A Scoping Review of the Demographic and Contextual Factors in Canada's Educational Opportunity Gaps.
Since James S. Coleman (1966) documented significant racial and socio-economic gaps in academic achievement, US researchers and education policymakers have studied the impact of demographic and contextual factors on academic achievement. Demographic factors are individual-level descriptors, like gender or race, while contextual factors are community-level descriptors, like the ethnic diversity or socio-economic status (SES) of a neighbourhood or community (Hillemeier, Lynch, Harper, & Casper, 2003). An informal literature search to examine differences in education attainment revealed that while there is widespread research in the United States on the "achievement gap," Canada lacks a cohesive examination of educational inequities along several contextual and demographic lines. This scoping review will therefore examine the breadth, depth, and gaps in the research on demographic and contextual factors in Canadian K-12 academic achievement.
This review, to the best of the authors' knowledge, is the first attempt to date to synthesize this literature. Complicating this review is the lack of an agreed-upon umbrella term for educational inequities in Canada. In the United States the term "achievement gap" is used, but in Canada a wide range of terms are utilized. Therefore, a scoping review was selected as the methodology, as it is designed for topics that are complex or being reviewed for the first time (Arksey & O'Malley, 2005); both are true in this case. Formal scoping reviews have been conducted to examine key demographic and contextual factors in published quantitative studies on the Canadian context. Following an overview of the research methodology and search results, the most significant findings are discussed and implications for future research are articulated.
It is important to examine which factors have been studied and which are missing, as this information is needed to identify and intervene in any systemic differences in academic opportunities for particular groups in Canada. In assessing the existing quantitative data, this review uses a critical educational theoretical framework (Ladson-Billings, 2006). The authors are aware of the dangers of replicating the US neoliberal school movement and discourse, which has too often placed blame on individual students and groups and has perpetuated inequalities through the privatization of schooling and standardized testing (Baldridge, 2014; Gillborn, 2005, 2008; Martino & Rezai-Rashti, 2013; Rebell & Wolff, 2008; Tuck, 2009). To this end, the scoping review in this research does not use the "achievement gap" framing, but instead opts for the theoretical framing and terminology of an "educational opportunity gap." The educational opportunity gap approach does not focus on individual failings, but considers systematic inequities in learning opportunities for particular groups (Darling-Hammond, 2013). Further, this research adopts an intersectional approach, which simultaneously considers the impact of multiple forms of identity and difference on an individual's or group's circumstances (Cole, 2009), in this case with the aim of understanding academic differences.
Overview of Scoping Review Method
A scoping review is a systematic literature review that is designed to map topics rapidly, summarize research findings, and identify gaps in the literature (Arksey & O'Malley, 2005). Unlike a meta-synthesis, which typically focuses only on qualitative research and is intended to be as exhaustive as possible (Thunder & Berry, 2016), a scoping review can assess quantitative or qualitative data and is specifically designed for topics being reviewed for the first time or complex subjects (Arksey & O'Malley, 2005). Scoping reviews, having identified commonalities and gaps in the research, are sometimes used to design a new study or to inform a subsequent systematic review.
This review used the model of scoping reviews proposed by Arksey and O'Malley's (2005), taking some of Levac, Colquhoun, and O'Brien's (2010) suggestions to improve this methodology. Arksey and O'Malley (2005) articulate a five-stage framework: (1) identify the research question, (2) identify relevant studies, (3) select the studies, (4) chart the data, and (5) summarize and report the results. Drawing from Levac et al.'s (2010) recommendations, steps three and four are treated as iterative, rather than linear, processes. This is appropriate because of the lack of a singular unified keyword or phrase to describe the phenomenon in question in the Canadian context.
Boundaries of the Review
This review centred on the following questions: What demographic and contextual factors are most commonly used in K-12 academic achievement studies in Canada? What, if any, research gaps exist?
Due to the limited literature, a decision was made to extend beyond the typical 10-year boundary for scoping reviews; thus, the search included peer-reviewed articles published between January 1, 2000 and April 1, 2017. Further, it was decided that articles included in the review must be written in English, due to the cost and time required for translation. This decision notably excludes articles in French, which is a limitation of this review, given the Canadian bilingual context.
After conducting an informal review, the two authors defined initial inclusion and exclusion criteria. The following inclusion criteria for articles were determined:
* Focused on academic achievement differences, inequities or gaps across demographic, social, identity and other contextual factors;
* Focused on K-12;
* Published between January 1, 2000 and April 1, 2017;
* Published in a peer-reviewed journal;
* Used a quantitative or mixed-method design;
* Used internal school or external academic measurement as the dependent factor (e.g., standardized test score or GPA);
* The review allowed data sets that contained preexisting secondary data analysis;
* The review allowed studies with cross-national comparison if Canadian-specific data were provided.
In addition, the following exclusion criteria were determined:
* Published as grey literature, including education policy documents;
* Provided theoretical or policy content with no new data on achievement;
* Focused on early childhood (prior to kindergarten);
* Focused on post-secondary education;
* Utilized only qualitative design;
* Utilized as the only independent variable: a physical, intellectual, or developmental dis/ability (e.g., students with hearing impairments); a psychological/cognitive factor (e.g., self-motivation); an individual educational variable (e.g., past literacy scores); or a school-related variable (e.g., teacher experience);
* Utilized as the only dependent variable a self-report of academic engagement, aspirations, or motivation, without any academic achievement measurement.
After the initial search selection, it was necessary to determine whether to include empirical multinational studies and evaluations of achievement gap interventions or policies. Using Levac et al.'s (2010) iterative process for scoping reviews, where new exclusion and inclusion criteria are added during the process if unexpected grey areas emerge during the selection process, both authors agreed to add two new exclusion criteria. Multinational studies that explicitly included Canadian data and disaggregated it from other countries would be included. Further, articles focused on intervention strategies would only be included when new information on Canadian demographic or context factors were offered. Therefore, two additional exclusion criteria were added in order to make determinations about several articles:
* Focused on educational interventions or policy;
* Provided cross-national data but did not include disaggregated data on Canada.
Figure 1 presents a flowchart demonstrating the study selection process. The search was conducted between April and June 2017, using Primo Central Index with the categories of education, law, psychology, public health, and social sciences selected.
* A first search for peer-reviewed articles from 2000 to 2017 was conducted using the terms "achievement gap" AND Canad*, yielding 1,119 articles.
* A second search used the terms "educational inequity" AND Canad*, yielding 1,015 articles.
* A third search used the terms "at risk" AND academic AND Canad*, yielding 1,004 articles.
* A fourth search used the terms "opportunity gap" AND academic AND Canad*, yielding 646 articles.
The 3,784 results were compiled.
An additional hand search for relevant articles was conducted, using five major Canadian education journals: Canadian Journal of Education (n = 63), Canadian Journal of Native Education (n = 15), Canadian Journal for New Scholars in Education (n = 6), McGill Journal of Education (n = 15), and Alberta Journal of Educational Research (n = 32). The hand search led to the inclusion of 131 articles. In total, 3,915 articles were selected during the identification phase.
Next, duplicates were removed and included articles were screened for the review, first at the abstract and then at the full text level. Notably, many articles were removed for articulating theory or policy recommendations (n = 71), presenting intervention data (n = 17), or providing only qualitative research (n = 64). Ten articles were deemed questionable by the first reviewer and were sent to the second reviewer for discussion; nine of these were excluded based on the criteria.
Fifty-four articles met all criteria and were read in full and synthesized by the first author. Please the table in Appendix A which was created to synthesize and chart the data found during the scoping review.
This section reports the terminology, demographic and contextual factors, and research design elements of the 54 reviewed articles.
The scoping review results yielded no consistent term for describing educational inequities in Canada, with 39 different terms identified. The most popular terms were as follows: "achievement gap" (n = 9), "academic achievement" (n = 5), "achievement" (n = 4), and "educational achievement" (n = 2). Other terms like "at risk," "dropout," and "educational inequity" were also noted. Many authors did not use a consistent term in their article, in which case the reviewers informally assessed which term was used most commonly in the article (see Appendix A). Further, the keywords for these articles demonstrated little consistency.
