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Indigenous Statistics

  • Tahu KukutaiEmail author
  • Maggie Walter
Living reference work entry

Abstract

Statistics about Indigenous peoples are a common feature of Anglo-colonizing nation states such as Canada, Australia, Aotearoa New Zealand, and the United States (CANZUS). The impetus for the production of most Indigenous statistics is the shared position of Indigenous disadvantage in health and socioeconomic status. In this chapter, we contrast statistics about Indigenous peoples with statistics for Indigenous people and statistics by Indigenous people. There are very significant differences between these categories of Indigenous statistics. At the heart of these differences is the methodology that informs the research processes and practices. Statistics about Indigenous peoples often reflect the dominant social norms, values, and racial hierarchy of the society in which they are created. In the CANZUS states, these statistics are deficit focused and, at times, victim blaming. Also missing from these statistical portrayals is the culture, interests, perspectives, and alternative narratives of the Indigenous peoples that they purport to represent. We contrast these statistics with those from statistical research using processes and practices that are shaped by Indigenous methodologies. Indigenous methodologies are distinguished by their prioritization of Indigenous methods, protocols, values, and epistemologies. We conclude with two examples of what Indigenous quantitative methodologies look like in practice from Aotearoa NZ and Australia.

Keywords

Indigenous Statistics New Zealand Australia Colonization Methodology 

1 Introduction

The estimated number of Indigenous peoples ranges between 300 and 370 million, and comprises thousands of distinct polities covering all of the world’s continents (Gracey and King 2009; Hall and Patrinos 2012). Statistics about Indigenous peoples (Given the diversity of Indigenous peoples, the United Nations does not have an official definition of “Indigenous” but rather invokes the following criteria: (1) Self- identification as indigenous peoples at the individual level and accepted by the community as their member; (2) Historical continuity with pre-colonial and/or pre-settler societies; (3) Strong link to territories and surrounding natural resources; (4) Distinct social, economic or political systems; (5) Distinct language, culture and beliefs; (6) Form non-dominant groups of society; (7) Resolve to maintain and reproduce their ancestral environments and systems as distinctive peoples and communities.) are a common feature of Anglo-colonizing nation states such as Canada, Australia , Aotearoa New Zealand , and the United States (the so-called CANZUS group; Meyer 2012). The impetus for the production of most of these Indigenous statistics is the shared position of socioeconomic and health disadvantage. In all of the four CANZUS nations, Indigenous peoples are far more likely to die younger, to experience much poorer health, to be unemployed, to be homeless, to be incarcerated, and to not have the same level of educational achievement as non-Indigenous citizens (Anderson et al. 2006; Cooke et al. 2007; Gracey and King 2009; Anderson et al. 2016; see also “Kaupapa Māori Health Research” and “Culturally Safe Research with Vulnerable Populations”).

In this chapter, we not only discuss statistics about Indigenous peoples but also statistics for Indigenous people and statistics by Indigenous people. There are very significant differences between these categories of Indigenous statistics. At the heart of these differences is the methodology that informs the research processes and practices. Methodology matters and we demonstrate how the methodology informing the standard trope of statistics about Indigenous people in the CANZUS states are deficit focused and, at times, victim blaming. We contrast these statistics with those from statistical research using processes and practices shaped by Indigenous methodologies.

2 Methodology

2.1 What Is a Methodology

The terms “method” and “methodology” tend to be used interchangeably within the health and social science research literature. However, they mean quite different things. While both are related to the practice of doing research, they differ conceptually. Researchers need to have both a methodology and a method for the conduct of good research. All kinds of research, not just research related to Indigenous peoples, have a methodology (see also “Ontology and Epistemology, Kaupapa Māori Health Research” and “Engaging Aboriginal People in Research”).

The term “method” has a straightforward meaning. It refers to the method of collecting and/or analyzing data. For a qualitative research project exploring how Aboriginal women experience breast cancer treatments, the method might be in-depth interviews. For a research project exploring heart disease rates among urban Māori, the method would be statistical analysis. Despite what is written in some texts, methodology is not primarily related to whether the research has a qualitative or a quantitative base.

