THE NATIVE AMERICAN STUDENT DROP-OUT RATE AT 50% (26% ... · running head: a persisting problem in search of a solution 1 the native american student drop-out rate at 50%

RUNNING HEAD: A PERSISTING PROBLEM IN SEARCH OF A SOLUTION 1

THE NATIVE AMERICAN STUDENT DROP-OUT RATE AT 50%

(26% HIGHER THAN FOR WHITE STUDENTS):

A PERSISTNG PROBLEM IN SEARCH OF A SOLUTION

By

Aaron A. Payment

Northern Michigan University

SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE

MASTERS IN EDUCATION ADMINISTRATION &

EDUCATIONAL SPECIALIST DEGREE AT

NORTHERN MICHIGAN UNIVERSITY

December 19, 2011

APPROVED BY: Derek L. Anderson, Ed.D.

DATE: December 20, 2011

A PERSISTING PROBLEM IN SEARCH OF A SOLUTION 2

ABSTRACT

Background

Researchers have studied the Native American (Natives) high school drop-out crisis at

least since the enactment of the Indian Education and Self-Determination Act of 1974. Today,

this crisis persists with one of every two Natives who enter high school dropping out. Only four

of every ten who enter college will graduate. Natives have the worst high school graduation rate

of all racial ethnic population (National Center for Educational Statistics, 2010). Michigan

statistics mirror national data (EPE Research Center, 2009). One study of Michigan Natives

(Cornell, Parish, Schweitzer, 2001) shows a Native drop-out rate of 47% with Natives tracked to

non-college curriculum at a rate of three times that of their non-Native counterparts.

Research Focus & Methods

The broad purpose of this research is to identify, understand and explain influences on

educational outcomes for Natives borrowing from research by Sixkiller-Clarke (1994) to

examine the extent to which school, personal, family and cultural factors influence or predict

educational outcomes for Michigan Natives using non-experimental quantitative retrospective

research methods. The target population is Michigan Natives who earned their college degree as

differentiated by entering college with a high school diploma or General Education Diploma

(GED). Given the small n problem in studying Natives, convenience and snowball sampling

techniques were used. A sample size of n=300 was achieved representing several Michigan

tribes. Correlations, t-tests, ANOVAs, and multiple and logical regression were used to answer

six related research questions that address two main areas of inquiry: whether individual

characteristics and pre-college education factors influence high school GPAs and if MI Natives

graduate with a high school diploma versus a GED; and if there is a significant difference in


college graduating GPAs between the two populations and if individuals characteristics (pre-

college and college level) influence college GPAs.

Findings

Significant differences were found between the two groups on family income, expectation

to graduate, family member substance abuse, connection to school, connection to class cohort,

use of financial aid, the Michigan Indian Tuition Waiver, the Native American Office and Multi-

Cultural Affairs offices. Additionally, while there was a significant difference in high school

GPAs (on a 4.0 scale) between GED recipients (2.51) and high school graduates (3.09), the

college graduating GPAs between these two groups were not significantly different with GED

recipients edging out high school graduates (3.37 versus 3.35 respectively). Significant factors at

the pre-college level influencing high school GPA include: knowing what students wanted to be

when they grow up, connection to school, connection to class cohort, change in this connection,

class subject grades, and school type. Also, both parental expectations to graduate and

connection to class significantly influence whether an individual graduated or earned a GED.

Those items which influenced college GPA include: class subject liked in high school,

connection to culture, academic GPA in high school, whether or not the student graduated or

earned a GED and the use of multicultural affairs in college.


TABLE OF CONTENTS

Chapter I: Introduction………………………………………………………………….... 6

Problem Statement…………………………………………………………...…………. 7

Research Purpose and Questions …………………………..…………………………... 8

Definitions………………………….………………………………………………….... 9

Chapter II: Literature Review ……………………………………………………………. 11

Data Related to Native American High School Graduation…………………………….

Native Entry and College Graduation………………………………….………………..

Visualizing the Crisis……………………………………………………………………

American Indian Education Shortcomings………………………………………………

11

13

13

16

Approaches: Deficit, Organizational/ Systems, Socio-Anthropological, Cultural………

Operationalizing Native American Retention Study…………………………………….

18

20

Related Studies

Quantitative…………………………………………………………………………

Qualitative…………………………………………………………………………..

22

25

Chapter III: Methodology

Research Design……………………………………………………………...………….

Quantitative Purpose and Research Question………………………………………

Conceptual Framework: Quantitative………………………………………….

Quantitative Research Design…………………………………………………

Population………………….……………………………..……...…….............

Small n problem in Native Populations………………………………………..

Sample Size……….. …...……………………………………..……...……….

29

29

29

30

31

32

32


Data Collection………………………………………………………………...

Variables……………………………………………………………………….

Reliability Assessment……………………………………………………

Limits on Variables in Data Collection…………………………………...

Quantitative Analytical Method……………..…………………………........…

Potential Researcher Subjectivity (Bias)……………………………………………

33

34

36

37

38

42

Chapter IV: Results

Quantitative Findings……………………………………………………………….

Descriptives (frequencies, correlations matrix)…...………...…………………

ANOVAS, t-tests……………………………………………………….…

Correlations……………………………………………………………...

Regression Analyses

RQ1……………………………………………………………………….

RQ2……………………………………………………………………….

RQ3……………………………………………………………………….

RQ4……………………………………………………………………….

RQ5……………………………………………………………………….

RQ6……………………………………………………………………….

RQ7……………………………………………………………………….

44

44

45

51

54

55

56

57

59

59

60

Chapter V: Recommendations and Conclusions

Conclusions………………………………………….………………..……………

Areas of Further Research: What Was Missed.………………………………….…

Limitations of Study……………………………………………………………….

62

64

65


References……………………………………………………………………………………

Appendices (A through G)…………………………………………………………………...

67

71


LIST OF TABLES

Table 1 Model of Hypothetical Differences in Outcomes for Natives versus Caucasian

Students from entry into high school through to college graduation………………..

14

Table 2

Applying Model of Differences in Outcomes for Natives versus Caucasian

Students from entry into high school through to college graduation (Nationally

and Michigan)………………………………………………………………………………

15

Table 3 Model of Hypothetical Differences in Outcomes for Natives (n=35) versus

Caucasian Students (n=65) from entry into high school through to college

graduation…………………………………………………………………………………...

16

Table 4 Proposed Factors, Independent and Dependent Variables for RQ1 through RQ4.. 35

Table 5 Descriptive Statistics for all Variables………………………………………………….. 44

Table 6 Mean Differences of Family Income While Growing Up (IV2) Between

Respondent Groups…………………………………………………………………………

47

Table 7 Mean Differences of School Connection (IV6) Between Respondent Groups……... 48

Table 8 Mean Differences of Connection to Class Cohort (IV7) Between Respondent

Groups………………………………………………………………………………………..

49

Table 9 Mean Differences of Class Subjects Liked (IV12) Between Respondent Groups….. 50

Table 10 RQ2 Coefficients for Final Model……………………………………………………….. 56





LIST OF FIGURES


Figure 1 Cumulative Promotion Index (CPI) formula developed by Christopher B.

Swanson, for the Urban Institute Education Policy Center………………………

21

Figure 2 Conceptual Framework for Intergenerational Historical Trauma for Native

Americans…………………………………………………………………………………..

26


CHAPTER I: INTRODUCTION

The disparity between Native American (Natives) student success versus that of the

general population and other racial-ethnic minorities is alarming. The attrition or narrowing of

academic success due to fractionating at each stage in education: those who enter high school;

those who graduate high school; those who enroll in college; those who graduate college,

underscores the importance of identifying factors that positively or negatively affect academic

success at each stage. Not all Natives who fail to graduate high school cease their educational

endeavors, but with a 50 percent drop-out rate for Natives (NCES, 2010), the alternative

educational path of a general education development (GED) certificate – appears to be the

Native tract to college for many Natives. There appears to be some factor, condition, or

phenomena for some Native students that make them less likely to graduate through the

conventional route. The fact that some Natives enter and graduate college through the GED

route, suggests there exists some set of characteristics that may predict success or failure.

Chapter I of this research will provide an orientation to the Native education experience

and focus the issue to the main problem statement. The purpose of this study will be identified

followed by the delineation of the main research question and subsidiary research questions and

methods to be tested through data collection and analysis. The significance of this study will

become apparent through the review of relevant statistics and analyses. An orientation to

commonly used terms in studying Natives and academic success will follow. Chapter II will

provide a more in-depth orientation to literature addressing both qualitative and quantitative

studies, as well as, address previous works related to Natives at various academic levels.

Chapter III will operationalize this two phased sequential mixed-methods study and describe the

methodology used to answer the main and subsidiary research question(s) including the analytics


applied. The results in Chapter IV will dually report the relevant statistical analytics which

describe the relationships of variables and a qualitative analysis to understand and describe the

phenomena associated with the Native drop-out crisis. Chapter V will conclude with some

recommendations for increasing the likelihood of Native American student graduation; identify

limitations of this study; and pose additional areas of both quantitative and qualitative

exploration to pursue a solution to the Native American drop-out crisis.

Problem Statement

Statistics show an enduring crisis of an alarmingly low high school graduation rate for

Native students. In 2006, the national high school graduation rate for Natives was 50 percent

versus 76 percent for Caucasian Americans (EPE Research Center, 2009). Compared to 2005

(NCES as cited by Faircloth and Tippeconnic, 2010), Natives experienced a three percent

improvement but the gap appears to be widening as the graduation rates for each population in

2005 were 47 percent and 70 percent respectively. Nationally, Natives continue to have the

lowest high school graduation rate of any racial-ethnic population at 1.2 percent lower than

African Americans; 29 percent lower than Asian/ Pacific Americans; and 5 percent lower than

Hispanic Americans. Michigan statistics for 2006, are consistent with national data with a

Native graduation rate of 49 percent versus 77 percent for Caucasian students representing a 28

percent gap (EPE Research Center, 2009). At the collegiate level, the experience for Natives is

also bleak. According to the National Center for Educational Statistics:

Sixty seven percent of Asians [graduate] compared with 60 percent of Whites; 48% of

Hispanics, 42 % of Blacks, and 40 % of Natives graduated with a bachelor's degree or its

equivalent within 6 years. Nonetheless, Natives consistently had the lowest graduation

rates of the five racial/ethnic groups (NCES, 2010).


While there is a great deal of descriptive statistical reporting of the incidence of Native

graduation rates and an abundance of literature steeped in qualitative socio-anthropological

theory to explain why Natives drop-out, there appears to be little research that comprehensively

studies the problem – namely through quasi-experimental research and rich phenomenological

explanations drawn from the same participants. More pointedly to the Michigan Native

population, broad studies on the Native drop-out crisis in Michigan appear non-existent.

Michigan Tribal leaders are reticent to facilitate implementation of intervention strategies

gleaned from communities outside of their own experience. With some Natives persisting

through to graduation in college after having dropped out of high school, there may exist some

discoverable characteristics for Native student success.

Research Purpose and Questions

The broad purpose of this research is to identify, understand and explain what influence

educational outcomes for Natives at both the high school and college level. This study will

explore the extent to which school, personal, family, and cultural factors influence educational

outcomes and seek to understand the experience of Michigan Natives in educational institutions.

Differences that exist between Natives who earned their college degree by way of earning a high

school diploma versus those who attained their general education development (GED) certificate

will be analyzed. This positivistic-oriented study will examine what factors explain and predict

the probability that some Natives may succeed. A second phase (not reported in this research)

will have a broader purpose of exploring phenomenological and ethnographic components to

inductively discover common or divergent cultural experiences of Michigan Natives and seek to

describe and understand the experiences of Michigan Native Americans in educational


institutions. The second phase will explore, describe, and explain these educational experiences

with the hope that related grounded theory will emerge for further study and problem solving.

The broad purpose of the current research is distilled to two main research areas:

1) Do individual characteristics and previous education factors influence whether or not

MI Natives graduate with a high school diploma versus a GED?; and

2) Is there a significant difference in college graduating grade point averages (GPAs)

between MI Natives who entered college with a high school diploma versus those who

attained their GEDs?

The following subsidiary research questions will be explored to contextualize the answer to the

initial areas of inquiry. Is there a difference between college graduating GPAs for the two

subsets of the population studied – high school graduates versus those who earned their GEDs?

Do various pre-college and select college variables predict college graduating GPA? Do select

college variables and whether an individual earned a high school diploma or GED predict college

graduating GPA? What is the effect of all pre-college and all college variables on college

graduating GPA? Is any difference between high school GPA of those who graduate high school

versus those who earned their GED? What predictive values does all pre-college variables have

on high school GPA? Finally, how well do these pre-college variables predict whether or not

someone will graduate with a high school diploma versus a GED? You will note that these

questions becoming increasing more complex with variations of additional factors added to the

design. The initial research question and subsidiary questions, method of analysis, and decision

factors are fully explored in Chapter Three.


Definition of Terms

Attrition and matriculation are used as inverse properties that extend the annual drop-out

rate beyond one year and explain those who do not continue and those who do (respectively).

Graduation rate and drop-out rate are used as inverse properties even though it is

understood that some students may eventually elect to re-enter education in at a later date.

Native American(s), Native(s), Native student(s) refer to those individual(s) who are

members of federally recognized American Indian tribes. This is different from those who self-

identify as American Indian but cannot prove this through legal documentation of blood lineage

and enrollment in a federally recognized tribe.

