New Scholarship in Critical Quantitative Research, Part 2 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Bildung
• Serie: J-B IR Single Issue Institutional Research
• Sprache: Englisch

Seitenzahl: 236

Veröffentlichungsjahr: 2015

Beschreibung

This volume is the second in a two-part series on differentiating approaches to quantitative research from more traditional positivistic and postpositivistic approaches. While the first volume provided an expanded conceptualization of critical quantitative inquiry, this volume concludes the series by: * applying critical quantitative approaches to new populations of college students who are rarely addressed in institutional and higher education research, such as American Indian, Alaska Native, and students with disabilities, * applying the principles of quantitative criticalism to advanced methods of statistical analysis, and * discussing the variety of challenges to overcome and presenting a future research agenda using these methods. This work is of interest to institutional and higher education researchers who want to expand and critique new ways of thinking about the broad array of populations participating in and served by higher education, while keeping in mind the goals of revealing inequity, challenging marginalization, and helping all students to succeed. This is the 163rd volume of this Jossey-Bass quarterly report series. Timely and comprehensive, New Directions for Institutional Research provides planners and administrators in all types of academic institutions with guidelines in such areas as resource coordination, information analysis, program evaluation, and institutional management.

Leseprobe

New Directions for Institutional Research

John F. Ryan EDITOR-IN-CHIEF

Gloria Crisp ASSOCIATE EDITOR

New Scholarship in Critical Quantitative Research—Part 2: New Populations, Approaches, and Challenges

Ryan S. Wells

Frances K. Stage

EDITORS

Number 163

Jossey-Bass

San Francisco

NEW SCHOLARSHIP IN CRITICAL QUANTITATIVE RESEARCH—PART 2: NEW POPULATIONS, APPROACHES, AND CHALLENGES

Ryan S. Wells, Frances K. Stage (eds.) New Directions for Institutional Research, no. 163 John F. Ryan, Editor-in-Chief Gloria Crisp, Associate Editor

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The Association for Institutional Research (AIR) is the world's largest professional association for institutional researchers. The organization provides educational resources, best practices, and professional development opportunities for more than 4,000 members. Its primary purpose is to support members in the process of collecting, analyzing, and converting data into information that supports decision making in higher education.

Contents

Editors' Notes

References

1: Use of Large-Scale Data Sets to Study Educational Pathways of American Indian and Alaska Native Students

State of Education for American Indians and Alaska Natives

Critical Approach to the Study of American Indian and Alaska Native Students

Large-Scale National Education Data Sets

Examples of Critical Approaches Using Large Data Sets

Implications for Research, Policy, and Practice

Recommendations for Future Research

Need for a New Critical Theory of Change

Conclusion

Notes

References

2: Researching Students with Disabilities: The Importance of Critical Perspectives

Introduction

Employ Challenging and Enriching Theories in Multiple Disciplines

Ask Relevant Questions

Choose and/or Collect Relevant Data

Apply Appropriate, Rigorous, Sophisticated, and Disaggregated Analyses

Know How to Interpret Results

Inform and Challenge Existing Educational Policies and Practices

Conclusion

References

3: Using Big (and Critical) Data to Unmask Inequities in Community Colleges

Big Data: Definitions and Approaches

Benefits of Big Data

Challenges of Big Data

Big (and Critical) Data in Higher Education Research

Implications of Using Big Data for Critical Quantitative Research in Higher Education

Notes

References

4: Application of Person-Centered Approaches to Critical Quantitative Research: Exploring Inequities in College Financing Strategies

Understanding Person-Centered Approaches

Use of Person-Centered Approaches in Higher Education Research

Why Use Person-Centered Approaches in Critical Quantitative Research?

Using LCA to Examine Racial/Ethnic Differences in College Financing Strategies

Conclusion

Notes

References

5: Critical Social Network Analysis in Community Colleges: Peer Effects and Credit Attainment

Brief History of SNA in Higher Education

Why Is Critical SNA Important?