So what is a methodology? Basically, it is the framework that guides how the researcher approaches the research. This framework is not always consciously understood by researchers, especially those from dominant social, cultural, and racial groups. As has been argued elsewhere (Walter 2010; Walter and Andersen 2013), the basis of this guiding framework is the social positioning of the researcher. Social positioning relates to the race, class, gender, and social and cultural space that the researcher/s occupy. With different social positioning attributes comes different sets of values and belief systems (axiological elements) that can, for example, help determine what research questions the researcher thinks are important. The social position of the researcher will also be important in shaping what data or knowledge sets will be gathered (epistemological elements) and, if there is more than one set of knowledge, which knowledge set is prioritized. Social positioning also influences how researchers see the world, their place in it, and the place of others who are not like themselves (ontological elements).

Methodology can also affect the choice of method. This is because it is important for a method to be able to gather the sort of data that the researcher needs to address the research questions. Sometimes this means developing new research methods. For example, Yarning is an Aboriginal research method built around Aboriginal ways of communication (see also “Engaging Aboriginal People in Research”). But Yarning is more than just Aboriginal people talking. As argued by Bessarab and Ng’andu (2010), Yarning as a research method is also a process of meaning making and communicating in culturally appropriate ways. It is, therefore, likely to be much better method fit for researchers working with Aboriginal people than the traditional (Western) method of in-depth interviewing.

2.2 Why Methodology Matters

Methodology matters to the way research is done and to the findings that result. Quality research should always make obvious the methodology that informed the research process. When the research relates to Indigenous populations or cultural minorities, a clear articulation of the research methodology is even more crucial. This is because while Indigenous peoples are the frequent objects of health and social science research, they are far less likely to be the commissioners, research designers, or data interpreters of that research. In all CANZUS countries, the vast majority of Indigenous-related research is still undertaken by non-Indigenous researchers and commissioned by non-Indigenous policy makers (Taylor 2008; Kukutai and Walter 2015).

The imbalance is important because the social position of the subject “knower” (e.g., policy analyst within government) and the social position of the object of statistical study (Aboriginal and Torres Strait Islander and Māori people) are not even remotely the same. If these differences are reflected in the values that inform the research, the prioritization of knowledge, the analysis, and the interpretation of results, then the outcome is likely to focus on Indigenous “deficits” (Valencia 2012). Deficit research focuses on Indigenous problems and locates the source of those problems within Indigenous populations and culture. The validity of this approach and its methodological underpinnings has long been challenged by Indigenous scholars (see Tuhiwai Smith 1999) but still remains a dominant trope in Indigenous data (see next section).

To demonstrate how methodology operates in practice, let’s relook at our research examples. In the qualitative example of exploring the experience of breast cancer treatment of Aboriginal women, there are some key methodological questions. More critically, differing answers to these questions will produce very different research projects and different findings. For example, do the researchers decide that the data are only going to be from Aboriginal women? Or will treatment personnel be included? If so, what will happen if the report from the treatment personnel and Aboriginal women differ? Whose perspective will be deemed more accurate? Will and how will the research process and practice be adjusted from mainstream models to capture the specific experiences of Aboriginal women? And which women? Are we talking about urban or remote experiences or both? Aggregation of “the” Aboriginal population into one category is a common practice in Australian research. This practice, however, ignores the reality of over 500 Aboriginal nations in Australia , all whom have different cultural, historical, and contemporary realities.

For our second example, cardiovascular disease (CVD), the statistical data already exists. However, the researcher’s methodology will still shape the research outcomes. In Aotearoa NZ, ischemic heart disease accounts for over half of all cardiovascular disease mortality and the age-standardized ischemic heart disease mortality rate among Māori (35+ years) is more than twice as high as that among non-Māori (RR 2.14, CI 2.02–2.27) (Age standardized rates can be accessed at: http://www.health.govt.nz/our-work/populations/Māori-health/tatau-kahukura-Māori-health-statistics/nga-mana-hauora-tutohu-health-status-indicators/cardiovascular-disease.). What factors might explain why Māori have higher rates of heart disease at younger ages? Researchers without a strong understanding of Māori culture, values, and life circumstances might line up the usual suspects of heart disease: smoking, diet, and exercise. Their choice of such variables may be influenced by negative stereotypes of Māori people that circulate in the public discourse and which define the “problem” of Māori CVD as primarily one of poor individual choices and health behaviors. Subsequent policy interventions may also be focused on promoting individual lifestyle changes. This is despite the substantial evidence that such an approach has a limited effect in disadvantaged populations because of the failure to address the issues that gave rise to the behaviors.