Persistence, persist and ‘did not persist’ are emerging as the standard as these terms do

not carry with it the negative connotations that the term drop-out does.

Racial Terms - though datasets and studies generally use the terms Whites, Blacks,

Asians, Hispanics, the more appropriate terms of Caucasian, African American, Asian Pacific

Americans, and Hispanic Americans are used. Even more appropriate but cumbersome terms of

Latino/a, Chicano/a, Cuban, Puerto Rican, Dominican, and other populations clustered into the

conglomerate Hispanic nomenclature are noted. Several additional terms are used throughout

and are sufficiently explained both in their context and often with brief descriptive asides.

Retention is simply the state of continuing to be enrolled and usually refers to a year to

year statistic. Typically, the retention rate is the number of students who enter one academic

year over those who were enrolled the previous year. Use as a static statistic is often confused

with a longitudinal retention rate which is the combined rate of retention year to year through to

graduation.

Succeed (academically). At this stage, the phrase “succeed along a conventional route”


will be generally defined as those from the target population who earn a high school diploma

within a four year time frame and those who earn their college degrees within the six years.


Chapter II: Literature Review

Chapter One sought to provide compelling statistics to demonstrate the serious need for

the issue of Native American high school and college retention to be studied further such that

practicable solutions are found. Chapter two gets into more specifics and describes how the

issue of Native student retention has been studied including qualitative explanations of the issue

and quantitative studies to begin to understand the relationships and effects of predictor variables

on outcomes. Review of the literature will elaborate the Native American drop-out crisis;

highlight the work that has been done to date with regard to the Native drop-out crisis at both the

high school and college levels; and provide an overview of the essential studies conducted since

Indian Education became a studied topic in and around the enactment of the 1974 Indian

Education and Self-Determination Act. Enduring conceptual frameworks will be described like

those developed by Reyhner (1991) in writing the seminal Indian Nations At Risk, to provide the

theoretical basis for what the literature describes as factors influencing Native retention. Finally,

the application of these factors through three quantitative research studies, two at the secondary

level and one at the post secondary level will be summarized for their value related to the

variables but also to demonstrate three different levels of statistical analyses (independent t-test,

ANOVA, and multiple regression) as relevant to the current study. Qualitative explorations will

draw liberally from the literature to help frame what we may find in the data.

Data Related to Native American high school graduation

The National Center for Education Statistics (NCES) reported in 1989 that Native

Americans (Natives) had a high school graduation rate of 35.5 percent which represented the

highest drop-out rate of any racial/ ethnic group (Reyhner, 1991). Though there are variances in

reported data, clearly Natives persist in having the worse high school completion rate of any


racial-ethnic group. According to Swanson (2003) for the high school class of 1999-2000, the

graduation rate for Natives was 38.1 percent. In reviewing more closely 2005 data from the

National Center for Education Statistics for the twelve states that have the largest proportion of

the total student population who are Natives, the graduation rate for Natives ranged from 30

percent (South Dakota) to 64 percent (Oklahoma). Even more striking is the 45 percent lower

graduation rate for Natives in South Dakota versus the general population. The average

graduation rate for these states for Native Americans was 47 percent versus Caucasians at 70

percent (a disparity of 23 percent). Again Natives have the lowest graduation rate of any racial-

ethnic group at 8 percent lower than African Americans; 31 percent lower than Asian/ Pacific

Americans; and 4 percent lower than Hispanic Americans. Finally, using the same dataset, there

appears to be a gender difference - 42 percent for male Natives versus 51 percent for female

Natives - with Native males graduating at a lower rate in all but one of these states (Faircloth and

Tippeconnic, 2010). In an historical archival study of a school district located in a small town in

the upper peninsula of Michigan of the 1990’s, Cornell (2001) reported a Native high school

drop-out rate of 47 percent. Thus, in Michigan and nationally, one out of two Native Americans

who enter high school do not graduate. By all measures, this data demonstrates a crisis.

Of course, though graduation is the ultimate measure of academic success it is by no

means the only measure. Twenty nine percent of Native students have had to repeat at least one

grade which is the highest percentage of any racial/ethnic group. Less than 10 percent of Native

students are in the upper quartile on achievement test scores in history, mathematics, reading and

science while 40 percent are in the lowest quartile (Reyhner, 1991). Looking at SAT and ACT

scores from 1987 to 1997, Natives lagged behind the rest of the nation and ranked below the

national average in completion of core curriculum for high school graduation (Gilbert, 2000).


According to Reyhner (1991) there is evidence of the ‘unequal’ practice of tracking Native

students into non-college preparation. In the study in Michigan noted previously, it was also

demonstrated that Native students were 3 times more likely to be tracked to non-college prep

over their non-Native counterparts (Cornell, 2001).

Native Entry and College Graduation

Literature for post secondary graduation rates of Native Americans is noticeably scarce.

Available statistics, however, show a persisting Native dropout crisis at the collegiate level as

well. The problem of retention does not begin with college enrollment as Native Americans are

the least likely to enroll in four-year public institutions of higher education and the least likely to

graduate in those institutions (Larimore and McClellan, 2005). According to Neisler (1992 as

cited in Larimore and McClellan, 2005) 60 percent of all students who graduated high school in

1989 immediately enrolled in college while Tierney (1992) cites several studies indicating that

only 40 percent of the Native students who graduate high school will enroll in college. Once in

college Native students are less likely to graduate. Tierney adds that a retention rate for Native

Americans may be as low as 15 percent overall. According to Brown and Robinson Kurpius

(1997 as cited in Larimore and McClellan, 2005) estimates of drop-out rates for Native students

in higher education range from between 75 to 93 percent . One incredulous statistic cited by

Gilbert (2000) for those who were enrolled in college (1994-95) one percent of Natives received

bachelor’s degrees compared to 79 percent of students from the dominant culture.

Visualizing the crisis

The critical nature of the crisis of the Native drop-out rate is perhaps better appreciated

when using numbers of students rather than percentages and tracking a hypothetical group of


students from entry into high school through to college graduation. From data presented earlier,

if 49 percent of Native students graduate high school, and 40 percent of this subset actually

enroll in college (versus 77 percent and 60 percent respectively for Caucasians) for a

hypothetical population of n= 100 for both populations, we see 28 of the Caucasian students who

start high school eventually finish college versus just eight for Natives (See Table 1). Note how

this model fractionates the total number for both populations at each juncture.

Table 1

Model of Hypothetical Differences in Outcomes for Natives versus Caucasian Students from

entry into high school through to college graduation.

A B C D E F G

n

%

Graduate

HS (A*B)

% of those

who

Graduate

HS who

Enroll in

College

# who

enroll in

College

(C*D)

% who

graduate

college

# who

graduate

college

(E*F)

Whites 100 77% 77 60% 46 60% 28

Natives 100 49% 49 40% 20 40% 8

Taking this a step further, using NCES (2010) data for high school graduates rates in

2006-07 for the Nation, a projected 3,913,871 (2,892,351 graduated / a rate of 73.9 percent who

entered high school four years earlier) students entered high school during the 2002-03 school

year. The comparable data in Michigan is 145,244 (111,838 who graduated/ a rate of 77 percent


who entered high school four years earlier) students who entered high school in 2002-03.

Inserting each of these figures into the model from Table 1 - Natives at a factor of 1.5 percent

nationally and .05 percent for Michigan (My Online Maps, 2011) of the total population at each

level (see Table 2), we see the following results in Table 2:

Table 2

Applying Model of Differences in Outcomes for Natives versus Caucasian Students from entry

into high school through to college graduation (Nationally and Michigan)

A B C D E F G

n

%

Graduate

HS (A*B)

% of those

who

Graduate

HS who

Enroll in

College

# who

enroll in

College

(C*D)

% who

graduate

college

# who

graduate

college

(E*F)

Nation

White 3,913,871 77% 3,013,681 60% 1,808,208 60% 1,084,925

NA 58,708 49% 29,354 40% 11,742 40% 4,697

MI

White 145,244 77% 111,838 60% 67,103 60% 40,262

NA 726 49% 356 40% 142 40% 57

Extrapolating the relative success rates noted above at each juncture, varying the n for

both Caucasian and Native students, for the nation we see that of the 3,913,871Caucasian


students who enter high school, hypothetically 1,084,925 (28%) will graduate college while the

comparable number for Natives is just 4,697 (8%). For Michigan, of the 145,244 Caucasian

students who enter high school, 40,262 will graduate college while just 57 of the 726 Natives

who enter high school will matriculate on through to college graduation.

Taking this hypothetical one step further to represent the proportion of a total population

of students Natives represent by using a 35% and setting the total population at 100, we see that

eighteen of the original sixty five Caucasian students will matriculate through to college

graduation while just three Natives out of the original thirty five Native Americans who enter

high school will graduated college. In this final hypothetical model, where Natives represent

35% of the total population who enter high school, they represent a mere 16% of the total

population who will graduate college.

If the extrapolations from this hypothetical model hold true, there exists a ready challenge

for educational leaders to not only endeavor to fix the high Native drop-out problem, but also

work to understand what factors influence the likelihood of Natives matriculating at each

juncture as these factors may ultimately relate to their academic success or failure beyond high

school. Once Natives enter college, it is also important to understand the factors (before and

during college) that influence the likelihood of Natives not only matriculating through to college

graduation but to also of success with measures of achievement like grade point average.


Table 3

Model of Hypothetical Differences in Outcomes for Natives (n=35) versus Caucasian Students

(n=65) from entry into high school through to college graduation.

A B C D E F G

n

%

Graduate

HS (A*B)

% of those

who

Graduate

HS who

Enroll in

College

# who

enroll in

College

(C*D)

% who

graduate

college

# who

graduate

college

(E*F)

Whites 65 77% 50 60% 30 60% 18

Natives 35 49% 17 40% 7 40% 3

American Indian education short comings

The apparent unrelenting crisis of a low Native American high school graduation rate and

inversely - the drop-out rate, warrants a comprehensive, valid and reliable study that

operationalizes, and triangulates methods in order to identify factors that influence Natives to

dropout in order to find solutions. In discharging it’s federal fiduciary responsibility to honor the

‘prepaid treaty right’ of education for American Indians, the federal government has had mixed

results with mostly adverse effects of Indian boarding schools designed to assimilate Natives by

stripping them of their cultural identity and worldview to nominal funding through the Johnson

O’Malley (JOM) tribal education funding program established under the Indian Reorganization

Act of 1934 and the Indian Education and Self-Determination Act of 1974 which provided equity


funding for federal Title initiatives like Indian Education (currently Title VII) to public schools

who have verifiable Native student counts from federally recognized tribes. Over the years, a

number of Congressional reports have been commissioned (NCLB Indian Education Workgroup,

National Advisory Council on Indian Education (NACIE) Annual Reports). Though positive in

some respects with shining a light on the crisis that is American Indian Education, these

retrospective historical archival studies, have generally failed to comprehensively quantify the

problem in a valid and reliable way; have not studied the issue in a longitudinal manner; and

have not systematically ascertained explanations that have any practicable use. Additionally,

irrespective of past efforts, Natives persist in having the highest rate of high school dropout of

any racial/ethical population. Past Indian education research has focused primarily on regional,

reservation or Bureau of Indian Affairs/ Education (BIA/BIE) funded residential or day schools

with none on Michigan Natives. Thus, these studies have limited generalizability to Michigan.

Survey research through a quantitative survey and historical document approach

(Creswell, 2009) is necessary for examining variables that effect Native student retention, but it

is by no means sufficient for solving the problem. To the extent that characteristics that

positively influence Native retention can be identified and replicated while mitigating negative

influences, successful intervention strategies may be discovered and developed. Of preeminent

interest is identifying access factors that have a positive impact while minimizing variables that

adversely influence a Native student’s likelihood of succeeding academically. Involving a

partnership between individual tribes, tribal nation groups, the National Congress of American

Indians, the Bureau of Indian Education and the tribal education departments may facilitate a

comprehensive and applied examination of the issue in order to make systemic and lasting

change. Putting federal bureau (administrative), Congressional, and tribal governance territorial


differences aside, the opportunity for partnerships exist for a meaningful and applied approach to

finding solutions with potential alignment of Title VII, JOM and other tribal resources to address

this crisis. If it takes a village to educate a child, it certainly takes more than an isolated

approach to ensure a Native child’s academic success. Fully understanding the phenomena of

the Native dropout problem is the first step.

Approaches and Theoretical Frameworks

Approaches to the study of Native student retention have varied depending on the

researchers’ theoretical and conceptual frameworks. Deficit theory posits that students who

come from broken homes need individual attention and enrichment for skill mastery (St.

Germaine, 1995). In the U.S. Department of Education commissioned study entitled, Indian

Nations at Risk, Reyhner (1991) noted that a survey of research demonstrated that studies

focused on so-called deficits of the student who drops out, such as intelligence, school

attendance, and parental income. Instead, he suggests a shift in focus to systemic institutional

and support issues endemic to schools like: large factory-like schools, uncaring teachers, passive

teaching methods, irrelevant curriculum, inappropriate testing, tracked classes, and lack of

parental involvement (Reyner, 1992, 2006). This systems focus has endured in Native

educational research. Organizational theorists postulate that ‘school-structures’ need to change

to retain students. A sociologist/ anthropologist focus on economic and political structures that

are endemic to society and “give voice to some and deny it to others” suggests, ‘that winners and

losers are inevitable” (McLaughlin, p. 53 as cited in St. Germaine 1995, p. 2). These critical

theorists call for "teachers as coaches, pedagogy as problem solving and curriculum that

addresses important themes connected to the lives of students” (St. Germaine, 1995, p. 2).