Demographic and Contextual Factors
The results revealed 40 discrete demographic or contextual factors, which are broken down by frequency in a table in Appendix B. Some of these factors were addressed by only one published study meeting the selection criteria for this review (e.g., disabled parents were addressed by Chen, Osberg, & Phipps, 2015; experience in childcare by Geoffroy et al., 2010; and age of arrival to Canada by Anisef, Brown, Phythian, Sweet, & Walters, 2010). Others factors, such as SES and gender, account for the focal terms in many of the articles. The most commonly studied factors were as follows: SES (n = 34); gender (n = 21); language (n = 11); immigrant status (n = 10); family structure (n = 10); and Indigenous status (n = 8). Importantly, we included factors either if they were the primary focus of the article or if the researchers specifically reported the results of controlling for this demographic or contextual factor. A summary of select demographic and contextual factors follows below.
SES. SES was the most commonly studied factor (n = 34). According to the Programme for International Student Assessment (2017), Canada consistently ranks as one of the most equitable education systems among OECD countries, which may be attributable to the provincial public school funding formula that ensures that rich and poor districts receive similar funding. While many of the studies in this scoping review used PISA or other major database data that present Canada in a favourable light (n = 9), researchers nevertheless indicated educational differences between students of lower and higher SES backgrounds in Canada (Benito, Alegre, & Gonzalez-Balletbo, 2014; Castejon & Zancajo, 2015; Chmielewski & Reardon, 2016; Edgerton, Peter, & Roberts, 2008, 2014; Hampden-Thompson, 2013; Hanushek & Luque, 2003; Perry, 2009; Schnepf, 2007).
Authors provided a range of theories to explain the relationship between SES and achievement. Anisef et al. (2010) suggested the relationship be understood through social capital theory, where students with higher SES have access to networks of support, information and services, and similar social backgrounds, which help with school success and future employment. Maggi, Kohen, and D'angiulli (2004) and Roos et al. (2006) focused not on individual SES, but the importance of neighbourhood SES. As Friesen and Krauth (2010) theorized, the provincial public school funding formula ignores that students typically live in relatively homogenous communities, and therefore systemic differences in fundraising, school resources, and teacher preferences may exacerbate SES-based educational gaps. Articles demonstrated a range of findings in terms of the importance of SES and understanding why this factor has remained a persistent predictor of educational success.
Gender. While gender was commonly studied (n = 21), there is little consensus in the findings, with some studies depicting girls outperforming boys, some showing boys outperforming girls, some demonstrating stratified differences across subjects, and others arguing that an intersectional approach is needed to determine which boys are not performing as well.
White (2007) argued that the panic over underachieving boys might be greatly overstated due to studies not controlling for other background factors. White's (2007) model suggested gender accounted for less than 1% of the variance in reading achievement, strengthening the notion that gender-based underachievement may be overstated. Similarly, Kingdon, Serbin, and Stack (2017) explored the intersectionality of SES and gender, finding that the gap between girls outperforming boys widened in groups of lower-income students. Interestingly, Bouchard and St-Amant's (2000) research suggested that the more an individual conforms to gender stereotypes, the more their achievement suffers. The review thus revealed that tensions and contradictions emerge in assessing "gender gaps." These examples point to the need for careful research that accounts for the complexity of student backgrounds in determining whether a "gap" exists between groups.
Immigrant status. Ten studies examined differences in educational attainment dependent on immigrant status. The authors of this review chose the general term "immigrant status" to indicate studies that explored the relationship between immigration and education; however, the studies' authors defined immigration status in a variety of ways, variously using the child's country of origin, the kind of immigration status or class that has been granted, or the generational wave of immigration to which the child belongs (e.g., born outside of Canada, born in Canada to parents who have recently immigrated).
Many of the cross-national studies (n = 5) demonstrated that immigrants in Canada fare well in comparison to other countries such as the United States, the United Kingdom, and France (Benito et al., 2014; Cobb-Clark, Sinning, & Stillman, 2012; Hochschild & Cropper, 2010; Riederer & Verwiebe, 2015; Schnepf, 2007). As Schnepf (2007) stated, Canada has one of the lowest differences in education attainment between immigrants and "native" Canadians; once language is controlled for, the difference largely disappears. Yet, it is important to note that several studies in this scoping review demonstrated there is a significant gap between those who are either first- or second-generation immigrants and "native" Canadians (Anisef et al., 2010; Cobb-Clark et al., 2012; Hochschild & Cropper, 2010; Ma, 2001; Ma & Crocker, 2007; Riederer & Verwiebe, 2015; Schnepf, 2007; Toohey & Derwing, 2008). Additionally, some of the studies examined how generational differences impact achievement (Anisef et al., 2010; Cobb-Clark et al., 2012; Hochschild & Cropper, 2010; Ma, 2001; Riederer & Verwiebe, 2015; Schnepf, 2007). Hochschild and Cropper (2010) demonstrated very small differences between second-generation immigrant students and "native" Canadian students, suggesting that by the second generation the gap narrows.
Importantly, several studies examined education differences in subgroups of immigrants, finding significant differences dependent on region of birth/ethnicity (Anisef et al., 2010; Bakhshaei, Georgiou, & McAndrew, 2016); language (Bakhshaei et al., 2016; Cobb-Clark et al., 2012; Toohey & Derwing, 2008); age of arrival (Cobb-Clark et al., 2012); and SES (Anisef et al., 2010; Bakhshaei et al., 2016; Toohey & Derwing, 2008). Toohey and Derwing's (2008) study explored differences in educational attainment based on immigration class (independent, family sponsored, or refugee), finding that students whose parents immigrated based on the independent category graduated in Vancouver at a far higher rate than those who immigrated under the family class or refugee status classes. The reviewed studies point to the importance of examining how well immigrants actually fare in Canada, particularly when additional demographic and contextual factors are explored.
Indigenous status. Eight articles included in the scoping review demonstrated that Indigenous students face educational disparities (Aman, 2008; Brade, Duncan, & Sokal, 2003; Friesen & Krauth, 2010; Ma & Klinger, 2000; Philpott & Nesbit, 2010; Puchala, Vu, & Muhajarine, 2010; Richards, Vining, & Weimer, 2010; Steeves, Carr-Stewart, & Marshall, 2010). Ma and Klinger (2000) found that "Native ethnicity" was the single most important factor in their multi-variable study, and remained strong even after controlling for SES, attributing these differences to "unsuccessful incorporation into the mainstream culture" (p. 51).
While some have written about this population's achievement differences, comparing it to the Black-White achievement differences in the United States (Friesen & Krauth, 2009), the authors of this review believe it is critical that these data be viewed through an opportunity gap theoretical lens. As explained in this article's theoretical framework, what matters is not simply the "output" differences, but rather the contextual "input" differences in terms of bias in teaching, structural disparities, underfunded educational programs, the historical context of residential schools, and so on. The studies included here that address educational disparities that Indigenous students face also discuss the importance of considering the influence of SES (Brade et al., 2003; Richards et al., 2010; Steeves et al., 2010); language issues (Brade et al., 2003); disability diagnoses (Friesen & Krauth, 2010); culturally appropriate curriculum, teaching, and/or testing (Philpott & Nesbitt, 2010; Richards et al., 2010; Steeves et al., 2010); historical trauma and the history of residential schooling (Brade et al., 2003; Philpott & Nesbitt, 2010; Steeves et al., 2010); students changing schools (Aman, 2008; Brade et al., 2003); teacher quality, experience, and/or turnover rate (Friesen & Krauth, 2010); school attendance (Philpott & Nesbit, 2010); and school environment and/or population (Brade et al., 2003; Friesen & Krauth, 2010; Puchala et al., 2010; Richards et al., 2010). A holistic view of educational differences is therefore critical when exploring Indigenous identity factors.
Other findings. While providing details of all 40 factors was not feasible within this scoping review, a few additional findings can be briefly outlined. Some factors, such as country of origin (n = 2), educational policy in country of origin (n = 3), language factors (n = 11), education factors (n = 4), and ethnicity (n = 5), overlap with a focus on immigration status and student success. The studies addressing these factors suggested that nuanced examinations of intersectional factors can illuminate which students struggle in Canadian systems and may reveal opportunities to develop policies that target the specific needs of newcomer youth. For example, Puchala et al.'s (2010) study found that high ethnic diversity in a child's neighborhood mitigates the negative effects of ESL status on achievement. Further, only three of the reviewed articles examined the role of Canada's national policies on student success (Hampden-Thompson, 2013; Perry, 2009; Riederer & Verwiebe, 2015), suggesting there may be a need to examine both the impact of the national context and the effectiveness of existing national educational policies.
Further, there are a large number of factors (n = 26) that have only been studied once or twice, including biological risk factors (n = 1), neighbourhood characteristics (n = 2), and urban versus rural settings (n = 2). (For a complete list of factors and their frequency, see Appendix B.) The authors also identified potentially significant factors that were not studied in the reviewed articles, including LGBTQ+ identity, religion, and racial identity. The importance of these understudied factors is discussed later in this article.