Implementing a CSNA Perspective to Study Peer Effects in Community Colleges

Discussion

Implications for Institutional Researchers

Notes

References

6: What Is “Good” Research? Revealing the Paradigmatic Tensions in Quantitative Criticalist Work

Quantitative Criticalism's Paradigmatic Shift in Quantitative Studies

Quantitative Criticalism Challenges Normative Assumptions About “Quantitative Research”

Quantitative Criticalism Requires a High Level of Expertise in Statistical Analyses and Critical Theory

Qualitative Criticalism Requires the Use of a Set of Critical Theoretical Standards/Tenets to Ensure Legitimacy and Rigor

Conclusion

Notes

References

7: Past, Present, and Future of Critical Quantitative Research in Higher Education

New Populations

New Approaches

New Challenges

Conclusion

References

Advert

Index

END USER LICENSE AGREEMENT

List of Tables

Chapter 1

Table 1.1

Table 1.2

Chapter 2

Table 2.1

Table 2.2

Chapter 3

Table 3.1

Chapter 4

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Chapter 5

Table 5.1

Table 5.2

Table 5.3

List of Illustrations

Chapter 4

Figure 4.1

College Financing Strategy Precedes Financial Aid Indicator Variables

Figure 4.2

Financial Aid Support Mechanism Probability Profiles

Chapter 5

Figure 5.1

Initial Transformation Followed in the Analytic Process

Figure 5.2

Two-Mode Network Matrix Form of the Edge List Presented in Figure 5.1.

Figure 5.3

One-Mode Network of the Matrix Represented in Figure 5.2

Figure 5.4

Diagonal Information Added as a Column and Changed to Zero to Avoid Self-Selection

Guide

Cover

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Editors' Notes

Seven years ago, New Directions for Institutional Research published the volume Using Quantitative Data to Answer Critical Questions (Stage, 2007). In that volume, a group of quantitative researchers sought to differentiate their approaches to quantitative research from more traditional positivistic and postpositivistic approaches. The term “quantitative criticalists” described researchers who used quantitative methods to represent educational processes and outcomes to reveal inequities and to identify perpetuation of systematic inequities. The term also included researchers who questioned models, measures, and political processes in order to ensure equity when describing educational experiences. These scholars resisted traditional quantitative research motivations that sought solely to confirm theory and explain processes. Kincheloe and McLaren's (1994) description of critical work was useful as grounding:

Thought is mediated by socially and historically created power relations.

Facts cannot be isolated from values.

The relationship between concept and object is never fixed and is often socially mediated.

Language is central to the formation of subjectivity.

Certain groups in society hold privilege over others that is maintained if subordinates accept their status as natural.

Oppression has many faces that must be examined simultaneously.

Mainstream research practices generally reproduce class, race, and gender oppression.

In the wake of the 2007 publication, there was an increase in the explicit use of critical approaches in quantitative higher education journal articles, dissertations, and other publications, although there were also several examples of resistance to this attempt. Overall though, there was enough continued interest at conferences and through informal conversations to provide the impetus for us to revisit the topic in print. Our recent New Directions for Institutional Research volume (No. 158, Stage & Wells, 2014), New Scholarship in Critical Quantitative Research—Part I: Studying Institutions and People in Context, was the first step in that direction. That volume provided an expanded conceptualization of the tasks that critical quantitative inquiry addresses to include the need to conduct culturally relevant research by studying institutions and people in context.

This final volume continues where that one left off, examining the current state of critical quantitative inquiry and looking to its use in the future. Specifically, this volume aims to apply the critical quantitative paradigm to new populations and new approaches, while acknowledging the challenges that accompany it. In the first two chapters, authors propose ways that critical quantitative perspectives can be useful in studying American Indian and Alaska Native students as well as college students with disabilities. These are populations that are not only underserved in college but are underrepresented in institutional and higher education research as well.