The conceptual model of cultural discontinuity focuses on student social adjustment to

the dominant culture (St. Germaine, 1995) and falls into the broad category of cultural theorists

which is borne out of the work of sociolinguists. According to St. Germaine (1995), “Cultural

difference theorists believe solutions lie in teachers becoming knowledgeable about the culture

and language of their students and adapting curriculum and teaching methods to students’ needs”

(p. 3). Cultural discontinuity theory suggests that minority children,

…having been initially raised in a distinctive culture of their own, are often thrust into a

school system that promotes the values of the majority culture - not those of their own.

If the resulting clash of cultures continues, the minority child may feel forced to choose

one culture at the expense of the other. Success (in school becomes failure (in the

community)…failure in school is a tacit cultural goal that must be achieved”

(McDermott, 1987; Spindler, 1987 as cited in St. Germaine, 1995, p. 3).

While St. Germaine cited that though cultural discontinuity plays a major role in Native

American student failure, some researchers caution that this construct is too narrow as it ignores

“macrostructural variables” when there is “overwhelming evidence that economic and social

issues…not culturally specific to being Indian” are significant to Natives dropping out (1995, p.

4). Certainly, the notion of cultural discontinuity is consistent with Reyhner’s findings in the

Indian Nation’s at Risk study where he noted the impact of “inappropriate” curriculum in

teaching methods where the curriculum does not reflect the Indian child’s unique cultural

background. He elaborates that, “textbooks are not written for Indian students…they enlarge the

cultural gap between home and school” (Reyhner, 1992, p. 5). With images of American Indians

in movies and television as uncivilized and blood thirsty savages, Indian sports mascots as the

savage warrior or with a goofy grin reminiscent of the Black Sambo, and little or no authentic


competing history taught in school to counter the objectification of American Indians, it’s no

wonder Natives flee school systems. Irrespective of the level at which a Native American

student has assimilated to the dominant culture or the extent to which an Native student is not

identifiably Indian (in visual appearance), the persistence of negative or incomplete cultural

stereotypes serves to perpetuate a lowered sense of self. This notion is reinforced through a

resolution enacted by the American Psychological Association which reads:

…the continued use of American Indian mascots…appears to have a negative impact on

the self-esteem of American Indian children…is a detrimental [to]…the cultural identity

of American Indian people through negative displays and/ or interpretations of spiritual

and traditional practices (American Psychological Association, 2005).

Though possibly well intended, sociological/ ethnographic studies provide a limited view

and application of Natives that is often one-dimensional. In the past, oversimplified

generalizations of Natives, as “good with their hands”, “good workers at heights” or “good with

arts and crafts” probably contributed to tracking Natives into non-college preparation programs.

Indian boarding schools were designed to assimilate Natives. One method was to train them to

become textile workers or seamstresses (St. Germaine, 1995). Federal funding for many Native

dropout studies originates from the Bureau of Indian Education and have limited application as

these studies follow one conceptual framework or another focusing on reservation-based Indians,

residential or Indian day-schools, geographically or to individual tribes. This leaves the

impression that Natives are a homogenous population and that there is one way to study Natives.

Operationalizing Native American Retention Study: Getting on the Same Page

One challenge in studying the Native American drop-out crisis is the lack of agreement of

which retention statistic to look at. According to Reyhner,


Studies by some school districts…only [report] the dropout rate for one year…such

studies ignore students who will drop out in subsequent years before graduating or who

have dropped out in previous years. Longitudinal studies are those that follow students

through high school… (1991, p. 3).

Some researchers (Swisher, 1992) have tried to establish a standard for defining Native student

dropouts and conversely retention rates by conducting a survey of methods. Pavel (1992) cites

several other studies to define reliable indicators including state equity scores or enrollment

(ESEs) created by dividing the proportion of Natives in a state’s population by the proportion of

Native enrollment in the state’s education institutions. Pavel prescribes the use of state equity

scores for graduation (ESGs) which can be computed by dividing the proportion of Natives in an

entering class by the proportion of Natives who graduate. Comparing these indices to the

general population provides a relative statistic of how Natives are doing compared to others.

Nonetheless, Pavel (1992) cited the lack of unanimity on the subject.

In reviewing more recent general literature on high school graduation, it appears as

though at least some states are now using the National Governor’s Association (NGA) Compact

Formula for calculating graduation rates which is calculated by, “taking the total number of

Native American students who graduated from high school within 4 years and dividing by the

total number of freshman Native American students entering high school four years earlier, then

multiplying by 100” (Zwiebel, 2010, p. 77). Another recent study used the National Center for

Education Statistics data, and the Cumulative Promotion Index (CPI) developed by Swanson

(2003 as cited in Faircloth and Tippeconnic, 2010) which collects and analyzes simulated

longitudinal data to allow researchers to track student progress toward graduation. Thus, the


statistic is the combined probability for each static statistic for each class to simulate the

probability of graduating. Figure 1 shows this calculation,

Figure 1. Cumulative Promotion Index (CPI) formula developed by Christopher B. Swanson, for

the Urban Institute Education Policy Center.

Related Studies

Quantitative Studies

The following quantitative studies represent two at the high school level and one at the

collegiate level examining the Native student drop out crisis from varying perspectives. These

studies were selected for their similarities to the quantitative portion of the current study. The

first, uses an independent t-test to study the effect of an intervention treatment at the Nizhoni

Academy which teaches Native students (Gilbert, 2000). The second, is a study using an ANOVA

test to examine cultural, school and family factor (Sixkiller-Clarke, 1994) differences between

Native high school drop-outs and those who graduated high school. The third, uses a multiple

regression study at the University of Oklahoma to make predictions on whether or not the fall

class of Native American students would matriculate to their sophomore year while examining a

number of pre-college and during-college independent variables on the outcome of persist or not

(Healy, 2000).

First, we start with a quantitative study of high school interventions by Gilbert (2000)

which used a ‘pre-experimental’ research design with a treatment afforded to a group of Native

high students who attended a five week summer academic program (Nizhoni Academy). The

purpose statement focused on the outcomes of an intervention or treatment on Native high school

sophomores and juniors in a five week summer program which provided direct instruction and


academic support for educationally disadvantaged students from rural high schools in the ‘four-

corners’ Southwest area. The program goals were to acquaint students to the rigor of college

life, and to prepare them for college and continued academic success after high school (Gilbert,

2000). The Nizhoni Academy philosophy focused on five aspects of learning including: meta-

cognition, concentrated learning, cooperative learning, a process approach, and critical thinking

skills. Each aspect emphasized structured learning opportunities that promoted culturally

oriented individual and communal learning. Three research questions asked how student

academic achievement was affected as evidenced by post-test scores in three subject areas. The

research design was ‘pre-experimental’ quantitative as no control group was used. Pre-test and

post-test scores were examined. The researcher set up a non-directional null hypothesis for

analyzing mathematics, English, and career development pre-test, post-test differences. A

convenience sample was used which represents a potential internal validity concern. Changes to

the curriculum (systems theory) were implemented including method of delivery, supplemental

instructional experiences and reinforcement of learning through collaborative activities, building

trust with the instructors, and creating social cohesion. The mean scores on pre-tests/post-tests

were examined to prove or disprove the null, thereby rejecting or accepting the alternative

hypothesis. The post-test scores were significantly higher (using a two tailed t- test) for math

pretest-posttest (M = 2.54, SD = 1.40 < M = 4.46, SD 1.69, p < .001); (M = 3.80, SD = 1.40 < M

= 5.82, SD = 1.50, p < .001); (M = 84.60, SD = 11.20 < M = 89.81, SD = 7.63, p < .001). Even

when factoring for the effects of gender, grade-level, and public/ private school, students showed

significant improvements thereby rejecting each null hypothesis (Gilbert, 2000).

Next, a study by Sixkiller-Clarke (1994) used an ANOVA test to: Identify the factors

that keep Native American students in school (graduation); and identify the factors that result in


their leaving school prematurely (i.e. dropping out). The participants were Native high school

students (n =156, 116 high school graduates, 40 drop-outs) living on reservations in1989, 1990,

or 1991 in the Montana, North Dakota and South Dakota regions. Selected variables were

grouped in areas of person problems, school factors, family factors and cultural issues.

Significant differences were found in skipping school between those who graduated high school

(M = 2.71) versus those who dropped out (M = 1.92) F = 19.30(1, 154), p < .001); retention at

grade level (failing a class) graduates (M = .18) versus those who dropped out (M = .43) F =

11.10(1, 154), p < .001); and for playing sports with graduates (M = 1.64%) versus those who

dropped out (M = 2.35%) F = 11.27(1,159), p < .05). Further, for differences in skipping school,

retention at grade level and sports, the r statistics were .11, .07, and .07 respectively. No other

variables examined under cultural, school and family factors were found to be significant.

The third and more complex level of study at the university level (Healy, 2000)

examined the entire 270 new enrolling freshmen Natives at the University of Oklahoma in the

fall of 1994 to predict retention to the second semester along a number of pre-college and during-

college variables. The Native student population represented 10 percent of the new freshmen

whereas the Caucasian population represented 79 percent. The purpose was to determine to what

extent they could predict retention as measured by successive enrollment in the fall semester

1995. This multivariate study using a multiple regression analysis explored family, tribal and

community experiences as factors broken down into: pre-college (mother’s and father’s

education level, state of residence, high school size, high school GPA, seven different ACT sorts,

and population of hometown); during-college variables (intended science related major as yes or

no, status of campus living, and first semester GPA). In the descriptives, it was reported that

89% of the Native students matriculated while 11% did not. The significant relationships


between various predictors and retention were: type of housing, semester GPA (p < .001,

respectively) and English ACT and Reading ACT (p < .05, respectively). Overall, the

combinations of these variables account for an R2 predictive value of .581 which Healy (2000)

reports means these three variables account for 34% of the variation in retention.

Qualitative Studies:

Collectively, the literature on Native American retention demonstrates a crisis with the

highest drop-out rate of any racial ethnic group. In addition to the survey of study types noted

previously, several qualitative studies have provided great insights into the problem with at least

a few studies demonstrating the consistency of the combination of personal, family, school, and

cultural factors that influence the Native drop-out phenomena. More specific regional studies

and comparisons to national databases like NCES might shed additional light to better appreciate

the situation. Building a body of literature that pinpoints the effect of variables and taking the

next (qualitative) step to prescribe how to enhance those positive conditions and minimize the

negative factors might lead to the development of effective intervention strategies.

Historical Trauma

Grounded theory seeks to remain open to emergent themes or patterns which then

may formulate a theory to describe some social phenomena (Patton 2002, Creswell, 2009).

Natives are in large part a heterogeneous population with a diversity of experience including

cultural identities, visual appearance, urban versus rural versus reservation, socioeconomic

status, and legacy Indian board school experiences. There are a few working theories or

conceptual frameworks regarding American Indians from which to borrow from academic

disciplines of psychology, sociology, cultural anthropology and ethnic studies which may

explain the common educational experience and facilitate data collection in the current research.


The prescience of these conceptual frameworks exist through an historical trauma

working hypothesis or conceptual framework. Social indicators like high rates of suicide,

alcoholism, transience, and normlessness in common Native experiences rings familiar with

Durkheim’s theory of social anomie (Young, 1991). Immigrant theory (Bender and Kagiwada,

1968) challenges the melting pot notions of cultural assimilation, acculturation with associated

cultural identity, and ethnic pride. Again, related cultural discontinuity and deficit theory are

often cited to explain the Native experience in general (St. Germain, 1995). Figure Two

provides a visualization of the American Indian educational experience, and the dynamics and

intergenerational effects of historical trauma.


Figure 2. Conceptual Framework for Intergenerational Historical Trauma

for Native Americans.

According to Newbreast (2011) and Yellow Horse Braveheart (2000, 2001, 2004),

historical trauma is the persisting condition of cultural shock for Natives including the

missionary and boarding school experiences, negative self-identify and learned helplessness, and

prescribed efforts to heal irrespective of a heterogeneous Native population and diversity of

experience. Policies of self-governance and self-determination likely play a role with tribes

having the opportunity to shape the educational destinies of their citizens. The conceptual

framework noted in Figure Two is directional and longitudinally modeled moving from top to

bottom with cultural and historical influences persisting from generation-to-generation with an

anticipated level of dilution over time. Theories of anomie, immigrant theory, historical trauma,

and U.S. Policies of Educational Self-Determination may be observable or articulated throughout

this study. The red circles labeled Z1 through Z4 refer to possible zones of cultural adaptation,

adherence or even resistance which may be observed. The last zone Z5 is undetermined as

increasing tribes have the financial wherewithal to set the direction of education and social

adaptation of their tribal citizens. This represents substantive opportunities for scholarship of an

advocacy or participatory action research nature.

Tribes are likely to be more critical consumers of research and will undoubtedly push for

substantive studies which are not ideologically one-dimensional. A burgeoning field that may

provide a scientific explanation to intergenerational trauma is that of epigenetics. Though

epigenetic study is biomedical and focuses on disease, the nature versus nurture argument

become inextricably intertwined as genetic precursors and social conditions explain behavior.


