nuanced interpretation of Indigenous knowledge (Kennedy et al., 2022; Marriott et al., 2019).
The strengths of these methods are complementary and contribute to a richer, more contextually relevant form of data analysis compared to traditional analysis methods for Indigenous research (Ingham et al., 2023). Kaupapa Māori's emphasis on self-determination and cultural integrity, and Yarning's focus on relationality and deep listening, provides a powerful framework for research that honours Indigenous ways of knowing, being, and doing. By centreing Indigenous perspectives, these methods challenge the dominance of Western paradigms and create space for culturally grounded methodologies that are responsive to the needs and realities of Indigenous communities (Geia et al., 2013). These approaches not only enhance the validity and relevance of the research but also promote decolonised, respectful engagement with Indigenous knowledge systems, and are therefore capable of addressing Indigenous priorities and realities (Geia et al., 2013).
At the outset of our scoping review, we conducted a preliminary exploration to identify Indigenous analytical methods specifically employed in primary research. We initially explored the Groundwater Method, anecdotally known for its potential in knowledge translation within Indigenous contexts. However, our review revealed no existing examples of the Groundwater Method explicitly applied as an analytical framework within primary health research. Hayashi et al. (2021) paper explores the ways water can be perceived and conserved in a decolonising way, however does not explicitly discuss or refer to the “groundwater method” as an Indigenous analysis method in research. Instead, it describes transdisciplinary approaches involving Western hydrological sciences and Indigenous (Yolŋu Aboriginal) knowledge and governance practices for sustainable water management on Milingimbi Island. This includes engagement strategies such as participatory three-dimensional mapping to bridge Yolŋu knowledge and Western scientific methodologies (Hayashi et al., 2021). Although it emphasises the importance of incorporating Aboriginal perspectives and ancestral knowledge into water management frameworks, it does not specifically identify or elaborate on a distinct “groundwater method” as an Indigenous analysis method.
Additionally, we identified the Collective Consensual Data Analytic Procedure, an Indigenous research method initially developed by Bartlett and colleagues in 2006, subsequently adapted by Bourassa et al. into NAKPA (Nanâtawihowin Âcimowina Kika-môsahkinikêhk Papiskîci-itascikêwin Astâcikowina) –translated as Cree Medicine/Healing Stories Picked, Sorted, Stored (Starblanket et al., 2019). Although the NAKPA method represents a robust Indigenous analytical framework, it remains largely descriptive and theoretical, lacking clear applications in published primary research studies. Furthermore, its credibility and applicability have been complicated by identity controversies surrounding its lead researcher, who faced challenges related to Indigenous identity and authenticity (Gabel et al., 2024). Although the term Nanâtawihowin Âcimowina Kika-môsahkinikêhk Papiskîci-itascikêwin Astâcikowina was included in our database search strategy, no primary research studies applying this method were identified through this approach; instead, the method was located in a study (McIlduff et al., 2022) through hand-searching reference lists. This highlights that even systematic scoping reviews cannot guarantee comprehensive identification of all relevant literature. One reason for this could be that we do not claim to know all terms and names associated with various Indigenous analysis methods and therefore may have inadvertently missed some relevant studies.
For non-Indigenous researchers, the adoption of these methods can lead to more authentic and respectful engagement with Indigenous communities (Sivertsen et al., 2022). By applying Indigenous analyses methods, non-Indigenous researchers can better align their work with ethical standards of respect, inclusivity, and community engagement (Kukutai and Taylor, 2016), leading to richer, more nuanced, and socially responsible outcomes (Sivertsen et al., 2022). In cross-cultural research teams, these methods introduce non-Indigenous researchers to alternative ways of thinking and interpreting data, challenging the dominance of Western paradigms. Additionally, this can optimise understanding and alternative thinking in all future research (Sivertsen et al., 2020). This approach fosters a broader understanding of complexity and interconnectedness, enhancing the potential for more innovative and interdisciplinary research. Ultimately, Indigenous analysis methods can provide a framework for more inclusive and ethical research practices that benefit both Indigenous and non-Indigenous communities alike.