Chapters 3, 4, and 5 have authors proposing a critical quantitative paradigm alongside the use of cutting-edge methods and approaches, pointing out the power as well as the dangers of doing so. Big data, person-centered approaches (rather than variable-centered approaches), and social network analysis are all presented as methods than can be leveraged critically to improve equity and outcomes for oppressed and marginalized groups in college.

In Chapter 6, a critical quantitative project is used as an example to highlight the tensions and dangers in this type of work, cautioning and yet encouraging researchers about the approach. Especially useful in this chapter are thoughts about the ways that qualitative and quantitative researchers, each with critical aims, can work productively together.

In the final chapter, we respond to each of the subsequent chapters and look back over the two-volume series. In doing so, we see where this type of work has come in the past decade and propose where this type of research may go in the future. We ultimately challenge institutional and higher education researchers to both expand and critique this growing body of work by generating new ways of thinking about the broad array of populations participating in and served by higher education while keeping in mind the goals of revealing inequity, challenging marginalization, and helping all students to succeed.

Ryan S. WellsFrances K. StageEditors

References

Kincheloe, J. L., & McLaren, P. L. (1994). Rethinking critical theory and qualitative research. In N. Denzin & Y. Lincoln (Eds.),

Handbook of qualitative research

(pp. 138–157). London, UK: Sage.

Stage, F. K. (Ed.). (2007).

New Directions for Institutional Research: No. 133. Using quantitative data to answer critical questions

. San Francisco, CA: Jossey-Bass.

Stage F. K., & Wells, R. S. (Eds.). (2014).

New Directions for Institutional Research: No. 158. New scholarship in critical quantitative research—Part 1: Studying institutions and people in context

. San Francisco, CA: Jossey-Bass.

Ryan S. Wells

is an associate professor of higher education in the Department of Educational Policy, Research, and Administration at the University of Massachusetts Amherst.

Frances K. Stage

is a professor of higher and postsecondary education in the Department of Administration, Leadership, and Technology at New York University.

1

This chapter discusses issues and challenges encountered in using large-scale data sets to study educational experiences and subsequent outcomes for American Indian and Alaska Native (AI/AN) students. In this chapter, we argue that the linguistic and cultural diversity of Native peoples, coupled with the legal and political ways in which education has been used to acculturate and assimilate them, makes it imperative that a full range of social, cultural, and demographic variables be collected and that these data be analyzed, using a theory of change that emphasizes the strengths of AI/AN students and their communities rather than their perceived deficits. We envision this theory of change espousing the use of data to critically unpack and respond to the role of language and culture in shaping pathways to success in education and beyond for AI/AN students. We conclude with recommendations for constructing and analyzing large-scale data sets to better represent the diversity of cultures and experiences among AI/AN students.

Use of Large-Scale Data Sets to Study Educational Pathways of American Indian and Alaska Native Students

Susan C. Faircloth, Cynthia M. Alcantar, Frances K. Stage

Today nearly 3 million people in the United States, slightly less than 1% of the total population, identify as American Indian or Alaska Native (AI/AN) (Norris, Vines, & Hoeffel, 2012). Although often grouped together for ease of reporting and analyzing quantitative data, it is important to recognize that American Indians and Alaska Natives are in fact two distinct cultural groups, each encompassing its own unique cultures, histories, and languages (Caldwell et al., 2005). While combining American Indians and Alaska Natives provides useful data, it can also serve to mask important social and economic differences that significantly impact the overall well-being and life outcomes of individuals from these two groups.

As researchers, we are particularly interested in the ways in which data are collected and analyzed to describe and explain the educational experiences and subsequent academic outcomes for AI/AN students from prekindergarten through postgraduate work (PK–20), institutionally as well as nationally. We situate this discussion within the larger domain of the social and institutional characteristics and the aims of elementary, secondary, and postsecondary education. While some practitioners and researchers (e.g., Tinto, 1987) use models of college success that emphasize students’ individual integration into college, both socially and academically, this notion has been challenged by other scholars who study the success of Native1 students in higher education (e.g., Pavel & Inglebret, 2007; Shotton, Lowe, & Waterman, 2013; Tierney, 1992). These scholars argue that rather than expecting AI/AN students to leave their cultures behind once they have entered institutions of higher education, these institutions must provide culturally relevant experiences for these students, thereby enabling them to be both academically and culturally successful.