An obvious overlap between neuroscience and education for example is Autism (Hopkins, 2011).

More pointedly, epigenetics seeks to isolate the conditions at which certain outcomes are

triggered. This might explain why some Native Americans succeed academically and others do

not. If Natives have a predisposition to say learning disabilities, and if certain environmental

conditions or events trigger manifestations of this predisposition, then discovering what these

triggers are becomes critical. Clearly not within the scope of this study or the literature search

conducted for this study, if there exists a common combination of factors to explain why Native

Americans have the highest drop-out rates, then irrespective of how offensive as it may seem,

keeping an open mind to epigenetics is a good idea as it may not only add credence to the notion

of historical trauma, but explain this seemingly perpetual state in Native America.


CHAPTER III: METHODOLOGY

Quantitative Purpose and Research Questions

Again, the broad purpose of this research is to identify what factors influence educational

outcomes for Native Americans at both the high school and college level. More pointedly, for

Michigan Native Americans, this research will explore the extent to which school, personal,

family, and cultural factors influence high school graduation or general education development

(GED) attainment, high school graduating grade point average (GPA), and college graduating

grade point average. Further, differences that exist between Native Americans who earned their

college degree by way of earning a high school diploma versus those who attained their general

education development (GED) will be analyzed.

Conceptual framework: Quantitative

The conceptual framework used for the quantitative portion of this research borrows

largely from the comprehensive correlational / multiple regression study (noted in the literature

review section of this research, Sixkiller-Clarke, 1994) which collected over 140 variables that

fall fit into five broad categories. These are: personal factors (substance abuse, peer pressure,

trouble with the law, low self-esteem, teen pregnancy, motivation toward school, career plans);

family background (family composition, SES, welfare access and generation use, parental and

other family education levels, birth order and family dropout/graduation/college attendance and

graduation status, substance abuse, family education expectations); school factors (academic

achievement, teacher attitudes and expectations, school attendance, sports and extra-curricular

participation, abuse by school employee, engagement with high school guidance counselor,

integration with cohort or school); cultural factors (tribal identification, discrimination/racism

and from whom, bilingualism, per-capita dividend) and access factors (proximity to a tribal or


other community college, college, or university and any outreach, availability and participation

in Title VII Indian Education public schools or Johnson O’ Malley tribal programs. Given

concerns with sampling error with such a large variable population and the problem of small n in

studying Native Americans, the current study will minimize the number of independent variables

but borrow from the broad categories (personal, family, school, and culture).

Quantitative Research Design

This study will employ a quantitative non-experimental or quasi-experimental research

design (Creswell, 2009) utilizing primary research. Statistical tools allows for the manipulation

of variables, constructs and factors after the fact to: identify their relationships between

variables; determine what predictor/independent variables are associated with particular

outcomes (dependent variables); and later to discover and develop inferential models that explain

and predict educational outcomes for Native Americans. Given the unique challenges of the

problem of a small sample size (small n) (Bufferfield, 2003 as cited in Faircloth and

Tippeconnic, III, 2010) with studying American Indian populations, it is anticipated that use of

archival data like NCES datasets in later studies would allow for a review of the reliability of

various combinations of variables, constructs, or factors collected in historical archival data to be

compared against those planned for use in survey research. Operationalizing future studies to be

consistent with comprehensive data collected in national databases like the National Center for

Educational Statistics (NCES) on high school retention with the ability to sort by Native

American populations would allow for a level of reliability for comparisons in answering the

seven research questions between existing data collected over time versus data collected in this

primary research of Michigan Native Americans. If primary survey research instruments can be


articulated to match historical datasets, a simulated longitudinal effect may be gained for greater

insights into trends in Native American high school and college persistence rates and factors.

As a quasi-experimental or non-experimental design, research questions are used as

opposed to null and alternative hypothesis statements. The research questions (RQ) for this

study follow.

RQ1: Is there a difference between college graduating GPAs for the two subsets of the

population studied – high school graduates versus those who earned their GEDs?

RQ2: Do various pre-college and select college variables predict college graduating

GPA?

RQ3: Do select college variables and whether an individual earned a high school

diploma or GED predict college graduating GPA?

RQ4: What is the effect of all pre-college and all college variables on college

graduating GPA?

RQ5: Is any difference between high school GPA of those who graduate high school

versus those who earned their GED?

RQ6: What predictive values does all pre-college variables have on high school GPA?

RQ7: How well do these pre-college variables predict whether or not someone will

graduate with a high school diploma versus a GED?

Population

The population for whom this research intends to draw inferences is Native American

students at both the high school and college levels. More specifically, given there appears to be

no comprehensive primary educational research studies of MI Natives, the population of interest

is MI Native Americans who possess college degrees. Of particular interest is Natives who are


members of federally recognized tribes whose reservations are situated in Michigan. There are

twelve such tribes in Michigan from which to draw participants.

Small n problem in studying the Native American populations

A serious challenge to studying Native students is the problem of small n and with a

geographically dispersed population which results in many researchers not studying or reporting

Native statistics due to the statistical insignificance of the results (Butterfield, 2003 as cited in

Faircloth and Tippeconnic, 2010). In 2008, there were about 644,000 American Indian and

Alaska Native students in kindergarten through twelfth grade. About 92 percent attended regular

public schools with just 8 percent attending federal Bureau of Indian Education (BIE) or

individual tribal schools. In Michigan, there exists two BIE Tribal/ State of Michigan Public

School Academy Charter Schools. Native students are 46 percent more likely than their non-

Native counterparts to attend rural schools. Though Indian education is mostly a public school

issue, one-third of Native students attend school where at least 50 percent of the population is

Native (Faircloth and Tippeconnic, 2010).

Sample Size

The problem of the small n necessitates operationalizing several paths this research may

take. Inevitably, the sample population is a convenience sample that took on a snow ball effect

in data collection in order to ensure a sufficient sample size. The targeted sample size was set

based on RQ3 which has the largest number of variables to be collected at 18. In order to

minimize sampling error, using a multiplier of 20 respondents per variable, the number of survey

returns needed for RQ3 is an n = 360. In a conventional hard copy survey, using a projected

response rate of 30 percent, this would necessitate the production of 1,200 surveys. The second

highest number of variables is for RQ1 (15 variables) for a targeted number of returns at 300


necessitating the projection of 1,000 surveys. Depending on reliability analyses, these variables

are expected to be combined into no more than seven factors or constructs which allows us to set

the maximum responses as a target but not a steadfast rule. See Appendix A for calculations.

Given the retrospective nature of this research, it is important to note that typically

research may limit the elapsed time since earning their diploma or GED to five years. This is

noted as a serious challenge to the reliability of the results and is recognized as an area of

improvement for future studies. Additionally, the potential threat of sampling bias exists in the

manner in which surveys were collected. Given there is no known available repository of

available names of MI Native high school graduates or those who earned their GED, several

innovative techniques will need to be employed to get surveys to potential respondents. Suffice

it to say that the trade off of convenience sampling to ensure a sufficient sample size, is a

potential sampling bias through convenience sampling in a snow ball manner.

Data Collection

Data was collected using an instrument created for this purpose (see Appendix B) and

implemented using several distribution methods. A mailing was done and follow-up phone

calls and/or visits to MI tribal administrations for distribution in the twelve federally recognized

tribal offices which typically have a high traffic flow of their tribal members. Tribal

governments in Michigan were asked to promote completion of the survey through their

respective tribal newspapers, web pages, and through mass emails to their members. Tribal

education directors generally have the most reliable lists of members who are in college or have

earned college degrees given they administer tribally based scholarships. The survey was made

accessible through hard copy or through an electronic version using the on-line data collector

Survey Monkey (Survey Monkey, 2011). For those who completed a hard copy of the survey, the


results were individually inputted into Survey Monkey. Respondents were asked to forward the

Survey Monkey link to an electronic on-line version of the survey to other MI Natives who

earned their college degrees within the last ten years.

The anonymous survey collected general demographic information for all research

questions including gender, tribal membership, year of birth, year high school diploma was

earned or GED attained, and year of college graduation. The closest previous research found to

match the current research identified four major correlates to the incidence of Native American

persistence in high school (Sixkiller-Clarke, 1994). The following variables for the current

study were paired with the variables used by Sixkiller-Clarke: personal factors (substance

abuse, peer pressure, trouble with the law, low self-esteem, teen pregnancy); family factors

(family composition, socioeconomic status, parental education levels, older siblings – drop-out

or graduate, substance abuse); school factors (academic achievement, teacher attitudes, teacher

expectations, school attendance, abuse by school employee); cultural factors (tribal self-

identify/pride, discrimination/racism, bilingualism) Sixkiller-Clarke (1994, p. 72).

Variables

Table 4 lists factors, constructs, and variables for both the predictor/ independent and

independent and outcome/dependent variables. For each research question, Table 4 also denotes

whether each variable is categorical (discrete) or continuous and the level of measurement.


Table 4

Proposed Factors, Independent and Dependent Variables for RQ1 through RQ4

Factor Value Label DV/ IV Measure

RQ 1 College GPA DV234 Scale

School High School Graduation or GED (used as IV) DV1 Nominal

RQ 2 College GPA DV 234 Ratio

ALL RQ7 Independent Variables plus…. IV1 thru IV15 Mixed


School English, Math, Science, History, Social Studies GPA IV17 Scale

School College Admissions Status IV18 Nominal



School English, Math, Science, History, Social Studies IV17 Scale

School College Admissions Status IV18 Nominal

Family College Financial aid IV19 Nominal

Family Loans IV20 Nominal

Cultural Michigan Indian Tuition Waiver IV21 Nominal

Cultural Tribal Scholarships IV22 Nominal

Cultural Natives Services IV23 Nominal

Cultural Multi-Cultural Office IV24 Nominal


All Pre-College IV (RQ7) + All College IV (IV17-IV24) Various Various

RQ 5 High School GPA IV17 Scale


High School Graduation or GED DV1 Nominal

RQ 6 High School GPA IV17 Scale

All Pre-College IV (RQ7) Various Various

RQ 7 High School Graduation or GED DV1 Nominal

Personal Be When You Grow Up IV1 Nominal

Family Family Income While Growing Up IV2 Ordinal

Family Highest Level of Education of Family IV3 Scale

Family Parental Expectations to Graduate IV4 Nominal

Family Family Substance Abuse IV5 Nominal

School Connection at Elementary, Middle and High School IV6 Ordinal

School Connection to Class Cohort IV7 Ordinal

School Change of Connection with Class Cohort IV8 Nominal

School Participated in Extra Curricular Activities IV9 Nominal

School School Intervention Available? IV10 Nominal

School Participate in School Intervention? IV11 Nominal

School Class Subjects Liked IV12 Ordinal

School Class Subjects Grades IV13 Scale

School School Type IV14 Nominal

Cultural Connection to Culture IV15 Ordinal

[Variable names and values for each variable appear in Appendix C]


Reliability assessment

The reliability of possible constructs or the combinations of variables that make up these

possible constructs was examined using PASW Statistics 18, Release Version 18.0.0 (PASW,

2009) reliability test feature. Given instrument construction and questions are very similar to

data collected by NCES (on a much smaller scale), the reliability of proposed constructs may be

compared against NCES datasets and reliability test results using PASW reliability test feature of

similar questions asked on NCES instrument. Though, no reliability statistic - alpha was

reported in the Sixkiller-Clarke study (1994), the results did not rule out any variables. For the

current research, the combinations of three or more variables in suspected combinations were

analyzed using the PASW Scale feature, selecting the reliability and scale item if deleted option

which shows the reliability of these questions taken together and how they could be improved

upon by omission of select variables.

The suggested constructs or combinations of variables at this stage included: personal

engagement at school (feeling of connection in school at the elementary, middle school and high

school level, feeling of connect with class cohort, and participation in school activities); school

engagement (interventions, attendance, like of class subjects, and grades per class subject); and

financial support (college financial aid, loans, Michigan Indian Tuition Waiver use, tribal

scholarship use). Additionally, any number of variables for each research question may

constitute a construct and the factors identified may result in broad constructs. Once the data

was collected, an analysis of all combinations of variables that make a factor was performed

using the PASW Scale feature noted previously.

Some of the constructs in the previous study which overlap with the current study have

some notable established reliability. For the variables - feeling of connection in school, class


cohort, and participation in school activities - combined as a construct, a reliable result was

found (= .81, M = 3.08, SD = .41). For the variables – college financial aid, loans, Michigan

Indian Tuition Waiver, and Tribal scholarships – combined as a construct found a reliable result

as well (= .70, M = 1.71, SD = .21). However, the variables – interventions, class subjects like,

class subject grades – combined found a close but less than reliable result (= .66, M = 13.33,

SD = 9) which doesn’t quite meet the (> .70) threshold to be combined as a construct. In

examining further the value of both the ratings of class subject like and class subject grades for

individual classes combined as constructs, these were found to be reliable with class subject like

(= .74, M =1.62 , SD = .06); and class subject grades (= .79, M = 3.19, SD = .20). The

reliability for ‘interventions’ could not be determined due the existence of just two variables.

The conclusion is that the variable interventions in high school does not fit with the other two

variables of class like and grades.