Limitations
This is the first review to systematically synthesise existing evidence on Indigenous research analyses methods. The strength of this scoping review is its novel nature. Additionally, a further strength is the inter-Indigenous international approach, encompassing Indigenous researchers from both northern and southern hemispheres. This review has considered primary research studies that reported on Indigenous data analysis methods from several Indigenous cultural groups including Aboriginal and/or Torres Strait Islander research in Australia, and Māori research from Aotearoa. However, the findings should be considered with acknowledgement of the following limitations. The review included only English-language studies, potentially missing relevant studies in other languages. Additionally, a further limitation is our inability to identify all possible keywords from all culture groups in our search. The generalisability of the results to non-English-speaking contexts is uncertain, and future research could expand to include non-English studies.
Although this review found two data analysis methods from two nations and potentially two cultural groups, it is important to note that singular Indigenous voices or perspectives do not adequately capture the diversity and complexity of Indigenous experiences, cultures, and worldviews. Indigenous communities across the globe are heterogeneous, with distinct languages, traditions, historical contexts, and health priorities. Therefore, the inclusion of few Indigenous voices, while valuable, cannot comprehensively represent the nuances of other Indigenous groups or speak on behalf of all.
This limitation is critical in understanding the scope of this review, as certain perspectives, particularly those from smaller or more isolated Indigenous communities, may be underrepresented. While the insights from the Indigenous voices included in this review contribute meaningfully to the discourse, the conclusions drawn may not fully reflect the variety of lived experiences across different Indigenous populations. Future research must prioritise engaging multiple, diverse Indigenous voices to develop a more holistic understanding of the subject matter. This limitation highlights the importance of avoiding overgeneralisations and reinforces the need for ongoing, culturally respectful research that recognises and incorporates the full breadth of Indigenous diversity.
Conclusion
Whilst decolonising research methodologies and paradigms are gaining in popularity, this review has demonstrated a critical gap in the research in terms of Indigenous analysis methods. Only two Indigenous analysis methods were discovered in a comprehensive review of the literature. The use of Kaupapa Māori is well established in Aotearoa and Australian literature included the Yarning method for use with Aboriginal and/or Torres Strait Islander people. A further Australian Indigenous analysis method ‘Thought Ritual’ was discussed but to our knowledge remains untested. Additionally, a gap remains in the research literature in terms of Indigenous analysis methods with no methods identified for research with any First nations groups in the US, Canada or Sápmi.
Supplemental Material
sj-docx-1-qrj-10.1177_14687941251350883 - Supplemental material for Connecting the dots and weaving peoples’ stories together – a systematic scoping review about indigenous data analysis methods in research
Supplemental material, sj-docx-1-qrj-10.1177_14687941251350883 for Connecting the dots and weaving peoples’ stories together – a systematic scoping review about indigenous data analysis methods in research by Nina Sivertsen, Tahlia Johnson, Susan Smith, Tara Struck, Larissa Taylor, Annette Briley, Megan Cooper, and Shanamae Davies in Journal of Interactive Marketing
Footnotes
Acknowledgements
As a research team, we extend our deepest respect to the First Peoples, Elders, and Ancestors across the lands and waters where we live and work. We acknowledge the trauma that Indigenous communities have endured as a result of healthcare policies and practices both in clinical care and research – systems in which we, as Nurses and Midwives, actively participate. We recognise the critical need for progress in building culturally safe and trusting environments for all. The resilience and leadership of First Peoples enrich society as they share their knowledge across diverse sectors. Indigenous peoples and their communities, cultures, and languages have existed since time immemorial, each with unique histories and tremendous diversity. As an inter-Indigenous research collective representing both Northern and Southern hemispheres, we are committed to improving research in healthcare and health outcomes for all.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by a grant from the Southern Adelaide Local Health Network Collaborative Grant Scheme.
Supplemental material
Supplemental material for this article is available online.
Author biographies
Nina Sivertsen is an Arctic Indigenous Sámi woman living and working with Flinders University on Kaurna Lands in Australia, while holding a position as Associate Professor with the Arctic University of Norway. Her work centres on the impacts of colonisation and assimilation on healthcare, with a particular focus on integrating culture into mainstream health care provision. Her academic contributions encompass First Nations curriculum design and implementation, with cultural responsiveness in the centre. Nina's research primarily addresses women's and family health, health systems and services research, and contributes to shaping restorative policies and practices in mainstream health that benefit Sámi, Aboriginal and Torres Strait Islander families.