This potential tension between students’ Indigenous cultures and the cultures of the institutions of higher education in which they are enrolled raises a number of questions regarding how institutions measure student success when implementing culturally relevant practices and pedagogies aimed at academically and socially engaging students. Other questions include: What are the common challenges to examining student success for AI/AN students? How can we examine the educational pathways of AI/ANs in order to identify their strengths and to determine areas in need of support?

In response to these and other questions, this chapter makes the case for the use of a critical quantitative approach to the use of large-scale national data sets to study the educational pathways and subsequent outcomes of AI/AN students. Research on academic success and postsecondary attainment is typically focused on either the kindergarten to grade 12 or postsecondary education system rather than connecting the PK–20 educational pathways of students. However, we recognize that education does not occur in a vacuum, and the educational conditions students encounter in elementary and secondary school serve to shape their aspirations, expectations, and eventual success along the pathway to college. In response, this chapter considers ways in which both PK–12 and postsecondary data sets together may be utilized to better understand the educational conditions and subsequent academic outcomes for Native students, as well as the challenges of using these data to study a culturally distinct, diverse, and historically marginalized student group.

We begin by reviewing the educational contexts in which AI/AN students attend school, followed by an overview of some of the PK–12 and postsecondary national data sets that include sufficient numbers of Native students, schools, and staff, primarily through the use of oversampling, to allow for quantitative analysis. We then discuss some of the limitations of these data and ways in which a critical quantitative approach may help to overcome these limitations. We conclude with recommendations for future research and implications for practice.

State of Education for American Indians and Alaska Natives

Any meaningful examination of the educational conditions and subsequent academic outcomes for American Indians and Alaska Natives requires, at minimum, a basic understanding of their sociopolitical history as it relates to education. Historically, education has been used as a means to civilize and Christianize Indigenous peoples worldwide (Deyhle & Swisher, 1997), resulting in the loss of Indigenous languages and, some would argue, cultures of many Indigenous groups, including those in the United States (Faircloth, 2009). One of the things that is unique about the education of Indigenous peoples in the United States is that many of these peoples’ ancestors ceded lands to the U.S. government in exchange for the provision of health, education, and basic well-being, provisions that have, more often than not, gone lacking.

Today, the majority (approximately 93%) of AI/AN students attend public schools while the remainder attend schools operated or funded by the Bureau of Indian Education and tribes, and a smaller percentage attend private schools (DeVoe & Darling-Churchill, 2008). Regardless of where they are educated, many AI/AN students experience cultural incongruence between home and school (Chrisjohn, Towson, & Peters, 1988; Lomawaima, 1995; Powers, Potthoff, Bearinger, & Resnick, 2003; Suina, 2001), making it difficult for them to reconcile their Indigenous language and culture with the Westernized academic culture of schools. Unfortunately, many of those who do succeed in school and go on to graduate are still unable to attend college due to either a real or perceived lack of access to necessary financial aid and other resources (e.g., Tierney, Sallee, & Venegas, 2007).

The lack of financial resources is due in large part to high rates of poverty among AI/AN persons. For example, nearly one third (31%) attend high-poverty schools (Ross et al., 2012), and 40% of American Indian children live at or below the poverty level (Children's Defense Fund, 2014). Poverty is especially concentrated in homes with female heads of household, with 53% of American Indian children who reside in homes with female heads of household also living in poverty. This is an important point given the relationship between poverty and academic performance and the fact that students from lower socioeconomic statuses tend to perform more poorly in school than do their peers from higher socioeconomic statuses (e.g., Duncan, Yeung, Brooks-Gunn, & Smith, 1998; Sirin, 2005).