Research Design Overview
The majority of studies were cross-sectional (n = 39), with fewer using longitudinal design (n = 14) or both (n = 1). Further, almost every study (n = 49) relied either exclusively on secondary data analysis of preexisting datasets, or included secondary data analysis along with survey research design. The most common dataset used was PISA (n = 16). A wide range of academic measurements (n = 29) were identified as dependent variables. Most studies utilized some form of standardized testing (n = 51). Relatively few used a measure that is cumulative (n = 13), such as high school completion/dropout rate, GPA, or failure to accumulate basic credits in Grade 9. (See Appendix A for a breakdown of the use of datasets and academic measurements.) Finally, a significant portion of the studies drew from multinational datasets that included Canada-specific data (n = 12) or were Canada-wide (n = 9). The provincial and territory breakdown for sample location is as follows: Ontario (n = 9), British Columbia (n = 7), Quebec (n = 6), Manitoba (n = 4), Alberta (n = 3), New Brunswick (n = 3), Saskatchewan (n = 2), Newfoundland and Labrador (n = 1), Nova Scotia (n = 1); no studies used samples that included Prince Edward Island, Nunavut, Yukon, or the Northwest Territories. Several provinces and territories are clearly understudied.
The first major finding is that, unlike the US literature, the Canadian literature has not uniformly adopted any umbrella term to describe educational inequities, with 39 different terms being used in the reviewed studies. This may potentially limit Canadian researchers' and educators' ability to access this information easily and build on previous research. It also suggests that there is a lack of consistent discourse or theorizing about educational inequity in Canada. Canada does not have a federal department of education or an integrated national education system, with each province and territory being responsible for the organization, delivery, and assessment of education, as well as determining what data are gathered on academic performance. Further, given Canada's high education rankings among Organisation for Economic Co-operation and Development (OECD) countries, there has not been the same push for data collection and school reform as in the United States. These differences may be useful in understanding why Canada has neither clear terminology nor a clearly organized research effort around educational inequities.
Adopting a unified term in the Canadian context may be warranted to facilitate research dissemination and to assess if educational inequities exist along various demographic and contextual lines. Despite its ubiquity in the United States, we would be wary of adopting the popular term "achievement gap," due to the significant critique of achievement gap discourse and education policy--see Baldridge (2014); Gillborn (2005, 2008); Ladson-Billings (2006); Martino and Rezai-Rashti (2013); Rebell and Wolff (2008); and Tuck (2009). These authors suggest that this theoretical framing promotes deficit language and "damage-centered narratives," reignites conversations about a genetic or "cultural deficit" basis for differences, and increases neoliberal ties to mass testing, market-driven education, and systems of accountability where data are presented discursively to suggest. We would not recommend Canada adopt this language or discourse, which centres on documenting individual failures rather than providing opportunities to change educational success. Instead, we suggest the use of "educational opportunity gap" (Baldridge, 2014). An opportunity gap perspective directs the research focus away from "individual failings" onto the system's inequities and the systematic denial of equal educational opportunities. We suggest that this shift in focus is more likely to encourage research and interventions focused on systemic changes to opportunities, rather than merely individual-centred interventions. Notably, none of the articles we reviewed use the term "educational opportunity gap."
The results of the scoping review revealed that a wide number of factors have been studied; however, only a relatively small number have been examined more than five times. Importantly, Klinger, Rogers, Anderson, Poth, and Calman (2006) suggest, "Canada has a long history of collecting information on student achievement of learning outcomes, as well as characteristics of students, schools, and communities; however, the anonymous and/or incomplete nature of the data have resulted in restricted analyses" (p. 773). Therefore, what follows is a discussion of select demographic factors and future research implications, as well as significant gaps in the research.
SES. As reported above, over half of the studies found SES significant in determining the magnitude and direction of educational opportunity gaps. This is important, given that the latest statistics show that the poverty rate in Canada is increasing every year, with approximately one in five children living below the national poverty line (Canada Without Poverty, n.d.). SES is one of the most persistent predictors of academic attainment; however, there is ongoing debate about why this is the case. As Davies and Aurini (2013) wrote, "Researchers continue to debate the relative weight of evidence that traces [SES gaps] to biases in school teaching, curricula, and organization, or to resource inequalities among households and neighbourhoods (Conley & Albright, 2004). These attributions each imply different policy solutions" (p. 288). As the reviewed research uses inferential statistics, it remains limited in understanding both the cause of the differences and what might be needed to reduce opportunity gaps. Further, while the scoping review allowed for mixed methods research studies, only one study (Bouchard & St-Amant, 2000) used a mixed methods research design. Mixed methods studies can often be useful in understanding the relationship between variables, because the qualitative data can offer rich information about lived experience.
Based on the analysis of these articles, researchers tended to attribute SES-based gaps to differences in early childhood, social capital, access to wrap-around or alternative learning opportunities, and/or school resources based on neighbourhood SES. Notably absent from the discussion sections of the reviewed articles was the potential impact that chronic stress stemming from poverty might have on learning. This absence is significant given recent studies that have demonstrated links between chronic stress and cognitive functioning, executive functioning, and learning (Evans & Fuller-Rowell, 2013; Kaplan et al., 2001; Mani, Mullainathan, Shafir, & Zhao, 2013).
Further, there was marked variation in how SES was operationalized in the reviewed articles. Due to the difficulties in determining household income, researchers used a variety of proxy factors to approximate SES (see Appendix C). About half of the studies (n = 19) used multiple proxies for SES or created a composite SES score based on a range of factors. As Appendix C indicates, nine drew on neighbourhood or zip code census data to determine an approximate income, which limits the specificity and accuracy of correlations between SES and academic achievement at the individual level and data at the national level. More problematically, educational-oriented SES proxies, such as parental highest level of education, educational possessions (e.g., number of books in a house), or access to social and cultural educational activities (e.g., visits to museums or art galleries), were at times implemented (Bouchard & St-Amant, 2000; Ma, 2001; Ma & Klinger, 2000). Using educational proxies as a substitute for SES may influence the results of a study focused on education, as the strength and nature of the associations among factors may be difficult to identify due to confounding explanations. For example, using proxies like a parent's education, access to reading material, and/or access to educational events might influence a child's education differently than the maternal age of the first child's birth or a parent's occupation.
While recognizing that many of these studies used preexisting datasets and were therefore limited by the information contained in them, we would recommend that whenever possible researchers seek the family income level, rather than neighborhood or zip code approximates. Further, when income level cannot be collected, we recommend that researchers use composite measures, but avoid relying solely on education variables (such as parental educational level or the number of books in a home) to approximate SES.
Immigrant status. As discussed, several of the multinational studies show Canada as having one of the smallest educational attainment gaps for immigrant students. However, based on the results of the scoping review, it can be suggested that these cross-national comparative studies do not provide the needed nuanced examination of Canada's immigrant and education policies. As Hochschild and Cropper (2010) discussed, Canada has perhaps too often been held up as the model for immigration policy and integration. Because Canada actively pursues an immigration policy that targets immigrants who have the capacity to be rapidly incorporated and who are highly skilled, educated, and have French or English proficiency, the comparative small differences may not be due to Canada's education or multiculturalism policies but rather due to immigration policy (Hochschild & Cropper, 2010; Schnepf, 2007).
Further, while the results of the scoping review suggest that studies have begun to carefully examine variations in academic success across various subgroups, these contextual and demographic variables should be examined intersectionally, for example considering immigrant status with ethno-racial identity. Additional nuanced research is needed on the differences between various generations of immigration (e.g., first-generation, second-generation); different forms of immigration (family sponsorship, immigration through point system, forced migration or refugee status); language considerations; age of migration; and intersectional considerations between immigrant status and other factors.
Indigenous status. As indicated in the results section, eight articles demonstrated that Indigenous students face educational disparities. It is critical that the inequity of education outcomes must be understood through the historical, legal, social, and economic contexts of Indigenous populations in Canada. This must include looking intersectionally at the impact of SES, reserve vs. provincial schooling, treaty agreements regarding education and land, and what Brave Heart and DeBruyn (1998) described as "historical unresolved grief' (p. 56). Furthermore, as the Truth and Reconciliation Commission of Canada (2015) powerfully articulated, residential schooling and education itself was a place of physical, sexual, and cultural violence. This context must be taken into account when examining any educational outcomes for Indigenous students. Researchers should consider the potential harm that may result from focusing simply on improving test scores for this community. As Brade et al. (2003) suggested, researchers must be careful to consider whether the pursuit of these scores is a worthy goal, or is simply about assimilation to white culture.