By contrast, a Māori researcher who is embedded in both their discipline and their culture will likely include elements of the social determinants of health, which are the underlying economic and social conditions that drive racial health inequities (Commission on Social Determinants of Health 2008). These factors are inclusive of the heavy socioeconomic disadvantage experienced by Māori related to dispossession, colonialism, and ongoing marginalization including institutional racism and unmet needs in access to high quality and culturally appropriate healthcare services (Ajwani et al. 2003; Kerr et al. 2010; Axelsson et al. 2016). Focusing on these distal determinants of health, and how they shape the distribution of more immediate risk factors such as poor diet and ultimately CVD, engenders a different understanding of health inequities and approaches to reducing them. Policy responses might include engaging Māori in the design and delivery of culturally grounded health services, addressing the institutional barriers to timely diagnosis and treatment pathways, and taking a broader whānau (family) approach to health promotion rather than a narrow individualistic focus (Durie 2003; Kerr et al. 2010; see also “Kaupapa Māori Health Research” and “Culturally Safe Research with Vulnerable Populations”).

The question then arises, if methodology is so important to the research process, why is it so frequently not articulated within research? The answer seems to be that researchers whose social positioning places them in the dominant racial or cultural group have not been trained to recognize that their social positioning directly affects how they “do” research. In cross-cultural research, such a lack of researcher reflexivity is a recipe for at best, poor quality research, and at worst, research that does harm to the group it is professing to research.

3 Exposing the Orthodoxy of Indigenous Statistics

In cross-cultural health and social science research, the traditional way of doing Indigenous research flows from the dominant model of what Indigenous statistics looks like within Aotearoa New Zealand , Australia and other first-world colonized nations. The privileging of mainstream “mental models” to frame and explain Indigenous peoples has real-life consequences for Indigenous peoples.

Indigenous researchers and communities have made numerous criticisms of how statistical agencies collect, disseminate, and analyze Indigenous data. The criticisms include a tendency to focus on Indigenous “problems” rather than strengths; a failure to recognize Indigenous culture, values, and practices in the measures and processes used to gather and analyze data; a failure to prioritize Indigenous needs in data system development; ineffective measures to address longstanding data quality issues such as Indigenous undercounting; and a tendency to use token consultation rather than meaningful Indigenous engagement and partnership (Taylor 2009; Robson and Reid 2001; Prout 2012). In response to these problems, Kukutai and Walter (2015) proposed five development principles aimed at enhancing the functionality of official statistics for both Indigenous peoples and national statistics agencies.

These concerns are not limited to domestic policy making. Global forums, such as the United Nations Permanent Forum on Indigenous Issues and the Special Rapporteur on the Rights of Indigenous Peoples, have stressed the importance of high quality and meaningful data for enabling Indigenous development. However, the extent to which governments recognize the existence of Indigenous peoples in official statistics varies widely. Preliminary findings from the Ethnicity Counts? project show that, of the 150 countries and territories that encompass Indigenous peoples, only 45% identify Indigenous peoples in the population census (Taylor and Kukutai 2015). In some countries, there are multiple questions relating to Indigenous identity. In Aotearoa NZ, for example, Māori can be identified by ethnicity, ancestry, tribal affiliation, and language. However, in the majority of countries, Indigenous peoples are statistically invisible. Ironically, some of these countries, such as Sweden and Norway, have some of the most well-developed official statistics systems in the world.

The census is the flagship of official statistics in many countries. It provides the population-level denominator for many indicators of well-being within countries, as well as for many of the UN’s Sustainable Development Goal indicators (United Nations General Assembly 2015). The extent of Indigenous invisibility in the census has far-reaching implications for the ability to monitor Indigenous development on a global scale. The 2015 State of the World’s Indigenous Peoples report noted that it is still often difficult to obtain a global assessment of Indigenous peoples’ health status because of the lack of data (United Nations Department of Economic and Social Affairs 2015).