Although many AI/AN students do graduate from high school and go on to college, a disproportionate number do not pursue postsecondary education. Data indicate only 18% of all American Indian children have parents with a bachelor's degree or higher compared to 59% of Asians, 44% of Whites, 20% of Blacks, and 16% of Latinos (Ross et al., 2012). These findings are important given the relationship between degree attainment and the potential to earn higher wages, with those graduating from college often earning more than those who do not (Faircloth & Tippeconnic, 2010). What is equally troubling is that more than half of those AI/AN students who go to college end up not graduating (Knapp, Kelly-Reid, & Ginder, 2012). This raises questions about the factors that account for student retention and graduation, as well as those for student aspirations to attend college.

Data from the 2009 National Indian Education Study (Mead, Grigg, Moran, & Kuang, 2010) indicate that 57% of American Indian eighth graders who participated in this study aspired to attend college; however, the 2011 National Indian Education Study found that 63% of AI/AN eighth graders who were surveyed had not met with their high school counselor to discuss their classes or their plans following high school (National Center for Education Statistics, 2012). This suggests a disconnect between AI/AN students’ aspirations and their preparation for college. This failure to seek college counseling may also help to explain Ross et al.'s (2012) finding that only 24% of American Indian males and 33% of American Indian females between the ages of 18 and 24 are enrolled in postsecondary education, at the undergraduate or graduate level. This is the lowest rate of college attendance of all racial/ethnic groups, with 43% of White males and 51% of White females, 31% of Black males and 42% of Black females, and 26% of Latino males and 35% of Latina females attending college. An analysis of Integrated Postsecondary Education Data System (IPEDS) data also showed that American Indian students earned less than 0.8% of all associate's and bachelor's degrees in the 2008–2009 academic year (Stage, Lundy-Wagner, & John, 2013). While 0.8% may not seem so different from the population percentage of 1%, it suggests a 20% lower achievement rate within the population. In other words, while AI/AN students represent 1% of the population, they earn only 0.8%, or 80% of their share, of the college degrees earned in the United States. In contrast, if Whites, who make up approximately 73% of the population, earned 0.2% less than their 73% representation, the difference would be almost meaningless.

Critical Approach to the Study of American Indian and Alaska Native Students

As mentioned earlier in this chapter, the historical, societal, and political marginalization of American Indians and Alaska Natives has rendered them, in many cases, invisible to those within the academic arena (Shotton et al., 2013). Critical quantitative approaches are needed to reverse this trend. As Stage (2007) has argued, “When models do not accurately reflect a given population's experiences, the task is to pose alternatives to those models. Rather than focus on explanation, or fairness, [the modeling] focuses on equity concerns that can often be highlighted through analysis of large data sets” (p. 9). The Tinto (1987) model is a good case in point. This model forms the basis of much current research on college student success. It was originally developed in the 1970s from studies of students enrolled in elite colleges. More recently, scholars have challenged aspects of the model and searched for culturally relevant variables for particular student groups (Brayboy, Fann, Castagno, & Solyom, 2012; Harper & Hurtado, 2007; Hurtado & Ponjuan, 2005). Unfortunately, many of these variables are absent from existing large-scale data sets used to examine the educational conditions and subsequent outcomes of students in PK–20.

Large-Scale National Education Data Sets

The federal government has developed a collection of hundreds of national data sets, 32 of which are education data sets managed by the National Center for Education Statistics (NCES). Researchers use these national data sets to make inferences about populations and institutions, and policy makers have used these results to inform policy and practice. Thus it is imperative that all racial and ethnic groups be represented within these data sets. Unfortunately, because of the small sample size of AI/AN students in these data sets, studies often (a) exclude AI/AN students, (b) group them as “Other” or as students who identify as multiracial, or (c) fail to report any outcomes for this group after analysis because low numbers resulted in nonsignificant findings and/or low effect sizes.