Philpott and Nesbit's (2010) discussion of the largest learning needs assessment of Indigenous students in Canada provides an excellent overview of the complexity of Indigenous education in the Canadian context, as well as policy and program recommendations following their assessment of ability, achievement, risk factors, attendance profiles, and other information on an entire culture of Innu children. It is to be strongly recommended that any research that reports differences in achievement with Indigenous students be framed from an opportunity gap theoretical lens, and include in its theoretical orientation and discussion both the historical and current inequities which contribute significantly to these differences.
Understudied factors. Finally, it is important to consider not only which factors were common in the scoping review, but also gaps in the research. The scoping review revealed some social factors that were not considered by any studies meeting the screening criteria, including LGBTQ+ identity, religion, and, most surprisingly, race. Climate surveys examining students' perceptions of safety and inclusion in school have suggested that LGBTQ+ students face greater stigma and peer harassment, thus warranting an examination of educational opportunity gaps (Craig, Tucker, & Wagner, 2008; Kosciw, Greytak, & Diaz, 2009; Ryan & Futterman, 1998). Craig and Smith's (2014) study, for example, indicated that perceived discrimination experienced by multiethnic sexual minority youth significantly influenced their school performance. Given this research, studies pertaining to opportunity gaps for LGBTQ+ students in the Canadian context are needed.
Similarly, given the rise of anti-Muslim rhetoric, a need for research exploring opportunity gaps for Muslim students may be indicated. Zine's (2004) qualitative study demonstrated a need to disrupt Islamophobia in Canadian schools. Additionally, Hindy's (2016) report on Ontario public schools suggested that Muslim students experience feelings of isolation, peers and teachers lack awareness about Islam, and there is a lack of representation of Muslims in the curriculum. It may be important to consider how other religious minority students, as well as agnostic or atheist students, are faring in Canada. Further studies exploring the relationship between religious identity and educational opportunities are needed.
Finally, given the overwhelming focus of "achievement gap" studies in the United States on educational differences for racialized students, it was surprising not to find any similar studies in Canada meeting the criteria for this scoping review. While ethnicity is sometimes used as a euphemism for racialization in Canada, only five studies included ethnicity as an independent factor. We interpret this lack of data on race and achievement as stemming from Canada's avoidance of collecting disaggregated racial data. Most school districts and standardized assessments do not collect race-based data, limiting researchers' ability to examine the ways in which particular groups may face greater academic challenges. As Pon (2009) suggested, the "ontology of forgetting" allows Canada to see itself as fair and multicultural, while ignoring pervasive racism (p. 66). Similarly, Rodney and Copeland (2009) suggested that "the official discourse of multiculturalism makes it difficult to speak of race and racism in Canada" (p. 817). They remind readers, however, that "whenever data are collected in Canada based on race, disparities are observed" (p. 821).
Importantly, the Toronto District School Board (TDSB) does collect disaggregated race and ethnicity demographics, and has recently made data demonstrating race-based disparities available to the public. James and Turner (2017), arguing for the reporting of disaggregated race-based data, wrote, "Despite its limitations, the TDSB data offers useful insights into the schooling and education of Black students beyond what any other data source currently provides--including the Canadian Census--and is the only source of its kind that exists in Ontario and in Canada generally" (p. 4). In September 2017, Ontario Education Minister Mitzie Hunter announced that all Ontario schools would collect this data, which may result in studies exploring the impact of racial identity on equitable educational opportunities (Government of Ontario, 2017).
When researchers conduct new studies, the collection of race and ethnicity based data is recommended in order to expand this research. Alternatively, when collecting data for secondary data analysis from either international studies or district school boards, researchers should be encouraged to ask that this information be collected in the future and to communicate its importance in understanding whether some students face racial inequities and systemic educational opportunity gaps.
Implications for Future Research Design
Analysis of the overarching research design of the studies included in the review (sample location, research design, data set, and academic measure) reveals a number of gaps to be filled by future research.
The sample locations show that the majority of studies drew from multinational datasets that included Canada-specific data. While useful as comparison studies, these articles drawing on multinational databases provided little nuanced information. Further, the provincial and territorial breakdown suggests several areas of Canada are understudied.
Cross-Sectional vs. Longitudinal Design
The majority of studies had a cross-sectional survey design, primarily relying on a single-time individual score on a standardized test. As Roos et al. (2006) argued,
Testing tells only part of the story ... What is not known is how many students missed a test because they were not in school on a test day, because they had fallen one or more years behind their cohort, or because they had dropped out. (p. 685)
Data are limited for academic measurements taken in schooling (e.g., Alberta's high school completion exams or PISA testing at age 15), where students facing larger opportunity gaps may no longer attend regularly, or the data may be gathered too late for meaningful educational interventions to be implemented. As Roos et al.'s (2006) longitudinal study revealed, a single-time assessment of educational achievement not only fails to capture a population of students who miss the test, but also "will overestimate the performance of groups at risk for poor outcomes and provide distorted, inaccurate comparisons of school performance" (p. 698).
Further, Jang, Dunlop, Wagner, Kim, and Gu's (2013) longitudinal study of immigrant English language learners demonstrated that early achievement gaps disappear the longer the students live in their target language community, and that these students outperform monolinguals after five years. They suggested that longitudinal studies challenge the short-term deficit view and provide a more holistic contextual picture of this population. Kingdon et al. (2017) provided the first longitudinal study tracking the academics of boys and girls across the full course of schooling, which established new information in understanding the development of the gender gap. The limited number of longitudinal studies suggests a need to report longitudinal educational research on inequities. We also suggest that cumulative academic measures, such as: GPA, literacy or numeracy, failure to accumulate basic credits in Grade 9, or the high school completion/dropout rate, might provide a more holistic account of a student's long-term educational success.
While many of the studies created complex, nested models and used Hierarchal Linear Modeling, it is important to note that many failed to explore interactions between two or more contextual factors on academic outcomes. Only three articles were framed from an intersectional theoretical lens--Kingdon et al. (2017); Serbin, Stack, and Kingdon (2013); and White (2007). Each of these highlighted the importance of examining the "gender gap" intersectionally, particularly in terms of SES and age, finding that differences tend to become larger during secondary school. Several researchers pointed to the importance of accounting and controlling for multiple demographic factors when studying gender in order to take into account the complexity of demographic backgrounds in determining whether a "gap" exists between groups (Bouchard & St. Amant, 2000; Kingdon et al., 2017; Serbin et al., 2013; White, 2007). Indeed, as Martino and Rezai-Rashti (2013) argued, "an interactional or intersectional analysis that takes into account questions of identity, culture, race, and social class is needed when interpreting test scores" (p. 599). It should be recommended that, when possible, researchers consider a wide variety of demographic and contextual factors, not only separately, but also to look at their potential interaction with one another.
Almost every study relied heavily or exclusively on secondary data analysis of preexisting datasets, with PISA serving as the most common source of data. Most used some form of standardized testing to measure the academic outcome. While large-scale learning outcome data can certainly be useful in understanding demographic and contextual factors in education, the over-reliance on these preexisting datasets is problematic in four ways. First, this reliance necessarily limits the type of information that can be analyzed, based on the previous design of the instrument and data collection. Given the need for studies on demographic factors not typically included in these datasets, ongoing reliance on these sources of data will continue to perpetuate the existing gaps.
Second, as Klinger et al. (2006) suggested, many of the large-scale assessments (PISA, PIRLS, TIMMS, SAIP, etc.) are "low-stakes" tests paired with survey data, which neither teachers nor students are particularly motivated to fill out. They write, "Because of the low-stakes of the test, it can be argued that students are less motivated to produce their best work, teachers are not motivated to encourage maximum student performance, and not all survey items are answered, leading to problems with data quality or missing data" (p. 775).
Third, as Perry (2009) articulated, some researchers have questioned whether these assessments are culturally relevant, pointing to problematic test items. Similarly, Cheng, Klinger, and Zheng (2009) wrote that "large-scale, high-stakes literacy testing is particularly problematic for vulnerable groups of students who are second language learners, or who have had little formal education in the language being tested" (p. 121). In these cases, the academic measure may fail to account for a student's academic ability based on cultural or linguistic differences.