Limitations on Variables and Data Collected

The possible limitations of the data and method of collection include: collecting a

sufficient sample size; the ability of the respondent to recall information with a level of precision

given the retrospective nature of the data; and whether or not respondents answer questions

truthfully which is always a potential threat to any survey. To the extent this can be ameliorated,

a cover letter stressed the importance of answering all questions truthfully and with the greatest

level of recall possible. In order to ensure that a sufficient sample size was drawn from the

respective high school graduation and GED attainment populations, the length of time for

retrospective data collection was extended beyond a standard five year period. It is understood

and noted that longer than this period represents a threat to the validity and reliability of the data

recollected.


Quantitative Analytical method

Preparatory work moving from data collection to analysis

Survey responses were collected via Survey Monkey with data downloaded into Microsoft

Excel 7.0 (2007) then uploaded into PASW for descriptive and inferential statistical data

analyses. The data collection period predated a recent Survey Monkey feature which

automatically encodes data from the on-line survey into SPSS. For this research, this was done

manually and painstakingly for approximately 200 data points. When downloading from Survey

Monkey to Excel 7.0, the variable names are preserved in column headings. Once uploaded into

PASW, the column headings serve as variable names. For each variable, value labels were added

for ease of use and for displaying results. Variables were encoded as either numeric or string

variables; level of measurement; and values assigned to selection of options within each variable.

Appendix C displays this information for all variables used in this research.

As part of the preparatory work for running analyses, the distribution of variables was

examined for the relative normality of each distribution using the PASW frequencies command.

In doing so, the distribution of the data for each variable was checked against the normal

distribution using the means, standard deviations, standard errors, kurtosis and skew, and

z-scores to detect if there are any outliers that necessitated making alternative arrangements like

setting the outlier value to the lowest or highest within + or - three standard deviations from the

mean value. This is an important step in order to gauge whether or not corrections need to be

made for non-normally distributed data. The results from frequency tables runs serves as the

basis for reporting ‘descriptives’ in the results to aid the reader in visualizing the data.

Additional general data runs were conducted to better understand the data and possible

relationships among variables using: the PASW Correlations – bivariate function selecting


flagging significant correlations among all variables; an analysis of variance between the

outcome/dependent variables and predictor/independent variable for each research question

using PASW Compare Means – ANOVA function selecting posthoc, tukey table to test for

significance of variances, homogeneity of variance test, and means plots to better understand

how the means differ.

Deciding on appropriate statistical tests

The most important aspect of moving from data collection to data analyses is interpreting

which statistical test to use. Table 4 listed the nature of the data for each variable used in this

research as either categorical (discrete) or continuous. This information is necessary to

determine the statistical test to use. According to Field (2009) and Mertler and Vannatta (2010),

answering a few simple questions reveals the appropriate test to use. These questions ask how

many outcome/dependent or dependent variables are used in each research question. Whether or

not the outcome/dependent or dependent variable is continuous or categorical and how many

predictor/independent variables there are at either one or two (or more) is asked. Next, what type

of predictor/independent variable(s) is(are) used and how many categories does each

predictor/independent variable fit into is decided? Finally, answering whether or not each

predictor/independent variable has the same or different participants and whether the data meets

parametric assumptions, determines which analytics to use. The following includes the

responses to each of these questions Field (2009) poses and therefore which statistical tests were

selected for use for RQ1 through RQ7.

RQ1: The number of dependent variables is one. The nature of the dependent variable is

continuous as college graduating GPA is any number between 0.00 and 4.00 depending


on the grade point average system at their respective college. There is one predictor/

independent variable that is categorical with two categories that also differentiate the

different populations (high school graduation versus GED attainment) and the data meets

the parametric assumptions so the statistical test to use is an independent t-test.

RQ2: The number of dependent variables is one. The nature of the dependent variable,

college graduating GPA, is continuous. There are eighteen predictor/independent

variables that are both continuous and categorical with different populations

(differentiated by high school graduation or GED attainment) and the data meets the

parametric assumptions so the statistical test to use is multiple regression.


college graduating GPA, is continuous. There are nine predictor/independent variables

that are both continuous and categorical and the data meets the parametric assumptions so

the statistical test to use is multiple regression.


college graduating GPA, is continuous. There are twenty four predictor/independent

variables that are both continuous and categorical and the data meets the parametric

assumptions so the statistical test to use is multiple regression.


continuous as high school GPA is any number between 0.00 and 4.00 depending on the

grade point average system at their respective college. There is one predictor/

independent variable that is categorical with two categories that also differentiate the

different populations (high school graduation versus GED attainment) and the data meets

the parametric assumptions so the statistical test to use is an independent t-test.



high school GPA, is continuous. There are fifteen predictor/independent variables that

are both continuous and categorical and the data meets the parametric assumptions so the

statistical test to use is multiple regression.


categorical as either possessing a high school diploma or GED. There are 15 predictor/

independent variables that are both continuous and categorical with different populations

(differentiated by high school graduation or GED attainment) so the statistical test to use

is logistical regression.

Decision factors for research questions

RQ1 and RQ5 are likely the most straight forward statistical test to conduct and interpret.

The independent t-test, should show whether or not the two groups are different with respect to

their mean high school and college graduating GPAs. The null hypothesis is stated that there is

no difference (to minimize Type I error) or that the means are equal. If the associated

significance value is (p < .05) for a 95 percent confidence interval for the t statistic, then we

reject the null and conclude that mean graduating college GPAs are different for those who

graduated high school with a diploma versus a GED. Conversely, typically, if the p value is not

significant (p > .05), then we fail to reject the null and conclude that the high school or college

graduating GPAs for the respondents is not significantly different for those graduated with a high

school diploma versus a GED.

RQ 2, RQ 3, RQ 4, and RQ6 were analyzed using a multiple regression analysis.

Multiple regression is simply the selection of several independent variables used as

regressors (using PASW regression feature and select linear regression) to find the


regression equation that best explains the role of the predictor/independent variables and

the extent of their effect per predictor/ independent variable unit change in the

outcome/dependent variable (Field, 2009). For RQ 2, RQ 3, RQ 4, and RQ6, the relative

effect of various predictor variables for personal, family, school and cultural factors have

on MI Native American college graduating GPAs will be explained.

For RQ 7, a logistical regression was the statistical test selected. Using PASW, select the

regression feature and choose the binary logistical regression option and display at each step

among other options. For linear regression equations like:

Y = + 1X1 + 2X2 + 3X3 + …k Xk +

the value of the beta coefficient or represents the change and significance in the

outcome/dependent or dependent variable resulting from a unit change in the

predictor/independent variable. The value reported in logistical regression is similar but

represents the change in the logit of the outcome/dependent variable associated with a one unit

change in the predictor/independent variable (Field, 2009). The sequential process in a logistical

regression begins with the constant at step zero and examines the value of including each

additional predictor/independent variable for increasing the overall predictability of the

regression model. In other words, the results indicate whether or not the model including the

predictor/independent variables is significantly better than without those predictor/independent

variables. For RQ7, the decision factor will be in determining at what step each of the 15

predictor/independent variables holds some value on predicting the outcome/dependent or

dependent variable. The response to RQ7, will take the form of the results for individual

predictor/independent variables, as well as, the overall model that best fits an explanation of the

outcome which is either obtaining a high school diploma or a GED.


Quantitative Methodological limitations

Again with the population of American Indians at about 1.5 percent of the total United

States population means that the problem if the small n is always in play. Fractioning this

population further through various sorts decreases the likelihood of a sufficient sample size.

Natives are generally a transitory population which makes studying this population difficult.

Swisher & Hoisch (1992) and St. Germaine (1995) note the incidence of Native American

student transfers confounding the calculation of Native student retention. This is not to suggest

that transferring is detrimental, but that continuity of data collection is interrupted. Finally, one

of the limitations of the quantitative paradigm, is though we can establish accurate baseline data

to begin to project hypotheses to explain relationships among variables, the small n and the

heterogeneity of the population of Native Americans resists simple or convenient classifications.

Potential Researcher Subjectivity (Bias)

Any researcher carries a bias in the manner in which they interpret what they see.

Disclosing inherent biases and how these will be managed, promotes transparency in order to

rely on the objectivity of the researcher. For this study, the researcher has a clear advocacy/

participatory (Creswell, 2009) motivation for studying the Native high school dropout crisis. As

a Native who dropped out of high school but went on to earn a graduate education, the researcher

has a firmly held belief that given the right academic opportunity, most all students can achieve

academic success. Borrowing from the literature, an organizational/systems view suggests

deficiencies exist in public education that continue to fail Natives. The researcher agrees with

both constructivist (social historical) and sociological/anthropological theories that explain how

Natives enter the school system (lower SES, high rates of poverty, poor nutrition, poor cognitive

preparation, undiagnosed special education needs, etc.) effects their propensity to succeed.



CHAPTER IV: RESULTS

Quantitative Findings

What follows is a comprehensive listing of the results and various analytics to address

each research question posed. First, descriptive will be shared to give a general birds-eye view

of the data with respect to their distributions. Next, both frequencies distributions and an

interpretation of this data using t-tests and ANOVA where appropriate are used to appreciate the

differences between the two populations research – namely high school graduates versus GED

recipients. Finally, the results of each research question are presents with the appropriate

statistical test ranging from t-tests, to multiple regression, and logistical regression where

appropriate.

Descriptive statistics and frequencies

What follows in Table 5 is a summary descriptive statistics to give a glimpse of the

nature of the data including the dispersion and frequencies.

Table 5

Descriptive Statistics for all Variables

N Min. Max. Mean Std. Deviation

High School Graduation or GED 300 1 2 1.12 .326

College GPA 265 2.00 4.00 3.35 .477

Be When You Growing Up 300 1 3 1.61 .514

Family Income While Growing Up 300 1 5 2.39 .856

Recoded for Family Education 296 1 22 13.49 4.140

Parental Expectation to Graduate 300 1 3 1.20 .499

Family Substance Abuse 300 1 3 1.65 .562


Connection (K-12) 300 1 5 2.52 .901

Connection to Class Cohort 281 1 5 3.24 1.441

Change of Connection with Class Cohort 300 1 3 1.79 .687

Participated Extra Curricular Activities 300 1 2 1.14 .351

School Intervention Available 300 1 2 1.69 .465

Table 5

Descriptive Statistics for all Variable (cont.)

N Min. Max. Mean Std. Deviation

Participate in School Intervention 300 1 2 1.85 .354

Class Subjects Liked 300 1 6 3.84 1.284

Class Subjects Grades 300 1 4 3.54 .719

IV14SCHtypeKthru12 300 1 4 2.81 .513

Connection to Culture 300 1 5 2.08 1.297

Eng., Math, Sci., History, Social Studies GPA 296 .40 4.00 3.03 .715

College Admissions Status 300 1 3 1.27 .641

College Financial Aid 300 1 3 1.59 .690

Loans 300 1 3 1.64 .726

Michigan Indian Tuition Waiver 300 1 3 1.80 .684

Tribal Scholarships 300 1 3 1.93 .690

Native Services 300 1 3 2.03 .817

Use Services at Multi-Cultural or NA Office 300 1 4 2.30 1.024


The full frequency distributions and default graphs are appended (F) as a separate PASW Output

file. Graphs depicting significant differences between High School Graduates versus those who

earned their GEDs are show in Appendix G.

Visual Inspection of Data with t-Test and ANOVA Confirmations

PASW (2009) formerly SPSS, has features that provide any number of ways to display

data. One such way is through the production of graphs to show differences or similarities

between respondent groups. The two main populations examined in this study are Michigan

Natives who complete high school with a diploma versus those who earned their GEDs. To

appreciate comparisons among these two groups, totals for each legend variable category in the

Chart Builder feature was selected to show the relative percentages for each separate group with

bar codes lined up side by side with the comparison group. Using the independent variable

which asked whether or not individuals knew what they wanted to be when they grow up (IV1)

for example, the responses were: 1=Yes; 2 = No; 3=Don’t Know. The percent who responded yes

for the two main populations (High School Graduate versus GED recipient) are displayed side by

side. Then, those who reported no are displayed side by side with those who responded don’t

know following. A visual inspection of the data suggests there is a distinct difference in the two

populations on several response items. What follows is a brief descriptive narrative of these

differences followed by a more robust analysis including the use of t-test and ANOVA tests to

determine if these differences are indeed significant. For variables with more than two response

categories, an ANOVA was used while those variables that have dichotomous responses, a t-test

was used. All 24 graphs appear in Appendix G.

For the variable question which asked if the respondents knew what they wanted to be

when they grow up (IV1), there was no appreciable difference. With respect to the variable


family income while growing up (IV2) there appears to be a difference with GED recipients

reporting a total low income of 69% versus high school diploma recipients at 51%. Twenty eight

percent of GED recipients reported middle income versus 44% for high school diploma

recipients. Thus, GED recipients appear to be from a lower socio-economic class than high

school diploma recipients. Given, there are more than two response categories for this variable,

however, an ANOVA was used to determine if these differences are significant. The results

show that there is a significant difference with respect to income between the groups F(4,295) =

4.17, p < .01, r = .23(2

=.04) which represents a small effect size (Kirk, 1996 as cited in

Field, 2009, p. 390) with the significant differences appearing in the following table:

Table 6

Mean Differences of Family Income While Growing Up (IV2) Between Respondent Groups

1 2 3 4 5

1 Very Low -

2 Low .185* -

3 Medium .210* .026 -

4 Upper .089 -.096 .121 -

5 Don’t Know .289 .104 .200 .200 -

* p < .05

Looking at educational attainment as represented in the variable family education (IV3),

there appears to be no appreciable difference between the two groups. With respective to

parental expectation of the respondent to graduate high school (IV4), however, there appears to

be a difference with 88% of high school graduates reporting yes, while 53% of GED earners


reporting yes – a 35% spread. Treating the don’t know response as missing values in order to run

a t-test, we see those who earned their GEDs (M=1.39, SE =.09) was significantly lower than

those who graduated high school (M=1.09, SE=.02) t(32) = -3.277, p < .01 and an effect size of

50%.