4 Indigenous Data Sovereignty

One of the questions raised by Indigenous quantitative methodologies is who has the power to control Indigenous data. In the CANZUS states, there has been a growing call for greater control over the collection, dissemination, analysis, and storage of Indigenous data. This call for “Indigenous data sovereignty” (Kukutai and Taylor 2016) is founded on Indigenous rights to self-determination which emanate from their inalienable relationships to lands, waters, and the natural world, and which are encapsulated in Articles 3 and 4 on the United Nations Declaration on the Rights of Indigenous Peoples (The full text of the UNDRIP can accessed at: http://www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf.). The idea of data sovereignty is a recent development of the digital age referring to the management of information in a way that is consistent with laws, practices, and customs of the nation-states where data are located. Indigenous data sovereignty sees Indigenous data as subject to the laws of the nation from which it is collected and requires a relocation of authority over relevant information from nation states back to Indigenous peoples (Snipp 2016). Indigenous data is broadly understood as data about Indigenous peoples, their territories, conditions (including health conditions), and ways of life. Such data includes genetic samples, linked “mega” datasets, digitized health records, and data on land and other natural resources. In the context of cross-cultural research, the implications of Indigenous data sovereignty are far reaching because it has the potential to transform power relationships in terms of who owns, governs, and controls access to and management of Indigenous data.

In the CANZUS states, Indigenous peoples are giving practical expression to various forms of Indigenous data sovereignty. In Canada, there are the First Nations’ principles and practices of ownership, control, access, and possession over First Nations data known as OCAP® (First Nations Information Governance Centre 2014). OCAP® was created by the First Nations Information Governance Centre to help guide the development of the First Nations Regional Health Survey (FNRHS), the only First Nations-governed, national health survey in Canada that collects information about First Nation on-reserve and northern communities. The development of OCAP® was motivated by negative experiences with research projects conducted by non-First Nations people that did not benefit First Nations people or communities. OCAP® ensures that First Nations own their information and respects the fact that they are stewards of their information, much in the same way that they are stewards over their own lands. It also reflects First Nation commitments to use and share information in a way that maximizes the benefit to a community, while minimizing harm. First Nation communities have passed their own privacy laws, established research review committees, entered data-sharing agreements, and set standards to ensure OCAP® compliance. Other Indigenous data sovereignty initiatives are being driven by Te Mana Raraunga, the Māori Data Sovereignty network in Aotearoa NZ, the US Indigenous Data Sovereignty Network, and the Yawuru Native Title holders of Broome in Western Australia (Yap and Yu 2016). Collectively, these networks and organizations, and others like them, are developing new ways of “doing” Indigenous data that are challenging conventional methods and methodologies.

5 Getting to Understand Indigenous Methodologies

The deficiencies of traditional Western research methodologies for Indigenous peoples have led Indigenous scholars, globally, to develop Indigenous methodologies. Indigenous methodologies are a paradigm rather than a category of methodologies. Each, however, shares a philosophical base. This base is concisely summed up by Sami scholar Porsanger (2004) when she states that Indigenous methodologies all reflect Indigenous ways of knowing, doing, and being. In doing so, they make visible what is meaningful and logical for Indigenous people and Indigenous understandings of the world.

The field of Indigenous methodology scholarship was led by the ground-breaking work of Linda Tuhiwai Smith (1999). Smith’s book, Decolonizing Methodology: Research and Indigenous Peoples, details the tenets of Kaupapa Māori, a methodology intricately connected to Māori philosophy and principles, the validity and legitimacy of Māori, Māori language (Te Reo Māori) and culture, and Māori autonomy over their own cultural well-being. Moewaka Barnes (2000) emphasizes three defining principles of this approach:
  • It is by Māori for Māori.

  • Māori worldviews are the normative frame

  • Research is for the benefit of Māori.

In a similar vein, Native Hawaiian scholar Ku Kukahalau, the first person to earn a PhD in Indigenous education, highlights the importance of Hawaiian cultural protocols in her integration of existing heuristic methodology and Indigenous epistemology (2004). In Australia , Aboriginal scholar Karen Martin (2003, 2008), aligns the philosophical underpinnings of Indigenous methodology into theoretical principles. These require a recognition of Aboriginal worldviews, knowledge, and realities; the honoring of Aboriginal social mores; the social, historical, and political contexts which shape Aboriginal experience, lives, positions, and futures; and the privileging of the voices, experiences, and lives of Aboriginal people and Aboriginal lands. Native American scholar Margaret Kovach (2009) focuses on qualitative research practices in her theorizing of Indigenous methodologies. She argues that Indigenous methodologies are distinctive from Western and other methodological frames, and are distinguished by their prioritization of Indigenous methods, protocols, meaning making, and epistemologies in how to undertake research processes and research practice.

Indigenous methodology scholarship is also emerging from non-Anglo colonized nation states. Botswanan scholar Bagele Chilisa (2011) for, example, uses a postcolonial frame to demonstrate how methodologies are not restricted to academic knowledge systems. Her Indigenous methodological stance focuses on how the paradigms and practices of research can support Indigenous epistemologies and honor integrative knowledge systems (see also “Indigenist and Decolonizing Research Methodology”).