Finally, many of these datasets utilized standardized testing results to determine success and equity. Martino and Rezai-Rashti (2013) pointed out the danger of using PISA as the measure, suggesting this form of evaluation might direct our attention towards school reform, testing, and privatizing the school system through charter schools, rather than considering the need for social policy to address disadvantages for particular populations. Indeed, the neoliberal educational reform efforts in the United States that focus on standardized testing have been shown to place marginalized youth at further disadvantage. Testing, rather than being used as a tool to illuminate disparity, has instead become the "solution" (Baldridge, 2014).
Similarly, Tuck (2009) points out the danger of relying on "damage-centered narratives" in education, where educational policies focus on documenting failure through testing and accountability, rather than providing opportunities to change inequities. Provincial agencies such as Ontario's Education Quality and Accountability Office, Alberta Education, and the British Columbia Ministry of Education Assessment have been criticized for focusing on large-scale testing to measure "success," creating high social and fiscal costs that divert money away from the classroom, and using potentially biased assessments, with important equity implications (Hauseman, 2015). As Martino and Rezai-Rashti (2013) stated, "What is needed, then, is more engagement with data generation from the bottom up, which includes both quantitative and rich qualitative data that are generated at school board and local school level and disaggregated in multiple ways" (p. 607). While there is certainly usefulness in secondary data analysis of these large-scale assessments, it is important that researchers and policymakers do not solely rely on them given the limitations addressed above.
One of the major limitations of this study, necessary in scoping review methodology, is the impact of the selection and exclusion criteria. Of particular significance, the decision to include only articles written in English may have eliminated articles that provide information on francophone communities. Additionally, the search and selection process, including hand searching Canadian educational journals, may have excluded some economics and sociology journals where studies containing empirical research on educational opportunity gaps have been published (e.g., Livingstone & Weinfeld, 2017). Further, the decision to include only studies with a quantitative component often excluded rich qualitative studies that focused on students' experiences in order to understand opportunity gaps with greater depth. This is particularly significant given that many scholars focusing on issues like race, religion, and sexual orientation may focus on qualitative approaches, given the difficulties of attaining disaggregated data in Canada or the desire to focus on the lived experience and voices of marginalized youth. For example, the extensive qualitative study conducted by James and Turner (2017) on race equity in the Greater Toronto Area was not included in this scoping review, among other examples of qualitative educational inequity (e.g., Dei, 2008; Dei & Kempf, 2013; Guo, 2011; James, 2012; Schroeter & James, 2015; Turner, 2015). The decision to include only peer-reviewed articles meant that potentially important data or reports produced by educational departments or local school districts were not examined. For example, TDSB data provide a wealth of information about educational disparities across race, ethnicity, language, and SES. Due to time and resource constraints, as well as the preliminary nature of this review, the authors did not consult with stakeholders and experts during the study selection process as recommended by Arskey and O'Malley (2005), which may have resulted in some studies not being reviewed.
To advance the study of educational inequities in Canada, a coordinated effort of research, including common terminology and attempts to fill gaps in research around contextual factors, is necessary. This scoping review demonstrates a dearth of published research into this topic as a whole, but specifically on racial, religious and LGBTQ+ social dimensions. Many studies lacked an intersectional approach, potentially hiding the ways in which combinations of various social identities, contextual environments, and policy factors may lead to increased systemic educational disparities. Further, this scoping review revealed a reliance on a few academic measurements and databases, largely cross-sectional in nature. Relying on single-time exam scores as the primary assessment of academic success fails to account for more meaningful measures of success, including graduation rates or functional literacy. This form of data collection makes it difficult to see a fuller picture of a student's education, and may unintentionally promote educational policies that focus on increasing a single score, rather than attend holistically to a student's education. From these findings, it is clear that there is a need for further research in tracking and understanding the contextual factors in K-12 educational opportunity gaps in Canada. Deeper opportunity gap research may result in important implications for Canadian educational, social, economic, and immigration policies. Intersectional data and
equitable education policies deserve more attention. The authors call on researchers to conduct studies that will support the educational needs of all of Canada's youth.
Appendix A Author/s Terminology Aman (2008) Academic achieve- ment; Equity Anderson et al. Achievement (2006) Anisef, Brown, Early school Phythian, Sweet, & leaving; Academ- Walters (2010) ic performance; Dropout Bakhshaei, Geor- Disparities in giou, & McAn- school success; drew (2016) Educational achievement Basque & Bou- Academic achieve- chamma (2013) ment Bassani (2008) Achievement disparities Benito, Alegre, & Educational Gonzalez-Balletbo equality (2014) Bouchamma & Academic achieve- Lapointe (2008) ment Bouchard & St- School success Amant (2000) Brade, Duncan, & Educational Sokal (2003) attainment Brownell et al. At risk (2010) Cadigan, Wei, & Educational out- Clifton (2014) comes; Education- al achievement Carson, Kirby, & Early reading Hutchinson (2000) achievement Castejon & Zanca- Academic perfor- jo (2015) mance Chen, Osberg, & Achievement gap Phipps (2015) Cheng, Klinger, & Literacy Zheng (2009) Chmielewski & Achievement gap Reardon (2016) Cobb-Clark, Sin- Achievement gap ning, & Stillman (2012) Corak & Lauzon Differences in (2009) achievement Davies & Aurini Learning inequal- (2013) ity; Achievement gap Demeris, Childs, Achievement & Jordan (2007) Edgerton, Peter, & Education in- Roberts (2008) equality Edgerton, Peter, & Academic achieve- Roberts (2014) ment Friesen & Krauth Achievement gap (2010) Garnett, Ada- Academic mobility muti-Trache, & Ungerleider (2008) Geoffroy et al. Achievement gap (2010) Hampden-Thomp- Educational son (2013) achievement Hanushek & Education quality; Luque (2003) School equity Hochschild & Educational Cropper (2010) achievement Jang, Dunlop, Reading achieve- Wagner, Kim, & ment Gu (2013) Jutte et al. (2010) Educational out- comes Kingdon, Serbin, Achievement gap & Stack (2017) Klinger, Rogers, Gaps in achieve- Anderson, Poth, & ment Calman (2006) Lloyd, Walsh, & Differences in Yailagh (2005) achievement Ma (2001) Gap in achieve- ment Ma & Crocker Achievement (2007) Ma & Klinger Academic achieve- (2000) ment Maggi, Hertzman, Conditions pre- Kohen, & D'angi- venting develop- ulli (2004) ment of learning potential Perry (2009) Equitable systems of education Philpott & Nesbit Educational em- (2010) powerment Pope, Wentzel, Differences