A visual inspection of IV5 (family substance abuse) 37% of high school graduates

reported someone in the immediate family with a substance abuse problem while the respondent

was growing up versus 58% for GED recipients, a 21% difference. Using a t-test, the mean of

responses for GED recipients (M=1.40, SE=.08) was significantly lower that for high school

graduates (M=1.61, SE=.03) t(285) = 2.391, p < .05 with an effect size of 14%. Recall that 1 =

yes a family member had a substance abuse problem and 2 = no.

With respect to a student’s connection or engagement in school (IV6), with their class

cohort (IV7) or any changes in connection with their class cohort (IV8) and participation in

school activities (IV9) a visual inspection appears to show differences. GED recipients report a

total low connection with school (IV6) at 83% while those who earned high school diplomas at

46%. Neither of these statistics are particularly encouraging given a level of disconnection or

disengagement of Native students overall in school. Applying an ANOVA, we see that these

differences are significant F(3,296) = 10.426, p < .001, r = .31(2

=.08) which represents

a medium effect size. Given there was only one respondent who reported a high connection to

school, this response category was collapsed into somewhat high & high. The significance

appears in the following table:


Table 7

Mean Differences of School Connection (IV6) Between Respondent Groups

1 2 3 4

1 Low -

2 Somewhat Low .192* -

3 Moderate .285* .093 -

4 Somewhat High & High .342* .150* .057 -

* p < .05

Connection class (IV7) shows some startling differences between the two groups with a

70% of GED recipients reporting a total low connection versus a total low connection of high

school graduates at 28% . This difference is significant F(4,276) = 12.896, p < .001, r = .40

(2

=.15) which represents a large effect size.. The respective differences are shown in

the following table:

Table 8

Mean Differences of Connection to Class Cohort (IV7) Between Respondent Groups

1 2 3 4 5

1 Low -

2 Somewhat Low .287* -

3 Moderate .253* -.034 -

4 Somewhat High .327* .040 .074 -

5 High .346* .060 .094 .020 -

* p < .05


With respect to any changes in the student’s connection to their class cohort (IV8), 50%

of GED recipients reported that there was a period in their education when there was a change in

their connection to class versus 34% for high school graduates. Applying a t-test to see if this

difference is significant, we see that it is with GED (M=1.33, SE=.09) versus high school

diploma (M=1.60, SE=.03) t(252) = 2.713, p < .01 with an effect size of 17%. Finally, 64% of

those who earned a GED reported participating in extra-curricular activities (IV9) while 89% of

the high school graduate group reported participation. The t-test borne out the significance of

these results as well with GED (M=1.36, SE=.08) and high school graduates (M=1.11, SE=.02)

t(39) = -.2.963, p < .01 with an effect size of 43%.

For the variables school intervention available (IV10), participate in school intervention

(IV11), class subjects grades (IV13), school type (IV14), and connection to culture (IV15) there

doesn’t appear to be any appreciable difference between the high school graduates versus GED

recipients. A visual inspection of class subjects liked (IV12) appears to show differences

between the two groups and an ANOVA test does show a significant differences at the .05 level,

F(4,235) = 2.408, p < .05, r = .21(2

=.02) which represents a medium effect size.

However, post hoc analysis though the application of a Tukey table does not show any

significance individuals differences in the table below.

The mean difference in academic grade point average (IV17) for high school graduates

versus GED recipients appears significant and a t-test bears this out with GED recipients

(M=2.51, SE=.12), high school graduate (M=3.09, SE=.04) t(294) = 4.588, p< .01 with an effect

size of 26%. Given GED recipients reported lower grade point averages and dropped out of high

school (potentially before SAT and ACT exams), it is intuitive that their college admissions

(IV18) would show a distinction between the two groups. A graphical depiction of the


differences shows that 86% of high school graduate respondents entered college under regular

admissions and 67% of the GED recipients entered college under regular admissions. This

difference, however, is not significant with GED recipients (M=1.11, SE=.06) and high school

graduates (M=1.05, SE=.02) t(29) = -.903, p >.01 (.374).

Table 9

Mean Differences of Class Subjects Liked (IV12) Between Respondent Groups

1 2 3 4 5

1 Did Not Like -

2 Moderate Dislike .100 -

3 Neutral .249 .149 -

4 Moderately Liked .258 .158 .009 -

5 Really Liked .333 .233 .084 .075 -

* p < .05

Regarding variables at the college level we also see some notable differences between

high school graduates versus those who earned their GED. First, high school graduates reported

accessing financial aid (IV19) at a rate of 50% versus GED recipients at 67% marking a 17%

difference. This difference is significant with GED recipients (M=1.17, SE=.07) and high school

graduates (M=1.44, SE=.03) t(263) = 2.759, p < .01 with an effect size of 47%. Access and use

of loans (IV20) between the two groups, however, shows no difference. The remaining college

variables show differences. Those who accessed and used the Michigan Indian Tuition Waiver

(IV21) - a State of Michigan program which provides full tuition funding for Michigan Natives

who are residents of Michigan for at least one year who have at least ¼ certified Indian blood -


shows a difference with GED recipients receiving the waiver at 53% versus 33% for high school

graduates, a 20% difference which is significant with GED recipients (M=1.32, SE=.09) and high

school graduates (M=1.62, SE=.03) t(252) = 3.013. p < .01) with an effect size of 19%. There

was also a discernable difference in those who received tribal scholarships (IV22) with 26% of

high school graduates versus 39% of GED recipients receiving tribal scholarships which

represents a significant difference with GED recipients (M=1.42, SE=.10) and high school

graduates (M=1.68, SE=.03) t(237) = 2.586, p < .01 with an effect size of 17%.

Finally, there were differences with respect to students who accessed Native (IV23) -

47% versus 30% respectively for GED recipients versus high school graduates - and Multi-

Cultural services (IV24) - 39% versus 17% respectively for GED recipients versus high school -

while in college. These difference in use of Native services in college are not significant at the p

< .05 or .01 level but are at the .07 level with GED recipients (M=1.35, SE=.10) and high school

graduates (M=1.54, SE=.04) t(34) = 1.874, p < .07 with an effect size of 31%. There was a more

definitive difference in those who accessed the services of the multi-cultural affairs office at their

respective colleges with GED recipients (M=1.42, SE=.10) and high school graduates (M=1.76,

SE=.03) t(27) = 3.228, p < .01) with an effect size of 53%.

Correlations

With twenty four independent and two dependent variables and the resulting 676

different pairings, there are too many correlations to give each individualized attention here.

There is some value, however, in looking at the strongest effect sizes of correlations in order to

decipher relationships among variables identified. One hundred and one variable pairs had

significant correlations with 35 pairings at the p < .05 level and 66 at p < .01 level. The

following will summarize those pairings of variables that have significant correlations and effect


sizes greater than or equal to 15% which translates to a correlation of nearly .39. Then those

with effect sizes at or greater than 10% up to 15% will be presented. This review provides a

simple review of the relationships of variables. The full correlations matrix is appended (E).

A student’s connection to their class cohort (IV7) is positively correlated with their

connection with school (IV6) across the all grade levels, r = .72, p < .01 and a 52% effect size

(i.e. variance in connection with school explained by a student’s connection to class). Not

surprisingly, the extent to which a student participates in school interventions during high

schools (IV11) is positively correlated with the availability of school interventions (IV10), r =

.614, p < .01 with a 38% effect size. Again, somewhat tautological, whether or not a student

received college financial aid (IV19) is positively correlated with their acceptance into college

(IV18), r = .420, p < .05 with an effect size of 18%. Whether or not a student receives college

loans (IV20) is positive correlated with their acceptance into college (IV18), r = .504, p < .05

with an effect size of 25%. Whether or not a student receives tribal scholarships (IV22) to

support attending college is positive correlated with their acceptance into college (IV18), r =

.415, p < .01 with an effect size of 17%. The use of the multi-cultural affairs office (IV24) is

positively correlated with their acceptance into college (IV18), r = .382, p < .05 with an effect

size of 15%. Correspondingly, the correlation for a student’s use of the Native student services

office is r = .309, p < .01 with an effect size of 10% and a student’s use of the multi-cultural

affairs office (IV24) is positively correlated with their use of a college’s Native office (IV23), r =

.465, p < .01 with an effect size of 22%. This is not surprising given these offices are often one

and the same, located in adjoining offices, or affiliated through organizational planning. A

student’s use of college loans (IV20) is positively correlated with their accessing financial aid

(IV19), r = .579, p < .01 with an effect size of 34%. Whether or not a student receives a tribal


scholarship (IV22) to support college is positively correlated with receipt of college loans

(IV20), r = 494, p < .01 and whether or not they received the Michigan Indian Tuition Waiver

(IV21), r = .401, p < .01 with effect sizes of 24% and 18% respectively.

Moderate positive pairings of variables with correlations greater than r = .30 and less

than .39 with effect sizes greater than or equal to 10% but less than 15% follow. Participation

in school extra-curricular activities (IV9) is negatively correlated with a student’s connection to

their class cohort (IV7), r = -.333, p < .01 with an effect size of 11%. Interpreting this is difficult

as it seems counterintuitive as it seems that the more involved in extra-curricular activities a

student is, the more connection they would have with their class cohort unless what is being

captured is a level of participation that lessens opportunity for leisure involvement with class

cohorts. A student’s high school academic grade point average (IV) is positively correlated with

their connection with school (IV6) and connection to their class cohort (IV7), r = .317 and r =

.309, p < .01 with effect sizes of 10% respectively. Again, not surprisingly, the grades a student

receives in high school per class subject (IV13) is positively related to how well they liked their

class subjects (IV12), r = .360, p < .01 with an effect size of 13%. A student’s use of the

Michigan Indian Tuition Waiver (IV21) is positively correlated with their acceptance to college

(IV18), and their use of college financial aid (IV19) and loans (IV20), r = .366, .330, .311, p <

.01 with effect sizes of 13%, 11%, and 10% respectively. Additionally, a student’s use of tribal

scholarships (IV22) is positively correlated with their use of college financial aid (IV19), r =

.352, p < .01 with an effect size of 12%. This is not surprising given tribal scholarships are

typically considered last resort funding with a recommendation made after all other forms of gift

assistance are applied and a recommendation made to the tribe by the college financial aid office.


A student’s reported use of their college’s Native office (IV24) is positively correlated

with acceptance into college (IV18), use of financial aid (IV19), and use of the Michigan Indian

Tuition Waiver (IV21), r = .309, .327, .311, p < .01 with effect sizes of 10%, 11% and 10%

respectively. It is also worth noting that both access to student loans (IV20) and tribal

scholarships (IV22) are positively correlated with use of the Native office (IV24), r = .280, .289,

p < .01 [effect size 8% respectively]. Additionally, a student’s use of the multi-cultural affairs

office (IV24) is positively correlated with their accessing financial aid (IV19), student loans

(IV20) and the Michigan Indian Tuition Waiver (IV21), r = .335, .329, .365, p < .01 with effect

sizes of 11%, 11% and 13% respectively. Taken together with the larger correlations and effect

sizes reported earlier for this combination of variables between accessing services and the multi-

cultural or Native office, the critical importance and value of these offices as a hub of service

delivery is underscored.

RQ1 Results – College Graduating GPA between High School Graduates versus GED

Earners

Is there a significant difference in college graduating GPAs between MI Natives who

graduated from high school versus those who attained their GEDs? The independent t-test,

should show whether or not the two groups are significantly different with respect to their mean

graduating college GPAs. The null hypothesis is stated that there is no difference (the means are

equal). In order to conduct a the independent t-test, we needed to first determine if the

homogeneity of variance is assumed using Levine’s test (Field, p. 152). The results indicated

that the assumption was not violated with F(1,261) = .32, ns, p > .05 [p=.56]. The results of the

t-test showed the average college GPA of respondents who earned their GEDs (M=3.37, SE =

.09) was higher than the average college GPA of those who earned high school diplomas


(M=3.35, SE .03). The difference between the two mean GPAs, however, was not significant

t(263) = -.269, p > .05 (.788), and had a very low effect size at r = .02. Given the associated

significance value is (p > .05) for a 95 percent confidence interval for the t statistic, we fail to

reject null and conclude that mean college graduating GPA for respondents is not significantly

different for those who graduated high school versus those who attained a GED.

RQ2 Results – All Pre-College and Select College Independent Variables on College GPA

Regression results indicate an overall model of eighteen predictor variables that in

combination significantly predict college GPA, R2 = .145, R

2adj = .077, F(18,228) =2.140, p <

.001 with IV12 (Class Subjects Liked), IV15 (Connection to Culture), DV1 (high school

graduate or GED), IV17 (Academic GPA in high school) having significant predictive value at p

< .10 or lower. This model accounted for 14.5% of the variance in college GPA’s. A summary

of the regression model is presented in Table 1. The unstandardized and standardized

coefficients and standard error between each predictor and dependent variable are presented in

the table below.