Tahlia Johnson is a proud Warramunga midwife with clinical experience from Kaurna Country, passionate about Aboriginal and Torres Strait Islander women's health, particularly birth and postpartum. Tahlia works as an academic at Flinders University, educating health students around incorporating culture in practice. She works as a researcher focusing on Indigenous curriculum, women's and family health, as well as researching colonisation in health systems and services. She specialises in implementing qualitative Indigenous research methods across research teams. Tahlia's goal is to find the evidence we need to make positive changes to the health services we provide Aboriginal and Torres Strait Islander families in mainstream health.
Susan Smith is a non-Indigenous woman living on Peramangk Land and working on Kaurna Land in Australia. She has a background in nursing, midwifery and maternal and infant welfare with a focus on Aboriginal and Torres Strait Island health, having worked on Ngarrindjeri Land for many years. She is currently employed conjointly as a research midwife with SA Health and Flinders University. Her research has a focus on optimal outcomes for mothers and babies, through both access to immunisation and equity of access to culturally safe services.
Tara Struck is an Adnyamathanha woman and Registered Midwife, born on Larrakia country, and currently working and living in Kaurna country. With clinical experience across all areas of midwifery, including Birthing on Country models of care, she has a deep passion for the improving outcomes for Aboriginal and Torres Strait Islander peoples and much of her professional focus has been dedicated to this. Alongside her clinical work, she is an academic at Flinders University, where she is pursuing a Bachelor of Health Sciences (Honours).
Larissa Taylor is first and foremost a very proud Gumbaynggirr/Dunghutti descendent and the current Topic Coordinator for PARA1001 Integrating Cultural Safety & Indigenous Health with Paramedic Professional Practice, and the Indigenous Lecturer for Aboriginal Health within the Public Health and Medicine discipline. She wishes to acknowledge the unceded Lands and Water ways of the Kaurna peoples, who's country I have the privilege to live love & work on. She has worked in multiple areas within Community and feels that Cultural responsiveness and humility are one of the biggest givers to support change. Her Goal is to be a driver for cultural changes within our health systems, to continue to educate and support truth telling processes.
Annette Briley, PhD is a non-Indigenous woman living and working on Kaurna lands in South Australia. She is an internationally qualified nurse and midwife who has worked in clinical research to improve pregnancy outcomes and longer-term health and wellbeing for women, babies and families. She has worked with women from different vulnerable and marginalised groups and been involved in many trials that have informed guidelines and practice. She is currently employed in a joint clinical academic position as Professor of Midwifery and Women's Health at Flinders University and Northern Adelaide Local Health Network and is a Visiting Professor at King's College London.
Megan Cooper, PhD is a non-Indigenous Australian woman living and working on Kaurna Land in South Australia, Australia. She is a proud midwife, academic, educator and researcher and currently holds the position of Senior Lecturer and Course Coordinator of Midwifery Programmes at Flinders University. Her research portfolio spans diverse foci including but not limited to women's experiences of maternity care, midwifery-led models of care, water immersion for labour and birth and innovative approaches to theoretical and clinical midwifery education. She is particularly passionate about the translation of research into practice with a special interest in clinical policies and guidelines. This is demonstrated by her lead role on multiple water immersion guidelines and the Australian National Midwifery Guidelines for Consultation and Referral (4th edition) and her contributions as a member of the Guidelines Leadership Group for both the Australian National COVID Taskforce and the Living Evidence for Pregnancy and Postnatal Care (LEAPP) guidelines.
Shanamae Davies, RM is a proud Kaurna, Narungga, Ngarrindjeri woman currently employed by SA Health as a Clinical Midwife.
Jaclyn Davey is a non-Indigenous Australian woman living and working on Kaurna land in South Australia. She is a clinical research nurse/midwife who currently holds a position in the Women's and Children's Division of Flinders Medical Centre located in Adelaide southern region. She is an early career researcher working on a multitude of projects all across the Women's and Children's Division including Paediatrics, Obstetrics, Gynaecology, Neonatology and Child Protection. She has a passion for inclusion and access to care for all, having grown up in a rural town in the Eyre Peninsular of South Australia where access for specialist care was an 8-h drive to attend. She is currently furthering her studies by commencing a Master of Health and Clinical Research in 2024.
Shannon Brown, Flinders University Library Services.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