across Braden, & Ander- performance son (2006) Pope, Wentzel, & Gender relation- Cammaert (2002) ship patterns with scores Puchala, Vu, & School readiness Muhajarine (2010) Quilliams & Beran At-risk (2009) Richards, Vining, Achievement gap & Weimer (2010) Riederer & Ver- Educational wiebe (2015) achievement Rogers et al. Achievement (2006) Roos et al. (2006) Educational achievement Schnepf (2007) Educational disad- vantage; Educa- tional achievement Serbin, Stack, & Academic success; Kingdon (2013) "Gender gap" Steeves, Achievement gap; Carr-Stewart, & Educational at- Marshall (2010) tainment; Inequal- ity of educational outcomes Toohey & Der- Student success wing (2008) Wei, Clifton, & Academic achieve- Roberts (2011) ment White (2007) Under-performing Author/s Demographic/con- textual variable Aman (2008) Indigenous status; School mobility Anderson et al. Family/parental sup- (2006) port; Gender; SES Anisef, Brown, Age at arrival in Phythian, Sweet, & Canada; Country of Walters (2010) origin; Education fac- tors; Family structure; Gender; Intersection- al: Immigrant status & SES; Language factors Bakhshaei, Geor- Country of origin; giou, & McAn- Ethnicity; Gender; drew (2016) Immigrant status; Language factors; SES Basque & Bou- Urban vs. rural chamma (2013) Bassani (2008) Community size; Family structure; Social capital; SES Benito, Alegre, & Gender; Immigrant Gonzalez-Balletbo status; SES (2014) Bouchamma & Language factors; Lapointe (2008) Psychological/cogni- tive factors Bouchard & St- Gender; SES Amant (2000) Brade, Duncan, & Identification with Sokal (2003) ethnicity; Indigenous status; Mobility Brownell et al. SES (2010) Cadigan, Wei, & SES Clifton (2014) Carson, Kirby, & Education factors; Hutchinson (2000) Family support; Psy- chological/ cognitive factors Castejon & Zanca- SES jo (2015) Chen, Osberg, & Disability benefits; Phipps (2015) Disabled parent; SES Cheng, Klinger, & Family practices; Zheng (2009) Language factors Chmielewski & Country education Reardon (2016) systems; Income gap in countries; SES Cobb-Clark, Sin- Immigrant status ning, & Stillman (2012) Corak & Lauzon Family structure; (2009) Province; SES Davies & Aurini Family practices; (2013) Family structure; SES Demeris, Childs, Proportion of special & Jordan (2007) need students; SES Edgerton, Peter, & Gender; Province; Roberts (2008) SES Edgerton, Peter, & Gender; Gender Roberts (2014) socialization; SES Friesen & Krauth Indigenous status (2010) Garnett, Ada- Ethnicity (ethno-lin- muti-Trache, & guistic groups); Gen- Ungerleider (2008) der; Language factors; Neighbourhood characteristics; SES Geoffroy et al. Childcare experience; (2010) SES Hampden-Thomp- Country policy; Fam- son (2013) ily structure; SES Hanushek & Family background; Luque (2003) Family structure; SES Hochschild & Immigrant status Cropper (2010) Jang, Dunlop, Language factors Wagner, Kim, & Gu (2013) Jutte et al. (2010) Biological risk factors (e.g. birth weight, gestational age, Apgar score); Social risk factors (e.g. mother's age, parent marital status); SES Kingdon, Serbin, Intersectional: SES & & Stack (2017) Gender Klinger, Rogers, Education factors; Anderson, Poth, & Home materials; Lan- Calman (2006) guage factors; SES Lloyd, Walsh, & Gender; Psychologi- Yailagh (2005) cal/cognitive factors (achievement beliefs) Ma (2001) Age; Family struc- ture (includes size); Gender; Immigrant status; SES Ma & Crocker Education factors; (2007) Family structure (including size); Fam- ily/parental support; Gender; Home envi- ronment; Immigrant status; Language factors; Part-time em- ployment for student; Province; Psycholog- ical/cognitive factors; SES; Urban vs. rural Ma & Klinger Family/parental (2000) support; Indigenous status; Ethnicity; Family structure; Gender; SES Maggi, Hertzman, Neighbourhood SES Kohen, & D'angi- ulli (2004) Perry (2009) Country policy differ- ences; SES Philpott & Nesbit Indigenous status (2010) (Innu) Pope, Wentzel, Gender Braden, & Ander- son (2006) Pope, Wentzel, & Gender Cammaert (2002) Puchala, Vu, & Age; Ethnicity; Muhajarine (2010) Gender; Indigenous status; Language fac- tors; Neighbourhood characteristics; SES; Special needs Quilliams & Beran Age; Ethnicity; Fam- (2009) ily/parental support; Gender; Psychologi- cal/cognitive factors Richards, Vining, Indigenous status & Weimer (2010) (including size of Indigenous cohort in a school); Presence of Indigenous education policies; SES Riederer & Ver- Country policy; wiebe (2015) Immigrant status Rogers et al. Disabling condition; (2006) Family structure; Family/parental support; Gender Roos et al. (2006) Neighbourhood char- acteristics (SES); SES Schnepf (2007) Immigrant status; Language factors; SES Serbin, Stack, & Intersectional: Gen- Kingdon (2013) der & SES Steeves, Indigenous status Carr-Stewart, & (including whether Marshall (2010) attending provincial or First Nations-man- aged schools) Toohey & Der- Immigration status wing (2008) (category); Language factors; SES Wei, Clifton, & Gender; Psychologi- Roberts (2011) cal/cognitive factors; SES White (2007) Gender Author/s Sample size Sample location Aman (2008) 4,460 BC Anderson et al. 43,314 Canada (2006) Anisef, Brown, 16,249 Toronto, ON Phythian, Sweet, & Walters (2010) Bakhshaei, Geor- 1,571 QC giou, & McAn- drew (2016) Basque & Bou- 2,436 NB (franco- chamma (2013) phone schools only) Bassani (2008) Not reported Canada Benito, Alegre, & 130,229 Multi-na- Gonzalez-Balletbo tional (2014) Bouchamma & 3,874 MB, NB, NS, Lapointe (2008) ON, QC Bouchard & St- 1,965 QC Amant (2000) Brade, Duncan, & 636 Canada Sokal (2003) Brownell et al. 11,703 MB (2010) Cadigan, Wei, & 1,736 Canada Clifton (2014) Carson, Kirby, & 72 ON city Hutchinson (2000) Castejon & Zanca- Not reported Multi-na- jo (2015) tional Chen, Osberg, & Not reported Canada Phipps (2015) Cheng, Klinger, & 14,311 ON Zheng (2009) Chmielewski & 148,306 Multi-na- Reardon (2016) tional Cobb-Clark, Sin- 288,056 Multi-na- ning, & Stillman tional (2012) Corak & Lauzon Approx. 30,000 Canada only (2009) data Davies & Aurini 1,376 ON, select (2013) boards Demeris, Childs, 1,973 classes ON & Jordan (2007) Edgerton, Peter, & 28,000 Multi-na- Roberts (2008) tional Edgerton, Peter, & 21,948 Multi-nation- Roberts (2014) al (PISA); Canada (YITS) Friesen & Krauth Not reported BC (2010) Garnett, Ada- 4,075 BC muti-Trache, & Ungerleider (2008) Geoffroy et al. 1,863 QC (2010) Hampden-Thomp- Approx. 115,000 Multi-na- son (2013) tional Hanushek & Not reported Multi-na- Luque (2003) tional Hochschild & Not reported Multi-na- Cropper (2010) tional Jang, Dunlop, 120,767 ON Wagner, Kim, & Gu (2013) Jutte et al. (2010) 4,667 Winnipeg, MB Kingdon, Serbin, 126 families QC & Stack (2017) Klinger, Rogers, 160,491 ON Anderson, Poth, & Calman (2006) Lloyd, Walsh, & 161 BC Yailagh (2005) Ma (2001) 32,583 Canada only data Ma & Crocker 29,687 Canada (2007) Ma & Klinger 6,883 NB (2000) Maggi, Hertzman, 78 schools Vancouver, Kohen, & D'angi- BC ulli (2004) Perry (2009) 95,952 Multi-na- tional Philpott & Nesbit 908 Labrador, NL (2010) Pope, Wentzel, Not reported AB Braden, & Ander- son (2006) Pope, Wentzel, & Not reported AB Cammaert (2002) Puchala, Vu, & 6,144 Saskatoon, SK Muhajarine (2010) Quilliams & Beran 148 Calgary, AB (2009) Richards, Vining, 366 schools BC & Weimer (2010) Riederer & Ver- 96,778 Multi-na- wiebe (2015) tional Rogers et al. 3,624 (language AB (2006) arts), 3,643 (math) Roos et al. (2006) 5,894 MB Schnepf (2007) 157,334 (total); Multi-na- 41,542 (Canada) tional Serbin, Stack, & 127 families QC Kingdon (2013) Steeves, 857,530 SK Carr-Stewart, & Marshall (2010) Toohey & Der- 1,554 Vancouver, wing (2008) BC Wei, Clifton, & 27,953 Canada Roberts (2011) White (2007) 113,050 ON Author/s Research design Data set Aman (2008) Longitudinal, BC Ministry