Table 10

RQ2 Coefficients for Final Model

B β S.E.

IV1 -.019 -.020 .060

IV2 -.024 -.041 .040

IV3 -.009 .078 .007

IV4 .068 .072 .064

IV5 -.028 -.034 .054

IV6 .021 .039 .051


IV7 -.003 -.009 .032

IV8 -.061 -.086 .045

IV9 -.053 -.038 .096

IV10 .015 .015 .084

IV11 .107 .079 .110

IV12 -.045*** -.119 .027

IV13 .046 .067 .050

IV14 .022 .023 .066

IV15 .043** .112 .025

DV1 .193*** .125 .110

IV17 .184* .266 .050

IV18 -.075 -.065 .079

* p < .001, ** p < .05, *** p < .10

RQ 3 - High School Graduate or GED and Select College Independent Variables on college

GPA

Regression results indicate an overall model of nine predictor variables that in

combination significantly predict college GPA, R2 = .112, R

2adj = .080, F(9,252) =3.523, p < .001

with DV1 (high school graduate or GED), IV17 (Academic GPA in high school), and IV24 (Use

of the Multi-Cultural Affairs Office in college) having some predictive value at p < .10 or lower.

This model accounted for 11.2% of the variance in college GPA. A summary of the regression

model is presented in Table 1. The unstandardized and standardized coefficients and standard

error between each predictor and dependent variable are presented in Table 11.


Table 11


B β S.E.

DV1 .185*** .121 .099

IV17 .198* .290 .044

IV18 -.113 -.100 .076

IV19 .011 .014 .055

IV20 .041 .054 .054

IV21 -.006 -.008 .049

IV22 .009 .012 .049

IV23 .021 .035 .040

IV24 .064** .127 .033

* p < .001, ** p < .05, *** p < .10

RQ4 - All Pre-College and All College Independent Variables on College GPA

Regression results indicate an overall model of 24 predictor variables that in combination

significantly predict college GPA, R2 = .168, R

2adj = .079, F(24,222) = 1.873, p < .01 with DV1

(high school graduate or GED), IV12 (class subjects liked), IV15 (connection to culture), and

IV17 (Academic GPA in high school) having some predictive value at p < .10 or lower. This

model accounted for 16.8% of the variance in college GPA. A summary of the regression model

is presented in Table 1. The unstandardized and standardized coefficients and standard error

between each predictor and dependent variable are presented in the following table:


Table 12


B Β S.E.

DV1 .220** .142 .112

IV1 -.031 -.034 .061

IV2 -.022 -.038 .041

IV3 .009 .078 .007

IV4 .064 .068 .064

IV5 -.045 -.054 .056

IV6 .017 .031 .052

IV7 -.001 -.002 .032

IV8 -.060 -.085 .046

IV9 -.069 -.049 .100

IV10 .024 .023 .085

IV11 .044 .032 .114

IV12 -.0528** -.137 .027

IV13 .036 .053 .050

IV14 .027 .028 .067

IV15 .0448** .115 .026

IV17 .194* .279 .051

IV18 -.099 -.086 .082

IV19 .034 .040 .060

IV20 .043 .056 .057


IV21 .000 .000 .053

IV22 .020 .026 .055

IV23 .031 .050 .043

IV24 .048 .093 .036

* p < .001, ** p < .05, *** p < .10

RQ5 Results – High School GPA between High School Graduates versus GED Earners

Is there a significant difference in high school GPAs between MI Natives who graduated

from high school versus those who attained their GEDs? The independent t-test, should show

whether or not the two groups are significantly different with respect to their mean high school

GPAs. The null hypothesis is stated that there is no difference (the means are equal). The results

of the t-test showed the average high school GPA of respondents who earned their GEDs

(M=2.51, SE=.12) was lower than the average college GPA of those who earned high school

diplomas (M=3.09, SE=.04). The difference between the two mean GPAs was significant t(294)

= 4.588, p< .01 with an effect size of 26%. Given the associated significance value is (p < .05)

for a 95 percent confidence interval for the t statistic, we reject null and conclude that mean high

school GPA for respondents is significantly different (greater) for those who graduated high

school versus those who attained a GED.

RQ6 - All Pre-College Independent Variables on High School GPA

Regression results indicate an overall model of 15predictor variables that in combination

significantly predict high school GPA, R2 = .230, R

2adj = .185, F(15,258) = 5.141, p < .001 with

IV1 (be when you grow up ), IV6 (connection in school), IV7(connection to class cohort), IV8

(change in connection to class cohort), IV13 (class subject grades), IV14 (school type), and IV15


(connection to culture) having some predictive value at p < .10 or lower. This model accounted

for 23% of the variance in high school GPA. A summary of the regression model is presented in

Table 1. The unstandardized and standardized coefficients and standard error between each

predictor and dependent variable are presented in the following table:

Table 13


B β S.E.

IV1 -.052** -.038 .078

IV2 .033 .040 .048

IV3 .003 .021 .010

IV4 .014 .010 .080

IV5 .025 .020 .073

IV6 .121*** .156 .063

IV7 .066*** .137 .040

IV8 .1218** .117 .058

IV9 -.188 -.092 .122

IV10 -.063 -.042 .104

IV11 -.105 -.053 .140

IV12 -.029 -.053 .034

IV13 .181* .184 .061

IV14 .233* .176 .078

IV15 .059*** .109 .031

* p < .001, ** p < .05, *** p < .10


RQ7 - Pre-College Independent Variables on High School Graduation versus GED

Forward logistic regression was conducted to determine which pre-college independent

variables were predictors of graduating high school versus obtaining a GED. Regression results

indicated that the overall model of just two of the fifteen predictors (parental expectation to

graduate and a student’s connection to their class cohort) were statistically reliable in

distinguishing between which outcome was more likely – earning a high school diploma versus a

GED (-2 log likelihood = 145.088, 2(2) =114.908, p < .001). The model correctly classified

89.2% of the cases. Regression coefficients are presented in the following table. Though, the

Wald statistics indicate that both variables significantly predict high school graduation versus

GED, the odds ratio for these variables indicate little change in the likelihood of high school

graduation versus GED.

Table 14


B Wald df

p Odds

Ratio

Parental Expectation to Graduate 1.096 11.718 1 .001 2.993

Connection to Class -.857 22.019 1 .000 .424


CHAPTER V: CONCLUSIONS

Conclusions

We began our inquiry with the intent of finding factors for conditions which influence

whether Michigan Native Americans surveyed graduated with a high school diploma or earned a

GED, and to determine if differences exist on key outcomes like high school and college grade

point averages. Though the outcome of this research is enlightening with notable and significant

differences, the election of variables to study did not result in findings in which to build a

comprehensive intervention strategy through the use of regression results. There are notable

differences between high school graduates and GED recipients in several pre-college variables

for which school administrators may want to dedicate some attention. When interpreting results

of the first research question – whether or not there is a difference in college graduating GPAs

between high school graduates and GED recipients, we do note that the college GPA for the

GED recipients. Again, however, the difference is not significant. This finding itself is a

revelation in that when we look back at the high school GPA differences between these two

groups, we do see a difference with an inverse outcome with high school graduates having a

significantly higher GPA than their counterparts who earned a GED. It would be logical to

assume the college GPAs for GED earners would also be significantly less than for high school

graduates. This is, however, not the case. The fact that GED recipients have higher GPAs (or

statistically equal) lends support to Tribal GED preparation programs which exist to ameliorate

the reality of the high drop-out rate for Natives at nearly 50%.

With respect to research questions two, three, four and six, we did see an increasingly

greater amount of variance in outcome variables explained by combinations of select

independent variables. This is encouraging as it identifies factors or conditions for which


educational administrators can try to facilitate the introduction or withholding of various

variables that effect educational outcomes for Michigan Natives. For example, though not a

large percentage, 14.5% of the variance in college GPA explainable by class subjects liked,

connection to culture, whether or not an individual earns a high school diploma or GED and the

students respective high school GPA, provides college administrators with some level of data to

affect the retention rate of Native American students. The combination of independent variables

high school graduate or GED recipients, high school GPA, and the use of the college multi-

cultural affairs office explains 11.2% of the variance in college GPA. Though college

administrators cannot control the first two variables, they can underscore the value of services to

minority populations and the utility this has for Michigan Natives. Admittedly, research

question four has some reliability concerns with only 300 survey respondents yet a projected 360

needed. Nonetheless, with 16.8% of the variance in college GPA explainable through a

combination of pre-college and college independent variables like high school graduate or GED

recipient, class subjects liked, connection to culture, and academic GPA in high school, this

provides college administrators with information in which to program interventions. Research

question six provides the greatest level of predictability with 23% of the variance in high school

GPA with be when you grow up, connection to school, connection to class cohort, change in

connection to class cohort, class subject grades, school type, and connection to culture all

playing a significant role in predicting high school GPAs. Intervention strategies which monitor

a student's feelings of being connected at school and offers options to facilitate a greater level of

connection hold promise for higher graduating GPAs. Identifying, the point at which students

feel this change is the point at which intensive interventions should be programmed.


Incorporating a student’s respective culture holds promise for adding to a student’s feeling of

connectedness.

Finally, for this research select questions were chosen to be analyzed in a consumable

manner. Many more variables were collected and will be retained in a PASW database for later

analyses as the researcher becomes more proficient in both quantitative methods, as well as, the

use of PASW as a tool. For now, however, two independent variables lend some predictability

for whether or not a student graduates with a high school diploma or a GED. These are parental

expectation to graduate and connection to class. These results mirror those of affecting high

school and college GPAs as an outcome. For the first – expectation to graduate – the students’

respective educational institution may not be able to directly affect this, but they could afford

information to parents to reinforce the value of having positive expectations of their child to

graduate high school and to demonstrate strategies for how to productively share these

expectations with their child. With the research shared above with respect to a level of alienation

Natives may be facing due to historical trauma, it is not surprising that feeling of connection to

class plays an important role in whether or not a student graduates high school. There are any

number of ways to make someone feel more connected and engaged with their cohort, with their

school, and to intervene at key times of change in order to retain students through to graduation.

Areas of Further Research: What Was Missed

Not all tribes responded to the survey. The distribution to the survey through a

convenience sample is a notable short coming and one that would explain what not all tribes

participated. More work is needed to ensure all tribes have a sufficient number of respondents in

order to make inferences. It is hoped that presenting the results to the Michigan Tribal Education


Directors to the Michigan Indian Education Consortium Annual Conference in March 2012, will

peak their interest with the current results and build support to engage in another level of study.

Given the research is primarily retrospective; there are some concerns with reliability of the

respondent responses. As noted previously, additional work in cross referencing these results

and designing future studies with national data sets, may help to better identify questions to ask

and possible more appropriate ways to elicit responses. One vary glaring result that came to

light after the data was collected was that of not having asked the question of gender on the

survey. This was in an earlier incarnation of the survey draft, but somehow did not make it into

the survey that was deployed. It would be interesting to see any differences between male and

females as national data sets do show a lower graduation rate for male Natives compared to their

female counterparts.

Limitations of study

There are many challenges to studying Natives in general. The condition or state of

being a high school drop-out has a negative connation for which some may be too embarrassed

to acknowledge. Given the possible reasons for dropping out dealing with impoverished

backgrounds or possible negative and racist experiences Natives face while growing up, some

potential respondents may simply choose to forge reliving such adverse experiences. There is

also the challenge of a cognitive disconnect with being Native American but not recognizing this

which some describe as an issue of cultural dissonance. In a pluralistic society where

assimilation was not only the ethic, but the official United States policy toward American

Indians with the abusive practices of Indian Boarding schools, it is not only surprising that some

Natives struggle with their Indian identities. This experience might very well explain a Natives’

hesitancy to remain part of an educational institution. Often, the struggle with understanding


one’s culture, heritage and their racial-ethnic (or in this case national) identity plays itself out

during a Native students’ high school and college years. Though some possible indicators in this

study will touch the surface of some of these issues, the research design employed is not

equipped to answers some of more qualitative questions of why. Data collection such as this

study will endeavor to undertake, certainly can provide a starting point for further research.


REFERENCES

American Psychological Association. (2005). APA Resolution Recommending the Immediate

Retirement of American Indian Mascots, Symbols, Images, and Personalities by Schools,

Colleges, Universities, Athletic Teams, and Organizations. Retrieved from

http://www.apa.org/pi/oema/resources/indian-mascots.aspx

Bender, E., Kagiwada, G. (1968). Hansen's law of third-generation return and the study of

American religio-ethnic groups. Phylon. 29 (4), pp. 360-370.

Brave Heart, M. Y. H. (2000) Wakiksuyapi: Carrying the historical trauma of the Lakota. Tulane

Studies in Social Welfare, 21-22, 245-266. 2Brave Heart, M. Y. H. (2000) Wakiksuyapi:

Carrying the historical trauma of the Lakota. Tulane Studies in Social Welfare, 21-22,

245-266.

Brave Heart, M.Y.H. (2001) Clinical assessment with American Indians. In R. Fong & S. Furuto

(Eds.), Cultural competent social work practice: Practice skills, interventions, and

evaluation (pp. 163-177). Reading: Longman Publishers.