6 Indigenous Quantitative Methodology

It is fair to say that, within the diverse spectrum of Indigenous methodologies, there is a strong preference toward qualitative methods and a widely held view that statistical research sits in tension with “Indigenous ways of knowing” (Kovach 2009). This is largely due to the perception that quantitative research methodologies are rooted in a Western positivist tradition that relies on “external evidence, testing and universal laws of generalizability…contradict[s] a more integrated, holistic and contextualized Indigenous approach to knowledge” (Kovach 2009, p. 78). The question then arises – what does a quantitative methodology built on Indigenous ways of knowing look like?

In their book Indigenous Statistics, Tasmanian Aboriginal scholar Maggie Walter and Metis scholar Chris Andersen (2013) tackle this question directly, proposing a way to move the understanding of Indigenous methodologies into the field of quantitative research. Dominant ways of doing Indigenous statistics, they argue, shortchange Indigenous peoples and communities through their narrow portrayal of who Indigenous peoples are, and their circumscription of how Indigenous people can be understood. Mainstream narratives of Aboriginal and other Indigenous populations in Anglo-colonizing nation states are based on data about Indigenous peoples that the nation state, rather than Indigenous peoples, deem to be important. The result is a depressing familiar role call that Walter (2016) calls 5D data: data about Indigenous people that focuses on disparity, deprivation, disadvantage, dysfunction, and difference.

The central problem of Indigenous statistics is that population or racial group statistics are not neutral data. Rather, they reflect the dominant social norms, values, and racial hierarchy of the society in which they are created. In Australia and Aotearoa NZ, these dominant social norms and values typically reflect those of Anglo/European settler descendants. Norms can be thought of as the shared expectations for social behavior around what is culturally desirable or acceptable. Norms are evident in everyday interactions, in institutions such as schools and healthcare services, and in policy approaches. The power of these norms comes from their “taken for granted nature” – very rarely are they made explicit or visible like formal rules. Statistics, and especially official statistics, embody norms but hold an aura of objectivity and tend to be presented and understood as “facts.” The trouble is that these “facts” only tell a very small, and specifically framed, part of the reality of Indigenous peoples. What is not present in these statistical portrayals is the culture, interests, perspectives, and alternative narratives of the Indigenous peoples that they purport to represent.

Indigenous quantitative methodologies, in contrast, can support the development of statistical portrayals that go beyond the narrow, frequently pejorative, reflections that dominate official statistics of Indigenous peoples. Moreover, Indigenous statistics developed from an Indigenous methodological frame can, as argued by Walter and Andersen (2013, p. 73) “speak back” to the state in a way that both incorporates Indigenous knowledge and is ontologically translatable to state actors. We illustrate this by way of our two case studies below.

7 Indigenous Quantitative Methodology in Practice

7.1 Case Study 1: Aotearoa NZ: Māori Concepts of Family

In this section, we discuss two examples of what Indigenous quantitative methodologies look like in practice from Aotearoa NZ and Australia. The first case study is from a project exploring Māori expressions of whānau or family (Kukutai et al. 2016). Families are a fundamental social unit in all societies but vary greatly in terms of their form, function, and meaning. Families are also an important focus for research, public policy, and service delivery, from the immunization of children, to state-funded assistance for single parents, and elder care. In Aotearoa NZ, statistical studies of Māori families have tended to focus on household structure and circumstances and, more recently, on vulnerable children and family violence (Vulnerable Children Act 2014). These portrayals are often deficit focused and viewed through the lens of Western theoretical models. Missing from these statistical narratives are Māori perceptions of who their whānau are, how their whānau are doing, and what whānau well-being entails (Cunningham et al. 2005; Tibble and Ussher 2012).

The whānau concept and well-being project is a collaboration between all-Māori research team and government policy agencies (We thank our colleagues at the Social Policy Evaluation and Research Unit (Superu), Te Puni Kōkiri, the Ministry of Māori Development, and the Superu Whānau Reference Group.). Much of the analysis is drawn from “Te Kupenga,” a nationally representative postcensal survey of well-being among Māori adults, which was conducted for the first time in 2013 (Kukutai et al. 2016). Unlike other official surveys such as the Census and General Social Survey, Te Kupenga was specifically designed with Māori values and priorities in mind and had substantial input from Māori researchers, communities, and policy makers (Statistics New Zealand 2009). The initial stage of the project focused on two key questions:
  1. 1.