Equally important is the fact that in the recent past, no national-level data were required to be collected for AI/AN students. For example, prior to 2003, the annual report commissioned by the U.S. Department of Education, The Condition of Education, did not report on AI/AN students. In 2003, the U.S. Office of Management and Budget (OMB), which “is responsible for the standards that govern the categories used to collect and present federal data on race and ethnicity” (Kena et al., 2014, p. vi), revised its guidelines to include the reporting of at least five racial categories including AI/ANs. Unfortunately, the lack of data prior to 2003 continues to limit longitudinal analysis of the educational pathways of AI/AN students.

In spite of this failure to systematically include AI/AN students in the bulk of the large-scale education data sets, a limited number of data sets have been constructed that do include AI/AN children, youth, and adults. In the following section, we describe some of these data sets and selected analyses that have been conducted involving AI/AN specific data. The focus here is on PK–20 education for American Indians and Alaska Natives.

PK–12 National Data Sets

Four national data sets have included sufficient numbers of AI/AN children, youth, and/or adults in PK–12 to allow for disaggregation and analysis of data. These data sets are discussed in brief next.

Early Childhood Longitudinal Study, Birth Cohort (ECLS-B)

The ECLS-B contains an oversample of American Indian and Alaska Native children from 2001 to 2006 (oversampling involves creating a sample of respondents that exceeds their actual representation in the population; see Table 1.1).2 This study tracks children from birth through entry into school and by collecting data on individual health, development, care, and education of children from birth through kindergarten entry. Using these data, Halle et al. (2009) found differences in cognitive development, as well as health disparities, between AI/AN children and their non-Native peers, as early as 9 months of age.

Table 1.1 Early Childhood Longitudinal Study Data Set, by Percentage Race/Ethnicity

Data sources: U.S. Department of Education, National Center for Education Statistics, Early Childhood Longitudinal Study, Birth Cohort (ECLS-B; 2001–2002, original data collected for children at 9 months of age); and the Early Childhood Longitudinal Study, Kindergarten Cohort (ECLS-K; 1998–1999 & 2011).

ECLS-B: 2001–2002

ECLS-K: 1998–1999

ECLS-K: 2010–2011

Total (N)

10,700

21,400

18,200

White

41.12%

55.08%

46.65%

American Indian/Alaska Native

2.80%

0.59%

2.09%

Asian

11.21%

1.33%

7.51%

Native Hawaiian/Other Pacific Islander

0.47%

0.45%

*

1.23%

Black/African American

15.89%

3.66%

17.71%

Hispanic/Latino

20.56%

4.10%

21.02%

Other/More than one race

∼7.94%

0.71%

3.30%

*Only Pacific Islanders. Data obtained from personal communication with project officer, October 1, 2014

Early Childhood Longitudinal Study, Kindergarten Cohort (ECLS-K)

The ECLS-K tracks students from kindergarten through eighth grade, in both public and private schools.2 The ECLS-K includes data from teachers, parents/families, and schools (Table 1.1). There are currently two iterations of this study, one begun in 1998–1999 and another begun in 2010–2011. Although AI/AN students were not oversampled in these data sets, they are included in sufficient numbers to allow for statistical analysis. For example, using data from the ECLS-K (1998–1999), Hibel, Faircloth, and Farkas (2008) found that AI/AN students’ referral and placement into special education could be predicted in kindergarten, based in large part on these children's scores on standardized achievement tests in reading and math. This analysis indicated that those students who performed poorly on the selected achievement tests were more likely to receive special education services in the early grades. Also drawing on the ECLS-K (1998–1999), Marks and Coll (2007) utilized latent growth modeling to examine similarities and differences in the academic preparedness and related characteristics of kindergarten students from different racial/ethnic groups. They found that AI/AN students who came to school academically prepared did much better in school than did their peers who are less ready to learn.

National Assessment of Educational Progress (NAEP)

The NAEP is the largest nationally representative data set in the United States.3