of secondary data Education data analysis 1991/2- 1998/9 Anderson et al. Cross-sectional, School Achieve- (2006) secondary data ment Indicators analysis Program (SAIP) 2001 Anisef, Brown, Longitudinal, Toronto District Phythian, Sweet, & secondary data School Board Walters (2010) analysis (TDSB) adminis- trative data Bakhshaei, Geor- Longitudinal, QC school admin- giou, & McAn- secondary data istrative databases drew (2016) analysis Basque & Bou- Longitudinal, NB Department of chamma (2013) observational Education admin- istrative data Bassani (2008) Cross-sectional, Program for secondary data International Stu- analysis dent Assessment (PISA) 1999 Benito, Alegre, & Cross-sectional, PISA 2009 Gonzalez-Balletbo secondary data (2014) analysis Bouchamma & Cross-sectional, Council of Min- Lapointe (2008) secondary data isters of Educa- analysis and tion of Canada survey database Bouchard & St- Mixed methods, n/a Amant (2000) cross-sectional, survey and focus groups Brade, Duncan, & Cross-sectional, Census 1991; Sokal (2003) secondary data Aboriginal Peoples analysis and Survey 1991 survey Brownell et al. Longitudinal, Manitoba Pop- (2010) secondary data ulation Health analysis Research Data Repository Cadigan, Wei, & Cross-sectional, PISA 2003 Clifton (2014) secondary data analysis and survey Carson, Kirby, & Longitudinal, n/a Hutchinson (2000) survey Castejon & Zanca- Cross-sectional, PISA 2009 jo (2015) secondary data analysis Chen, Osberg, & Longitudinal and National Longi- Phipps (2015) cross-sectional tudinal Survey data, secondary of Children and data analysis Youth (NLSCY), 1994-2008 Cheng, Klinger, & Cross-sectional, Ontario Secondary Zheng (2009) secondary data School Literacy analysis and Test (OSSLT), survey 2003 Chmielewski & Cross-sectional, Progress in Inter- Reardon (2016) secondary data national Reading analysis Literacy Study (PIRLS) 2001; PISA 2006, 2009, 2012 Cobb-Clark, Sin- Cross-sectional, PISA 2009 ning, & Stillman secondary data (2012) analysis Corak & Lauzon Cross-sectional, PISA 2000 (2009) secondary data analysis Davies & Aurini Longitudinal, sec- n/a (2013) ondary data analy- sis and survey Demeris, Childs, Cross-sectional, Education Equal- & Jordan (2007) secondary data ity and Account- analysis ability Office (EQAO) 1997/8 Edgerton, Peter, & Cross-sectional, PISA 2003 Roberts (2008) secondary data analysis Edgerton, Peter, & Cross-sectional, PISA 2003 and Roberts (2014) secondary data Youth in Transi- analysis tion Survey (YITS) 2003 Friesen & Krauth Longitudinal, BC Ministry of (2010) secondary data Education enrol- analysis ment database, 1999/0- 2003/4 Garnett, Ada- Longitudinal, BC Ministry of muti-Trache, & secondary data Education dataset Ungerleider (2008) analysis Geoffroy et al. Longitudinal, sec- Quebec Longitudi- (2010) ondary data analy- nal Study of Child sis and survey Development (QLSCD), cohort 1997/8 Hampden-Thomp- Cross-sectional, PISA 2000; Social son (2013) secondary data Policy Research analysis Unit database Hanushek & Cross-sectional, Third Internation- Luque (2003) secondary data al Mathematics analysis and Science Study (TIMSS) 1995 Hochschild & Cross-sectional, PISA 2000, 2003, Cropper (2010) secondary data 2006; several na- analysis tional databases Jang, Dunlop, Cross-sectional, EQAO 2006 Wagner, Kim, & secondary data Gu (2013) analysis and survey Jutte et al. (2010) Longitudinal, Manitoba Pop- secondary data ulation Health analysis Research Data Repository, April- December 1984 births Kingdon, Serbin, Longitudinal, sec- Concordia Lon- & Stack (2017) ondary data analy- gitudinal Risk sis and survey Project--recruited members to the study from origi- nal sample Klinger, Rogers, Cross-sectional, OSSLT 2003; Anderson, Poth, & secondary data Educational Calman (2006) analysis and Quality Indicator survey Framework (EQI) database Lloyd, Walsh, & Cross-sectional, FSA2001 Yailagh (2005) secondary data analysis and survey Ma (2001) Cross-sectional, TIMSS secondary data analysis Ma & Crocker Cross-sectional, PISA 2000 (2007) secondary data analysis Ma & Klinger Cross-sectional, New Brunswick (2000) secondary data School Climate analysis Study (NBSCS) Maggi, Hertzman, Cross-sectional, FSA 1999/2000 Kohen, & D'angi- secondary data ulli (2004) analysis and survey Perry (2009) Cross-sectional, PISA 2003, math secondary data analysis Philpott & Nesbit Cross-sectional, (2010) mixed methods, focus groups, survey Pope, Wentzel, Cross-sectional, AB Achievement Braden, & Ander- secondary data Testing Program, son (2006) analysis June 1999-2002 Pope, Wentzel, & Cross-sectional, AB diploma exam Cammaert (2002) secondary data scores 2000 analysis Puchala, Vu, & Cross-sectional, Census 2001 Muhajarine (2010) secondary data analysis and survey Quilliams & Beran Cross-sectional, n/a (2009) survey Richards, Vining, Cross-sectional, FSA 2001/2002- & Weimer (2010) secondary data 2005/2006; Census analysis Riederer & Ver- Cross-sectional, PISA 2000-2012 wiebe (2015) secondary data analysis Rogers et al. Cross-sectional, AB Provincial (2006) secondary data Language Arts analysis and and Mathematics survey Achievement Tests Roos et al. (2006) Longitudinal, Manitoba Pop- secondary data ulation Health analysis Research Data Re- pository; Census 2001 Schnepf (2007) Cross-sectional, TIMSS 1995, 1999; secondary data PISA 2003; PIRLS analysis 2001 Serbin, Stack, & Longitudinal, sec- Concordia Longi- Kingdon (2013) ondary data analy- tudinal Research sis and survey Project; Statistics Canada 2010 Steeves, Cross-sectional, SK Education Carr-Stewart, & secondary data Indicators Report Marshall (2010) analysis 2008; Census Toohey & Der- Cross-sectional, BC Ministry wing (2008) secondary data of Education analysis 1997-2002 Wei, Clifton, & Cross-sectional, PISA 2003 Roberts (2011) secondary data analysis and survey White (2007) Cross-sectional, OSSLT 2002 secondary data analysis Author/s Academic measure Aman (2008) High school com- pletion Anderson et al. SAIP, math, content (2006) and problem solving Anisef, Brown, School drop out (not Phythian, Sweet, & completed within 6 Walters (2010) years) Bakhshaei, Geor- School delay; Grad- giou, & McAn- uation; Dropout drew (2016) rates Basque & Bou- NB Department of chamma (2013) Education mandato- ry exam 2009-2010, math Bassani (2008) PISA 1999, mathe- matics Benito, Alegre, & PISA 2009, reading Gonzalez-Balletbo test (2014) Bouchamma & SAIP 2002 Writing Lapointe (2008) Assessment III Bouchard & St- Grades Amant (2000) Brade, Duncan, & Highest level of Sokal (2003) schooling Brownell et al. High school (2010) completion within 7 years; Grade 9 grades and credits; Failure to accumu- late 8 credits in first year of grade 9 Cadigan, Wei, & PISA 2003, math Clifton (2014) Carson, Kirby, & Woodcock Reading Hutchinson (2000) Mastery Tests--Re- vised Castejon & Zanca- PISA 2009 jo (2015) Chen, Osberg, & CAT/2 test, math Phipps (2015) scores Cheng, Klinger, & OSSLT, 2003 Zheng (2009) Chmielewski & PIRLS 2001, grade 4 Reardon (2016) reading; PISA 2006, 2009, 2012, reading, math and science Cobb-Clark, Sin- PISA 2009, reading, ning, & Stillman math, science (2012) Corak & Lauzon PISA 2000, reading (2009) Davies & Aurini STAR Reading (2013) scores; report card Demeris, Childs, EQAO 1997/8, & Jordan (2007) Grade 3 provincial exam Edgerton, Peter, & PISA 2003, reading, Roberts (2008) science, math Edgerton, Peter, & PISA 2003 Roberts (2014) Friesen & Krauth Foundational Skills (2010) Assessment tests, Grades 4 and 7 Garnett, Ada- Final Grade 12 muti-Trache, & grades, language Ungerleider (2008) arts, math, biology, chemistry, physics, geography, history Geoffroy et al. Lollipop Test for (2010) School Readiness; Peabody Picture Vocabulary Test Revised; Number Knowledge Test; Kaufman Assess- ment Battery for Children