    How do Māori define who belongs to their whānau?

     
  2. 2.

    How are expressions of whānau related to factors such as cultural identity, household living arrangements, and social context?

     

The word whānau literally means to “to be born” or to “give life.” While there is no univocal definition of whānau, there is a broad consensus that genealogical relationships form the basis of whānau, and that these relationships are intergenerational, shaped by context, and given meaning through roles and responsibilities (Lawson-Te Aho 2010). From a Māori standpoint, to be part of a whānau is to share common “whakapapa.” In a traditional sense, whakapapa is understood as descent-based relationships which extend from the physical world to the spiritual world (Kruger et al. 2004). Whakapapa also refers to the layers of relationships that connect individuals to ancestors, to the living, and to the natural environment (Te Rito 2007). Whakapapa relationships are not just ways of situating individuals within a kin group but are connected to roles, responsibilities, and obligations including mutual acts of giving and receiving, and the intergenerational transmission of knowledge.

The literature also refers to the concept of kaupapa whānau which is based on a common purpose or shared interests (Lawson-Te Aho 2010). In kaupapa whānau, “family-like” relationships of support and reciprocity are established as individuals purposefully engage to achieve a common goal. An oft-cited example is that of Māori language revitalization and preschool Māori language nests called kōhanga reo (Smith 1995). This expansive understanding of family is far removed from Euro normative concepts of family, especially those emphasizing the household as the economic unit of production. But, how do these culturally grounded understandings of whānau play out in the context of a representative national survey?

In defining whānau, the approach taken in Te Kupenga was to acknowledge kinship and interest-based whānau and leave it to the individual to define their own whānau within four broad relationship categories (Tibble and Ussher 2012). The question and response categories are shown in Fig. 1. Respondents could select as many categories as they needed. For the statistical analysis they were grouped into one of four mutually exclusive categories describing the broadest concept of whānau category reported, ranging from nuclear family to friends and others. The distribution can be seen in Table 1.
Fig. 1

Whānau question from Te Kupenga 2013

Table 1

Broadest concept of whnau (family) reported by Māori respondents in Te Kupenga 2013

 

Per cent

A. Parents, partner/spouse, brothers and sisters, brothers/sisters/ parents-in-law, children

40.2

B. Grandparents/grandchildren

15.2

C. Aunts, uncles, cousins, nephews, nieces, other in-laws

31.9

D. Close friends/others

12.5

Just over 40% of respondents in Te Kupenga reported that their whānau only comprised immediate relatives, that is, parents, partner/spouse, brothers, sisters, brother, sister, parent in-laws, and children. A further 15% reported that their whānau included grandparents and grandchildren, and about one-third included extended whānau such as aunts, uncles, and cousins. Interestingly, nearly 13% of Māori counted close friends and others as part of their whānau.

Regression analyses showed that household-based living arrangement – the conventional way of measuring family in Aotearoa NZ – is a very poor predictor of how Māori see their whānau. More important are demographic factors (age, region) and cultural factors including connectedness to customary communities, access to cultural support, and having a high regard for Māori culture. Māori with strong cultural connections tend to have a broader concept of whānau. The analysis has also helped to clarify the contexts within which nongenealogical relationships are perceived as being “whānau-like.” Interestingly, those who have participated in Māori language education and lived in homes where Māori is spoken are more likely to include friends and others as part of their whānau. Similarly, Māori who provide support to people living in other households, and those in challenging economic circumstances, are also more likely to count nonrelatives as part of their whānau. The project has important implications for research and policy focused on families. It suggests that, for Māori, household-based measures of family are a very poor proxy for the more complex set of whānau relationships that exist and that policy responses based on these narrow Western concepts may have limited relevance.

7.2 Case Study 2: Australia: How Do Indigenous Children Grow Up Strong in Education

The following case study demonstrates practically that it is not the method, in this case statistical analysis, but the methodological frame that shapes research. A key element is that the focus is not describing or investigating “the problem” of lower educational achievement for Aboriginal and Torres Strait Islander children as is the traditional research approach. Rather, the focus is on identifying the causes and the best ways to achieve good educational outcomes.

The all Aboriginal research team are researching Aboriginal and Torres Strait children’s (0–18 years) lived experience of schooling and education. The study’s objectives are to:
  1. 1.