Brave Heart, M. Y. H. (2004). The historical trauma response among Natives and its relationship

to substance abuse: A Lakota illustration. In E. Nebelkopf & M. Phillips (Eds.), Healing

and mental health for Native Americans: Speaking in red (pp.7-18). Walnut Creek: Alta

Mira Press. Also in Journal of Psychoactive Drugs, 35 (1), 7-13.

Cornell, G., Parish, A., & Schweitzer, J. (2001). Sault Ste. Marie area public schools: dropout/

retention study of Native American students. Michigan State University, East Lansing,

MI.

Council of Energy Resource Tribes. (1992). Tribal Workshop Methodology. Retrieved July 24,

2009, from http://www.certredearth.com/services.php

http://www.apa.org/pi/oema/resources/indian-mascots.aspx


Creswell, J. (2009). Research design: qualitative, quantitative and mixed methods Approaches.

Los Angeles, CA: Sage Publication.

EPE Research Center (2009). Graduation in the United States: Recent Gains at Risk of Eroding.

Education Week Online. Retrieved from www.edweek.org/go/dc09

Field, A. (2009). Discovering statistics using SPSS, Third Edition. Los Angeles, CA: Sage

Publication.

Gilbert, W.S. (2000). Bridging the gap between high school and college. Journal of American

Indian Education, 39, (3) page numbers not listed.

Healy, S. (2000). Persistence of Native American students at a comprehensive university.

(Ed.D. dissertation) Pennsylvania State University. United States – Pennsylvania.

Retrieved from Proquest Dissertations and Theses database (9998355).

Hopkins, R. (2011). Epigenetics: Scientific evidence of intergenerational trauma. Indian

Country Today, Retrieved:

http://indiancountrytodaymedianetwork.com/ict_sbc/epigenetics-scientific-evidence-of-

intergenerational-trauma

My Online Maps (2010). Michigan maps and data. My Online Maps.com Retrieved from

http://www.myonlinemaps.com/michigan.php

National Center for Education Statistics (2010). Student effort and educational progress: Public

high school graduation rates by state. U.S. Department of Education, Institute for

Education Sciences. Retrieved from

http://nces.ed.gov/programs/coe/2010/section3/table-scr-1.asp

Newbreast, Theda (2011). Historical trauma and Native Americans: Training of trainer

workshop. Chico: CA. Connecting Circles of Care. Retrieved

http://www.edweek.org/go/dc09

http://www.myonlinemaps.com/michigan.php


Nizhoni Academy (2009) Retrieved from http://home.nau.edu/edsup/nz/

PASW (2009). PASW Statistics 18, Release Version 18.0.0 (=D3 SPSS, Inc., 2009, Chicago,

IL, www.spss.com).

Patton, M.Q. (2002). Qualitative Research & Evaluation Methods (3rd ed.). Thousand Oaks,

CA: Sage Publications, Inc.

Pavel, M. (1992). American Indians and Alaska Natives in higher education: research on

participation and graduation. ERIC Clearing House on Rural Education and Small

Schools. Charleston, WV.

Reyhner, J. (1991). Indian nations at risk: Plans for dropout prevention and special school

support services for American Indian and Alaska Native students. Commissioned papers,

U.S. Department of Education.

Reyhner, J. (1992). American Indians out of school: a review of school-based causes and

solutions. Journal of American Indian Education, 31, (3), pp. 1 – 15.

Reyhner, J. (2006). Drop out nation. Indian Education Today: Native American Review, June,

pp. 28-30. Retrieved from http://jan.ucc.nau.edu/~jar/AIE/IETdropout.html

Sixkiller-Clarke, A. (1994). OERI Native American youth at risk study. Published Report,

Montana State University, Bozeman. Office of Education Research and Improvement.

St. Germaine, R. (1995). Dropout rates among American Indian and Alaska Native students:

beyond cultural discontinuity. ERIC Clearing House on Rural Education and Small

Schools. Charleston, WV.

Survey Monkey (2011). Survey Monkey 1999-2011. (Survey Monkey.com LLC, Palo Alto, CA,

http://www.surveymonkey.com)

Swanson, C. (2003). Keeping count and losing count: Calculating graduation rates for all

http://home.nau.edu/edsup/nz/

http://www.spss.com/

http://jan.ucc.nau.edu/~jar/AIE/IETdropout.html


students under NCLB accountability. The Urban Institute Education Policy Center.

Retrieved from http://www.urban.org/UploadedPDF/410843_keeping_count.pdf

Swisher, K. & Hoisch, M. (1992). Dropping out among American Indians and Alaska Natives: a

review of studies. Journal of American Indian Education, 31 (2), 3-23.

Tierney, W.G. (1992). An anthropological analysis of student participation in college. Journal

of Higher Education, 63, 603-618.

Young T.J.(1991). Suicide and homicide among Native Americans: anomie or social learning?

Psychology Reports. Jun; 68(3):1137-8. Retrieved

http://www.amsciepub.com/doi/abs/10.2466/pr0.1991.68.3c.1137

Zwiebel, J. S. (2010). The relationship between the high school exit exam graduation

requirement and graduate rates in Arizona. (Ed.D. dissertation) Northcentral University

United States – Arizona. Retrieved from Proquest Dissertations and Theses database

(AAT3417117).

http://www.urban.org/UploadedPDF/410843_keeping_count.pdf


APPENDIX A

Respondent Calculator Based on Number of Variables

RQ 1

2 # of Variables*

15 Sample X

30 Needed Returns

30% Projected return rate

100 Survey Total

RQ 2

19 # of Variables*

15 Sample X

285 Needed Returns


950 Survey Total

RQ 3

10 # of Variables*

15 Sample X

150 Needed Returns


500 Survey Total

RQ 4

24 # of Variables*

15 Sample X

360 Needed Returns



1200 Survey Total

RQ 5

2 # of Variables*

15 Sample X

30 Needed Returns


100 Survey Total

RQ 6

16 # of Variables*

15 Sample X

240 Needed Returns


800 Survey Total

RQ 7

16 # of Variables*

15 Sample X

240 Needed Returns


800 Survey Total

* Includes both IV and DV variables


APPENDIX B (1 OF 3)

Survey Instrument


APPENDIX B (2 OF 3)

Draft Survey Instrument


APPENDIX B (3 OF 3)

Draft Survey Instrument


APPENDIX C

Variable Names, Value Labels & Values Sorted by Research Question

Factor Variable Name Value Label Values Measure

RQ 1 DV234_GPACol College GPA Actual Scale

School DV1_GEDorHSGrad High School Graduation or GED 1=HS Graduate; 2 = GED Nominal

RQ 2 DV234_GPACol College GPA Actual Ratio

RQ1 IV+


School IV17_AcadGPA English, Math, Science, History, Social

Studies GPA Actual Scale

School IV18_CollAccept College Admissions Status 1=Regular; 2=Probationary; 3=Don't Know Nominal

RQ 3 DV234_GPACol College GPA Actual Scale


School IV17_AcadGPA English, Math, Science, History, Social

Studies Actual Scale

School IV18_CollAccept College Admissions Status 1=Regular; 2=Probationary; 3=Don't Know Nominal

Family IV19_FFAPELL College Financial aid 1=Yes; 2=No; 3=Don't Know Nominal

Family IV20_LOANS Loans 1=Yes; 2=No; 3=Don't Know Nominal

Cultural IV21_MITW Michigan Indian Tuition Waiver 1=Yes; 2=No; 3=Don't Know Nominal

Cultural IV22_TSCHOLSHIP Tribal Scholarships 1=Yes; 2=No; 3=Don't Know Nominal

Cultural IV23_NAOffice Natives Services 1=Yes; 2=No; 3=Don't Know Nominal

Cultural IV24_MultiCultOff Multi-Cultural Office 1=Yes; 2=No; 3=Don't Know Nominal

RQ 4 DV234_GPACol College GPA Scale

All Pre-College IV (RQ7) + All College

IV (IV17-IV24)

Various

Various

RQ 5 IV17 High School GPA Scale

High School Graduation or GED Nominal

RQ 6 IV17 High School GPA Scale

All Pre-College IV (RQ7) Various Various

RQ 7 DV1_GEDorHSGrad High School Graduation or GED 1=HS Graduate; 2 = GED Nominal

Personal IV1_BwhenGrown Be When You Grow Up 1=Yes; 2=No; 3=Don't Know Nominal

Family IV2_Income Family Income While Growing Up 1=Very Low; 2=Low; 3=Middle; 4=Upper; 5=Don't

Know Ordinal


Family IV3_FamEDlvl Highest Level of Education of Family 1=Elementary & Middle School; 2=High School;

3=12th Grade or GED; 4=College; 5 = Don't Know Scale

Family IV4_EX2Grad Parental Expectations to Graduate 1=Yes; 2=No; 3=Don't Know Nominal

Family IV5_Sabuse Family Substance Abuse 1=Yes; 2=No; 3=Don't Know Nominal

School IV6_SCHCon Connection at Elementary, Middle and

High School

1=Low; 2=Somewhat Low; 3=Moderate;

4=Somewhat High; 5=High Ordinal

School IV7_ClsCon Connection to Class Cohort 1=Low; 2=Somewhat Low; 3=Moderate;


School IV8_ChgCon Change of Connection with Class Cohort 1=Yes; 2=No; 3=Don't Know Nominal

School IV9_SCHPartiALL Participated in Extra Curricular Activities 1=Yes; 2=No Nominal

School IV10_SCHIntot School Intervention Available? 1=Yes; 2=No; 3=Don't Know Nominal

School IV11_SCHIntotParti Participate in School Intervention? 1=Yes; 2=No or Blank Nominal

School IV12_ClsSubLike Class Subjects Liked 1=Did Not Like; 2=Moderate Dislike; 3=Neutral;

4=Moderate Like; 5=Really Like; 6= No Opinion Ordinal

School IV13_ClsSubGr Class Subjects Grades 0=F; 1=D; 2=C; 3=B; 4=A Scale

School IV14_SCHtype School Type 1=Parochial; 2=Private; 3=Public; 4=Tribal Nominal

Cultural IV15_NACon Connection to Culture 1=Low; 2=Somewhat Low; 3=Moderate;



APPENDIX D

Interview Prompts for Phase II

GENERAL EXPERIENCE:

1. What was your experience and frame of mind throughout your educational experience [elementary, middle

school, high school, post high school]?

2. Have you ever wanted to give up on your education?

3. What reason(s) [did/ didn’t] you [not] give up?

4. What is your view of formal education for your child?

5. In what ways is this view different that your parent’s view for you when you were in school?

6. What differences do you see today in education versus when you were going to school?

7. What is your role (Dad) in your child’s education? What is your wife [girlfriend]’s role?

8. What is your role (Mom) in your child’s education? What is your husband [boyfriend]’s role?

9. What is your role [grandma, grandpa, uncle, auntie, cousin] in your child’s education?

10. In what way[s] is your role as it is today the same or different [Dad, Mom, Grandpa, Grandma, boyfriend,

girlfriend, uncle, auntie, cousin] that it was when you were growing up?

CONNECTEDNESS TO ‘SCHOOL’

1. In what ways did you feel a connection (if any) to your school or classmates?

2. In what ways did you feel disconnected (if at all) from your school or classmates?

3. Are your core group of friends those you know from school or outside of school?

4. What factor(s), if any, made you feel more or less connected at school?

ACCESS OR BARRIERS TO EDUCATION:

1. Were there any barriers you feel you had to overcome while in K-12 education?

2. What about college access issues?

3. If so, what factors aided you in graduating high school?

4. College?


FOR THOSE WHO TAKE A LESS CONVENTION ROUTE TO GRADUATE:

1. For those times you took time off from school, what compelled you to [want/need] to take time off?

2. What motivated you to continue and/or return?

3. What would it take for you to return?

AMERICAN INDIAN ID:

1. What does it mean to you to be an American Indian or Native American?

2. Does being an American Indian or Native American mean the same thing for your generation compared to

your parents? How so?

3. Does being an American Indian or Native American mean the same thing for you compared your

children’s? How so?

4. What does it mean to be an American Indian or Native American attending a [public/private/parochial]

school [K-12, community college, university, vocational trade school]?

5. In what ways do you think the experience is the same or different for American Indians or Native

Americans versus non-Natives? Please explain.

6. Do you think it is possible to be authentically an American Indian or Native American while also being a

college student? Please explain.

EDUCATION PARTICIPATION QUESTIONS:

1. Do you volunteer or participate in your child’s education?

2. What led you to want to volunteer or get involved in your child’s education?

3. Why do you suppose others volunteer or get involved in their child’s education?

4. Is your involvement with your child’s education something that began with you or your generation or is it

something you learned from others?


APPENDIX E.1

Correlation Matrix – Page 1 of 2


APPENDIX E.2

Correlation Matrix – Page 2 of 2


APPENDIX F

Frequency Distributions and Graphs

[Appended as a separate PASW Output File]


APPENDIX G.1

Graphs of Significant Differences on Select Variables Between

MI Native High School Graduates Versus those who earned their GED.


APPENDIX G.2




APPENDIX G.3




APPENDIX G.4



Documents

THE NATIVE AMERICAN STUDENT DROP-OUT RATE AT 50% (26% ... · running head: a persisting problem in search of a solution 1 the native american student drop-out rate at 50%