Hampden-Thomp- PISA 2000 son (2013) Hanushek & TIMSS 1995 Luque (2003) Hochschild & PISA 2000, 2003, Cropper (2010) 2006 Jang, Dunlop, EQAO testing 2006 Wagner, Kim, & Gu (2013) Jutte et al. (2010) On time passage of required Grade 12 exam Kingdon, Serbin, Bilan Qualitatif de & Stack (2017) l'Appentissage de la Lecture, 2nd ed.; Wechsler Individual Achievement Test (numerical subtest); GPA; grades Klinger, Rogers, OSSLT 2003 Anderson, Poth, & Calman (2006) Lloyd, Walsh, & FSA 2001, numeracy Yailagh (2005) subscores; math report card grades Ma (2001) TIMSS Ma & Crocker PISA 2000, reading (2007) Ma & Klinger Achievement (2000) test scores, math, science, reading, writing Maggi, Hertzman, FSA 1999/2000, Kohen, & D'angi- reading, math ulli (2004) Perry (2009) PISA 2003, math Philpott & Nesbit Reading at grade (2010) level; attendance; drop out Pope, Wentzel, AB Achievement Braden, & Ander- Testing Program, son (2006) Grades 3, 6, 9 Pope, Wentzel, & AB diploma exam Cammaert (2002) scores 2000; school-awarded scores, 2000 Puchala, Vu, & Early Development Muhajarine (2010) Instrument (EDI) Quilliams & Beran Grades; teacher (2009) report Richards, Vining, FSA 2001/2002- & Weimer (2010) 2005/2006 Riederer & Ver- PISA 2000-2012, wiebe (2015) reading Rogers et al. AB Provincial (2006) Language Arts and Mathematics Achievement Tests; Highest Level of Achievement Test (Grade 5 reading) Roos et al. (2006) Grade 12 provincial standard tests, ab- sences, completion, grades, dropout rates Schnepf (2007) TIMSS 1995, 1999; PISA 2003; PIRLS 2001 Serbin, Stack, & Test de rendement Kingdon (2013) pour francophones; Wechsler Individual Achievement Test; IQ; report cards Steeves, High school com- Carr-Stewart, & pletion rate Marshall (2010) Toohey & Der- Graduation; provin- wing (2008) cial exam scores Wei, Clifton, & PISA 2003 Roberts (2011) White (2007) OSSLT 2002 Appendix B Demographic or Frequency Authors contextual factor Indigenous status 8 Aman (2008); Brade, Duncan, & Sokal (2003); Friesen & Krauth (2010); Ma & Klinger (2000); Philpott & Nesbit (2010); Puchala, Vu, & Muhajarine (2010); Richards, Vining, & Weimer (2010); Steeves, Carr-Stewart, & Marshall (2010) Age 3 Ma (2001); Puchala et al. (2010); Quilliams & Beran (2009) Age at arrival in 1 Anisef, Brown, Phythian, Canada Sweet, & Walters (2010) Biological risk factors 1 Jutte, Brownell, Roos, Schippers, Boyce, & Syme (2010) Childcare experience 1 Geoffroy et al. (2010) Community size 1 Bassani (2008) Country education 1 Chmielewski & Reardon systems (2016) Country of origin 2 Anisef et al. (2010); Bakhshaei, Georgiou, & McAndrew (2016) Country policy 3 Hampden-Thompson (2013); Perry (2009); Riederer & Verwiebe (2015) Disability benefits 1 Chen, Osberg, & Phipps (2015) Disabled parent 1 Chen et al. (2015) Disabling condition 1 Rogers et al. (2006) Education factors 4 Anisef et al. (2010); Carson, Kirby, & Hutchinson (2000); Klinger, Rogers, Anderson, Poth, & Calman (2006); Ma & Crocker (2007) Ethnicity 5 Bakhshaei et al. (2016); Garnett, Adamuti-Trache, & Ungerleider (2008); Ma & Klinger (2000); Puchala et al. (2010); Quilliams & Beran (2009) Family background 1 Hanushek & Luque (2003) Family practices 2 Cheng, Klinger, & Zheng (2009); Davies & Aurini (2013) Family structure 10 Anisef et al. (2010); Bassani (2008); Corak & Lauzon (2009); Davies & Aurini (2013); Hampden- Thompson (2013); Hanushek & Luque (2003); Ma (2001); Ma & Crocker (2007); Ma & Klinger (2000); Rogers et al. (2006) Family/parental support 6 Anderson et al. (2006); Carson et al. (2000); Ma & Crocker (2007); Ma & Klinger (2000); Quilliams & Beran (2009); Rogers et al. (2006) Gender 21 Anderson et al. (2006); Anisef et al. (2010); Bakhshaei et al. (2016); Benito, Alegre, & Gonzalez-Balletbo (2014); Bouchard & St-Amant (2000); Edgerton, Peter, & Roberts (2008, 2014); Garnett et al. (2008); Kingdon, Serbin, & Stack (2017); Lloyd, Walsh, & Yailagh (2005); Ma (2001); Ma & Crocker (2007); Ma & Klinger (2000); Pope, Wentzel, & Cammaert (2002); Pope, Wentzel, Braden, & Anderson (2006); Puchala et al. (2010); Quilliams & Beran (2009); Rogers et al. (2006); Serbin, Stack, & Kingdon (2013); Wei, Clifton, & Roberts (2011); White (2007) Gender socialization 1 Edgerton et al. (2014) Home environment 1 Ma & Crocker (2007) Home materials 1 Klinger et al. (2006) Identification with 1 Brade et al. (2003) ethnicity Immigrant status 10 Anisef et al. (2010); Bakhshaei et al. (2016); Benito et al. (2013); Cobb-Clark, Sinning, & Stillman (2012); Hoch- schild & Cropper (2010); Ma (2001); Ma & Crocker (2007); Riederer & Verwiebe (2015); Schnepf (2007); Toohey & Derwing (2008) Income gap in countries 1 Chmielewski & Reardon (2016) Language factors 11 Anisef et al. (2010); Bakhshaei et al. (2016); Bouchamma & Lapointe (2008); Cheng et al. (2009); Garnett et al. (2008); Jang, Dunlop, Wagner, Kim, & Gu (2013); Klinger et al. (2006); Ma & Crocker (2007); Puchala et al. (2010); Schnepf (2007); Toohey & Derwing (2008) Mobility 1 Brade et al. (2003) Neighbourhood 2 Garnett et al. (2008); characteristics Puchala et al. (2010) Neighbourhood SES 2 Maggi, Hertzman, Kohen, & D'angiulli (2004); Roos et al. (2006) Part-time employment 1 Ma & Crocker (2007) for student Presence of Indigenous 1 Richards et al. (2010) education policies Proportion of special 1 Demeris, Childs, & Jordan need students (2007) Province 3 Corak & Lauzon (2009); Edgerton et al. (2008); Ma & Crocker (2007) Psychological/cognitive 6 Bouchamma & Lapointe factors (2008); Carson et al. (2000); Lloyd et al. (2005); Ma & Crocker (2007); Quilliams & Beran (2009); Wei et al. (2011) School mobility 1 Aman (2008) SES 34 Anderson et al. (2006); Anisef et al. (2010); Bakhshaei, et al. (2016); Bassani (2008); Benito et al. (2014); Bouchard & St-Amant (2000); Brownell et al. (2010); Cadigan, Wei, & Clifton (2013); Castejon & Zancajo (2015); Chen et al. (2015); Chmielewski & Reardon (2016); Corak & Lauzon (2009); Davies & Aurini (2013); Demeris et al. (2007); Edgerton et al. (2008, 2014); Garnett et al. (2008); Geoffroy et al. (2010); Hampden- Thompson (2013); Hanushek & Luque (2003); Jutte et al. (2010); Kingdon et al. (2017); Klinger et al. (2006); Ma (2001); Ma & Crocker (2007); Ma & Klinger (2000); Perry (2009); Puchala et al. (2010); Richards et al. (2010); Roos et al. (2006); Schnepf (2007); Serbin et al. (2013); Toohey & Derwing (2008); Wei et al. (2011) Social capital 1 Bassani (2008) Social risk factors 1 Jutte et al. (2010) Special needs 1 Puchala et al. (2010) Urban vs. rural 2 Basque & Bouchamma (2013); Ma & Crocker (2007) Appendix C Operationalizing SES via Proxies Composite or multiple proxies Anderson et al. (2006) Benito et al. (2013) Brownell (2010) Castejon & Zancajo (2015) Chen et al. (2015) Corak & Lauzon (2008) Davies & Aurini (2013) Edgerton et al. (2008) Edgerton et al. (2014) Geoffroy et al. (2010) Hampden-Thompson (2013) Hanushek & Luque (2003) Jutte et al. (2010) Kingdon et al. (2016) Ma & Crocker (2007) Roos et al. (2006) Schnepf(2006) Serbin et al. (2013) Wei et al. (2012) Neighbourhood or zip code census proxies Anisef et al. (2010) Bakhshaei et al. (2016) Demeris et al. (2007) Garnett et al. (2008) Klinger et al. (2006) Maggi et al. (2004) Perry (2009) Puchala et al. (2010) Richards et al. (2010) Toohey & Derwing (2008) Educational-oriented SES proxies Bouchard & St-Amant (2000) Ma (2001) Ma & Klinger (2000)
* Included in the scoping review
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Wilfrid Laurier University
Eliana B. Suarez
Wilfrid Laurier University
Caption: Figure 1. Scoping review process
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|Author:||Mayor, Christine; Suarez, Eliana B.|
|Publication:||Canadian Journal of Education|
|Date:||Jul 1, 2019|
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