    Identify the critical intersections of events that impact on Indigenous children’s educational chances across the childhood life course across locations.

     
  2. 2.

    Identify the pathways, protective factors, and resilience dimensions that support educational achievement for Indigenous children irrespective of disadvantage.

     

The project uses data from the Longitudinal Study of Indigenous Children (LSIC) a national longitudinal panel study conducting annual waves of data collection, with Wave 1 (2008) surveying families of 1,670 Indigenous children from 11 sites across Australia . Face-to-face interviews are conducted between the study child’s primary parent and locally employed Indigenous research administration officers. Use of the LSIC data is a key is part of the research project’s methodological frame. The study is guided by an Indigenous-led Steering Committee and its question topics, question design and conduct and are overtly shaped by Aboriginal and Torres Strait Islander perspectives and values.

The starting premise of this Strong in Education research project is that while a lot is known about Indigenous children, it is a certain sort of knowledge from a particular perspective. Government statistics tell us that Aboriginal and Torres Strait Islander children are much more likely to live in poor households and do far less well in the education system than non-Indigenous children. They also consistently record that Aboriginal and Torres Strait Island children are more likely to miss school, be suspended, and less likely to go on to higher education. What such existing statistics do not do is tell us what factors support good education and resilience for Aboriginal and Torres Strait Islander children. Identifying those factors is the key aim of this research. Within this, epistemologically, the analysis centers Indigenous people’s knowledge, concepts, and worldviews.

The research’s key concepts also reflect an Aboriginal and Torres Strait Islander methodological frame. The term “Strong” is conceptualized as the deployment of resilience to achieve good education despite adverse life circumstances. “Good education” refers to academic achievement to non-Indigenous median norms but also to cultural and community education (Malin and Maidment 2003; Andersen and Walter 2010). “Resilience” refers to the ability to cope with stress and adversity and do well in life despite difficulties (Gunnestad 2006). Its conceptualization within the research recognizes the interface of Indigenous social and cultural resilience with individual/family resilience and that social, cultural, and identity practices that support positive adaptation are integrally connected to resilience (Lalonde 2006).

Recent results from examinations of LSIC data in relation to educational outcomes find that parental and child social and emotional well-being are strong predictive factors for children’s reading scores (Anderson et al. in press) and that how well the primary parent thought their child’s school understands the needs of Indigenous families was a consistent predictor in how involved parents were with their child’s schooling (Trudgett et al. in press).

8 Conclusion and Future Directions

In the CANZUS states, governments continue to invest substantial time and resources in monitoring the well-being outcomes of Indigenous peoples. In recent decades, governments in these countries have amassed a wealth of statistical data on Indigenous populations, all of whom are a significant focus of population research and policy in their respective countries. However, the categories and contexts employed in statistics about Indigenous peoples typically reflect dominant group norms, and their social and economic institutions. Because statistics about Indigenous peoples rarely encompass Indigenous methodologies, key aspects of Indigenous life are either missing or misrepresented. These epistemological and methodological shortcomings have stimulated calls for approaches for statistics that are by and for Indigenous peoples, rather than simply about them. In this chapter, we have identified key differences between these statistical approaches and the crucial importance of methodology for determining which questions are asked, and which processes and practices are employed.

In terms of future directions, major data transformations will raise new challenges along with potential opportunities. Technological innovations in the private sector involving big data are changing how data are used, most evident in the area of health. In the United States, genomics and big data science are being exploited in new ways to provide targeted, predictive, and personalized care in a “precision health” approach. Official data practices are also being transformed as governments seek alternatives to traditional data collection practices. Aotearoa NZ is at the forefront of these changes with several major initiatives that will fundamentally alter the national data ecosystem. These include legislative reform to enable greater data sharing across agencies (NZ Government 2014), a greater emphasis on extracting social and economic value from data, and the use of linked data on individuals and families to inform the government’s social investment spending through targeted inventions (The Treasury 2016). Given that Māori are disproportionately the subject of government interventions, these shifts raise a number of key issues about Māori data governance, ownership, and access. The rise of linked mega datasets and broader data sharing is likely to become a standard feature of official statistics in all of the CANZSUS states in the near future. The capacity of Indigenous peoples to benefit from the “data revolution” will likely depend on the extent to which they are able to exert meaningful influence and oversight of the practices, processes, and principles that emerge over the next decade.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.University of WaikatoHamiltonNew Zealand
  2. 2.University of TasmaniaHobartAustralia

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